Posts Tagged Data Analytics

Getting Started with Presto Federated Queries using Ahana’s PrestoDB Sandbox on AWS

Posted by Gary A. Stafford in AWS, Big Data, Software Development, SQL on September 3, 2020

Introduction

According to The Presto Foundation, Presto (aka PrestoDB), not to be confused with PrestoSQL, is an open-source, distributed, ANSI SQL compliant query engine. Presto is designed to run interactive ad-hoc analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto is used in production at an immense scale by many well-known organizations, including Facebook, Twitter, Uber, Alibaba, Airbnb, Netflix, Pinterest, Atlassian, Nasdaq, and more.

In the following post, we will gain a better understanding of Presto’s ability to execute federated queries, which join multiple disparate data sources without having to move the data. Additionally, we will explore Apache Hive, the Hive Metastore, Hive partitioned tables, and the Apache Parquet file format.

Presto on AWS

There are several options for Presto on AWS. AWS recommends Amazon EMR and Amazon Athena. Presto comes pre-installed on EMR 5.0.0 and later. The Athena query engine is a derivation of Presto 0.172 and does not support all of Presto’s native features. However, Athena has many comparable features and deep integrations with other AWS services. If you need full, fine-grain control, you could deploy and manage Presto, yourself, on Amazon EC2, Amazon ECS, or Amazon EKS. Lastly, you may decide to purchase a Presto distribution with commercial support from an AWS Partner, such as Ahana or Starburst. If your organization needs 24x7x365 production-grade support from experienced Presto engineers, this is an excellent choice.

Federated Queries

In a modern Enterprise, it is rare to find all data living in a monolithic datastore. Given the multitude of available data sources, internal and external to an organization, and the growing number of purpose-built databases, analytics engines must be able to join and aggregate data across many sources efficiently. AWS defines a federated query as a capability that ‘enables data analysts, engineers, and data scientists to execute SQL queries across data stored in relational, non-relational, object, and custom data sources.’

Presto allows querying data where it lives, including Apache Hive, Thrift, Kafka, Kudu, and Cassandra, Elasticsearch, and MongoDB. In fact, there are currently 24 different Presto data source connectors available. With Presto, we can write queries that join multiple disparate data sources, without moving the data. Below is a simple example of a Presto federated query statement that correlates a customer’s credit rating with their age and gender. The query federates two different data sources, a PostgreSQL database table, postgresql.public.customer, and an Apache Hive Metastore table, hive.default.customer_demographics, whose underlying data resides in Amazon S3.

WITH credit_demographics AS (
SELECT
(year (now()) – c_birth_year) AS age,
cd_credit_rating AS credit_rating,
cd_gender AS gender,
count(cd_gender) AS gender_count
FROM
postgresql.public.customer
LEFT JOIN hive.default.customer_demographics ON c_current_cdemo_sk = cd_demo_sk
WHERE
c_birth_year IS NOT NULL
AND cd_credit_rating IS NOT NULL
AND lower(cd_credit_rating) != 'unknown'
AND cd_gender IS NOT NULL
GROUP BY
cd_credit_rating,
c_birth_year,
cd_gender
)
SELECT
age,
credit_rating,
gender,
gender_count
FROM
credit_demographics
WHERE
age BETWEEN 21 AND 65
ORDER BY
age,
credit_rating,
gender;
view raw presto_query_federated.sql hosted with ❤ by GitHub

Ahana

The Linux Foundation’s Presto Foundation member, Ahana, was founded as the first company focused on bringing PrestoDB-based ad hoc analytics offerings to market and working to foster growth and evangelize the Presto community. Ahana’s mission is to simplify ad hoc analytics for organizations of all shapes and sizes. Ahana has been successful in raising seed funding, led by GV (formerly Google Ventures). Ahana’s founders have a wealth of previous experience in tech companies, including Alluxio, Kinetica, Couchbase, IBM, Apple, Splunk, and Teradata.

PrestoDB Sandbox

This post will use Ahana’s PrestoDB Sandbox, an Amazon Linux 2, AMI-based solution available on AWS Marketplace, to execute Presto federated queries.

Ahana’s PrestoDB Sandbox AMI allows you to easily get started with Presto to query data wherever your data resides. This AMI configures a single EC2 instance Sandbox to be both the Presto Coordinator and a Presto Worker. It comes with an Apache Hive Metastore backed by PostgreSQL bundled in. In addition, the following catalogs are bundled in to try, test, and prototype with Presto:

  • JMX: useful for monitoring and debugging Presto
  • Memory: stores data and metadata in RAM, which is discarded when Presto restarts
  • TPC-DS: provides a set of schemas to support the TPC Benchmark DS
  • TPC-H: provides a set of schemas to support the TPC Benchmark H

Apache Hive

In this demonstration, we will use Apache Hive and an Apache Hive Metastore backed by PostgreSQL. Apache Hive is data warehouse software that facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. The structure can be projected onto data already in storage. A command-line tool and JDBC driver are provided to connect users to Hive. The Metastore provides two essential features of a data warehouse: data abstraction and data discovery. Hive accomplishes both features by providing a metadata repository that is tightly integrated with the Hive query processing system so that data and metadata are in sync.

Getting Started

To get started creating federated queries with Presto, we first need to create and configure our AWS environment, as shown below.

Architecture of the demonstration’s AWS environment and resources

Subscribe to Ahana’s PrestoDB Sandbox

To start, subscribe to Ahana’s PrestoDB Sandbox on AWS Marketplace. Make sure you are aware of the costs involved. The AWS current pricing for the default, Linux-based r5.xlarge on-demand EC2 instance hosted in US East (N. Virginia) is USD 0.252 per hour. For the demonstration, since performance is not an issue, you could try a smaller EC2 instance, such as r5.large instance costs USD 0.126 per hour.

The configuration process will lead you through the creation of an EC2 instance based on Ahana’s PrestoDB Sandbox AMI.

I chose to create the EC2 instance in my default VPC. Part of the demonstration includes connecting to Presto locally using JDBC. Therefore, it was also necessary to include a public IP address for the EC2 instance. If you chose to do so, I strongly recommend limiting the required ports 22 and 8080 in the instance’s Security Group to just your IP address (a /32 CIDR block).

Limiting access to ports 22 and 8080 from only my current IP address

Lastly, we need to assign an IAM Role to the EC2 instance, which has access to Amazon S3. I assigned the AWS managed policy, AmazonS3FullAccess, to the EC2’s IAM Role.

Attaching the AmazonS3FullAccess AWS managed policy to the Role

Part of the configuration also asks for a key pair. You can use an existing key or create a new key for the demo. For reference in future commands, I am using a key named ahana-presto and my key path of ~/.ssh/ahana-presto.pem. Be sure to update the commands to match your own key’s name and location.

Once complete, instructions for using the PrestoDB Sandbox EC2 are provided.

You can view the running EC2 instance, containing Presto, from the web-based AWS EC2 Management Console. Make sure to note the public IPv4 address or the public IPv4 DNS address as this value will be required during the demo.

AWS CloudFormation

We will use Amazon RDS for PostgreSQL and Amazon S3 as additional data sources for Presto. Included in the project files on GitHub is an AWS CloudFormation template, cloudformation/presto_ahana_demo.yaml. The template creates a single RDS for PostgreSQL instance in the default VPC and an encrypted Amazon S3 bucket.

AWSTemplateFormatVersion: "2010-09-09"
Description: "This template deploys a RDS PostgreSQL database and an Amazon S3 bucket"
Parameters:
DBInstanceIdentifier:
Type: String
Default: "ahana-prestodb-demo"
DBEngine:
Type: String
Default: "postgres"
DBEngineVersion:
Type: String
Default: "12.3"
DBAvailabilityZone:
Type: String
Default: "us-east-1f"
DBInstanceClass:
Type: String
Default: "db.t3.medium"
DBStorageType:
Type: String
Default: "gp2"
DBAllocatedStorage:
Type: Number
Default: 20
DBName:
Type: String
Default: "shipping"
DBUser:
Type: String
Default: "presto"
DBPassword:
Type: String
Default: "5up3r53cr3tPa55w0rd"
# NoEcho: True
Resources:
MasterDatabase:
Type: AWS::RDS::DBInstance
Properties:
DBInstanceIdentifier:
Ref: DBInstanceIdentifier
DBName:
Ref: DBName
AllocatedStorage:
Ref: DBAllocatedStorage
DBInstanceClass:
Ref: DBInstanceClass
StorageType:
Ref: DBStorageType
Engine:
Ref: DBEngine
EngineVersion:
Ref: DBEngineVersion
MasterUsername:
Ref: DBUser
MasterUserPassword:
Ref: DBPassword
AvailabilityZone: !Ref DBAvailabilityZone
PubliclyAccessible: true
Tags:
– Key: Project
Value: "Demo of RDS PostgreSQL"
DataBucket:
DeletionPolicy: Retain
Type: AWS::S3::Bucket
Properties:
BucketEncryption:
ServerSideEncryptionConfiguration:
– ServerSideEncryptionByDefault:
SSEAlgorithm: AES256
PublicAccessBlockConfiguration:
BlockPublicAcls: true
BlockPublicPolicy: true
IgnorePublicAcls: true
RestrictPublicBuckets: true
Outputs:
Endpoint:
Description: "Endpoint of RDS PostgreSQL database"
Value: !GetAtt MasterDatabase.Endpoint.Address
Port:
Description: "Port of RDS PostgreSQL database"
Value: !GetAtt MasterDatabase.Endpoint.Port
JdbcConnString:
Description: "JDBC connection string of RDS PostgreSQL database"
Value: !Join
– ""
– – "jdbc:postgresql://"
– !GetAtt MasterDatabase.Endpoint.Address
– ":"
– !GetAtt MasterDatabase.Endpoint.Port
– "/"
– !Ref DBName
– "?user="
– !Ref DBUser
– "&password="
– !Ref DBPassword
Bucket:
Description: "Name of Amazon S3 data bucket"
Value: !Ref DataBucket
view raw presto_ahana_demo.yaml hosted with ❤ by GitHub

All the source code for this post is on GitHub. Use the following command to git clone a local copy of the project.

git clone \
  –branch master –single-branch –depth 1 –no-tags \
  https://github.com/garystafford/presto-aws-federated-queries.git

To create the AWS CloudFormation stack from the template, cloudformation/rds_s3.yaml, execute the following aws cloudformation command. Make sure you change the DBAvailabilityZone parameter value (shown in bold) to match the AWS Availability Zone in which your Ahana PrestoDB Sandbox EC2 instance was created. In my case, us-east-1f.

aws cloudformation create-stack \
  --stack-name ahana-prestodb-demo \
  --template-body file://cloudformation/presto_ahana_demo.yaml \
  --parameters ParameterKey=DBAvailabilityZone,ParameterValue=us-east-1f

To ensure the RDS for PostgreSQL database instance can be accessed by Presto running on the Ahana PrestoDB Sandbox EC2, manually add the PrestoDB Sandbox EC2’s Security Group to port 5432 within the database instance’s VPC Security Group’s Inbound rules. I have also added my own IP to port 5432, which enables me to connect to the RDS instance directly from my IDE using JDBC.

The AWS CloudFormation stack’s Outputs tab includes a set of values, including the JDBC connection string for the new RDS for PostgreSQL instance, JdbcConnString, and the Amazon S3 bucket’s name, Bucket. All these values will be required during the demonstration.

Preparing the PrestoDB Sandbox

There are a few steps we need to take to properly prepare the PrestoDB Sandbox EC2 for our demonstration. First, use your PrestoDB Sandbox EC2 SSH key to scp the properties and sql directories to the Presto EC2 instance. First, you will need to set the EC2_ENDPOINT value (shown in bold) to your EC2’s public IPv4 address or public IPv4 DNS value. You can hardcode the value or use the aws ec2 API command is shown below to retrieve the value programmatically.

# on local workstation
EC2_ENDPOINT=$(aws ec2 describe-instances \
  --filters "Name=product-code,Values=ejee5zzmv4tc5o3tr1uul6kg2" \
    "Name=product-code.type,Values=marketplace" \
  --query "Reservations[*].Instances[*].{Instance:PublicDnsName}" \
  --output text)
scp -i "~/.ssh/ahana-presto.pem" \
  -r properties/ sql/ \
  ec2-user@${EC2_ENDPOINT}:~/
ssh -i "~/.ssh/ahana-presto.pem" ec2-user@${EC2_ENDPOINT}

Environment Variables

Next, we need to set several environment variables. First, replace the DATA_BUCKET and POSTGRES_HOST values below (shown in bold) to match your environment. The PGPASSWORD value should be correct unless you changed it in the CloudFormation template. Then, execute the command to add the variables to your .bash_profile file.

echo """
export DATA_BUCKET=prestodb-demo-databucket-CHANGE_ME
export POSTGRES_HOST=presto-demo.CHANGE_ME.us-east-1.rds.amazonaws.com
export PGPASSWORD=5up3r53cr3tPa55w0rd
export JAVA_HOME=/usr
export HADOOP_HOME=/home/ec2-user/hadoop
export HADOOP_CLASSPATH=$HADOOP_HOME/share/hadoop/tools/lib/*
export HIVE_HOME=/home/ec2-user/hive
export PATH=$HIVE_HOME/bin:$HADOOP_HOME/bin:$PATH
""" >>~/.bash_profile

Optionally, I suggest updating the EC2 instance with available updates and install your favorite tools, likehtop, to monitor the EC2 performance.

yes | sudo yum update
yes | sudo yum install htop
View of htop running on an r5.xlarge EC2 instance

Before further configuration for the demonstration, let’s review a few aspects of the Ahana PrestoDB EC2 instance. There are several applications pre-installed on the instance, including Java, Presto, Hadoop, PostgreSQL, and Hive. Versions shown are current as of early September 2020.

java -version
  # openjdk version "1.8.0_252"
  # OpenJDK Runtime Environment (build 1.8.0_252-b09)
  # OpenJDK 64-Bit Server VM (build 25.252-b09, mixed mode)
hadoop version
  # Hadoop 2.9.2
postgres --version
  # postgres (PostgreSQL) 9.2.24
psql --version
  # psql (PostgreSQL) 9.2.24
hive --version
  # Hive 2.3.7
presto-cli --version
  # Presto CLI 0.235-cb21100

The Presto configuration files are in the /etc/presto/ directory. The Hive configuration files are in the ~/hive/conf/ directory. Here are a few commands you can use to gain a better understanding of their configurations.

ls /etc/presto/
cat /etc/presto/jvm.config
cat /etc/presto/config.properties
cat /etc/presto/node.properties
# installed and configured catalogs
ls /etc/presto/catalog/
cat ~/hive/conf/hive-site.xml

Configure Presto

To configure Presto, we need to create and copy a new Presto postgresql catalog properties file for the newly created RDS for PostgreSQL instance. Modify the properties/rds_postgresql.properties file, replacing the value, connection-url (shown in bold), with your own JDBC connection string, shown in the CloudFormation Outputs tab.

connector.name=postgresql
connection-url=jdbc:postgresql://presto-demo.abcdefg12345.us-east-1.rds.amazonaws.com:5432/shipping
connection-user=presto
connection-password=5up3r53cr3tPa55w0rd

Move the rds_postgresql.properties file to its correct location using sudo.

sudo mv properties/rds_postgresql.properties /etc/presto/catalog/

We also need to modify the existing Hive catalog properties file, which will allow us to write to non-managed Hive tables from Presto.

connector.name=hive-hadoop2
hive.metastore.uri=thrift://localhost:9083
hive.non-managed-table-writes-enabled=true

The following command will overwrite the existing hive.properties file with the modified version containing the new property.

sudo mv properties/hive.properties |
  /etc/presto/catalog/hive.properties

To finalize the configuration of the catalog properties files, we need to restart Presto. The easiest way is to reboot the EC2 instance, then SSH back into the instance. Since our environment variables are in the .bash_profile file, they will survive a restart and logging back into the EC2 instance.

sudo reboot

Add Tables to Apache Hive Metastore

We will use RDS for PostgreSQL and Apache Hive Metastore/Amazon S3 as additional data sources for our federated queries. The Ahana PrestoDB Sandbox instance comes pre-configured with Apache Hive and an Apache Hive Metastore, backed by PostgreSQL (a separate PostgreSQL 9.x instance pre-installed on the EC2).

The Sandbox’s instance of Presto comes pre-configured with schemas for the TPC Benchmark DS (TPC-DS). We will create identical tables in our Apache Hive Metastore, which correspond to three external tables in the TPC-DS data source’s sf1 schema: tpcds.sf1.customer, tpcds.sf1.customer_address, and tpcds.sf1.customer_demographics. A Hive external table describes the metadata/schema on external files. External table files can be accessed and managed by processes outside of Hive. As an example, here is the SQL statement that creates the external customer table in the Hive Metastore and whose data will be stored in the S3 bucket.

CREATE EXTERNAL TABLE IF NOT EXISTS `customer`(
  `c_customer_sk` bigint, 
  `c_customer_id` char(16),
  `c_current_cdemo_sk` bigint, 
  `c_current_hdemo_sk` bigint, 
  `c_current_addr_sk` bigint, 
  `c_first_shipto_date_sk` bigint, 
  `c_first_sales_date_sk` bigint, 
  `c_salutation` char(10),
  `c_first_name` char(20),
  `c_last_name` char(30),
  `c_preferred_cust_flag` char(1), 
  `c_birth_day` integer, 
  `c_birth_month` integer, 
  `c_birth_year` integer, 
  `c_birth_country` char(20),
  `c_login` char(13),
  `c_email_address` char(50),
  `c_last_review_date_sk` bigint)
STORED AS PARQUET
LOCATION
  's3a://prestodb-demo-databucket-CHANGE_ME/customer'
TBLPROPERTIES ('parquet.compression'='SNAPPY');

The threeCREATE EXTERNAL TABLE SQL statements are included in the sql/ directory: sql/hive_customer.sql, sql/hive_customer_address.sql, and sql/hive_customer_demographics.sql. The bucket name (shown in bold above), needs to be manually updated to your own bucket name in all three files before continuing.

Next, run the following hive commands to create the external tables in the Hive Metastore within the existing default schema/database.

hive --database default -f sql/hive_customer.sql
hive --database default -f sql/hive_customer_address.sql
hive --database default -f sql/hive_customer_demographics.sql

To confirm the tables were created successfully, we could use a variety of hive commands.

hive --database default -e "SHOW TABLES;"
hive --database default -e "DESCRIBE FORMATTED customer;"
hive --database default -e "SELECT * FROM customer LIMIT 5;"
Using the ‘DESCRIBE FORMATTED customer_address;’ Hive command

Alternatively, you can also create the external table interactively from within Hive, using the hive command to access the CLI. Copy and paste the contents of the SQL files to the hive CLI. To exit hive use quit;.

Interactively querying within Apache Hive

Amazon S3 Data Source Setup

With the external tables created, we will now select all the data from each of the three tables in the TPC-DS data source and insert that data into the equivalent Hive tables. The physical data will be written to Amazon S3 as highly-efficient, columnar storage format, SNAPPY-compressed Apache Parquet files. Execute the following commands. I will explain why the customer_address table statements are a bit different, next.

# inserts 100,000 rows
presto-cli --execute """
  INSERT INTO hive.default.customer
  SELECT * FROM tpcds.sf1.customer;
  """
# inserts 50,000 rows across 52 partitions
presto-cli --execute """
  INSERT INTO hive.default.customer_address
  SELECT ca_address_sk, ca_address_id, ca_street_number, 
    ca_street_name, ca_street_type, ca_suite_number, 
    ca_city, ca_county, ca_zip, ca_country, ca_gmt_offset, 
    ca_location_type, ca_state
  FROM tpcds.sf1.customer_address
  ORDER BY ca_address_sk;
  """
# add new partitions in metastore
hive -e "MSCK REPAIR TABLE default.customer_address;"
# inserts 1,920,800 rows
presto-cli --execute """
  INSERT INTO hive.default.customer_demographics
  SELECT * FROM tpcds.sf1.customer_demographics;
  """

Confirm the data has been loaded into the correct S3 bucket locations and is in Parquet-format using the AWS Management Console or AWS CLI. Rest assured, the Parquet-format data is SNAPPY-compressed even though the S3 console incorrectly displays Compression as None. You can easily confirm the compression codec with a utility like parquet-tools.

Data organized by key prefixes in Amazon S3
Using S3’s ‘Select from’ feature to preview the SNAPPY-compressed Parquet format data

Partitioned Tables

The customer_address table is unique in that it has been partitioned by the ca_state column. Partitioned tables are created using the PARTITIONED BY clause.

CREATE EXTERNAL TABLE `customer_address`(
    `ca_address_sk` bigint,
    `ca_address_id` char(16),
    `ca_street_number` char(10),
    `ca_street_name` char(60),
    `ca_street_type` char(15),
    `ca_suite_number` char(10),
    `ca_city` varchar(60),
    `ca_county` varchar(30),
    `ca_zip` char(10),
    `ca_country` char(20),
    `ca_gmt_offset` double precision,
    `ca_location_type` char(20)
)
PARTITIONED BY (`ca_state` char(2))
STORED AS PARQUET
LOCATION
  's3a://prestodb-demo-databucket-CHANGE_ME/customer'
TBLPROPERTIES ('parquet.compression'='SNAPPY');

According to Apache Hive, a table can have one or more partition columns and a separate data directory is created for each distinct value combination in the partition columns. Since the data for the Hive tables are stored in Amazon S3, that means that when the data is written to the customer_address table, it is automatically separated into different S3 key prefixes based on the state. The data is physically “partitioned”.

customer_address data, partitioned by the state, in Amazon S3

Whenever add new partitions in S3, we need to run the MSCK REPAIR TABLE command to add that table’s new partitions to the Hive Metastore.

hive -e "MSCK REPAIR TABLE default.customer_address;"

In SQL, a predicate is a condition expression that evaluates to a Boolean value, either true or false. Defining the partitions aligned with the attributes that are frequently used in the conditions/filters (predicates) of the queries can significantly increase query efficiency. When we execute a query that uses an equality comparison condition, such as ca_state = 'TN', partitioning means the query will only work with a slice of the data in the corresponding ca_state=TN prefix key. There are 50,000 rows of data in the customer_address table, but only 1,418 rows (2.8% of the total data) in the ca_state=TN partition. With the additional advantage of Parquet format with SNAPPY compression, partitioning can significantly reduce query execution time.

Adding Data to RDS for PostgreSQL Instance

For the demonstration, we will also replicate the schema and data of the tpcds.sf1.customer_address table to the new RDS for PostgreSQL instance’s shipping database.

CREATE TABLE customer_address (
    ca_address_sk bigint,
    ca_address_id char(16),
    ca_street_number char(10),
    ca_street_name char(60),
    ca_street_type char(15),
    ca_suite_number char(10),
    ca_city varchar(60),
    ca_county varchar(30),
    ca_state char(2),
    ca_zip char(10),
    ca_country char(20),
    ca_gmt_offset double precision,
    ca_location_type char(20)
);

Like Hive and Presto, we can create the table programmatically from the command line or interactively; I prefer the programmatic approach. Use the following psql command, we can create the customer_address table in the public schema of the shipping database.

psql -h ${POSTGRES_HOST} -p 5432 -d shipping -U presto \
  -f sql/postgres_customer_address.sql

Now, to insert the data into the new PostgreSQL table, run the following presto-cli command.

# inserts 50,000 rows
presto-cli --execute """
  INSERT INTO rds_postgresql.public.customer_address
  SELECT * FROM tpcds.sf1.customer_address;
  """

To confirm that the data was imported properly, we can use a variety of commands.

-- Should be 50000 rows in table
psql -h ${POSTGRES_HOST} -p 5432 -d shipping -U presto \
  -c "SELECT COUNT(*) FROM customer_address;"
psql -h ${POSTGRES_HOST} -p 5432 -d shipping -U presto \
  -c "SELECT * FROM customer_address LIMIT 5;"

Alternatively, you could use the PostgreSQL client interactively by copying and pasting the contents of the sql/postgres_customer_address.sql file to the psql command prompt. To interact with PostgreSQL from the psql command prompt, use the following command.

psql -h ${POSTGRES_HOST} -p 5432 -d shipping -U presto

Use the \dt command to list the PostgreSQL tables and the \q command to exit the PostgreSQL client. We now have all the new data sources created and configured for Presto!

Interacting with Presto

Presto provides a web interface for monitoring and managing queries. The interface provides dashboard-like insights into the Presto Cluster and queries running on the cluster. The Presto UI is available on port 8080 using the public IPv4 address or the public IPv4 DNS.

There are several ways to interact with Presto, via the PrestoDB Sandbox. The post will demonstrate how to execute ad-hoc queries against Presto from an IDE using a JDBC connection and the Presto CLI. Other options include running queries against Presto from Java and Python applications, Tableau, or Apache Spark/PySpark.

Below, we see a query being run against Presto from JetBrains PyCharm, using a Java Database Connectivity (JDBC) connection. The advantage of using an IDE like JetBrains is having a single visual interface, including all the project files, multiple JDBC configurations, output results, and the ability to run multiple ad hoc queries.

Below, we see an example of configuring the Presto Data Source using the JDBC connection string, supplied in the CloudFormation stack Outputs tab.

Make sure to download and use the latest Presto JDBC driver JAR.

With JetBrains’ IDEs, we can even limit the databases/schemas displayed by the Data Source. This is helpful when we have multiple Presto catalogs configured, but we are only interested in certain data sources.

We can also run queries using the Presto CLI, three different ways. We can pass a SQL statement to the Presto CLI, pass a file containing a SQL statement to the Presto CLI, or work interactively from the Presto CLI. Below, we see a query being run, interactively from the Presto CLI.

As the query is running, we can observe the live Presto query statistics (not very user friendly in my terminal).

And finally, the view the query results.

Federated Queries

The example queries used in the demonstration and included in the project were mainly extracted from the scholarly article, Why You Should Run TPC-DS: A Workload Analysis, available as a PDF on the tpc.org website. I have modified the SQL queries to work with Presto.

In the first example, we will run the three versions of the same basic query statement. Version 1 of the query is not a federated query; it only queries a single data source. Version 2 of the query queries two different data sources. Finally, version 3 of the query queries three different data sources. Each of the three versions of the SQL statement should return the same results — 93 rows of data.

Version 1: Single Data Source

The first version of the query statement, sql/presto_query2.sql, is not a federated query. Each of the query’s four tables (catalog_returns, date_dim, customer, and customer_address) reference the TPC-DS data source, which came pre-installed with the PrestoDB Sandbox. Note table references on lines 11–13 and 41–42 are all associated with the tpcds.sf1 schema.

— Modified version of
— Figure 7: Reporting Query (Query 40)
— http://www.tpc.org/tpcds/presentations/tpcds_workload_analysis.pdf
WITH customer_total_return AS (
SELECT
cr_returning_customer_sk AS ctr_cust_sk,
ca_state AS ctr_state,
sum(cr_return_amt_inc_tax) AS ctr_return
FROM
catalog_returns,
date_dim,
customer_address
WHERE
cr_returned_date_sk = d_date_sk
AND d_year = 1998
AND cr_returning_addr_sk = ca_address_sk
GROUP BY
cr_returning_customer_sk,
ca_state
)
SELECT
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return
FROM
customer_total_return ctr1,
customer_address,
customer
WHERE
ctr1.ctr_return > (
SELECT
avg(ctr_return) * 1.2
FROM
customer_total_return ctr2
WHERE
ctr1.ctr_state = ctr2.ctr_state)
AND ca_address_sk = c_current_addr_sk
AND ca_state = 'TN'
AND ctr1.ctr_cust_sk = c_customer_sk
ORDER BY
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return;
view raw presto_query2.sql hosted with ❤ by GitHub

We will run each query non-interactively using the presto-cli. We will choose the sf1 (scale factor of 1) tpcds schema. According to Presto, every unit in the scale factor (sf1, sf10, sf100) corresponds to a gigabyte of data.

presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query2.sql \
  --output-format ALIGNED \
  --client-tags "presto_query2"

Below, we see the query results in the presto-cli.

Below, we see the first query running in Presto’s web interface.

Below, we see the first query’s results detailed in Presto’s web interface.

Version 2: Two Data Sources

In the second version of the query statement, sql/presto_query2_federated_v1.sql, two of the tables (catalog_returns and date_dim) reference the TPC-DS data source. The other two tables (customer and customer_address) now reference the Apache Hive Metastore for their schema and underlying data in Amazon S3. Note table references on lines 11 and 12, as opposed to lines 13, 41, and 42.

— Modified version of
— Figure 7: Reporting Query (Query 40)
— http://www.tpc.org/tpcds/presentations/tpcds_workload_analysis.pdf
WITH customer_total_return AS (
SELECT
cr_returning_customer_sk AS ctr_cust_sk,
ca_state AS ctr_state,
sum(cr_return_amt_inc_tax) AS ctr_return
FROM
tpcds.sf1.catalog_returns,
tpcds.sf1.date_dim,
hive.default.customer_address
WHERE
cr_returned_date_sk = d_date_sk
AND d_year = 1998
AND cr_returning_addr_sk = ca_address_sk
GROUP BY
cr_returning_customer_sk,
ca_state
)
SELECT
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return
FROM
customer_total_return ctr1,
hive.default.customer_address,
hive.default.customer
WHERE
ctr1.ctr_return > (
SELECT
avg(ctr_return) * 1.2
FROM
customer_total_return ctr2
WHERE
ctr1.ctr_state = ctr2.ctr_state)
AND ca_address_sk = c_current_addr_sk
AND ca_state = 'TN'
AND ctr1.ctr_cust_sk = c_customer_sk
ORDER BY
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return;
view raw presto_query2_federated_v1.sql hosted with ❤ by GitHub

Again, run the query using the presto-cli.

presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query2_federated_v1.sql \
  --output-format ALIGNED \
  --client-tags "presto_query2_federated_v1"

Below, we see the second query’s results detailed in Presto’s web interface.

Even though the data is in two separate and physically different data sources, we can easily query it as though it were all in the same place.

Version 3: Three Data Sources

In the third version of the query statement, sql/presto_query2_federated_v2.sql, two of the tables (catalog_returns and date_dim) reference the TPC-DS data source. One of the tables (hive.default.customer) references the Apache Hive Metastore. The fourth table (rds_postgresql.public.customer_address) references the new RDS for PostgreSQL database instance. The underlying data is in Amazon S3. Note table references on lines 11 and 12, and on lines 13 and 41, as opposed to line 42.

— Modified version of
— Figure 7: Reporting Query (Query 40)
— http://www.tpc.org/tpcds/presentations/tpcds_workload_analysis.pdf
WITH customer_total_return AS (
SELECT
cr_returning_customer_sk AS ctr_cust_sk,
ca_state AS ctr_state,
sum(cr_return_amt_inc_tax) AS ctr_return
FROM
tpcds.sf1.catalog_returns,
tpcds.sf1.date_dim,
rds_postgresql.public.customer_address
WHERE
cr_returned_date_sk = d_date_sk
AND d_year = 1998
AND cr_returning_addr_sk = ca_address_sk
GROUP BY
cr_returning_customer_sk,
ca_state
)
SELECT
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return
FROM
customer_total_return ctr1,
rds_postgresql.public.customer_address,
hive.default.customer
WHERE
ctr1.ctr_return > (
SELECT
avg(ctr_return) * 1.2
FROM
customer_total_return ctr2
WHERE
ctr1.ctr_state = ctr2.ctr_state)
AND ca_address_sk = c_current_addr_sk
AND ca_state = 'TN'
AND ctr1.ctr_cust_sk = c_customer_sk
ORDER BY
c_customer_id,
c_salutation,
c_first_name,
c_last_name,
ca_street_number,
ca_street_name,
ca_street_type,
ca_suite_number,
ca_city,
ca_county,
ca_state,
ca_zip,
ca_country,
ca_gmt_offset,
ca_location_type,
ctr_return;
view raw presto_query2_federated_v2.sql hosted with ❤ by GitHub

Again, we have run the query using the presto-cli.

presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query2_federated_v2.sql \
  --output-format ALIGNED \
  --client-tags "presto_query2_federated_v2"

Below, we see the third query’s results detailed in Presto’s web interface.

Again, even though the data is in three separate and physically different data sources, we can easily query it as though it were all in the same place.

Additional Query Examples

The project contains several additional query statements, which I have extracted from Why You Should Run TPC-DS: A Workload Analysis and modified work with Presto and federate across multiple data sources.

# non-federated
presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query1.sql \
  --output-format ALIGNED \
  --client-tags "presto_query1"
# federated - two sources
presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query1_federated.sql \
  --output-format ALIGNED \
  --client-tags "presto_query1_federated"
# non-federated
presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query4.sql \
  --output-format ALIGNED \
  --client-tags "presto_query4"
# federated - three sources
presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query4_federated.sql \
  --output-format ALIGNED \
  --client-tags "presto_query4_federated"
# non-federated
presto-cli \
  --catalog tpcds \
  --schema sf1 \
  --file sql/presto_query5.sql \
  --output-format ALIGNED \
  --client-tags "presto_query5"

Conclusion

In this post, we gained a better understanding of Presto using Ahana’s PrestoDB Sandbox product from AWS Marketplace. We learned how Presto queries data where it lives, including Apache Hive, Thrift, Kafka, Kudu, and Cassandra, Elasticsearch, MongoDB, etc. We also learned about Apache Hive and the Apache Hive Metastore, Apache Parquet file format, and how and why to partition Hive data in Amazon S3. Most importantly, we learned how to write federated queries that join multiple disparate data sources without having to move the data into a single monolithic data store.

This blog represents my own viewpoints and not of my employer, Amazon Web Services.

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Getting Started with IoT Analytics on AWS

Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Cloud, Python, Software Development, Technology Consulting on July 15, 2020

 

Introduction

AWS defines AWS IoT as a set of managed services that enable ‘internet-connected devices to connect to the AWS Cloud and lets applications in the cloud interact with internet-connected devices.’ AWS IoT services span three categories: Device Software, Connectivity and Control, and Analytics. In this post, we will focus on AWS IoT Analytics, one of four services, which are part of the AWS IoT Analytics category. According to AWS, AWS IoT Analytics is a fully-managed IoT analytics service, designed specifically for IoT, which collects, pre-processes, enriches, stores, and analyzes IoT device data at scale.

Certainly, AWS IoT Analytics is not the only way to analyze the Internet of Things (IoT) or Industrial Internet of Things (IIoT) data on AWS. It is common to see Data Analyst teams using a more general AWS data analytics stack, composed of Amazon S3, Amazon Kinesis, AWS Glue, and Amazon Athena or Amazon Redshift and Redshift Spectrum, for analyzing IoT data. So then why choose AWS IoT Analytics over a more traditional AWS data analytics stack? According to AWS, IoT Analytics was purpose-built to manage the complexities of IoT and IIoT data on a petabyte-scale. According to AWS, IoT data frequently has significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Additionally, IoT data must often be enriched and transformed to be meaningful. IoT Analytics can filter, transform, and enrich IoT data before storing it in a time-series data store for analysis.

In the following post, we will explore the use of AWS IoT Analytics to analyze environmental sensor data, in near real-time, from a series of IoT devices. To follow along with the post’s demonstration, there is an option to use sample data to simulate the IoT devices (see the ‘Simulating IoT Device Messages’ section of this post).

IoT Devices

In this post, we will explore IoT Analytics using IoT data generated from a series of custom-built environmental sensor arrays. Each breadboard-based sensor array is connected to a Raspberry Pi single-board computer (SBC), the popular, low cost, credit-card sized Linux computer. The IoT devices were purposely placed in physical locations that vary in temperature, humidity, and other environmental conditions.

rasppi

Each device includes the following sensors:

  1. MQ135 Air Quality Sensor Hazardous Gas Detection Sensor: CO, LPG, Smoke (link)
    (requires an MCP3008 – 8-Channel 10-Bit ADC w/ SPI Interface (link))
  2. DHT22/AM2302 Digital Temperature and Humidity Sensor (link)
  3. Onyehn IR Pyroelectric Infrared PIR Motion Sensor (link)
  4. Anmbest Light Intensity Detection Photosensitive Sensor (link)

rasppi_detail

AWS IoT Device SDK

Each Raspberry Pi device runs a custom Python script, sensor_collector_v2.py. The script uses the AWS IoT Device SDK for Python v2 to communicate with AWS. The script collects a total of seven different readings from the four sensors at a regular interval. Sensor readings include temperature, humidity, carbon monoxide (CO), liquid petroleum gas (LPG), smoke, light, and motion.

The script securely publishes the sensor readings, along with a device ID and timestamp, as a single message, to AWS using the ISO standard Message Queuing Telemetry Transport (MQTT) network protocol. Below is an example of an MQTT message payload, published by the collector script.

{
"data": {
"co": 0.006104480269226063,
"humidity": 55.099998474121094,
"light": true,
"lpg": 0.008895956948783413,
"motion": false,
"smoke": 0.023978358312270912,
"temp": 31.799999237060547
},
"device_id": "6e:81:c9:d4:9e:58",
"ts": 1594419195.292461
}

view raw
iot_message_01.json
hosted with ❤ by GitHub

As shown below, using tcpdump on the IoT device, the MQTT message payloads generated by the script average approximately 275 bytes. The complete MQTT messages average around 300 bytes.

screen_shot_2020-07-15_at_1_56_21_pm

AWS IoT Core

Each Raspberry Pi is registered with AWS IoT Core. IoT Core allows users to quickly and securely connect devices to AWS. According to AWS, IoT Core can reliably scale to billions of devices and trillions of messages. Registered devices are referred to as things in AWS IoT Core. A thing is a representation of a specific device or logical entity. Information about a thing is stored in the registry as JSON data.

IoT Core provides a Device Gateway, which manages all active device connections. The Gateway currently supports MQTT, WebSockets, and HTTP 1.1 protocols. Behind the Message Gateway is a high-throughput pub/sub Message Broker, which securely transmits messages to and from all IoT devices and applications with low latency. Below, we see a typical AWS IoT Core architecture.

AWS_IoT_Diagram_01_Ingest_blog

At a message frequency of five seconds, the three Raspberry Pi devices publish a total of roughly 50,000 IoT messages per day to AWS IoT Core.

monitoring

AWS IoT Security

AWS IoT Core provides mutual authentication and encryption, ensuring all data is exchanged between AWS and the devices are secure by default. In the demo, all data is sent securely using Transport Layer Security (TLS) 1.2 with X.509 digital certificates on port 443. Authorization of the device to access any resource on AWS is controlled by individual AWS IoT Core Policies, similar to AWS IAM Policies. Below, we see an example of an X.509 certificate, assigned to a registered device.

thing_cert

AWS IoT Core Rules

Once an MQTT message is received from an IoT device (a thing), we use AWS IoT Rules to send message data to an AWS IoT Analytics Channel. Rules give your devices the ability to interact with AWS services. Rules are written in standard Structured Query Language (SQL). Rules are analyzed, and Actions are performed based on the MQTT topic stream. Below, we see an example rule that forwards our messages to IoT Analytics, in addition to AWS IoT Events and Amazon Kinesis Data Firehose.

rule

Simulating IoT Device Messages

Building and configuring multiple Raspberry Pi-based sensor arrays, and registering the devices with AWS IoT Core would require a lot of work just for this post. Therefore, I have provided everything you need to simulate the three IoT devices, on GitHub. Use the following command to git clone a local copy of the project.

git clone \
–branch master –single-branch –depth 1 –no-tags \
https://github.com/garystafford/aws-iot-analytics-demo.git

view raw
iot_github.sh
hosted with ❤ by GitHub

AWS CloudFormation

Use the CloudFormation template, iot-analytics.yaml, to create an IoT Analytics stack containing (17) resources, including the following.

  • (3) AWS IoT Things
  • (1) AWS IoT Core Topic Rule
  • (1) AWS IoT Analytics Channel, Pipeline, Data store, and Data set
  • (1) AWS Lambda and Lambda Permission
  • (1) Amazon S3 Bucket
  • (1) Amazon SageMaker Notebook Instance
  • (5) AWS IAM Roles

Please be aware of the costs involved with the AWS resources used in the CloudFormation template before continuing. To build the AWS CloudFormation stack, run the following AWS CLI command.

aws cloudformation create-stack \
–stack-name iot-analytics-demo \
–template-body file://cloudformation/iot-analytics.yaml \
–parameters ParameterKey=ProjectName,ParameterValue=iot-analytics-demo \
ParameterKey=IoTTopicName,ParameterValue=iot-device-data \
–capabilities CAPABILITY_NAMED_IAM

view raw
iot_cfn.sh
hosted with ❤ by GitHub

Below, we see a successful deployment of the IoT Analytics Demo CloudFormation Stack.

cfn_stack

Publishing Sample Messages

Once the CloudFormation stack is created successfully, use an included Python script, send_sample_messages.py, to send sample IoT data to an AWS IoT Topic, from your local machine. The script will use your AWS identity and credentials, instead of an actual IoT device registered with IoT Core. The IoT data will be intercepted by an IoT Topic Rule and redirected, using a Topic Rule Action, to the IoT Analytics Channel.

First, we will ensure the IoT stack is running correctly on AWS by sending a few test messages. Go to the AWS IoT Core Test tab. Subscribe to the iot-device-data topic.

screen_shot_2020-07-13_at_2_06_32_pm

Then, run the following command using the smaller data file, raw_data_small.json.

cd sample_data/
time python3 ./send_sample_messages.py \
-f raw_data_small.json -t iot-device-data

view raw
sample_iot_data_small.sh
hosted with ❤ by GitHub

If successful, you should see the five messages appear in the Test tab, shown above. Example output from the script is shown below.

screen_shot_2020-07-15_at_10.30.58_pm

Then, run the second command using the larger data file, raw_data_large.json, containing 9,995 messages (a few hours worth of data). The command will take approximately 12 minutes to complete.

time python3 ./send_sample_messages.py \
-f raw_data_large.json -t iot-device-data

view raw
sample_iot_data_large.sh
hosted with ❤ by GitHub

Once the second command completes successfully, your IoT Analytics Channel should contain 10,000 unique messages. There is an optional extra-large data file containing approximately 50,000 IoT messages (24 hours of IoT messages).

AWS IoT Analytics

AWS IoT Analytics is composed of five primary components: Channels, Pipelines, Data stores, Data sets, and Notebooks. These components enable you to collect, prepare, store, analyze, and visualize your IoT data.

iot_analytics

Below, we see a typical AWS IoT Analytics architecture. IoT messages are pulled from AWS IoT Core, thought a Rule Action. Amazon QuickSight provides business intelligence, visualization. Amazon QuickSight ML Insights adds anomaly detection and forecasting.

AWS_IoT_Diagram_02_IoT_Analytics_blog

IoT Analytics Channel

An AWS IoT Analytics Channel pulls messages or data into IoT Analytics from other AWS sources, such as Amazon S3, Amazon Kinesis, or Amazon IoT Core. Channels store data for IoT Analytics Pipelines. Both Channels and Data store support storing data in your own Amazon S3 bucket or in an IoT Analytics service-managed S3 bucket. In the demonstration, we are using a service managed S3 bucket.

When creating a Channel, you also decide how long to retain the data. For the demonstration, we have set the data retention period for 14 days. Channels are generally not used for long term storage of data. Typically, you would only retain data in the Channel for the time period you need to analyze. For long term storage of IoT message data, I recommend using an AWS IoT Core Rule to send a copy of the raw IoT data to Amazon S3, using a service such as Amazon Kinesis Data Firehose.

screen_shot_2020-07-13_at_3_03_09_pm

IoT Analytics Pipeline

An AWS IoT Analytics Pipeline consumes messages from one or more Channels. Pipelines transform, filter, and enrich the messages before storing them in IoT Analytics Data stores. A Pipeline is composed of an array of activities. Logically, you must specify both a Channel (source) and a Datastore (destination) activity. Optionally, you may choose as many as 23 additional activities in the pipelineActivities array.

In our demonstration’s Pipeline, iot_analytics_pipeline, we have specified five additional activities, including DeviceRegistryEnrich, Filter, Math, Lambda, and SelectAttributes. There are two additional Activity types we did not choose, RemoveAttributes and AddAttributes.

screen_shot_2020-07-14_at_3_11_01_pm

The demonstration’s Pipeline created by CloudFormation starts with messages from the demonstration’s Channel, iot_analytics_channel, similar to the following.

{
"co": 0.004782974313835918,
"device_id": "ae:c4:1d:34:1c:7b",
"device": "iot-device-01",
"humidity": 68.81000305175781,
"light": true,
"lpg": 0.007456714657976871,
"msg_received": "2020-07-13T19:44:58.690+0000",
"motion": false,
"smoke": 0.019858593777432054,
"temp": 19.200000762939453,
"ts": 1594496359.235107
}

view raw
iot_message_02.json
hosted with ❤ by GitHub

The demonstration’s Pipeline transforms the messages through a series of Pipeline Activities and then stores the resulting message in the demonstration’s Data store, iot_analytics_data_store. The resulting messages appear similar to the following.

{
"co": 0.0048,
"device": "iot-device-01",
"humidity": 68.81,
"light": true,
"lpg": 0.0075,
"metadata": "{defaultclientid=iot-device-01, thingname=iot-device-01, thingid=5de1c2af-14b4-49b5-b20b-b25cf251b01a, thingarn=arn:aws:iot:us-east-1:864887685992:thing/iot-device-01, thingtypename=null, attributes={installed=1594665292, latitude=37.4133144, longitude=-122.1513069}, version=2, billinggroupname=null}",
"msg_received": "2020-07-13T19:44:58.690+0000",
"motion": false,
"smoke": 0.0199,
"temp": 66.56,
"ts": 1594496359.235107
}

view raw
iot_message_03.json
hosted with ❤ by GitHub

In our demonstration, transformations to the messages include dropping the device_id attribute and converting the temp attribute value to Fahrenheit. In addition, the Lambda Activity rounds down the temp, humidity, co, lpg, and smoke attribute values to between 2–4 decimal places of precision.

screen_shot_2020-07-14_at_4_38_33_pm

The demonstration’s Pipeline also enriches the message with the metadata attribute, containing metadata from the IoT device’s AWS IoT Core Registry. The metadata includes additional information about the device that generated the message, including custom attributes we input, such as location (longitude and latitude) and the device’s installation date.

screen_shot_2020-07-14_at_3_15_27_pm

A significant feature of Pipelines is the ability to reprocess messages. If you make a change to the Pipeline, which often happens during the data preparation stage, you can reprocess any or all messages in the associated Channel, and overwrite the messages in the Data set.

screen_shot_2020-07-14_at_4_55_24_pm

IoT Analytics Data store

An AWS IoT Analytics Data store stores prepared data from an AWS IoT Analytics Pipeline, in a fully-managed database. Both Channels and Data store support storing data in your own Amazon S3 bucket or in an IoT Analytics managed S3 bucket. In the demonstration, we are using a service-managed S3 bucket to store messages in our Data store.

screen_shot_2020-07-13_at_3_03_22_pm

IoT Analytics Data set

An AWS IoT Analytics Data set automatically provides regular, up-to-date insights for data analysts by querying a Data store using standard SQL. Regular updates are provided through the use of a cron expression. For the demonstration, we are using a 15-minute interval.

Below, we see the sample messages in the Result preview pane of the Data set. These are the five test messages we sent to check the stack. Note the SQL query used to obtain the messages, which queries the Data store. The Data store, as you will recall, contains the transformed messages from the Pipeline.

screen_shot_2020-07-14_at_4_46_03_pm

IoT Analytics Data sets also support sending content results, which are materialized views of your IoT Analytics data, to an Amazon S3 bucket.

screen_shot_2020-07-14_at_3_32_18_pm

The CloudFormation stack contains an encrypted Amazon S3 Bucket. This bucket receives a copy of the messages from the IoT Analytics Data set whenever the scheduled update is run by the cron expression.

screen_shot_2020-07-14_at_3_38_40_pm

IoT Analytics Notebook

An AWS IoT Analytics Notebook allows users to perform statistical analysis and machine learning on IoT Analytics Data sets using Jupyter Notebooks. The IoT Analytics Notebook service includes a set of notebook templates that contain AWS-authored machine learning models and visualizations. Notebooks Instances can be linked to a GitHub or other source code repository. Notebooks created with IoT Analytics Notebook can also be accessed directly through Amazon SageMaker. For the demonstration, the Notebooks Instance is associated with the project’s GitHub repository.

screen_shot_2020-07-14_at_10_18_12_pm

The repository contains a sample Jupyter Notebook, IoT_Analytics_Demo_Notebook.ipynb, based on the conda_python3 kernel. This preinstalled environment includes the default Anaconda installation and Python 3. The Notebook uses pandas, matplotlib, and plotly to manipulate and visualize the sample IoT messages we published earlier and stored in the Data set.

screen_shot_2020-07-14_at_10_00_39_pm

screen_shot_2020-07-14_at_10_44_54_pm

screen_shot_2020-07-14_at_9_34_27_pm

screen_shot_2020-07-14_at_9_53_27_pm

Notebooks can be modified, and the changes pushed back to GitHub. You could easily fork a copy of my GitHub repository and modify the CloudFormation template, to include your own GitHub repository URL.

screen_shot_2020-07-14_at_10_14_51_pm

Amazon QuickSight

Amazon QuickSight provides business intelligence (BI) and visualization. Amazon QuickSight ML Insights adds anomaly detection and forecasting. We can use Amazon QuickSight to visualize the IoT message data, stored in the IoT Analytics Data set.

Amazon QuickSight has both a Standard and an Enterprise Edition. AWS provides a detailed product comparison of each edition. For the post, I am demonstrating the Enterprise Edition, which includes additional features, such as ML Insights, hourly refreshes of SPICE (super-fast, parallel, in-memory, calculation engine), and theme customization. Please be aware of the costs of Amazon QuickSight if you choose to follow along with this part of the demo. Amazon QuickSight is enabled or configured with the demonstration’s CloudFormation template.

QuickSight Data Sets

Amazon QuickSight has a wide variety of data source options for creating Amazon QuickSight Data sets, including the ones shown below. Do not confuse Amazon QuickSight Data sets with IoT Analytics Data sets. These are two different, yet similar, constructs.

screen_shot_2020-07-15_at_8.50.26_am

For the demonstration, we will create an Amazon QuickSight Data set that will use our IoT Analytics Data set as a data source.

screen_shot_2020-07-15_at_9_06_50_am

Amazon QuickSight gives you the ability to modify QuickSight Data sets. For the demonstration, I have added two additional fields, converting the boolean light and motion values of true and false to binary values of 0 or 1. I have also deselected two fields that I do not need for QuickSight Analysis.

screen_shot_2020-07-15_at_8.53.02_am

QuickSight provides a wide variety of functions, enabling us to perform dynamic calculations on the field values. Below, we see a new calculated field, light_dec, containing the original light field’s Boolean values converted to binary values. I am using a if...else formula to change the field’s value depending on the value in another field.

screen_shot_2020-07-15_at_8.53.16_am

QuickSight Analysis

Using the QuickSight Data set, built from the IoT Analytics Data set as a data source, we create a QuickSight Analysis. The QuickSight Analysis user interface is shown below. An Analysis is primarily a collection of Visuals (Visual types). QuickSight provides a number of Visual types. Each visual is associated with a Data set. Data for the QuickSight Analysis or for each individual visual can be filtered. For the demo, I have created a QuickSight Analysis, including several typical QuickSight Visuals.

screen_shot_2020-07-10_at_12_21_32_pm

QuickSight Dashboards

To share a QuickSight Analysis, we can create a QuickSight Dashboard. Below, we see a few views of the QuickSight Analysis, shown above, as a Dashboard. A viewer of the Dashboard cannot edit the visuals, though they can apply filtering and interactively drill-down into data in the Visuals.

screen_shot_2020-07-15_at_11_38_17_am

screen_shot_2020-07-15_at_11_40_17_am

screen_shot_2020-07-15_at_11_43_23_am

Geospatial Data

Amazon QuickSight understands geospatial data. If you recall, in the IoT Analytics Pipeline, we enriched the messages in the metadata from the device registry. The metadata attributes contained the device’s longitude and latitude. Quicksight will recognize those fields as geographic fields. In our QuickSight Analysis, we can visualize the geospatial data, using the geospatial chart (map) Visual type.

screen_shot_2020-07-10_at_12_22_12_pm

QuickSight Mobile App

Amazon QuickSight offers free iOS and Android versions of the Amazon QuickSight Mobile App. The mobile application makes it easy for registered QuickSight end-users to securely connect to QuickSight Dashboards, using their mobile devices. Below, we see two views of the same Dashboard, shown in the iOS version of the Amazon QuickSight Mobile App.

mobile_quicksight

Amazon QuickSight ML Insights

According to Amazon, ML Insights leverages AWS’s machine learning (ML) and natural language capabilities to gain deeper insights from data. QuickSight’s ML-powered Anomaly Detection continuously analyze data to discover anomalies and variations inside of the aggregates, giving you the insights to act when business changes occur. QuickSight’s ML-powered Forecasting can be used to accurately predict your business metrics, and perform interactive what-if analysis with point-and-click simplicity. QuickSight’s built-in algorithms make it easy for anyone to use ML that learns from your data patterns to provide you with accurate predictions based on historical trends.

Below, we see the ML Insights tab in the demonstration’s QuickSight Analysis. Individually detected anomalies can be added to the QuickSight Analysis, similar to Visuals, and configured to tune the detection parameters.

screen_shot_2020-07-15_at_12_04_21_pm

Below, we see an example of humidity anomalies across all devices, based on their Anomaly Score and are higher or lower with a minimum delta of five percent.

screen_shot_2020-07-15_at_12_24_12_pm

Cleaning Up

You are charged hourly for the SageMaker Notebook Instance. Do not forget to delete your CloudFormation stack when you are done with the demonstration. Note the Amazon S3 bucket will not be deleted; you must do this manually.

aws cloudformation delete-stack \
–stack-name iot-analytics-demo

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iot_cfn_delete.sh
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Conclusion

In this post, we demonstrated how to use AWS IoT Analytics to analyze and visualize streaming messages from multiple IoT devices, in near real-time. Combined with other AWS IoT analytics services, such as AWS IoT SiteWise, AWS IoT Events, and AWS IoT Things Graph, you can create a robust, full-featured IoT Analytics platform, capable of handling millions of industrial, commercial, and residential IoT devices, generating petabytes of data.

This blog represents my own viewpoints and not of my employer, Amazon Web Services.

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Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight: Part 1

Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Cloud, Python, Serverless, Software Development on January 5, 2020

Introduction

According to Wikipedia, data analysis is “a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusion, and supporting decision-making.” In this two-part post, we will explore how to get started with data analysis on AWS, using the serverless capabilities of Amazon Athena, AWS Glue, Amazon QuickSight, Amazon S3, and AWS Lambda. We will learn how to use these complementary services to transform, enrich, analyze, and visualize semi-structured data.

Data Analysis—discovering useful information, informing conclusion, and supporting decision-making. –Wikipedia

In part one, we will begin with raw, semi-structured data in multiple formats. We will discover how to ingest, transform, and enrich that data using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We will build an S3-based data lake, and learn how AWS leverages open-source technologies, such as Presto, Apache Hive, and Apache Parquet. In part two, we will learn how to further analyze and visualize the data using Amazon QuickSight. Here’s a quick preview of what we will build in part one of the post.

Demonstration

In this demonstration, we will adopt the persona of a large, US-based electric energy provider. The energy provider has developed its next-generation Smart Electrical Monitoring Hub (Smart Hub). They have sold the Smart Hub to a large number of residential customers throughout the United States. The hypothetical Smart Hub wirelessly collects detailed electrical usage data from individual, smart electrical receptacles and electrical circuit meters, spread throughout the residence. Electrical usage data is encrypted and securely transmitted from the customer’s Smart Hub to the electric provider, who is running their business on AWS.

Customers are able to analyze their electrical usage with fine granularity, per device, and over time. The goal of the Smart Hub is to enable the customers, using data, to reduce their electrical costs. The provider benefits from a reduction in load on the existing electrical grid and a better distribution of daily electrical load as customers shift usage to off-peak times to save money.

screen_shot_2020-01-13_at_7_57_47_pm_v4.pngPreview of post’s data in Amazon QuickSight.

The original concept for the Smart Hub was developed as part of a multi-day training and hackathon, I recently attended with an AWSome group of AWS Solutions Architects in San Francisco. As a team, we developed the concept of the Smart Hub integrated with a real-time, serverless, streaming data architecture, leveraging AWS IoT Core, Amazon Kinesis, AWS Lambda, and Amazon DynamoDB.

SA_Team_PhotoFrom left: Bruno Giorgini, Mahalingam (‘Mahali’) Sivaprakasam, Gary Stafford, Amit Kumar Agrawal, and Manish Agarwal.

This post will focus on data analysis, as opposed to the real-time streaming aspect of data capture or how the data is persisted on AWS.

athena-glue-architecture-v2High-level AWS architecture diagram of the demonstration.

Featured Technologies

The following AWS services and open-source technologies are featured prominently in this post.

Athena-Glue-v2.png

Amazon S3-based Data Lake

Screen Shot 2020-01-02 at 5.09.05 PMAn Amazon S3-based Data Lake uses Amazon S3 as its primary storage platform. Amazon S3 provides an optimal foundation for a data lake because of its virtually unlimited scalability, from gigabytes to petabytes of content. Amazon S3 provides ‘11 nines’ (99.999999999%) durability. It has scalable performance, ease-of-use features, and native encryption and access control capabilities.

AWS Glue

Screen Shot 2020-01-02 at 5.11.37 PMAWS Glue is a fully managed extract, transform, and load (ETL) service to prepare and load data for analytics. AWS Glue discovers your data and stores the associated metadata (e.g., table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL.

AWS Glue Data Catalog

Screen Shot 2020-01-02 at 5.13.01 PM.pngThe AWS Glue Data Catalog is an Apache Hive Metastore compatible, central repository to store structural and operational metadata for data assets. For a given data set, store table definition, physical location, add business-relevant attributes, as well as track how the data has changed over time.

AWS Glue Crawler

Screen Shot 2020-01-02 at 5.14.57 PMAn AWS Glue Crawler connects to a data store, progresses through a prioritized list of classifiers to extract the schema of your data and other statistics, and then populates the Glue Data Catalog with this metadata. Crawlers can run periodically to detect the availability of new data as well as changes to existing data, including table definition changes. Crawlers automatically add new tables, new partitions to an existing table, and new versions of table definitions. You can even customize Glue Crawlers to classify your own file types.

AWS Glue ETL Job

Screen Shot 2020-01-02 at 5.11.37 PMAn AWS Glue ETL Job is the business logic that performs extract, transform, and load (ETL) work in AWS Glue. When you start a job, AWS Glue runs a script that extracts data from sources, transforms the data, and loads it into targets. AWS Glue generates a PySpark or Scala script, which runs on Apache Spark.

Amazon Athena

Screen Shot 2020-01-02 at 5.17.49 PMAmazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena supports and works with a variety of standard data formats, including CSV, JSON, Apache ORC, Apache Avro, and Apache Parquet. Athena is integrated, out-of-the-box, with AWS Glue Data Catalog. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

The underlying technology behind Amazon Athena is Presto, the open-source distributed SQL query engine for big data, created by Facebook. According to the AWS, the Athena query engine is based on Presto 0.172 (released April 9, 2017). In addition to Presto, Athena uses Apache Hive to define tables.

Amazon QuickSight

Screen Shot 2020-01-02 at 5.18.40 PMAmazon QuickSight is a fully managed business intelligence (BI) service. QuickSight lets you create and publish interactive dashboards that can then be accessed from any device, and embedded into your applications, portals, and websites.

AWS Lambda

Screen Shot 2020-01-02 at 5.25.57 PMAWS Lambda automatically runs code without requiring the provisioning or management servers. AWS Lambda automatically scales applications by running code in response to triggers. Lambda code runs in parallel. With AWS Lambda, you are charged for every 100ms your code executes and the number of times your code is triggered. You pay only for the compute time you consume.

Smart Hub Data

Everything in this post revolves around data. For the post’s demonstration, we will start with four categories of raw, synthetic data. Those data categories include Smart Hub electrical usage data, Smart Hub sensor mapping data, Smart Hub residential locations data, and electrical rate data. To demonstrate the capabilities of AWS Glue to handle multiple data formats, the four categories of raw data consist of three distinct file formats: XML, JSON, and CSV. I have attempted to incorporate as many ‘real-world’ complexities into the data without losing focus on the main subject of the post. The sample datasets are intentionally small to keep your AWS costs to a minimum for the demonstration.

To further reduce costs, we will use a variety of data partitioning schemes. According to AWS, by partitioning your data, you can restrict the amount of data scanned by each query, thus improving performance and reducing cost. We have very little data for the demonstration, in which case partitioning may negatively impact query performance. However, in a ‘real-world’ scenario, there would be millions of potential residential customers generating terabytes of data. In that case, data partitioning would be essential for both cost and performance.

Smart Hub Electrical Usage Data

The Smart Hub’s time-series electrical usage data is collected from the customer’s Smart Hub. In the demonstration’s sample electrical usage data, each row represents a completely arbitrary five-minute time interval. There are a total of ten electrical sensors whose electrical usage in kilowatt-hours (kW) is recorded and transmitted. Each Smart Hub records and transmits electrical usage for 10 device sensors, 288 times per day (24 hr / 5 min intervals), for a total of 2,880 data points per day, per Smart Hub. There are two days worth of usage data for the demonstration, for a total of 5,760 data points. The data is stored in JSON Lines format. The usage data will be partitioned in the Amazon S3-based data lake by date (e.g., ‘dt=2019-12-21’).

{"loc_id":"b6a8d42425fde548","ts":1576915200,"data":{"s_01":0,"s_02":0.00502,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04167}}
{"loc_id":"b6a8d42425fde548","ts":1576915500,"data":{"s_01":0,"s_02":0.00552,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04147}}
{"loc_id":"b6a8d42425fde548","ts":1576915800,"data":{"s_01":0.29267,"s_02":0.00642,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04207}}
{"loc_id":"b6a8d42425fde548","ts":1576916100,"data":{"s_01":0.29207,"s_02":0.00592,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04137}}
{"loc_id":"b6a8d42425fde548","ts":1576916400,"data":{"s_01":0.29217,"s_02":0.00622,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04157}}
{"loc_id":"b6a8d42425fde548","ts":1576916700,"data":{"s_01":0,"s_02":0.00562,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04197}}
{"loc_id":"b6a8d42425fde548","ts":1576917000,"data":{"s_01":0,"s_02":0.00512,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04257}}
{"loc_id":"b6a8d42425fde548","ts":1576917300,"data":{"s_01":0,"s_02":0.00522,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04177}}
{"loc_id":"b6a8d42425fde548","ts":1576917600,"data":{"s_01":0,"s_02":0.00502,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04267}}
{"loc_id":"b6a8d42425fde548","ts":1576917900,"data":{"s_01":0,"s_02":0.00612,"s_03":0,"s_04":0,"s_05":0,"s_06":0,"s_07":0,"s_08":0,"s_09":0,"s_10":0.04237}}

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smart_data.json
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Note the electrical usage data contains nested data. The electrical usage for each of the ten sensors is contained in a JSON array, within each time series entry. The array contains ten numeric values of type, double.

{
"loc_id": "b6a8d42425fde548",
"ts": 1576916400,
"data": {
"s_01": 0.29217,
"s_02": 0.00622,
"s_03": 0,
"s_04": 0,
"s_05": 0,
"s_06": 0,
"s_07": 0,
"s_08": 0,
"s_09": 0,
"s_10": 0.04157
}
}

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usage-date-nested.json
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Real data is often complex and deeply nested. Later in the post, we will see that AWS Glue can map many common data types, including nested data objects, as illustrated below.

screen_shot_2020-01-05_at_7_46_19_am

Smart Hub Sensor Mappings

The Smart Hub sensor mappings data maps a sensor column in the usage data (e.g., ‘s_01’ to the corresponding actual device (e.g., ‘Central Air Conditioner’). The data contains the device location, wattage, and the last time the record was modified. The data is also stored in JSON Lines format. The sensor mappings data will be partitioned in the Amazon S3-based data lake by the state of the residence (e.g., ‘state=or’ for Oregon).

{"loc_id":"b6a8d42425fde548","id":"s_01","description":"Central Air Conditioner","location":"N/A","watts":3500,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_02","description":"Ceiling Fan","location":"Master Bedroom","watts":65,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_03","description":"Clothes Dryer","location":"Basement","watts":5000,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_04","description":"Clothes Washer","location":"Basement","watts":1800,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_05","description":"Dishwasher","location":"Kitchen","watts":900,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_06","description":"Flat Screen TV","location":"Living Room","watts":120,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_07","description":"Microwave Oven","location":"Kitchen","watts":1000,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_08","description":"Coffee Maker","location":"Kitchen","watts":900,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_09","description":"Hair Dryer","location":"Master Bathroom","watts":2000,"last_modified":1559347200}
{"loc_id":"b6a8d42425fde548","id":"s_10","description":"Refrigerator","location":"Kitchen","watts":500,"last_modified":1559347200}

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sensor_mappings.json
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Smart Hub Locations

The Smart Hub locations data contains the geospatial coordinates, home address, and timezone for each residential Smart Hub. The data is stored in CSV format. The data for the four cities included in this demonstration originated from OpenAddresses, ‘the free and open global address collection.’ There are approximately 4k location records. The location data will be partitioned in the Amazon S3-based data lake by the state of the residence where the Smart Hub is installed (e.g., ‘state=or’ for Oregon).


lon lat number street unit city district region postcode id hash tz
-122.8077278 45.4715614 6635 SW JUNIPER TER 97008 b6a8d42425fde548 America/Los_Angeles
-122.8356634 45.4385864 11225 SW PINTAIL LOOP 97007 08ae3df798df8b90 America/Los_Angeles
-122.8252379 45.4481709 9930 SW WRANGLER PL 97008 1c7e1f7df752663e America/Los_Angeles
-122.8354211 45.4535977 9174 SW PLATINUM PL 97007 b364854408ee431e America/Los_Angeles
-122.8315771 45.4949449 15040 SW MILLIKAN WAY # 233 97003 0e97796ba31ba3b4 America/Los_Angeles
-122.7950339 45.4470259 10006 SW CONESTOGA DR # 113 97008 2b5307be5bfeb026 America/Los_Angeles
-122.8072836 45.4908594 12600 SW CRESCENT ST # 126 97005 4d74167f00f63f50 America/Los_Angeles
-122.8211801 45.4689303 7100 SW 140TH PL 97008 c5568631f0b9de9c America/Los_Angeles
-122.831154 45.4317057 15050 SW MALLARD DR # 101 97007 dbd1321080ce9682 America/Los_Angeles
-122.8162856 45.4442878 10460 SW 136TH PL 97008 008faab8a9a3e519 America/Los_Angeles

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smart_hub_locations.csv
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Electrical Rates

Lastly, the electrical rate data contains the cost of electricity. In this demonstration, the assumption is that the rate varies by state, by month, and by the hour of the day. The data is stored in XML, a data export format still common to older, legacy systems. The electrical rate data will not be partitioned in the Amazon S3-based data lake.

<?xml version="1.0" encoding="UTF-8"?>
<root>
<row>
<state>or</state>
<year>2019</year>
<month>12</month>
<from>19:00:00</from>
<to>19:59:59</to>
<type>peak</type>
<rate>12.623</rate>
</row>
<row>
<state>or</state>
<year>2019</year>
<month>12</month>
<from>20:00:00</from>
<to>20:59:59</to>
<type>partial-peak</type>
<rate>7.232</rate>
</row>
<row>
<state>or</state>
<year>2019</year>
<month>12</month>
<from>21:00:00</from>
<to>21:59:59</to>
<type>partial-peak</type>
<rate>7.232</rate>
</row>
<row>
<state>or</state>
<year>2019</year>
<month>12</month>
<from>22:00:00</from>
<to>22:59:59</to>
<type>off-peak</type>
<rate>4.209</rate>
</row>
</root>

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rates.xml
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Data Analysis Process

Due to the number of steps involved in the data analysis process in the demonstration, I have divided the process into four logical stages: 1) Raw Data Ingestion, 2) Data Transformation, 3) Data Enrichment, and 4) Data Visualization and Business Intelligence (BI).

athena-glue-0.pngFull data analysis workflow diagram (click to enlarge…)

Raw Data Ingestion

In the Raw Data Ingestion stage, semi-structured CSV-, XML-, and JSON-format data files are copied to a secure Amazon Simple Storage Service (S3) bucket. Within the bucket, data files are organized into folders based on their physical data structure (schema). Due to the potentially unlimited number of data files, files are further organized (partitioned) into subfolders. Organizational strategies for data files are based on date, time, geographic location, customer id, or other common data characteristics.

This collection of semi-structured data files, S3 buckets, and partitions form what is referred to as a Data Lake. According to AWS, a data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale.

A series of AWS Glue Crawlers process the raw CSV-, XML-, and JSON-format files, extracting metadata, and creating table definitions in the AWS Glue Data Catalog. According to AWS, an AWS Glue Data Catalog contains metadata tables, where each table specifies a single data store.

Athena-Glue-1

Data Transformation

In the Data Transformation stage, the raw data in the previous stage is transformed. Data transformation may include both modifying the data and changing the data format. Data modifications include data cleansing, re-casting data types, changing date formats, field-level computations, and field concatenation.

The data is then converted from CSV-, XML-, and JSON-format to Apache Parquet format and written back to the Amazon S3-based data lake. Apache Parquet is a compressed, efficient columnar storage format. Amazon Athena, like many Cloud-based services, charges you by the amount of data scanned per query. Hence, using data partitioning, bucketing, compression, and columnar storage formats, like Parquet, will reduce query cost.

Lastly, the transformed Parquet-format data is cataloged to new tables, alongside the raw CSV, XML, and JSON data, in the Glue Data Catalog.

Athena-Glue-2

Data Enrichment

According to ScienceDirect, data enrichment or augmentation is the process of enhancing existing information by supplementing missing or incomplete data. Typically, data enrichment is achieved by using external data sources, but that is not always the case.

Data Enrichment—the process of enhancing existing information by supplementing missing or incomplete data. –ScienceDirect

In the Data Enrichment stage, the Parquet-format Smart Hub usage data is augmented with related data from the three other data sources: sensor mappings, locations, and electrical rates. The customer’s Smart Hub usage data is enriched with the customer’s device types, the customer’s timezone, and customer’s electricity cost per monitored period based on the customer’s geographic location and time of day.

Athena-Glue-3a

Once the data is enriched, it is converted to Parquet and optimized for query performance, stored in the data lake, and cataloged. At this point, the original CSV-, XML-, and JSON-format raw data files, the transformed Parquet-format data files, and the Parquet-format enriched data files are all stored in the Amazon S3-based data lake and cataloged in the Glue Data Catalog.

Athena-Glue-3b

Data Visualization

In the final Data Visualization and Business Intelligence (BI) stage, the enriched data is presented and analyzed. There are many enterprise-grade services available for visualization and Business Intelligence, which integrate with Athena. Amazon services include Amazon QuickSight, Amazon EMR, and Amazon SageMaker. Third-party solutions from AWS Partners, available on the AWS Marketplace, include Tableau, Looker, Sisense, and Domo. In this demonstration, we will focus on Amazon QuickSight.

Athena-Glue-4

Getting Started

Requirements

To follow along with the demonstration, you will need an AWS Account and a current version of the AWS CLI. To get the most from the demonstration, you should also have Python 3 and jq installed in your work environment.

Source Code

All source code for this post can be found on GitHub. Use the following command to clone a copy of the project.

git clone \
–branch master –single-branch –depth 1 –no-tags \
https://github.com/garystafford/athena-glue-quicksight-demo.git

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git_clone.sh
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Source code samples in this post are displayed as GitHub Gists, which will not display correctly on some mobile and social media browsers.

TL;DR?

Just want the jump in without reading the instructions? All the AWS CLI commands, found within the post, are consolidated in the GitHub project’s README file.

CloudFormation Stack

To start, create the ‘smart-hub-athena-glue-stack’ CloudFormation stack using the smart-hub-athena-glue.yml template. The template will create (3) Amazon S3 buckets, (1) AWS Glue Data Catalog Database, (5) Data Catalog Database Tables, (6) AWS Glue Crawlers, (1) AWS Glue ETL Job, and (1) IAM Service Role for AWS Glue.

Make sure to change the DATA_BUCKET, SCRIPT_BUCKET, and LOG_BUCKET variables, first, to your own unique S3 bucket names. I always suggest using the standard AWS 3-part convention of 1) descriptive name, 2) AWS Account ID or Account Alias, and 3) AWS Region, to name your bucket (e.g. ‘smart-hub-data-123456789012-us-east-1’).

# *** CHANGE ME ***
BUCKET_SUFFIX="123456789012-us-east-1"
DATA_BUCKET="smart-hub-data-${BUCKET_SUFFIX}"
SCRIPT_BUCKET="smart-hub-scripts-${BUCKET_SUFFIX}"
LOG_BUCKET="smart-hub-logs-${BUCKET_SUFFIX}"
aws cloudformation create-stack \
–stack-name smart-hub-athena-glue-stack \
–template-body file://cloudformation/smart-hub-athena-glue.yml \
–parameters ParameterKey=DataBucketName,ParameterValue=${DATA_BUCKET} \
ParameterKey=ScriptBucketName,ParameterValue=${SCRIPT_BUCKET} \
ParameterKey=LogBucketName,ParameterValue=${LOG_BUCKET} \
–capabilities CAPABILITY_NAMED_IAM

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step1-2.sh
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Raw Data Files

Next, copy the raw CSV-, XML-, and JSON-format data files from the local project to the DATA_BUCKET S3 bucket (steps 1a-1b in workflow diagram). These files represent the beginnings of the S3-based data lake. Each category of data uses a different strategy for organizing and separating the files. Note the use of the Apache Hive-style partitions (e.g., /smart_hub_data_json/dt=2019-12-21). As discussed earlier, the assumption is that the actual, large volume of data in the data lake would necessitate using partitioning to improve query performance.

# location data
aws s3 cp data/locations/denver_co_1576656000.csv \
s3://${DATA_BUCKET}/smart_hub_locations_csv/state=co/
aws s3 cp data/locations/palo_alto_ca_1576742400.csv \
s3://${DATA_BUCKET}/smart_hub_locations_csv/state=ca/
aws s3 cp data/locations/portland_metro_or_1576742400.csv \
s3://${DATA_BUCKET}/smart_hub_locations_csv/state=or/
aws s3 cp data/locations/stamford_ct_1576569600.csv \
s3://${DATA_BUCKET}/smart_hub_locations_csv/state=ct/
# sensor mapping data
aws s3 cp data/mappings/ \
s3://${DATA_BUCKET}/sensor_mappings_json/state=or/ \
–recursive
# electrical usage data
aws s3 cp data/usage/2019-12-21/ \
s3://${DATA_BUCKET}/smart_hub_data_json/dt=2019-12-21/ \
–recursive
aws s3 cp data/usage/2019-12-22/ \
s3://${DATA_BUCKET}/smart_hub_data_json/dt=2019-12-22/ \
–recursive
# electricity rates data
aws s3 cp data/rates/ \
s3://${DATA_BUCKET}/electricity_rates_xml/ \
–recursive

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step3.sh
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Confirm the contents of the DATA_BUCKET S3 bucket with the following command.

aws s3 ls s3://${DATA_BUCKET}/ \
–recursive –human-readable –summarize

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step3.sh
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There should be a total of (14) raw data files in the DATA_BUCKET S3 bucket.

2020-01-04 14:39:51 20.0 KiB electricity_rates_xml/2019_12_1575270000.xml
2020-01-04 14:39:46 1.3 KiB sensor_mappings_json/state=or/08ae3df798df8b90_1550908800.json
2020-01-04 14:39:46 1.3 KiB sensor_mappings_json/state=or/1c7e1f7df752663e_1559347200.json
2020-01-04 14:39:46 1.3 KiB sensor_mappings_json/state=or/b6a8d42425fde548_1568314800.json
2020-01-04 14:39:47 44.9 KiB smart_hub_data_json/dt=2019-12-21/08ae3df798df8b90_1576915200.json
2020-01-04 14:39:47 44.9 KiB smart_hub_data_json/dt=2019-12-21/1c7e1f7df752663e_1576915200.json
2020-01-04 14:39:47 44.9 KiB smart_hub_data_json/dt=2019-12-21/b6a8d42425fde548_1576915200.json
2020-01-04 14:39:49 44.6 KiB smart_hub_data_json/dt=2019-12-22/08ae3df798df8b90_15770016000.json
2020-01-04 14:39:49 44.6 KiB smart_hub_data_json/dt=2019-12-22/1c7e1f7df752663e_1577001600.json
2020-01-04 14:39:49 44.6 KiB smart_hub_data_json/dt=2019-12-22/b6a8d42425fde548_15770016001.json
2020-01-04 14:39:39 89.7 KiB smart_hub_locations_csv/state=ca/palo_alto_ca_1576742400.csv
2020-01-04 14:39:37 84.2 KiB smart_hub_locations_csv/state=co/denver_co_1576656000.csv
2020-01-04 14:39:44 78.6 KiB smart_hub_locations_csv/state=ct/stamford_ct_1576569600.csv
2020-01-04 14:39:42 91.6 KiB smart_hub_locations_csv/state=or/portland_metro_or_1576742400.csv
Total Objects: 14
Total Size: 636.7 KiB

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raw_data_files.txt
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Lambda Functions

Next, package the (5) Python3.8-based AWS Lambda functions for deployment.

pushd lambdas/athena-json-to-parquet-data || exit
zip -r package.zip index.py
popd || exit
pushd lambdas/athena-csv-to-parquet-locations || exit
zip -r package.zip index.py
popd || exit
pushd lambdas/athena-json-to-parquet-mappings || exit
zip -r package.zip index.py
popd || exit
pushd lambdas/athena-complex-etl-query || exit
zip -r package.zip index.py
popd || exit
pushd lambdas/athena-parquet-to-parquet-elt-data || exit
zip -r package.zip index.py
popd || exit

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step4.sh
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Copy the five Lambda packages to the SCRIPT_BUCKET S3 bucket. The ZIP archive Lambda packages are accessed by the second CloudFormation stack, smart-hub-serverless. This CloudFormation stack, which creates the Lambda functions, will fail to deploy if the packages are not found in the SCRIPT_BUCKET S3 bucket.

I have chosen to place the packages in a different S3 bucket then the raw data files. In a real production environment, these two types of files would be separated, minimally, into separate buckets for security. Remember, only data should go into the data lake.

aws s3 cp lambdas/athena-json-to-parquet-data/package.zip \
s3://${SCRIPT_BUCKET}/lambdas/athena_json_to_parquet_data/
aws s3 cp lambdas/athena-csv-to-parquet-locations/package.zip \
s3://${SCRIPT_BUCKET}/lambdas/athena_csv_to_parquet_locations/
aws s3 cp lambdas/athena-json-to-parquet-mappings/package.zip \
s3://${SCRIPT_BUCKET}/lambdas/athena_json_to_parquet_mappings/
aws s3 cp lambdas/athena-complex-etl-query/package.zip \
s3://${SCRIPT_BUCKET}/lambdas/athena_complex_etl_query/
aws s3 cp lambdas/athena-parquet-to-parquet-elt-data/package.zip \
s3://${SCRIPT_BUCKET}/lambdas/athena_parquet_to_parquet_elt_data/

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step5.sh
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Create the second ‘smart-hub-lambda-stack’ CloudFormation stack using the smart-hub-lambda.yml CloudFormation template. The template will create (5) AWS Lambda functions and (1) Lambda execution IAM Service Role.

aws cloudformation create-stack \
–stack-name smart-hub-lambda-stack \
–template-body file://cloudformation/smart-hub-lambda.yml \
–capabilities CAPABILITY_NAMED_IAM

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step6.sh
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At this point, we have deployed all of the AWS resources required for the demonstration using CloudFormation. We have also copied all of the raw CSV-, XML-, and JSON-format data files in the Amazon S3-based data lake.

AWS Glue Crawlers

If you recall, we created five tables in the Glue Data Catalog database as part of the CloudFormation stack. One table for each of the four raw data types and one table to hold temporary ELT data later in the demonstration. To confirm the five tables were created in the Glue Data Catalog database, use the Glue Data Catalog Console, or run the following AWS CLI / jq command.

aws glue get-tables \
–database-name smart_hub_data_catalog \
| jq -r '.TableList[].Name'

view raw
step8.sh
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The five data catalog tables should be as follows.

electricity_rates_xml
etl_tmp_output_parquet
sensor_mappings_json
smart_hub_data_json
smart_hub_locations_csv

view raw
step8.txt
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We also created six Glue Crawlers as part of the CloudFormation template. Four of these Crawlers are responsible for cataloging the raw CSV-, XML-, and JSON-format data from S3 into the corresponding, existing Glue Data Catalog database tables. The Crawlers will detect any new partitions and add those to the tables as well. Each Crawler corresponds to one of the four raw data types. Crawlers can be scheduled to run periodically, cataloging new data and updating data partitions. Crawlers will also create a Data Catalog database tables. We use Crawlers to create new tables, later in the post.

Run the four Glue Crawlers using the AWS CLI (step 1c in workflow diagram).

aws glue start-crawler –name smart-hub-locations-csv
aws glue start-crawler –name smart-hub-sensor-mappings-json
aws glue start-crawler –name smart-hub-data-json
aws glue start-crawler –name smart-hub-rates-xml

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step7.sh
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You can check the Glue Crawler Console to ensure the four Crawlers finished successfully.

screen_shot_2020-01-03_at_3_05_29_pm

Alternately, use another AWS CLI / jq command.

aws glue get-crawler-metrics \
| jq -r '.CrawlerMetricsList[] | "\(.CrawlerName): \(.StillEstimating), \(.TimeLeftSeconds)"' \
| grep "^smart-hub-[A-Za-z-]*"

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step8.sh
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When complete, all Crawlers should all be in a state of ‘Still Estimating = false’ and ‘TimeLeftSeconds = 0’. In my experience, the Crawlers can take up one minute to start, after the estimation stage, and one minute to stop when complete.

smart-hub-data-json: true, 0
smart-hub-etl-tmp-output-parquet: false, 0
smart-hub-locations-csv: false, 15
smart-hub-rates-parquet: false, 0
smart-hub-rates-xml: false, 15
smart-hub-sensor-mappings-json: false, 15

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step8.txt
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Successfully running the four Crawlers completes the Raw Data Ingestion stage of the demonstration.

Converting to Parquet with CTAS

With the Raw Data Ingestion stage completed, we will now transform the raw Smart Hub usage data, sensor mapping data, and locations data into Parquet-format using three AWS Lambda functions. Each Lambda subsequently calls Athena, which executes a CREATE TABLE AS SELECT SQL statement (aka CTAS) . Each Lambda executes a similar command, varying only by data source, data destination, and partitioning scheme. Below, is an example of the command used for the Smart Hub electrical usage data, taken from the Python-based Lambda, athena-json-to-parquet-data/index.py.

query = \
"CREATE TABLE IF NOT EXISTS " + data_catalog + "." + output_directory + " " \
"WITH ( " \
" format = 'PARQUET', " \
" parquet_compression = 'SNAPPY', " \
" partitioned_by = ARRAY['dt'], " \
" external_location = 's3://" + data_bucket + "/" + output_directory + "' " \
") AS " \
"SELECT * " \
"FROM " + data_catalog + "." + input_directory + ";"

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athena_command.py
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This compact, yet powerful CTAS statement converts a copy of the raw JSON- and CSV-format data files into Parquet-format, and partitions and stores the resulting files back into the S3-based data lake. Additionally, the CTAS SQL statement catalogs the Parquet-format data files into the Glue Data Catalog database, into new tables. Unfortunately, this method will not work for the XML-format raw data files, which we will tackle next.

The five deployed Lambda functions should be visible from the Lambda Console’s Functions tab.

screen_shot_2020-01-04_at_5_57_31_pm

Invoke the three Lambda functions using the AWS CLI. (part of step 2a in workflow diagram).

aws lambda invoke \
–function-name athena-json-to-parquet-data \
response.json
aws lambda invoke \
–function-name athena-csv-to-parquet-locations \
response.json
aws lambda invoke \
–function-name athena-json-to-parquet-mappings \
response.json

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step9.sh
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Here is an example of the same CTAS command, shown above for the Smart Hub electrical usage data, as it is was executed successfully by Athena.

CREATE TABLE IF NOT EXISTS smart_hub_data_catalog.smart_hub_data_parquet
WITH (format = 'PARQUET',
parquet_compression = 'SNAPPY',
partitioned_by = ARRAY['dt'],
external_location = 's3://smart-hub-data-demo-account-1-us-east-1/smart_hub_data_parquet')
AS
SELECT *
FROM smart_hub_data_catalog.smart_hub_data_json

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athena_command.sql
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We can view any Athena SQL query from the Athena Console’s History tab. Clicking on a query (in pink) will copy it to the Query Editor tab and execute it. Below, we see the three SQL statements executed by the Lamba functions.

screen_shot_2020-01-04_at_7_08_32_pm

AWS Glue ETL Job for XML

If you recall, the electrical rate data is in XML format. The Lambda functions we just executed, converted the CSV and JSON data to Parquet using Athena. Currently, unlike CSV, JSON, ORC, Parquet, and Avro, Athena does not support the older XML data format. For the XML data files, we will use an AWS Glue ETL Job to convert the XML data to Parquet. The Glue ETL Job is written in Python and uses Apache Spark, along with several AWS Glue PySpark extensions. For this job, I used an existing script created in the Glue ETL Jobs Console as a base, then modified the script to meet my needs.

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
args = getResolvedOptions(sys.argv, [
'JOB_NAME',
's3_output_path',
'source_glue_database',
'source_glue_table'
])
s3_output_path = args['s3_output_path']
source_glue_database = args['source_glue_database']
source_glue_table = args['source_glue_table']
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
datasource0 = glueContext. \
create_dynamic_frame. \
from_catalog(database=source_glue_database,
table_name=source_glue_table,
transformation_ctx="datasource0")
applymapping1 = ApplyMapping.apply(
frame=datasource0,
mappings=[("from", "string", "from", "string"),
("to", "string", "to", "string"),
("type", "string", "type", "string"),
("rate", "double", "rate", "double"),
("year", "int", "year", "int"),
("month", "int", "month", "int"),
("state", "string", "state", "string")],
transformation_ctx="applymapping1")
resolvechoice2 = ResolveChoice.apply(
frame=applymapping1,
choice="make_struct",
transformation_ctx="resolvechoice2")
dropnullfields3 = DropNullFields.apply(
frame=resolvechoice2,
transformation_ctx="dropnullfields3")
datasink4 = glueContext.write_dynamic_frame.from_options(
frame=dropnullfields3,
connection_type="s3",
connection_options={
"path": s3_output_path,
"partitionKeys": ["state"]
},
format="parquet",
transformation_ctx="datasink4")
job.commit()

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rates_xml_to_parquet.py
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The three Python command-line arguments the script expects (lines 10–12, above) are defined in the CloudFormation template, smart-hub-athena-glue.yml. Below, we see them on lines 10–12 of the CloudFormation snippet. They are injected automatically when the job is run and can be overridden from the command line when starting the job.

GlueJobRatesToParquet:
Type: AWS::Glue::Job
Properties:
GlueVersion: 1.0
Command:
Name: glueetl
PythonVersion: 3
ScriptLocation: !Sub "s3://${ScriptBucketName}/glue_scripts/rates_xml_to_parquet.py"
DefaultArguments: {
"–s3_output_path": !Sub "s3://${DataBucketName}/electricity_rates_parquet",
"–source_glue_database": !Ref GlueDatabase,
"–source_glue_table": "electricity_rates_xml",
"–job-bookmark-option": "job-bookmark-enable",
"–enable-spark-ui": "true",
"–spark-event-logs-path": !Sub "s3://${LogBucketName}/glue-etl-jobs/"
}
Description: "Convert electrical rates XML data to Parquet"
ExecutionProperty:
MaxConcurrentRuns: 2
MaxRetries: 0
Name: rates-xml-to-parquet
Role: !GetAtt "CrawlerRole.Arn"
DependsOn:
– CrawlerRole
– GlueDatabase
– DataBucket
– ScriptBucket
– LogBucket

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elt-job-cfn.yml
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First, copy the Glue ETL Job Python script to the SCRIPT_BUCKET S3 bucket.

aws s3 cp glue-scripts/rates_xml_to_parquet.py \
s3://${SCRIPT_BUCKET}/glue_scripts/

view raw
step10.sh
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Next, start the Glue ETL Job (part of step 2a in workflow diagram). Although the conversion is a relatively simple set of tasks, the creation of the Apache Spark environment, to execute the tasks, will take several minutes. Whereas the Glue Crawlers took about 2 minutes on average, the Glue ETL Job could take 10–15 minutes in my experience. The actual execution time only takes about 1–2 minutes of the 10–15 minutes to complete. In my opinion, waiting up to 15 minutes is too long to be viable for ad-hoc jobs against smaller datasets; Glue ETL Jobs are definitely targeted for big data.

aws glue start-job-run –job-name rates-xml-to-parquet

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step11.sh
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To check on the status of the job, use the Glue ETL Jobs Console, or use the AWS CLI.

# get status of most recent job (the one that is running)
aws glue get-job-run \
–job-name rates-xml-to-parquet \
–run-id "$(aws glue get-job-runs \
–job-name rates-xml-to-parquet \
| jq -r '.JobRuns[0].Id')"

view raw
step11.sh
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When complete, you should see results similar to the following. Note the ‘JobRunState’ is ‘SUCCEEDED.’ This particular job ran for a total of 14.92 minutes, while the actual execution time was 2.25 minutes.

{
"JobRun": {
"Id": "jr_f7186b26bf042ea7773ad08704d012d05299f080e7ac9b696ca8dd575f79506b",
"Attempt": 0,
"JobName": "rates-xml-to-parquet",
"StartedOn": 1578022390.301,
"LastModifiedOn": 1578023285.632,
"CompletedOn": 1578023285.632,
"JobRunState": "SUCCEEDED",
"PredecessorRuns": [],
"AllocatedCapacity": 10,
"ExecutionTime": 135,
"Timeout": 2880,
"MaxCapacity": 10.0,
"LogGroupName": "/aws-glue/jobs",
"GlueVersion": "1.0"
}
}

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job-results.json
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The job’s progress and the results are also visible in the AWS Glue Console’s ETL Jobs tab.

screen_shot_2020-01-04_at_7_42_51_pm

Detailed Apache Spark logs are also available in CloudWatch Management Console, which is accessible directly from the Logs link in the AWS Glue Console’s ETL Jobs tab.

screen_shot_2020-01-04_at_7_44_08_pm

The last step in the Data Transformation stage is to convert catalog the Parquet-format electrical rates data, created with the previous Glue ETL Job, using yet another Glue Crawler (part of step 2b in workflow diagram). Start the following Glue Crawler to catalog the Parquet-format electrical rates data.

aws glue start-crawler –name smart-hub-rates-parquet

view raw
step11b.sh
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This concludes the Data Transformation stage. The raw and transformed data is in the data lake, and the following nine tables should exist in the Glue Data Catalog.

electricity_rates_parquet
electricity_rates_xml
etl_tmp_output_parquet
sensor_mappings_json
sensor_mappings_parquet
smart_hub_data_json
smart_hub_data_parquet
smart_hub_locations_csv
smart_hub_locations_parquet

view raw
data_catalog_tables.txt
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If we examine the tables, we should observe the data partitions we used to organize the data files in the Amazon S3-based data lake are contained in the table metadata. Below, we see the four partitions, based on state, of the Parquet-format locations data.

screen_shot_2020-01-05_at_7_45_46_am

Data Enrichment

To begin the Data Enrichment stage, we will invoke the AWS Lambda, athena-complex-etl-query/index.py. This Lambda accepts input parameters (lines 28–30, below), passed in the Lambda handler’s event parameter. The arguments include the Smart Hub ID, the start date for the data requested, and the end date for the data requested. The scenario for the demonstration is that a customer with the location id value, using the electrical provider’s application, has requested data for a particular range of days (start date and end date), to visualize and analyze.

The Lambda executes a series of Athena INSERT INTO SQL statements, one statement for each of the possible Smart Hub connected electrical sensors, s_01 through s_10, for which there are values in the Smart Hub electrical usage data. Amazon just released the Amazon Athena INSERT INTO a table using the results of a SELECT query capability in September 2019, an essential addition to Athena. New Athena features are listed in the release notes.

Here, the SELECT query is actually a series of chained subqueries, using Presto SQL’s WITH clause capability. The queries join the Parquet-format Smart Hub electrical usage data sources in the S3-based data lake, with the other three Parquet-format, S3-based data sources: sensor mappings, locations, and electrical rates. The Parquet-format data is written as individual files to S3 and inserted into the existing ‘etl_tmp_output_parquet’ Glue Data Catalog database table. Compared to traditional relational database-based queries, the capabilities of Glue and Athena to enable complex SQL queries across multiple semi-structured data files, stored in S3, is truly amazing!

The capabilities of Glue and Athena to enable complex SQL queries across multiple semi-structured data files, stored in S3, is truly amazing!

Below, we see the SQL statement starting on line 43.

import boto3
import os
import logging
import json
from typing import Dict
# environment variables
data_catalog = os.getenv('DATA_CATALOG')
data_bucket = os.getenv('DATA_BUCKET')
# variables
output_directory = 'etl_tmp_output_parquet'
# uses list comprehension to generate the equivalent of:
# ['s_01', 's_02', …, 's_09', 's_10']
sensors = [f's_{i:02d}' for i in range(1, 11)]
# logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# athena client
athena_client = boto3.client('athena')
def handler(event, context):
args = {
"loc_id": event['loc_id'],
"date_from": event['date_from'],
"date_to": event['date_to']
}
athena_query(args)
return {
'statusCode': 200,
'body': json.dumps("function 'athena-complex-etl-query' complete")
}
def athena_query(args: Dict[str, str]):
for sensor in sensors:
query = \
"INSERT INTO " + data_catalog + "." + output_directory + " " \
"WITH " \
" t1 AS " \
" (SELECT d.loc_id, d.ts, d.data." + sensor + " AS kwh, l.state, l.tz " \
" FROM smart_hub_data_catalog.smart_hub_data_parquet d " \
" LEFT OUTER JOIN smart_hub_data_catalog.smart_hub_locations_parquet l " \
" ON d.loc_id = l.hash " \
" WHERE d.loc_id = '" + args['loc_id'] + "' " \
" AND d.dt BETWEEN cast('" + args['date_from'] + \
"' AS date) AND cast('" + args['date_to'] + "' AS date)), " \
" t2 AS " \
" (SELECT at_timezone(from_unixtime(t1.ts, 'UTC'), t1.tz) AS ts, " \
" date_format(at_timezone(from_unixtime(t1.ts, 'UTC'), t1.tz), '%H') AS rate_period, " \
" m.description AS device, m.location, t1.loc_id, t1.state, t1.tz, t1.kwh " \
" FROM t1 LEFT OUTER JOIN smart_hub_data_catalog.sensor_mappings_parquet m " \
" ON t1.loc_id = m.loc_id " \
" WHERE t1.loc_id = '" + args['loc_id'] + "' " \
" AND m.state = t1.state " \
" AND m.description = (SELECT m2.description " \
" FROM smart_hub_data_catalog.sensor_mappings_parquet m2 " \
" WHERE m2.loc_id = '" + args['loc_id'] + "' AND m2.id = '" + sensor + "')), " \
" t3 AS " \
" (SELECT substr(r.to, 1, 2) AS rate_period, r.type, r.rate, r.year, r.month, r.state " \
" FROM smart_hub_data_catalog.electricity_rates_parquet r " \
" WHERE r.year BETWEEN cast(date_format(cast('" + args['date_from'] + \
"' AS date), '%Y') AS integer) AND cast(date_format(cast('" + args['date_to'] + \
"' AS date), '%Y') AS integer)) " \
"SELECT replace(cast(t2.ts AS VARCHAR), concat(' ', t2.tz), '') AS ts, " \
" t2.device, t2.location, t3.type, t2.kwh, t3.rate AS cents_per_kwh, " \
" round(t2.kwh * t3.rate, 4) AS cost, t2.state, t2.loc_id " \
"FROM t2 LEFT OUTER JOIN t3 " \
" ON t2.rate_period = t3.rate_period " \
"WHERE t3.state = t2.state " \
"ORDER BY t2.ts, t2.device;"
logger.info(query)
response = athena_client.start_query_execution(
QueryString=query,
QueryExecutionContext={
'Database': data_catalog
},
ResultConfiguration={
'OutputLocation': 's3://' + data_bucket + '/tmp/' + output_directory
},
WorkGroup='primary'
)
logger.info(response)

view raw
athena_query.py
hosted with ❤ by GitHub

Below, is an example of one of the final queries, for the s_10 sensor, as executed by Athena. All the input parameter values, Python variables, and environment variables have been resolved into the query.

INSERT INTO smart_hub_data_catalog.etl_tmp_output_parquet
WITH t1 AS (SELECT d.loc_id, d.ts, d.data.s_10 AS kwh, l.state, l.tz
FROM smart_hub_data_catalog.smart_hub_data_parquet d
LEFT OUTER JOIN smart_hub_data_catalog.smart_hub_locations_parquet l ON d.loc_id = l.hash
WHERE d.loc_id = 'b6a8d42425fde548'
AND d.dt BETWEEN cast('2019-12-21' AS date) AND cast('2019-12-22' AS date)),
t2 AS (SELECT at_timezone(from_unixtime(t1.ts, 'UTC'), t1.tz) AS ts,
date_format(at_timezone(from_unixtime(t1.ts, 'UTC'), t1.tz), '%H') AS rate_period,
m.description AS device,
m.location,
t1.loc_id,
t1.state,
t1.tz,
t1.kwh
FROM t1
LEFT OUTER JOIN smart_hub_data_catalog.sensor_mappings_parquet m ON t1.loc_id = m.loc_id
WHERE t1.loc_id = 'b6a8d42425fde548'
AND m.state = t1.state
AND m.description = (SELECT m2.description
FROM smart_hub_data_catalog.sensor_mappings_parquet m2
WHERE m2.loc_id = 'b6a8d42425fde548'
AND m2.id = 's_10')),
t3 AS (SELECT substr(r.to, 1, 2) AS rate_period, r.type, r.rate, r.year, r.month, r.state
FROM smart_hub_data_catalog.electricity_rates_parquet r
WHERE r.year BETWEEN cast(date_format(cast('2019-12-21' AS date), '%Y') AS integer)
AND cast(date_format(cast('2019-12-22' AS date), '%Y') AS integer))
SELECT replace(cast(t2.ts AS VARCHAR), concat(' ', t2.tz), '') AS ts,
t2.device,
t2.location,
t3.type,
t2.kwh,
t3.rate AS cents_per_kwh,
round(t2.kwh * t3.rate, 4) AS cost,
t2.state,
t2.loc_id
FROM t2
LEFT OUTER JOIN t3 ON t2.rate_period = t3.rate_period
WHERE t3.state = t2.state
ORDER BY t2.ts, t2.device;

view raw
athena_query_final.sql
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Along with enriching the data, the query performs additional data transformation using the other data sources. For example, the Unix timestamp is converted to a localized timestamp containing the date and time, according to the customer’s location (line 7, above). Transforming dates and times is a frequent, often painful, data analysis task. Another example of data enrichment is the augmentation of the data with a new, computed column. The column’s values are calculated using the values of two other columns (line 33, above).

Invoke the Lambda with the following three parameters in the payload (step 3a in workflow diagram).

aws lambda invoke \
–function-name athena-complex-etl-query \
–payload "{ \"loc_id\": \"b6a8d42425fde548\",
\"date_from\": \"2019-12-21\", \"date_to\": \"2019-12-22\"}" \
response.json

view raw
step12.sh
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The ten INSERT INTO SQL statement’s result statuses (one per device sensor) are visible from the Athena Console’s History tab.

screen_shot_2020-01-05_at_9_17_23_pm

Each Athena query execution saves that query’s results to the S3-based data lake as individual, uncompressed Parquet-format data files. The data is partitioned in the Amazon S3-based data lake by the Smart Meter location ID (e.g., ‘loc_id=b6a8d42425fde548’).

Below is a snippet of the enriched data for a customer’s clothes washer (sensor ‘s_04’). Note the timestamp is now an actual date and time in the local timezone of the customer (e.g., ‘2019-12-21 20:10:00.000’). The sensor ID (‘s_04’) is replaced with the actual device name (‘Clothes Washer’). The location of the device (‘Basement’) and the type of electrical usage period (e.g. ‘peak’ or ‘partial-peak’) has been added. Finally, the cost column has been computed.


ts device location type kwh cents_per_kwh cost state loc_id
2019-12-21 19:40:00.000 Clothes Washer Basement peak 0.0 12.623 0.0 or b6a8d42425fde548
2019-12-21 19:45:00.000 Clothes Washer Basement peak 0.0 12.623 0.0 or b6a8d42425fde548
2019-12-21 19:50:00.000 Clothes Washer Basement peak 0.1501 12.623 1.8947 or b6a8d42425fde548
2019-12-21 19:55:00.000 Clothes Washer Basement peak 0.1497 12.623 1.8897 or b6a8d42425fde548
2019-12-21 20:00:00.000 Clothes Washer Basement partial-peak 0.1501 7.232 1.0855 or b6a8d42425fde548
2019-12-21 20:05:00.000 Clothes Washer Basement partial-peak 0.2248 7.232 1.6258 or b6a8d42425fde548
2019-12-21 20:10:00.000 Clothes Washer Basement partial-peak 0.2247 7.232 1.625 or b6a8d42425fde548
2019-12-21 20:15:00.000 Clothes Washer Basement partial-peak 0.2248 7.232 1.6258 or b6a8d42425fde548
2019-12-21 20:20:00.000 Clothes Washer Basement partial-peak 0.2253 7.232 1.6294 or b6a8d42425fde548
2019-12-21 20:25:00.000 Clothes Washer Basement partial-peak 0.151 7.232 1.092 or b6a8d42425fde548

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elt_data.csv
hosted with ❤ by GitHub

To transform the enriched CSV-format data to Parquet-format, we need to catalog the CSV-format results using another Crawler, first (step 3d in workflow diagram).

aws glue start-crawler –name smart-hub-etl-tmp-output-parquet

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step13.sh
hosted with ❤ by GitHub

Optimizing Enriched Data

The previous step created enriched Parquet-format data. However, this data is not as optimized for query efficiency as it should be. Using the Athena INSERT INTO WITH SQL statement, allowed the data to be partitioned. However, the method does not allow the Parquet data to be easily combined into larger files and compressed. To perform both these optimizations, we will use one last Lambda, athena-parquet-to-parquet-elt-data/index.py. The Lambda will create a new location in the Amazon S3-based data lake, containing all the enriched data, in a single file and compressed using Snappy compression.

aws lambda invoke \
–function-name athena-parquet-to-parquet-elt-data \
response.json

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step14.sh
hosted with ❤ by GitHub

The resulting Parquet file is visible in the S3 Management Console.

screen_shot_2020-01-04_at_6_07_23_pm

The final step in the Data Enrichment stage is to catalog the optimized Parquet-format enriched ETL data. To catalog the data, run the following Glue Crawler (step 3i in workflow diagram

aws glue start-crawler –name smart-hub-etl-output-parquet

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step15.sh
hosted with ❤ by GitHub

Final Data Lake and Data Catalog

We should now have the following ten top-level folders of partitioned data in the S3-based data lake. The ‘tmp’ folder may be ignored.

aws s3 ls s3://${DATA_BUCKET}/

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step16.sh
hosted with ❤ by GitHub

PRE electricity_rates_parquet/
PRE electricity_rates_xml/
PRE etl_output_parquet/
PRE etl_tmp_output_parquet/
PRE sensor_mappings_json/
PRE sensor_mappings_parquet/
PRE smart_hub_data_json/
PRE smart_hub_data_parquet/
PRE smart_hub_locations_csv/
PRE smart_hub_locations_parquet/

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final_data_lake_contents.txt
hosted with ❤ by GitHub

Similarly, we should now have the following ten corresponding tables in the Glue Data Catalog. Use the AWS Glue Console to confirm the tables exist.

screen_shot_2020-01-04_at_8_30_50_pm

Alternately, use the following AWS CLI / jq command to list the table names.

aws glue get-tables \
–database-name smart_hub_data_catalog \
| jq -r '.TableList[].Name'

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step17.sh
hosted with ❤ by GitHub

electricity_rates_parquet
electricity_rates_xml
etl_output_parquet
etl_tmp_output_parquet
sensor_mappings_json
sensor_mappings_parquet
smart_hub_data_json
smart_hub_data_parquet
smart_hub_locations_csv
smart_hub_locations_parquet

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gistfile1.txt
hosted with ❤ by GitHub

‘Unknown’ Bug

You may have noticed the four tables created with the AWS Lambda functions, using the CTAS SQL statement, erroneously have the ‘Classification’ of ‘Unknown’ as opposed to ‘parquet’. I am not sure why, I believe it is a possible bug with the CTAS feature. It seems to have no adverse impact on the table’s functionality. However, to fix the issue, run the following set of commands. This aws glue update-table hack will switch the table’s ‘Classification’ to ‘parquet’.

database=smart_hub_data_catalog
tables=(smart_hub_locations_parquet sensor_mappings_parquet smart_hub_data_parquet etl_output_parquet)
for table in ${tables}; do
fixed_table=$(aws glue get-table \
–database-name "${database}" \
–name "${table}" \
| jq '.Table.Parameters.classification = "parquet" | del(.Table.DatabaseName) | del(.Table.CreateTime) | del(.Table.UpdateTime) | del(.Table.CreatedBy) | del(.Table.IsRegisteredWithLakeFormation)')
fixed_table=$(echo ${fixed_table} | jq .Table)
aws glue update-table \
–database-name "${database}" \
–table-input "${fixed_table}"
echo "table '${table}' classification changed to 'parquet'"
done

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fix_table_classification.sh
hosted with ❤ by GitHub

The results of the fix may be seen from the AWS Glue Console. All ten tables are now classified correctly.

screen_shot_2020-01-05_at_11_43_50_pm

Explore the Data

Before starting to visualize and analyze the data with Amazon QuickSight, try executing a few Athena queries against the tables in the Glue Data Catalog database, using the Athena Query Editor. Working in the Editor is the best way to understand the data, learn Athena, and debug SQL statements and queries. The Athena Query Editor has convenient developer features like SQL auto-complete and query formatting capabilities.

Be mindful when writing queries and searching the Internet for SQL references, the Athena query engine is based on Presto 0.172. The current version of Presto, 0.229, is more than 50 releases ahead of the current Athena version. Both Athena and Presto functionality has changed and diverged. There are additional considerations and limitations for SQL queries in Athena to be aware of.

screen_shot_2020-01-05_at_10_32_25_am

Here are a few simple, ad-hoc queries to run in the Athena Query Editor.

— preview the final etl data
SELECT *
FROM smart_hub_data_catalog.etl_output_parquet
LIMIT 10;
— total cost in $'s for each device, at location 'b6a8d42425fde548'
— from high to low, on December 21, 2019
SELECT device,
concat('$', cast(cast(sum(cost) / 100 AS decimal(10, 2)) AS varchar)) AS total_cost
FROM smart_hub_data_catalog.etl_tmp_output_parquet
WHERE loc_id = 'b6a8d42425fde548'
AND date (cast(ts AS timestamp)) = date '2019-12-21'
GROUP BY device
ORDER BY total_cost DESC;
— count of smart hub residential locations in Oregon and California,
— grouped by zip code, sorted by count
SELECT DISTINCT postcode, upper(state), count(postcode) AS smart_hub_count
FROM smart_hub_data_catalog.smart_hub_locations_parquet
WHERE state IN ('or', 'ca')
AND length(cast(postcode AS varchar)) >= 5
GROUP BY state, postcode
ORDER BY smart_hub_count DESC, postcode;
— electrical usage for the clothes washer
— over a 30-minute period, on December 21, 2019
SELECT ts, device, location, type, cost
FROM smart_hub_data_catalog.etl_tmp_output_parquet
WHERE loc_id = 'b6a8d42425fde548'
AND device = 'Clothes Washer'
AND cast(ts AS timestamp)
BETWEEN timestamp '2019-12-21 08:45:00'
AND timestamp '2019-12-21 09:15:00'
ORDER BY ts;

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athena_queries.sql
hosted with ❤ by GitHub

Cleaning Up

You may choose to save the AWS resources created in part one of this demonstration, to be used in part two. Since you are not actively running queries against the data, ongoing AWS costs will be minimal. If you eventually choose to clean up the AWS resources created in part one of this demonstration, execute the following AWS CLI commands. To avoid failures, make sure each command completes before running the subsequent command. You will need to confirm the CloudFormation stacks are deleted using the AWS CloudFormation Console or the AWS CLI. These commands will not remove Amazon QuickSight data sets, analyses, and dashboards created in part two. However, deleting the AWS Glue Data Catalog and the underlying data sources will impact the ability to visualize the data in QuickSight.

# delete s3 contents first
aws s3 rm s3://${DATA_BUCKET} –recursive
aws s3 rm s3://${SCRIPT_BUCKET} –recursive
aws s3 rm s3://${LOG_BUCKET} –recursive
# then, delete lambda cfn stack
aws cloudformation delete-stack –stack-name smart-hub-lambda-stack
# finally, delete athena-glue-s3 stack
aws cloudformation delete-stack –stack-name smart-hub-athena-glue-stack

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step18.sh
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Part Two

In part one, starting with raw, semi-structured data in multiple formats, we learned how to ingest, transform, and enrich that data using Amazon S3, AWS Glue, Amazon Athena, and AWS Lambda. We built an S3-based data lake and learned how AWS leverages open-source technologies, including Presto, Apache Hive, and Apache Parquet. In part two of this post, we will use the transformed and enriched datasets, stored in the data lake, to create compelling visualizations using Amazon QuickSight.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

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2 Comments

Getting Started with Data Analytics using Jupyter Notebooks, PySpark, and Docker

Posted by Gary A. Stafford in Bash Scripting, Big Data, Build Automation, DevOps, Python, Software Development on December 6, 2019

There is little question, big data analytics, data science, artificial intelligence (AI), and machine learning (ML), a subcategory of AI, have all experienced a tremendous surge in popularity over the last few years. Behind the marketing hype, these technologies are having a significant influence on many aspects of our modern lives. Due to their popularity and potential benefits, commercial enterprises, academic institutions, and the public sector are rushing to develop hardware and software solutions to lower the barriers to entry and increase the velocity of ML and Data Scientists and Engineers.

Machine Learning and Data Science Search Results_ 5-Year Trend r2
(courtesy Google Trends and Plotly)

Many open-source software projects are also lowering the barriers to entry into these technologies. An excellent example of one such open-source project working on this challenge is Project Jupyter. Similar to Apache Zeppelin and the newly open-sourced Netflix’s Polynote, Jupyter Notebooks enables data-driven, interactive, and collaborative data analytics.

This post will demonstrate the creation of a containerized data analytics environment using Jupyter Docker Stacks. The particular environment will be suited for learning and developing applications for Apache Spark using the Python, Scala, and R programming languages. We will focus on Python and Spark, using PySpark.

Featured Technologies

pyspark_article_00b_feature

The following technologies are featured prominently in this post.

Jupyter Notebooks

According to Project Jupyter, the Jupyter Notebook, formerly known as the IPython Notebook, is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. Uses include data cleansing and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. The word, Jupyter, is a loose acronym for Julia, Python, and R, but today, Jupyter supports many programming languages.

Interest in Jupyter Notebooks has grown dramatically over the last 3–5 years, fueled in part by the major Cloud providers, AWS, Google Cloud, and Azure. Amazon Sagemaker, Amazon EMR (Elastic MapReduce), Google Cloud Dataproc, Google Colab (Collaboratory), and Microsoft Azure Notebooks all have direct integrations with Jupyter notebooks for big data analytics and machine learning.

Jupyter Search Results_ 5-Year Trend
(courtesy Google Trends and Plotly)

Jupyter Docker Stacks

To enable quick and easy access to Jupyter Notebooks, Project Jupyter has created Jupyter Docker Stacks. The stacks are ready-to-run Docker images containing Jupyter applications, along with accompanying technologies. Currently, the Jupyter Docker Stacks focus on a variety of specializations, including the r-notebook, scipy-notebook, tensorflow-notebook, datascience-notebook, pyspark-notebook, and the subject of this post, the all-spark-notebook. The stacks include a wide variety of well-known packages to extend their functionality, such as scikit-learn, pandas, Matplotlib, Bokeh, NumPy, and Facets.

Apache Spark

According to Apache, Spark is a unified analytics engine for large-scale data processing. Starting as a research project at the UC Berkeley AMPLab in 2009, Spark was open-sourced in early 2010 and moved to the Apache Software Foundation in 2013. Reviewing the postings on any major career site will confirm that Spark is widely used by well-known modern enterprises, such as Netflix, Adobe, Capital One, Lockheed Martin, JetBlue Airways, Visa, and Databricks. At the time of this post, LinkedIn, alone, had approximately 3,500 listings for jobs that reference the use of Apache Spark, just in the United States.

With speeds up to 100 times faster than Hadoop, Apache Spark achieves high performance for static, batch, and streaming data, using a state-of-the-art DAG (Directed Acyclic Graph) scheduler, a query optimizer, and a physical execution engine. Spark’s polyglot programming model allows users to write applications quickly in Scala, Java, Python, R, and SQL. Spark includes libraries for Spark SQL (DataFrames and Datasets), MLlib (Machine Learning), GraphX (Graph Processing), and DStreams (Spark Streaming). You can run Spark using its standalone cluster mode, Apache Hadoop YARN, Mesos, or Kubernetes.

PySpark

The Spark Python API, PySpark, exposes the Spark programming model to Python. PySpark is built on top of Spark’s Java API and uses Py4J. According to Apache, Py4J, a bridge between Python and Java, enables Python programs running in a Python interpreter to dynamically access Java objects in a Java Virtual Machine (JVM). Data is processed in Python and cached and shuffled in the JVM.

Docker

According to Docker, their technology gives developers and IT the freedom to build, manage, and secure business-critical applications without the fear of technology or infrastructure lock-in. For this post, I am using the current stable version of Docker Desktop Community version for macOS, as of March 2020.

Screen Shot 2020-03-07 at 9.16.03 PM

Docker Swarm

Current versions of Docker include both a Kubernetes and Swarm orchestrator for deploying and managing containers. We will choose Swarm for this demonstration. According to Docker, the cluster management and orchestration features embedded in the Docker Engine are built using swarmkit. Swarmkit is a separate project that implements Docker’s orchestration layer and is used directly within Docker.

PostgreSQL

PostgreSQL is a powerful, open-source, object-relational database system. According to their website, PostgreSQL comes with many features aimed to help developers build applications, administrators to protect data integrity and build fault-tolerant environments, and help manage data no matter how big or small the dataset.

Demonstration

In this demonstration, we will explore the capabilities of the Spark Jupyter Docker Stack to provide an effective data analytics development environment. We will explore a few everyday uses, including executing Python scripts, submitting PySpark jobs, and working with Jupyter Notebooks, and reading and writing data to and from different file formats and a database. We will be using the latest jupyter/all-spark-notebook Docker Image. This image includes Python, R, and Scala support for Apache Spark, using Apache Toree.

Architecture

As shown below, we will deploy a Docker stack to a single-node Docker swarm. The stack consists of a Jupyter All-Spark-Notebook, PostgreSQL (Alpine Linux version 12), and Adminer container. The Docker stack will have two local directories bind-mounted into the containers. Files from our GitHub project will be shared with the Jupyter application container through a bind-mounted directory. Our PostgreSQL data will also be persisted through a bind-mounted directory. This allows us to persist data external to the ephemeral containers. If the containers are restarted or recreated, the data is preserved locally.

JupyterDiagram

Source Code

All source code for this post can be found on GitHub. Use the following command to clone the project. Note this post uses the v2 branch.

git clone \
  --branch v2 --single-branch --depth 1 --no-tags \
  https://github.com/garystafford/pyspark-setup-demo.git

Source code samples are displayed as GitHub Gists, which may not display correctly on some mobile and social media browsers.

Deploy Docker Stack

To start, create the $HOME/data/postgres directory to store PostgreSQL data files.

mkdir -p ~/data/postgres

This directory will be bind-mounted into the PostgreSQL container on line 41 of the stack.yml file, $HOME/data/postgres:/var/lib/postgresql/data. The HOME environment variable assumes you are working on Linux or macOS and is equivalent to HOMEPATH on Windows.

The Jupyter container’s working directory is set on line 15 of the stack.yml file, working_dir: /home/$USER/work. The local bind-mounted working directory is $PWD/work. This path is bind-mounted to the working directory in the Jupyter container, on line 29 of the Docker stack file, $PWD/work:/home/$USER/work. The PWD environment variable assumes you are working on Linux or macOS (CD on Windows).

By default, the user within the Jupyter container is jovyan. We will override that user with our own local host’s user account, as shown on line 21 of the Docker stack file, NB_USER: $USER. We will use the host’s USER environment variable value (equivalent to USERNAME on Windows). There are additional options for configuring the Jupyter container. Several of those options are used on lines 17–22 of the Docker stack file (gist).

# docker stack deploy -c stack.yml jupyter
# optional pgadmin container
version: "3.7"
networks:
demo-net:
services:
spark:
image: jupyter/all-spark-notebook:latest
ports:
– "8888:8888/tcp"
– "4040:4040/tcp"
networks:
– demo-net
working_dir: /home/$USER/work
environment:
CHOWN_HOME: "yes"
GRANT_SUDO: "yes"
NB_UID: 1000
NB_GID: 100
NB_USER: $USER
NB_GROUP: staff
user: root
deploy:
replicas: 1
restart_policy:
condition: on-failure
volumes:
– $PWD/work:/home/$USER/work
postgres:
image: postgres:12-alpine
environment:
POSTGRES_USERNAME: postgres
POSTGRES_PASSWORD: postgres1234
POSTGRES_DB: bakery
ports:
– "5432:5432/tcp"
networks:
– demo-net
volumes:
– $HOME/data/postgres:/var/lib/postgresql/data
deploy:
restart_policy:
condition: on-failure
adminer:
image: adminer:latest
ports:
– "8080:8080/tcp"
networks:
– demo-net
deploy:
restart_policy:
condition: on-failure
# pgadmin:
# image: dpage/pgadmin4:latest
# environment:
# PGADMIN_DEFAULT_EMAIL: user@domain.com
# PGADMIN_DEFAULT_PASSWORD: 5up3rS3cr3t!
# ports:
# – "8180:80/tcp"
# networks:
# – demo-net
# deploy:
# restart_policy:
# condition: on-failure

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stack.yml
hosted with ❤ by GitHub

The jupyter/all-spark-notebook Docker image is large, approximately 5 GB. Depending on your Internet connection, if this is the first time you have pulled this image, the stack may take several minutes to enter a running state. Although not required, I usually pull new Docker images in advance.

docker pull jupyter/all-spark-notebook:latest
docker pull postgres:12-alpine
docker pull adminer:latest

Assuming you have a recent version of Docker installed on your local development machine and running in swarm mode, standing up the stack is as easy as running the following docker command from the root directory of the project.

docker stack deploy -c stack.yml jupyter

The Docker stack consists of a new overlay network, jupyter_demo-net, and three containers. To confirm the stack deployed successfully, run the following docker command.

docker stack ps jupyter --no-trunc

screen-shot-2019-12-04-at-10_34_40-am

To access the Jupyter Notebook application, you need to obtain the Jupyter URL and access token. The Jupyter URL and the access token are output to the Jupyter container log, which can be accessed with the following command.

docker logs $(docker ps | grep jupyter_spark | awk '{print $NF}')

You should observe log output similar to the following. Retrieve the complete URL, for example, http://127.0.0.1:8888/?token=f78cbe..., to access the Jupyter web-based user interface.

screen-shot-2019-12-04-at-10_34_52-am

From the Jupyter dashboard landing page, you should see all the files in the project’s work/ directory. Note the types of files you can create from the dashboard, including Python 3, R, and Scala (using Apache Toree or spylon-kernal) notebooks, and text. You can also open a Jupyter terminal or create a new Folder from the drop-down menu. At the time of this post (March 2020), the latest jupyter/all-spark-notebook Docker Image runs Spark 2.4.5, Scala 2.11.12, Python 3.7.6, and OpenJDK 64-Bit Server VM, Java 1.8.0 Update 242.

screen_shot_2019-12-01_at_4_40_12_pm

Bootstrap Environment

Included in the project is a bootstrap script, bootstrap_jupyter.sh. The script will install the required Python packages using pip, the Python package installer, and a requirement.txt file. The bootstrap script also installs the latest PostgreSQL driver JAR, configures Apache Log4j to reduce log verbosity when submitting Spark jobs, and installs htop. Although these tasks could also be done from a Jupyter terminal or from within a Jupyter notebook, using a bootstrap script ensures you will have a consistent work environment every time you spin up the Jupyter Docker stack. Add or remove items from the bootstrap script as necessary.

#!/bin/bash
set -ex
# update/upgrade and install htop
sudo apt-get update -y && sudo apt-get upgrade -y
sudo apt-get install htop
# install required python packages
python3 -m pip install –user –upgrade pip
python3 -m pip install -r requirements.txt –upgrade
# download latest postgres driver jar
POSTGRES_JAR="postgresql-42.2.17.jar"
if [ -f "$POSTGRES_JAR" ]; then
echo "$POSTGRES_JAR exist"
else
wget -nv "https://jdbc.postgresql.org/download/${POSTGRES_JAR}"
fi
# spark-submit logging level from INFO to WARN
sudo cp log4j.properties /usr/local/spark/conf/log4j.properties

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bootstrap_jupyter.sh
hosted with ❤ by GitHub

That’s it, our new Jupyter environment is ready to start exploring.

Running Python Scripts

One of the simplest tasks we could perform in our new Jupyter environment is running Python scripts. Instead of worrying about installing and maintaining the correct versions of Python and multiple Python packages on your own development machine, we can run Python scripts from within the Jupyter container. At the time of this post update, the latest jupyter/all-spark-notebook Docker image runs Python 3.7.3 and Conda 4.7.12. Let’s start with a simple Python script, 01_simple_script.py.

#!/usr/bin/python3
import random
technologies = [
'PySpark', 'Python', 'Spark', 'Scala', 'Java', 'Project Jupyter', 'R'
]
print("Technologies: %s\n" % technologies)
technologies.sort()
print("Sorted: %s\n" % technologies)
print("I'm interested in learning about %s." % random.choice(technologies))

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01_simple_script.py
hosted with ❤ by GitHub

From a Jupyter terminal window, use the following command to run the script.

python3 01_simple_script.py

You should observe the following output.

screen_shot_2019-12-01_at_4_46_38_pm

Kaggle Datasets

To explore more features of the Jupyter and PySpark, we will use a publicly available dataset from Kaggle. Kaggle is an excellent open-source resource for datasets used for big-data and ML projects. Their tagline is ‘Kaggle is the place to do data science projects’. For this demonstration, we will use the Transactions from a bakery dataset from Kaggle. The dataset is available as a single CSV-format file. A copy is also included in the project.

pyspark_article_03_kaggle

The ‘Transactions from a bakery’ dataset contains 21,294 rows with 4 columns of data. Although certainly not big data, the dataset is large enough to test out the Spark Jupyter Docker Stack functionality. The data consists of 9,531 customer transactions for 21,294 bakery items between 2016-10-30 and 2017-04-09 (gist).


Date Time Transaction Item
2016-10-30 09:58:11 1 Bread
2016-10-30 10:05:34 2 Scandinavian
2016-10-30 10:05:34 2 Scandinavian
2016-10-30 10:07:57 3 Hot chocolate
2016-10-30 10:07:57 3 Jam
2016-10-30 10:07:57 3 Cookies
2016-10-30 10:08:41 4 Muffin
2016-10-30 10:13:03 5 Coffee
2016-10-30 10:13:03 5 Pastry
2016-10-30 10:13:03 5 Bread

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bakery_data.csv
hosted with ❤ by GitHub

Submitting Spark Jobs

We are not limited to Jupyter notebooks to interact with Spark. We can also submit scripts directly to Spark from the Jupyter terminal. This is typically how Spark is used in a Production for performing analysis on large datasets, often on a regular schedule, using tools such as Apache Airflow. With Spark, you are load data from one or more data sources. After performing operations and transformations on the data, the data is persisted to a datastore, such as a file or a database, or conveyed to another system for further processing.

The project includes a simple Python PySpark ETL script, 02_pyspark_job.py. The ETL script loads the original Kaggle Bakery dataset from the CSV file into memory, into a Spark DataFrame. The script then performs a simple Spark SQL query, calculating the total quantity of each type of bakery item sold, sorted in descending order. Finally, the script writes the results of the query to a new CSV file, output/items-sold.csv.

#!/usr/bin/python3
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.appName('spark-demo') \
.getOrCreate()
df_bakery = spark.read \
.format('csv') \
.option('header', 'true') \
.option('delimiter', ',') \
.option('inferSchema', 'true') \
.load('BreadBasket_DMS.csv')
df_sorted = df_bakery.cube('item').count() \
.filter('item NOT LIKE \'NONE\'') \
.filter('item NOT LIKE \'Adjustment\'') \
.orderBy(['count', 'item'], ascending=[False, True])
df_sorted.show(10, False)
df_sorted.coalesce(1) \
.write.format('csv') \
.option('header', 'true') \
.save('output/items-sold.csv', mode='overwrite')

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02_bakery_dataframes.py
hosted with ❤ by GitHub

Run the script directly from a Jupyter terminal using the following command.

python3 02_pyspark_job.py

An example of the output of the Spark job is shown below.
screen-shot-2019-12-03-at-9_31_38-am

Typically, you would submit the Spark job using the spark-submit command. Use a Jupyter terminal to run the following command.

$SPARK_HOME/bin/spark-submit 02_pyspark_job.py

Below, we see the output from the spark-submit command. Printing the results in the output is merely for the purposes of the demo. Typically, Spark jobs are submitted non-interactively, and the results are persisted directly to a datastore or conveyed to another system.
screen-shot-2019-12-03-at-9_32_25-am

Using the following commands, we can view the resulting CVS file, created by the spark job.

ls -alh output/items-sold.csv/
head -5 output/items-sold.csv/*.csv

An example of the files created by the spark job is shown below. We should have discovered that coffee is the most commonly sold bakery item with 5,471 sales, followed by bread with 3,325 sales.
screen-shot-2019-12-03-at-9_32_52-am

Interacting with Databases

To demonstrate the flexibility of Jupyter to work with databases, PostgreSQL is part of the Docker Stack. We can read and write data from the Jupyter container to the PostgreSQL instance, running in a separate container. To begin, we will run a SQL script, written in Python, to create our database schema and some test data in a new database table. To do so, we will use the psycopg2, the PostgreSQL database adapter package for the Python, we previously installed into our Jupyter container using the bootstrap script. The below Python script, 03_load_sql.py, will execute a set of SQL statements contained in a SQL file, bakery.sql, against the PostgreSQL container instance.

#!/usr/bin/python3
import psycopg2
# connect to database
connect_str = 'host=postgres port=5432 dbname=bakery user=postgres password=postgres1234'
conn = psycopg2.connect(connect_str)
conn.autocommit = True
cursor = conn.cursor()
# execute sql script
sql_file = open('bakery.sql', 'r')
sqlFile = sql_file.read()
sql_file.close()
sqlCommands = sqlFile.split(';')
for command in sqlCommands:
print(command)
if command.strip() != '':
cursor.execute(command)
# import data from csv file
with open('BreadBasket_DMS.csv', 'r') as f:
next(f) # Skip the header row.
cursor.copy_from(
f,
'transactions',
sep=',',
columns=('date', 'time', 'transaction', 'item')
)
conn.commit()
# confirm by selecting record
command = 'SELECT COUNT(*) FROM public.transactions;'
cursor.execute(command)
recs = cursor.fetchall()
print('Row count: %d' % recs[0])

view raw
03_load_sql.py
hosted with ❤ by GitHub

The SQL file, bakery.sql.

DROP TABLE IF EXISTS "transactions";
DROP SEQUENCE IF EXISTS transactions_id_seq;
CREATE SEQUENCE transactions_id_seq INCREMENT 1 MINVALUE 1 MAXVALUE 2147483647 START 1 CACHE 1;
CREATE TABLE "public"."transactions"
(
"id" integer DEFAULT nextval('transactions_id_seq') NOT NULL,
"date" character varying(10) NOT NULL,
"time" character varying(8) NOT NULL,
"transaction" integer NOT NULL,
"item" character varying(50) NOT NULL
) WITH (oids = false);

view raw
bakery.sql
hosted with ❤ by GitHub

To execute the script, run the following command.

python3 03_load_sql.py

This should result in the following output, if successful.
screen-shot-2019-12-03-at-9_34_26-am

Adminer

To confirm the SQL script’s success, use Adminer. Adminer (formerly phpMinAdmin) is a full-featured database management tool written in PHP. Adminer natively recognizes PostgreSQL, MySQL, SQLite, and MongoDB, among other database engines. The current version is 4.7.6 (March 2020).

Adminer should be available on localhost port 8080. The password credentials, shown below, are located in the stack.yml file. The server name, postgres, is the name of the PostgreSQL Docker container. This is the domain name the Jupyter container will use to communicate with the PostgreSQL container.
screen_shot_2019-12-01_at_6_09_57_pm

Connecting to the new bakery database with Adminer, we should see the transactions table.
screen_shot_2019-12-01_at_6_10_20_pm

The table should contain 21,293 rows, each with 5 columns of data.

screen_shot_2019-12-01_at_6_11_32_pm

pgAdmin

Another excellent choice for interacting with PostgreSQL, in particular, is pgAdmin 4. It is my favorite tool for the administration of PostgreSQL. Although limited to PostgreSQL, the user interface and administrative capabilities of pgAdmin is superior to Adminer, in my opinion. For brevity, I chose not to include pgAdmin in this post. The Docker stack also contains a pgAdmin container, which has been commented out. To use pgAdmin, just uncomment the container and re-run the Docker stack deploy command. pgAdmin should then be available on localhost port 81. The pgAdmin login credentials are in the Docker stack file.

screen_shot_2019-12-05_at_10_11_15_amscreen_shot_2019-12-05_at_10_11_44_am

Developing Jupyter Notebooks

The real power of the Jupyter Docker Stacks containers is Jupyter Notebooks. According to the Jupyter Project, the notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process, including developing, documenting, and executing code, as well as communicating the results. Notebook documents contain the inputs and outputs of an interactive session as well as additional text that accompanies the code but is not meant for execution.

To explore the capabilities of Jupyter notebooks, the project includes two simple Jupyter notebooks. The first notebooks, 04_notebook.ipynb, demonstrates typical PySpark functions, such as loading data from a CSV file and from the PostgreSQL database, performing basic data analysis with Spark SQL including the use of PySpark user-defined functions (UDF), graphing the data using BokehJS, and finally, saving data back to the database, as well as to the fast and efficient Apache Parquet file format. Below we see several notebook cells demonstrating these features.screen_shot_2019-12-04_at_11_05_00_pmscreen_shot_2019-12-04_at_11_05_07_pmscreen_shot_2019-12-04_at_11_05_22_pmscreen_shot_2019-12-05_at_3_54_11_pmscreen_shot_2019-12-04_at_11_14_22_pm

IDE Integration

Recall, the working directory, containing the GitHub source code for the project, is bind-mounted to the Jupyter container. Therefore, you can also edit any of the files, including notebooks, in your favorite IDE, such as JetBrains PyCharm and Microsoft Visual Studio Code. PyCharm has built-in language support for Jupyter Notebooks, as shown below.
screen_shot_2019-12-01_at_9_21_49_pm

As does Visual Studio Code using the Python extension.

screen_shot_2019-12-08_at_8_02_43_pm.png

Using Additional Packages

As mentioned in the Introduction, the Jupyter Docker Stacks come ready-to-run, with a wide variety of Python packages to extend their functionality. To demonstrate the use of these packages, the project contains a second Jupyter notebook document, 05_notebook.ipynb. This notebook uses SciPy, the well-known Python package for mathematics, science, and engineering, NumPy, the well-known Python package for scientific computing, and the Plotly Python Graphing Library. While NumPy and SciPy are included on the Jupyter Docker Image, the bootstrap script uses pip to install the required Plotly packages. Similar to Bokeh, shown in the previous notebook, we can use these libraries to create richly interactive data visualizations.

Plotly

To use Plotly from within the notebook, you will first need to sign up for a free account and obtain a username and API key. To ensure we do not accidentally save sensitive Plotly credentials in the notebook, we are using python-dotenv. This Python package reads key/value pairs from a .env file, making them available as environment variables to our notebook. Modify and run the following two commands from a Jupyter terminal to create the .env file and set you Plotly username and API key credentials. Note that the .env file is part of the .gitignore file and will not be committed to back to git, potentially compromising the credentials.

echo "PLOTLY_USERNAME=your-username" >> .env
echo "PLOTLY_API_KEY=your-api-key" >> .env

The notebook expects to find the two environment variables, which it uses to authenticate with Plotly.

screen_shot_2019-12-04_at_11_20_06_pm

Shown below, we use Plotly to construct a bar chart of daily bakery items sold. The chart uses SciPy and NumPy to construct a linear fit (regression) and plot a line of best fit for the bakery data and overlaying the vertical bars. The chart also uses SciPy’s Savitzky-Golay Filter to plot the second line, illustrating a smoothing of our bakery data.

screen_shot_2019-12-05_at_11_14_27_am

Plotly also provides Chart Studio Online Chart Maker. Plotly describes Chart Studio as the world’s most modern enterprise data visualization solutions. We can enhance, stylize, and share our data visualizations using the free version of Chart Studio Cloud.

screen_shot_2019-12-05_at_11_15_55_am

Jupyter Notebook Viewer

Notebooks can also be viewed using nbviewer, an open-source project under Project Jupyter. Thanks to Rackspace hosting, the nbviewer instance is a free service.

screen_shot_2019-12-05_at_11_28_01_am.png

Using nbviewer, below, we see the output of a cell within the 04_notebook.ipynb notebook. View this notebook, here, using nbviewer.

screen_shot_2019-12-04_at_11_39_28_pm

Monitoring Spark Jobs

The Jupyter Docker container exposes Spark’s monitoring and instrumentation web user interface. We can review each completed Spark Job in detail.
screen-shot-2019-12-04-at-11_56_19-pm

We can review details of each stage of the Spark job, including a visualization of the DAG (Directed Acyclic Graph), which Spark constructs as part of the job execution plan, using the DAG Scheduler.
screen-shot-2019-12-04-at-11_57_18-pm

We can also review the task composition and timing of each event occurring as part of the stages of the Spark job.
screen-shot-2019-12-04-at-11_57_48-pm

We can also use the Spark interface to review and confirm the runtime environment configuration, including versions of Java, Scala, and Spark, as well as packages available on the Java classpath.
screen-shot-2019-12-04-at-11_58_02-pm

Local Spark Performance

Running Spark on a single node within the Jupyter Docker container on your local development system is not a substitute for a true Spark cluster, Production-grade, multi-node Spark clusters running on bare metal or robust virtualized hardware, and managed with Apache Hadoop YARN, Apache Mesos, or Kubernetes. In my opinion, you should only adjust your Docker resources limits to support an acceptable level of Spark performance for running small exploratory workloads. You will not realistically replace the need to process big data and execute jobs requiring complex calculations on a Production-grade, multi-node Spark cluster.
screen_shot_2019-12-03_at_9_18_36_am

We can use the following docker stats command to examine the container’s CPU and memory metrics.

docker stats --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}\t{{.MemPerc}}"

Below, we see the stats from the Docker stack’s three containers showing little or no activity.
Screen Shot 2019-12-05 at 12.16.14 AM

Compare those stats with the ones shown below, recorded while a notebook was reading and writing data, and executing Spark SQL queries. The CPU and memory output show spikes, but both appear to be within acceptable ranges.
Screen Shot 2019-12-05 at 12.13.49 AM

Linux Process Monitors

Another option to examine container performance metrics is top, which is pre-installed in our Jupyter container. For example, execute the following top command from a Jupyter terminal, sorting processes by CPU usage.

top -o %CPU

We should observe the individual performance of each process running in the Jupyter container.

screen_shot_2019-12-05_at_12_25_51_pm.png

A step up from top is htop, an interactive process viewer for Unix. It was installed in the container by the bootstrap script. For example, we can execute the htop command from a Jupyter terminal, sorting processes by CPU % usage.

htop --sort-key PERCENT_CPU

With htop, observe the individual CPU activity. Here, the four CPUs at the top left of the htop window are the CPUs assigned to Docker. We get insight into the way Spark is using multiple CPUs, as well as other performance metrics, such as memory and swap.

screen_shot_2019-12-02_at_12_08_18_pm.png

Assuming your development machine is robust, it is easy to allocate and deallocate additional compute resources to Docker if required. Be careful not to allocate excessive resources to Docker, starving your host machine of available compute resources for other applications.
screen_shot_2019-12-03_at_9_18_50_am

Notebook Extensions

There are many ways to extend the Jupyter Docker Stacks. A popular option is jupyter-contrib-nbextensions. According to their website, the jupyter-contrib-nbextensions package contains a collection of community-contributed unofficial extensions that add functionality to the Jupyter notebook. These extensions are mostly written in JavaScript and will be loaded locally in your browser. Installed notebook extensions can be enabled, either by using built-in Jupyter commands, or more conveniently by using the jupyter_nbextensions_configurator server extension.

The project contains an alternate Docker stack file, stack-nbext.yml. The stack uses an alternative Docker image, garystafford/all-spark-notebook-nbext:latest, This Dockerfile, which builds it, uses the jupyter/all-spark-notebook:latest image as a base image. The alternate image adds in the jupyter-contrib-nbextensions and jupyter_nbextensions_configurator packages. Since Jupyter would need to be restarted after nbextensions is deployed, it cannot be done from within a running jupyter/all-spark-notebook container.

FROM jupyter/all-spark-notebook:latest
USER root
RUN pip install jupyter_contrib_nbextensions \
&& jupyter contrib nbextension install –system \
&& pip install jupyter_nbextensions_configurator \
&& jupyter nbextensions_configurator enable –system \
&& pip install yapf # for code pretty
USER $NB_UID

view raw
Dockerfile
hosted with ❤ by GitHub

Using this alternate stack, below in our Jupyter container, we see the sizable list of extensions available. Some of my favorite extensions include ‘spellchecker’, ‘Codefolding’, ‘Code pretty’, ‘ExecutionTime’, ‘Table of Contents’, and ‘Toggle all line numbers’.

screen_shot_2019-12-05_at_7_53_17_pm.png

Below, we see five new extension icons that have been added to the menu bar of 04_notebook.ipynb. You can also observe the extensions have been applied to the notebook, including the table of contents, code-folding, execution time, and line numbering. The spellchecking and pretty code extensions were both also applied.

screen_shot_2019-12-05_at_7_47_12_pm

Conclusion

In this brief post, we have seen how easy it is to get started learning and performing data analytics using Jupyter notebooks, Python, Spark, and PySpark, all thanks to the Jupyter Docker Stacks. We could use this same stack to learn and perform machine learning using Scala and R. Extending the stack’s capabilities is as simple as swapping out this Jupyter image for another, with a different set of tools, as well as adding additional containers to the stack, such as MySQL, MongoDB, RabbitMQ, Apache Kafka, and Apache Cassandra.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers, their clients.

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1 Comment

Getting Started with Apache Zeppelin on Amazon EMR, using AWS Glue, RDS, and S3: Part 1

Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Build Automation, Cloud, DevOps, Python, Software Development on November 22, 2019

Introduction

There is little question big data analytics, data science, artificial intelligence (AI), and machine learning (ML), a subcategory of AI, have all experienced a tremendous surge in popularity over the last 3–5 years. Behind the hype cycles and marketing buzz, these technologies are having a significant influence on many aspects of our modern lives. Due to their popularity, commercial enterprises, academic institutions, and the public sector have all rushed to develop hardware and software solutions to decrease the barrier to entry and increase the velocity of ML and Data Scientists and Engineers.

screen_shot_2019-11-17_at_7_24_10_am1Data Science: 5-Year Search Trend (courtesy Google Trends)

screen_shot_2019-11-17_at_7_24_10_am2Machine Learning: 5-Year Search Trend (courtesy Google Trends)

Technologies

All three major cloud providers, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, have rapidly maturing big data analytics, data science, and AI and ML services. AWS, for example, introduced Amazon Elastic MapReduce (EMR) in 2009, primarily as an Apache Hadoop-based big data processing service. Since then, according to Amazon, EMR has evolved into a service that uses Apache Spark, Apache Hadoop, and several other leading open-source frameworks to quickly and cost-effectively process and analyze vast amounts of data. More recently, in late 2017, Amazon released SageMaker, a service that provides the ability to build, train, and deploy machine learning models quickly and securely.

Simultaneously, organizations are building solutions that integrate and enhance these Cloud-based big data analytics, data science, AI, and ML services. One such example is Apache Zeppelin. Similar to the immensely popular Project Jupyter and the newly open-sourced Netflix’s Polynote, Apache Zeppelin is a web-based, polyglot, computational notebook. Zeppelin enables data-driven, interactive data analytics and document collaboration using a number of interpreters such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown, Shell and so on. Zeppelin is one of the core applications supported natively by Amazon EMR.

screen_shot_2019-11-15_at_11_59_16_pm

In the following two-part post, we will explore the use of Apache Zeppelin on EMR for data science and data analytics using a series of Zeppelin notebooks. The notebooks feature the use of AWS Glue, the fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. The notebooks also feature the use of Amazon Relational Database Service (RDS) for PostgreSQL and Amazon Simple Cloud Storage Service (S3). Amazon S3 will serve as a Data Lake to store our unstructured data. Given the current choice of Zeppelin’s more than twenty different interpreters, we will use Python3 and Apache Spark, specifically Spark SQL and PySpark, for all notebooks.

zeppelin_header

We will build an economical single-node EMR cluster for data exploration, as well as a larger multi-node EMR cluster for analyzing large data sets. Amazon S3 will be used to store input and output data, while intermediate results are stored in the Hadoop Distributed File System (HDFS) on the EMR cluster. Amazon provides a good overview of EMR architecture. Below is a high-level architectural diagram of the infrastructure we will construct during Part 1 for this demonstration.

EMR-Zeppelin.png

Notebook Features

Below is an overview of each Zeppelin notebook with a link to view it using Zepl’s free Notebook Explorer. Zepl was founded by the same engineers that developed Apache Zeppelin, including Moonsoo Lee, Zepl CTO and creator for Apache Zeppelin. Zepl’s enterprise collaboration platform, built on Apache Zeppelin, enables both Data Science and AI/ML teams to collaborate around data.

Notebook 1

The first notebook uses a small 21k row kaggle dataset, Transactions from a Bakery. The notebook demonstrates Zeppelin’s integration capabilities with the Helium plugin system for adding new chart types, the use of Amazon S3 for data storage and retrieval, and the use of Apache Parquet, a compressed and efficient columnar data storage format, and Zeppelin’s storage integration with GitHub for notebook version control.

screen_shot_2019-11-27_at_11_13_53_am

Notebook 2

The second notebook demonstrates the use of a single-node and multi-node Amazon EMR cluster for the exploration and analysis of public datasets ranging from approximately 100k rows up to 27MM rows, using Zeppelin. We will use the latest GroupLens MovieLens rating datasets to examine the performance characteristics of Zeppelin, using Spark, on single- verses multi-node EMR clusters for analyzing big data using a variety of Amazon EC2 Instance Types.

screen_shot_2019-11-27_at_11_14_01_am

Notebook 3

The third notebook demonstrates Amazon EMR and Zeppelin’s integration capabilities with AWS Glue Data Catalog as an Apache Hive-compatible metastore for Spark SQL. We will create an Amazon S3-based Data Lake using the AWS Glue Data Catalog and a set of AWS Glue Crawlers.

screen_shot_2019-11-27_at_11_44_44_pm.png

Notebook 4

The fourth notebook demonstrates Zeppelin’s ability to integrate with an external data source. In this case, we will interact with data in an Amazon RDS PostgreSQL relational database using three methods, including the Psycopg 2 PostgreSQL adapter for Python, Spark’s native JDBC capability, and Zeppelin’s JDBC Interpreter.

screen-shot-2019-11-27-at-11_26_34-am

Demonstration

In Part 1 of the post, as a DataOps Engineer, we will create and configure the AWS resources required to demonstrate the use of Apache Zeppelin on EMR, using an AWS Glue Data Catalog, Amazon RDS PostgreSQL database, and an S3-based data lake. In Part 2 of this post, as a Data Scientist, we will explore Apache Zeppelin’s features and integration capabilities with a variety of AWS services using the Zeppelin notebooks.

Source Code

The demonstration’s source code is contained in two public GitHub repositories. The first repository, zeppelin-emr-demo, includes the four Zeppelin notebooks, organized according to the conventions of Zeppelin’s pluggable notebook storage mechanisms.

.
├── 2ERVVKTCG
│   └── note.json
├── 2ERYY923A
│   └── note.json
├── 2ESH8DGFS
│   └── note.json
├── 2EUZKQXX7
│   └── note.json
├── LICENSE
└── README.md

Zeppelin GitHub Storage

During the demonstration, changes made to your copy of the Zeppelin notebooks running on EMR will be automatically pushed back to GitHub when a commit occurs. To accomplish this, instead of just cloning a local copy of my zeppelin-emr-demo project repository, you will want your own copy, within your personal GitHub account. You could folk my zeppelin-emr-demo GitHub repository or copy a clone into your own GitHub repository.

To make a copy of the project in your own GitHub account, first, create a new empty repository on GitHub, for example, ‘my-zeppelin-emr-demo-copy’. Then, execute the following commands from your terminal, to clone the original project repository to your local environment, and finally, push it to your GitHub account.

# change me
GITHUB_ACCOUNT="your-account-name" # i.e. garystafford
GITHUB_REPO="your-new-project-name" # i.e. my-zeppelin-emr-demo-copy

# shallow clone into new directory
git clone --branch master \
    --single-branch --depth 1 --no-tags \
    https://github.com/garystafford/zeppelin-emr-demo.git \
    ${GITHUB_REPO}

# re-initialize repository
cd ${GITHUB_REPO}
rm -rf .git
git init

# re-commit code
git add -A
git commit -m "Initial commit of my copy of zeppelin-emr-demo"

# push to your repo
git remote add origin \
    https://github.com/$GITHUB_ACCOUNT/$GITHUB_REPO.git
git push -u origin master

GitHub Personal Access Token

To automatically push changes to your GitHub repository when a commit occurs, Zeppelin will need a GitHub personal access token. Create a personal access token with the scope shown below. Be sure to keep the token secret. Make sure you do not accidentally check your token value into your source code on GitHub. To minimize the risk, change or delete the token after completing the demo.

screen_shot_2019-11-18_at_8_44_27_pm

The second repository, zeppelin-emr-config, contains the necessary bootstrap files, CloudFormation templates, and PostgreSQL DDL (Data Definition Language) SQL script.

.
├── LICENSE
├── README.md
├── bootstrap
│   ├── bootstrap.sh
│   ├── emr-config.json
│   ├── helium.json
├── cloudformation
│   ├── crawler.yml
│   ├── emr_single_node.yml
│   ├── emr_cluster.yml
│   └── rds_postgres.yml
└── sql
    └── ratings.sql

Use the following AWS CLI command to clone the GitHub repository to your local environment.

git clone --branch master \
    --single-branch --depth 1 --no-tags \
    https://github.com/garystafford/zeppelin-emr-demo-setup.git

Requirements

To follow along with the demonstration, you will need an AWS Account, an existing Amazon S3 bucket to store EMR configuration and data, and an EC2 key pair. You will also need a current version of the AWS CLI installed in your work environment. Due to the particular EMR features, we will be using, I recommend using the us-east-1 AWS Region to create the demonstration’s resources.

# create secure emr config and data bucket
# change me
ZEPPELIN_DEMO_BUCKET="your-bucket-name"

aws s3api create-bucket \
    --bucket ${ZEPPELIN_DEMO_BUCKET}
aws s3api put-public-access-block \
    --bucket ${ZEPPELIN_DEMO_BUCKET} \
    --public-access-block-configuration \
    BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true

Copy Configuration Files to S3

To start, we need to copy three configuration files, bootstrap.sh, helium.json, and ratings.sql, from the zeppelin-emr-demo-setup project directory to our S3 bucket. Change the ZEPPELIN_DEMO_BUCKET variable value, then run the following s3 cp API command, using the AWS CLI. The three files will be copied to a bootstrap directory within your S3 bucket.

# change me
ZEPPELIN_DEMO_BUCKET="your-bucket-name"
 
aws s3 cp bootstrap/bootstrap.sh s3://${ZEPPELIN_DEMO_BUCKET}/bootstrap/
aws s3 cp bootstrap/helium.json s3://${ZEPPELIN_DEMO_BUCKET}/bootstrap/
aws s3 cp sql/ratings.sql s3://${ZEPPELIN_DEMO_BUCKET}/bootstrap/

Below, sample output from copying local files to S3.

screen-shot-2019-11-21-at-2_03_49-pm

Create AWS Resources

We will start by creating most of the required AWS resources for the demonstration using three CloudFormation templates. We will create a single-node Amazon EMR cluster, an Amazon RDS PostgresSQL database, an AWS Glue Data Catalog database, two AWS Glue Crawlers, and a Glue IAM Role. We will wait to create the multi-node EMR cluster due to the compute costs of running large EC2 instances in the cluster. You should understand the cost of these resources before proceeding, and that you ensure they are destroyed immediately upon completion of the demonstration to minimize your expenses.

Single-Node EMR Cluster

We will start by creating the single-node Amazon EMR cluster, consisting of just one master node with no core or task nodes (a cluster of one). All operations will take place on the master node.

Default EMR Resources

The following EMR instructions assume you have already created at least one EMR cluster in the past, in your current AWS Region, using the EMR web interface with the ‘Create Cluster – Quick Options’ option. Creating a cluster this way creates several additional AWS resources, such as the EMR_EC2_DefaultRole EC2 instance profile, the default EMR_DefaultRole EMR IAM Role, and the default EMR S3 log bucket.

screen_shot_2019-11-17_at_5_46_56_pm.png

If you haven’t created any EMR clusters using the EMR ‘Create Cluster – Quick Options’ in the past, don’t worry, you can also create the required resources with a few quick AWS CLI commands. Change the following LOG_BUCKET variable value, then run the aws emr and aws s3api API commands, using the AWS CLI. The LOG_BUCKET variable value follows the convention of aws-logs-awsaccount-region. For example, aws-logs-012345678901-us-east-1.

# create emr roles
aws emr create-default-roles

# create log secure bucket
# change me
LOG_BUCKET="aws-logs-your_aws_account_id-your_region"

aws s3api create-bucket --bucket ${LOG_BUCKET}
aws s3api put-public-access-block --bucket ${LOG_BUCKET} \
    --public-access-block-configuration \
    BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true

The new EMR IAM Roles can be viewed in the IAM Roles web interface.

screen-shot-2019-11-21-at-2_05_35-pm

Often, I see tutorials that reference these default EMR resources from the AWS CLI or CloudFormation, without any understanding or explanation of how they are created.

EMR Bootstrap Script

As part of creating our EMR cluster, the CloudFormation template, emr_single_node.yml, will call the bootstrap script we copied to S3, earlier, bootstrap.sh. The bootstrap script pre-installs required Python and Linux software packages, and the PostgreSQL driver JAR. The bootstrap script also clones your copy of the zeppelin-emr-demo GitHub repository.

#!/bin/bash
set -ex

if [[ $# -ne 2 ]] ; then
    echo "Script requires two arguments"
    exit 1
fi

GITHUB_ACCOUNT=$1
GITHUB_REPO=$2

# install extra python packages
sudo python3 -m pip install psycopg2-binary boto3

# install extra linux packages
yes | sudo yum install git htop

# clone github repo
cd /tmp
git clone "https://github.com/${GITHUB_ACCOUNT}/${GITHUB_REPO}.git"

# install extra jars
POSTGRES_JAR="postgresql-42.2.8.jar"
wget -nv "https://jdbc.postgresql.org/download/${POSTGRES_JAR}"
sudo chown -R hadoop:hadoop ${POSTGRES_JAR}
mkdir -p /home/hadoop/extrajars/
cp ${POSTGRES_JAR} /home/hadoop/extrajars/

EMR Application Configuration

The EMR CloudFormation template will also modify the EMR cluster’s Spark and Zeppelin application configurations. Amongst other configuration properties, the template sets the default Python version to Python3, instructs Zeppelin to use the cloned GitHub notebook directory path, and adds the PostgreSQL Driver JAR to the JVM ClassPath. Below we can see the configuration properties applied to an existing EMR cluster.

screen-shot-2019-11-21-at-2_24_43-pm
EMR Application Versions

As of the date of this post, EMR is at version 5.28.0. Below, as shown in the EMR web interface, are the current (21) applications and frameworks available for installation on EMR.

emr-28.png

For this demo, we will install Apache Spark v2.4.4, Ganglia v3.7.2, and Zeppelin 0.8.2.

screen_shot_2019-11-17_at_8_32_17_pmApache Zeppelin: Web Interface

screen_shot_2019-11-13_at_5_40_12_pmApache Spark: DAG Visualization

screen_shot_2019-11-13_at_8_31_13_pmGanglia: Cluster CPU Monitoring

Create the EMR CloudFormation Stack

Change the following (7) variable values, then run the emr cloudformation create-stack API command, using the AWS CLI.

# change me
ZEPPELIN_DEMO_BUCKET="your-bucket-name"
EC2_KEY_NAME="your-key-name"
LOG_BUCKET="aws-logs-your_aws_account_id-your_region"
GITHUB_ACCOUNT="your-account-name" # i.e. garystafford
GITHUB_REPO="your-new-project-name" # i.e. my-zeppelin-emr-demo
GITHUB_TOKEN="your-token-value"
MASTER_INSTANCE_TYPE="m5.xlarge" # optional
 
aws cloudformation create-stack \
    --stack-name zeppelin-emr-dev-stack \
    --template-body file://cloudformation/emr_single_node.yml \
    --parameters ParameterKey=ZeppelinDemoBucket,ParameterValue=${ZEPPELIN_DEMO_BUCKET} \
                 ParameterKey=Ec2KeyName,ParameterValue=${EC2_KEY_NAME} \
                 ParameterKey=LogBucket,ParameterValue=${LOG_BUCKET} \
                 ParameterKey=MasterInstanceType,ParameterValue=${MASTER_INSTANCE_TYPE} \
                 ParameterKey=GitHubAccount,ParameterValue=${GITHUB_ACCOUNT} \
                 ParameterKey=GitHubRepository,ParameterValue=${GITHUB_REPO} \
                 ParameterKey=GitHubToken,ParameterValue=${GITHUB_TOKEN}

You can use the Amazon EMR web interface to confirm the results of the CloudFormation stack. The cluster should be in the ‘Waiting’ state.

screen_shot_2019-11-15_at_7_42_09_pm

PostgreSQL on Amazon RDS

Next, create a simple, single-AZ, single-master, non-replicated Amazon RDS PostgreSQL database, using the included CloudFormation template, rds_postgres.yml. We will use this database in Notebook 4. For the demo, I have selected the current-generation general purpose db.m4.large EC2 instance type to run PostgreSQL. You can easily change the instance type to another RDS-supported instance type to suit your own needs.

Change the following (3) variable values, then run the cloudformation create-stack API command, using the AWS CLI.

# change me
DB_MASTER_USER="your-db-username" # i.e. masteruser
DB_MASTER_PASSWORD="your-db-password" # i.e. 5up3r53cr3tPa55w0rd
MASTER_INSTANCE_TYPE="db.m4.large" # optional
 
aws cloudformation create-stack \
    --stack-name zeppelin-rds-stack \
    --template-body file://cloudformation/rds_postgres.yml \
    --parameters ParameterKey=DBUser,ParameterValue=${DB_MASTER_USER} \
                 ParameterKey=DBPassword,ParameterValue=${DB_MASTER_PASSWORD} \
                 ParameterKey=DBInstanceClass,ParameterValue=${MASTER_INSTANCE_TYPE}

You can use the Amazon RDS web interface to confirm the results of the CloudFormation stack.

screen_shot_2019-11-17_at_8_06_44_pm.png

AWS Glue

Next, create the AWS Glue Data Catalog database, the Apache Hive-compatible metastore for Spark SQL, two AWS Glue Crawlers, and a Glue IAM Role (ZeppelinDemoCrawlerRole), using the included CloudFormation template, crawler.yml. The AWS Glue Data Catalog database will be used in Notebook 3.

Change the following variable value, then run the cloudformation create-stack API command, using the AWS CLI.

# change me
ZEPPELIN_DEMO_BUCKET="your-bucket-name"
 
aws cloudformation create-stack \
    --stack-name zeppelin-crawlers-stack \
    --template-body file://cloudformation/crawler.yml \
    --parameters ParameterKey=ZeppelinDemoBucket,ParameterValue=${ZEPPELIN_DEMO_BUCKET} \
    --capabilities CAPABILITY_NAMED_IAM

You can use the AWS Glue web interface to confirm the results of the CloudFormation stack. Note the Data Catalog database and the two Glue Crawlers. We will not run the two crawlers until Part 2 of the post, so no tables will exist in the Data Catalog database, yet.

screen_shot_2019-11-27_at_8_50_09_pmscreen_shot_2019-11-27_at_8_56_47_pm

At this point in the demonstration, you should have successfully created a single-node Amazon EMR cluster, an Amazon RDS PostgresSQL database, and several AWS Glue resources, all using CloudFormation templates.

screen_shot_2019-11-21_at_4_19_01_pm

Post-EMR Creation Configuration

RDS Security

For the new EMR cluster to communicate with the RDS PostgreSQL database, we need to ensure that port 5432 is open from the RDS database’s VPC security group, which is the default VPC security group, to the security groups of the EMR nodes. Obtain the Group ID of the ElasticMapReduce-master and ElasticMapReduce-slave Security Groups from the EMR web interface.

screen_shot_2019-11-20_at_11_51_10_am

Access the Security Group for the RDS database using the RDS web interface. Change the inbound rule for port 5432 to include both Security Group IDs.

screen_shot_2019-11-20_at_11_52_23_am

SSH to EMR Master Node

In addition to the bootstrap script and configurations, we already applied to the EMR cluster, we need to make several post-EMR creation configuration changes to the EMR cluster for our demonstration. These changes will require SSH’ing to the EMR cluster. Using the master node’s public DNS address and SSH command provided in the EMR web console, SSH into the master node.

screen_shot_2019-11-15_at_7_42_09_pm_v3

If you cannot access the node using SSH, check that port 22 is open on the associated EMR master node IAM Security Group (ElasticMapReduce-master) to your IP address or address range.

screen_shot_2019-11-15_at_7_42_09_pm_v2

screen_shot_2019-11-21_at_4_51_01_pm.png

Git Permissions

We need to change permissions on the git repository we installed during the EMR bootstrapping phase. Typically, with an EC2 instance, you perform operations as the ec2-user user. With Amazon EMR, you often perform actions as the hadoop user. With Zeppelin on EMR, the notebooks perform operations, including interacting with the git repository as the zeppelin user. As a result of the bootstrap.sh script, the contents of the git repository directory, /tmp/zeppelin-emr-demo/, are owned by the hadoop user and group by default.

screen_shot_2019-11-17_at_8_01_24_pm

We will change their owner to the zeppelin user and group. We could not perform this step as part of the bootstrap script since the the zeppelin user and group did not exist at the time the script was executed.

cd /tmp/zeppelin-emr-demo/
sudo chown -R zeppelin:zeppelin .

The results should look similar to the following output.

screen_shot_2019-11-17_at_8_02_16_pm

Pre-Install Visualization Packages

Next, we will pre-install several Apache Zeppelin Visualization packages. According to the Zeppelin website, an Apache Zeppelin Visualization is a pluggable package that can be loaded/unloaded on runtime through the Helium framework in Zeppelin. We can use them just like any other built-in visualization in the notebook. A Visualization is a javascript npm package. For example, here is a link to the ultimate-pie-chart on the public npm registry.

We can pre-load plugins by replacing the /usr/lib/zeppelin/conf/helium.json file with the version of helium.json we copied to S3, earlier, and restarting Zeppelin. If you have a lot of Visualizations or package types or use any DataOps automation to create EMR clusters, this approach is much more efficient and repeatable than manually loading plugins using the Zeppelin UI, each time you create a new EMR cluster. Below, the helium.json file, which pre-loads (8) Visualization packages.

{
    "enabled": {
        "ultimate-pie-chart": "ultimate-pie-chart@0.0.2",
        "ultimate-column-chart": "ultimate-column-chart@0.0.2",
        "ultimate-scatter-chart": "ultimate-scatter-chart@0.0.2",
        "ultimate-range-chart": "ultimate-range-chart@0.0.2",
        "ultimate-area-chart": "ultimate-area-chart@0.0.1",
        "ultimate-line-chart": "ultimate-line-chart@0.0.1",
        "zeppelin-bubblechart": "zeppelin-bubblechart@0.0.4",
        "zeppelin-highcharts-scatterplot": "zeppelin-highcharts-scatterplot@0.0.2"
    },
    "packageConfig": {},
    "bundleDisplayOrder": [
        "ultimate-pie-chart",
        "ultimate-column-chart",
        "ultimate-scatter-chart",
        "ultimate-range-chart",
        "ultimate-area-chart",
        "ultimate-line-chart",
        "zeppelin-bubblechart",
        "zeppelin-highcharts-scatterplot"
    ]
}

Run the following commands to load the plugins and adjust the permissions on the file.

# change me
ZEPPELIN_DEMO_BUCKET="your-bucket-name"
 
sudo aws s3 cp s3://${ZEPPELIN_DEMO_BUCKET}/bootstrap/helium.json \
    /usr/lib/zeppelin/conf/helium.json
sudo chown zeppelin:zeppelin /usr/lib/zeppelin/conf/helium.json

Create New JDBC Interpreter

Lastly, we need to create a new Zeppelin JDBC Interpreter to connect to our RDS database. By default, Zeppelin has several interpreters installed. You can review a list of available interpreters using the following command.

sudo sh /usr/lib/zeppelin/bin/install-interpreter.sh --list

screen_shot_2019-11-18_at_6_29_40_am

The new JDBC interpreter will allow us to connect to our RDS PostgreSQL database, using Java Database Connectivity (JDBC). First, ensure all available interpreters are installed, including the current Zeppelin JDBC driver (org.apache.zeppelin:zeppelin-jdbc:0.8.0) to /usr/lib/zeppelin/interpreter/jdbc.

Creating a new interpreter is a two-part process. In this stage, we install the required interpreter files on the master node using the following command. Then later, in the Zeppelin web interface, we will configure the new PostgreSQL JDBC interpreter. Note we must provide a unique name for the interpreter (I chose ‘postgres’ in this case), which we will refer to in part two of the interpreter creation process.

sudo sh /usr/lib/zeppelin/bin/install-interpreter.sh --all
 
sudo sh /usr/lib/zeppelin/bin/install-interpreter.sh \
    --name "postgres" \
    --artifact org.apache.zeppelin:zeppelin-jdbc:0.8.0

To complete the post-EMR creation configuration on the master node, we must restart Zeppelin for our changes to take effect.

sudo stop zeppelin && sudo start zeppelin

In my experience, it could take 2–3 minutes for the Zeppelin UI to become fully responsive after a restart.

screen_shot_2019-11-18_at_10_01_54_pm

Zeppelin Web Interface Access

With all the EMR application configuration complete, we will access the Zeppelin web interface running on the master node. Use the Zeppelin connection information provided in the EMR web interface to setup SSH tunneling to the Zeppelin web interface, running on the master node. Using this method, we can also access the Spark History Server, Ganglia, and Hadoop Resource Manager web interfaces; all links are provided from EMR.

screen_shot_2019-11-15_at_7_42_09_pm

To set up a web connection to the applications installed on the EMR cluster, I am using FoxyProxy as a proxy management tool with Google Chrome.

screen_shot_2019-11-17_at_8_22_09_pm.png

If everything is working so far, you should see the Zeppelin web interface with all four Zeppelin notebooks available from the cloned GitHub repository. You will be logged in as the anonymous user. Zeppelin offers authentication for accessing notebooks on the EMR cluster. For brevity, we will not cover setting up authentication in Zeppelin, using Shiro Authentication.

screen_shot_2019-11-17_at_8_32_17_pm.png

To confirm the path to the local, cloned copy of the GitHub notebook repository, is correct, check the Notebook Repos interface, accessible under the Settings dropdown (anonymous user) in the upper right of the screen. The value should match the ZEPPELIN_NOTEBOOK_DIR configuration property value in the emr_single_node.yml CloudFormation template we executed earlier.

screen_shot_2019-11-18_at_10_04_23_pm

To confirm the Helium Visualizations were pre-installed correctly, using the helium.json file, open the Helium interface, accessible under the Settings dropdown (anonymous user) in the upper right of the screen.

screen_shot_2019-11-15_at_7_45_28_pmNote the enabled visualizations. And, it is easy to enable additional plugins through the web interface.screen_shot_2019-11-15_at_7_45_33_pm

New PostgreSQL JDBC Interpreter

If you recall, earlier, we install the required interpreter files on the master node using the following command using the bootstrap script. We will now complete the process of configuring the new PostgreSQL JDBC interpreter. Open the Interpreter interface, accessible under the Settings dropdown (anonymous user) in the upper right of the screen.

The title of the new interpreter must match the name we used to install the interpreter files, ‘postgres’. The interpreter group will be ‘jdbc’. There are, minimally, three properties we need to configure for your specific RDS database instance, including default.url, default.user, and default.password. These should match the values you used to create your RDS instance, earlier. Make sure to includes the database name in the default.url. An example is shown below.

default.url: jdbc:postgresql://zeppelin-demo.abcd1234efg56.us-east-1.rds.amazonaws.com:5432/ratings
default.user: masteruser
default.password: 5up3r53cr3tPa55w0rd

We also need to provide a path to the PostgreSQL driver JAR dependency. This path is the location where we placed the JAR using the bootstrap.sh script, earlier, /home/hadoop/extrajars/postgresql-42.2.8.jar. Save the new interpreter and make sure it starts successfully (shows a green icon).

screen_shot_2019-11-17_at_9_03_02_pm.png

screen_shot_2019-11-18_at_6_42_13_am

Switch Interpreters to Python 3

The last thing we need to do is change the Spark and Python interpreters to use Python 3 instead of the default Python 2. On the same screen you used to create a new interpreter, modify the Spark and Python interpreters. First, for the Python interpreter, change the zeppelin.python property to python3.

screen_shot_2019-11-18_at_3_30_39_pm

Next, for the Spark interpreter, change the zeppelin.pyspark.python property to python3.

screen_shot_2019-11-18_at_3_32_34_pm

Part 2

In Part 1 of this post, we created and configured the AWS resources required to demonstrate the use of Apache Zeppelin on EMR, using an AWS Glue Data Catalog, Amazon RDS PostgreSQL database, and an S3 data lake. In Part 2 of this post, we will explore some of Apache Zeppelin’s features and integration capabilities with a variety of AWS services using the notebooks.

All opinions expressed in this post are my own and not necessarily the views of my current or past employers or their clients.

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Big Data Analytics with Java and Python, using Cloud Dataproc, Google’s Fully-Managed Spark and Hadoop Service

Posted by Gary A. Stafford in Big Data, Cloud, GCP, Java Development, Python, Software Development, Technology Consulting on December 11, 2018

There is little question, big data analytics, data science, artificial intelligence (AI), and machine learning (ML), a subcategory of AI, have all experienced a tremendous surge in popularity over the last few years. Behind the hype curves and marketing buzz, these technologies are having a significant influence on all aspects of our modern lives.

However, installing, configuring, and managing the technologies that support big data analytics, data science, ML, and AI, at scale and in Production, often demands an advanced level of familiarity with Linux, distributed systems, cloud- and container-based platforms, databases, and data-streaming applications. The mere ability to manage terabytes and petabytes of transient data is beyond the capability of many enterprises, let alone performing analysis of that data.

To ease the burden of implementing these technologies, the three major cloud providers, AWS, Azure, and Google Cloud, all have multiple Big Data Analytics-, AI-, and ML-as-a-Service offerings. In this post, we will explore one such cloud-based service offering in the field of big data analytics, Google Cloud Dataproc. We will focus on Cloud Dataproc’s ability to quickly and efficiently run Spark jobs written in Java and Python, two widely adopted enterprise programming languages.

Featured Technologies

The following technologies are featured prominently in this post.

dataproc

Google Cloud Dataproc

dataproc_logoAccording to Google, Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Dataproc is a complete platform for data processing, analytics, and machine learning. Dataproc offers per-second billing, so you only pay for exactly the resources you consume. Dataproc offers frequently updated and native versions of Apache Spark, Hadoop, Pig, and Hive, as well as other related applications. Dataproc has built-in integrations with other Google Cloud Platform (GCP) services, such as Cloud Storage, BigQuery, Bigtable, Stackdriver Logging, and Stackdriver Monitoring. Dataproc’s clusters are configurable and resizable from a three to hundreds of nodes, and each cluster action takes less than 90 seconds on average.

Similar Platform as a Service (PaaS) offerings to Dataproc, include Amazon Elastic MapReduce (EMR), Microsoft Azure HDInsight, and Qubole Data Service. Qubole is offered on AWS, Azure, and Oracle Cloud Infrastructure (Oracle OCI).

According to Google, Cloud Dataproc and Cloud Dataflow, both part of GCP’s Data Analytics/Big Data Product offerings, can both be used for data processing, and there’s overlap in their batch and streaming capabilities. Cloud Dataflow is a fully-managed service for transforming and enriching data in stream and batch modes. Dataflow uses the Apache Beam SDK to provide developers with Java and Python APIs, similar to Spark.

Apache Spark

spark_logoAccording to Apache, Spark is a unified analytics engine for large-scale data processing, used by well-known, modern enterprises, such as Netflix, Yahoo, and eBay. With in-memory speeds up to 100x faster than Hadoop, Apache Spark achieves high performance for static, batch, and streaming data, using a state-of-the-art DAG (Directed Acyclic Graph) scheduler, a query optimizer, and a physical execution engine.

According to a post by DataFlair, ‘the DAG in Apache Spark is a set of Vertices and Edges, where vertices represent the RDDs and the edges represent the Operation to be applied on RDD (Resilient Distributed Dataset). In Spark DAG, every edge directs from earlier to later in the sequence. On the calling of Action, the created DAG submits to DAG Scheduler which further splits the graph into the stages of the task.’ Below, we see a three-stage DAG visualization, displayed using the Spark History Server Web UI, from a job demonstrated in this post.

Screen Shot 2018-12-15 at 11.20.57 PM

Spark’s polyglot programming model allows users to write applications in Scala, Java, Python, R, and SQL. Spark includes libraries for Spark SQL (DataFrames and Datasets), MLlib (Machine Learning), GraphX (Graph Processing), and DStreams (Spark Streaming). Spark may be run using its standalone cluster mode or on Apache Hadoop YARN, Mesos, and Kubernetes.

PySpark

pyspark_logoThe Spark Python API, PySpark, exposes the Spark programming model to Python. PySpark is built on top of Spark’s Java API. Data is processed in Python and cached and shuffled in the JVM. According to Apache, Py4J enables Python programs running in a Python interpreter to dynamically access Java objects in a JVM.

Apache Hadoop

hadoop_logo1According to Apache, the Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. This is a rather modest description of such a significant and transformative project. When we talk about Hadoop, often it is in the context of the project’s well-known modules, which includes:

  • Hadoop Common: The common utilities that support the other Hadoop modules
  • Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data
  • Hadoop YARN (Yet Another Resource Negotiator): A framework for job scheduling and cluster resource management, also known as ‘Hadoop NextGen’
  • Hadoop MapReduce: A YARN-based system for parallel processing of large datasets
  • Hadoop Ozone: An object store for Hadoop

Based on the Powered by Apache Hadoop list, there are many well-known enterprises and academic institutions using Apache Hadoop, including Adobe, eBay, Facebook, Hulu, LinkedIn, and The New York Times.

Spark vs. Hadoop

There are many articles and posts that delve into the Spark versus Hadoop debate, this post is not one of them. Although both are mature technologies, Spark, the new kid on the block, reached version 1.0.0 in May 2014, whereas Hadoop reached version 1.0.0, earlier, in December 2011. According to Google Trends, interest in both technologies has remained relatively high over the last three years. However, interest in Spark, based on the volume of searches, has been steadily outpacing Hadoop for well over a year now. The in-memory speed of Spark over HDFS-based Hadoop and ease of Spark SQL for working with structured data are likely big differentiators for many users coming from a traditional relational database background and users with large or streaming datasets, requiring near real-time processing.

spark-to-hadoop

In this post, all examples are built to run on Spark. This is not meant to suggest Spark is necessarily superior or that Spark runs better on Dataproc than Hadoop. In fact, Dataproc’s implementation of Spark relies on Hadoop’s core HDFS and YARN technologies to run.

Demonstration

To show the capabilities of Cloud Dataproc, we will create both a single-node Dataproc cluster and three-node cluster, upload Java- and Python-based analytics jobs and data to Google Cloud Storage, and execute the jobs on the Spark cluster. Finally, we will enable monitoring and notifications for the Dataproc clusters and the jobs running on the clusters with Stackdriver. The post will demonstrate the use of the Google Cloud Console, as well as Google’s Cloud SDK’s command line tools, for all tasks.

In this post, we will be uploading and running individual jobs on the Dataproc Spark cluster, as opposed to using the Cloud Dataproc Workflow Templates. According to Google, Workflow Template is a reusable workflow configuration. It defines a graph of jobs with information on where to run those jobs. Workflow Templates are useful for automating your Datapoc workflows, however, automation is not the primary topic of this post.

Source Code

All open-sourced code for this post can be found on GitHub in two repositories, one for Java with Spark and one for Python with PySpark. Source code samples are displayed as GitHub Gists, which may not display correctly on all mobile and social media browsers.

Cost

Of course, there is a cost associated with provisioning cloud services. However, if you manage the Google Cloud Dataproc resources prudently, the costs are negligible. Regarding pricing, according to Google, Cloud Dataproc pricing is based on the size of Cloud Dataproc clusters and the duration of time that they run. The size of a cluster is based on the aggregate number of virtual CPUs (vCPUs) across the entire cluster, including the master and worker nodes. The duration of a cluster is the length of time, measured in minutes, between cluster creation and cluster deletion.

Over the course of writing the code for this post, as well as writing the post itself, the entire cost of all the related resources was a minuscule US$7.50. The cost includes creating, running, and deleting more than a dozen Dataproc clusters and uploading and executing approximately 75-100 Spark and PySpark jobs. Given the quick creation time of a cluster, 2 minutes on average or less in this demonstration, there is no reason to leave a cluster running longer than it takes to complete your workloads.

Kaggle Datasets

To explore the features of Dataproc, we will use a publicly-available dataset from Kaggle. Kaggle is a popular open-source resource for datasets used for big-data and ML applications. Their tagline is ‘Kaggle is the place to do data science projects’.

For this demonstration, I chose the IBRD Statement Of Loans Data dataset, from World Bank Financial Open Data, and available on Kaggle. The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. This dataset contains historical snapshots of the Statement of Loans including the latest available snapshots.

screen_shot_2018-12-04_at_7.02.53_pm

There are two data files available. The ‘Statement of Loans’ latest available snapshots data file contains 8,713 rows of loan data (~3 MB), ideal for development and testing. The ‘Statement of Loans’ historic data file contains approximately 750,000 rows of data (~265 MB). Although not exactly ‘big data’, the historic dataset is large enough to sufficiently explore Dataproc. Both IBRD files have an identical schema with 33 columns of data (gist).


End of Period Loan Number Region Country Code Country Borrower Guarantor Country Code Guarantor Loan Type Loan Status Interest Rate Currency of Commitment Project ID Project Name Original Principal Amount Cancelled Amount Undisbursed Amount Disbursed Amount Repaid to IBRD Due to IBRD Exchange Adjustment Borrower's Obligation Sold 3rd Party Repaid 3rd Party Due 3rd Party Loans Held First Repayment Date Last Repayment Date Agreement Signing Date Board Approval Date Effective Date (Most Recent) Closed Date (Most Recent) Last Disbursement Date
2018-10-31T00:00:00 IBRD00010 EUROPE AND CENTRAL ASIA FR France CREDIT NATIONAL FR France NON POOL Fully Repaid 4.2500 P037383 RECONSTRUCTION 250000000.00 0.00 0.00 250000000.00 38000.00 0.00 0.00 0.00 249962000.00 249962000.00 0.00 0.00 1952-11-01T00:00:00 1977-05-01T00:00:00 1947-05-09T00:00:00 1947-05-09T00:00:00 1947-06-09T00:00:00 1947-12-31T00:00:00
2018-10-31T00:00:00 IBRD00020 EUROPE AND CENTRAL ASIA NL Netherlands NON POOL Fully Repaid 4.2500 P037452 RECONSTRUCTION 191044211.75 0.00 0.00 191044211.75 103372211.75 0.00 0.00 0.00 87672000.00 87672000.00 0.00 0.00 1952-04-01T00:00:00 1972-10-01T00:00:00 1947-08-07T00:00:00 1947-08-07T00:00:00 1947-09-11T00:00:00 1948-03-31T00:00:00
2018-10-31T00:00:00 IBRD00021 EUROPE AND CENTRAL ASIA NL Netherlands NON POOL Fully Repaid 4.2500 P037452 RECONSTRUCTION 3955788.25 0.00 0.00 3955788.25 0.00 0.00 0.00 0.00 3955788.25 3955788.25 0.00 0.00 1953-04-01T00:00:00 1954-04-01T00:00:00 1948-05-25T00:00:00 1947-08-07T00:00:00 1948-06-01T00:00:00 1948-06-30T00:00:00
2018-10-31T00:00:00 IBRD00030 EUROPE AND CENTRAL ASIA DK Denmark NON POOL Fully Repaid 4.2500 P037362 RECONSTRUCTION 40000000.00 0.00 0.00 40000000.00 17771000.00 0.00 0.00 0.00 22229000.00 22229000.00 0.00 0.00 1953-02-01T00:00:00 1972-08-01T00:00:00 1947-08-22T00:00:00 1947-08-22T00:00:00 1947-10-17T00:00:00 1949-03-31T00:00:00
2018-10-31T00:00:00 IBRD00040 EUROPE AND CENTRAL ASIA LU Luxembourg NON POOL Fully Repaid 4.2500 P037451 RECONSTRUCTION 12000000.00 238016.98 0.00 11761983.02 1619983.02 0.00 0.00 0.00 10142000.00 10142000.00 0.00 0.00 1949-07-15T00:00:00 1972-07-15T00:00:00 1947-08-28T00:00:00 1947-08-28T00:00:00 1947-10-24T00:00:00 1949-03-31T00:00:00
2018-10-31T00:00:00 IBRD00050 LATIN AMERICA AND CARIBBEAN CL Chile Ministry of Finance CL Chile NON POOL Fully Repaid 4.5000 P006578 POWER 13500000.00 0.00 0.00 13500000.00 12167000.00 0.00 0.00 0.00 1333000.00 1333000.00 0.00 0.00 1953-07-01T00:00:00 1968-07-01T00:00:00 1948-03-25T00:00:00 1948-03-25T00:00:00 1949-04-07T00:00:00 1954-12-31T00:00:00
2018-10-31T00:00:00 IBRD00060 LATIN AMERICA AND CARIBBEAN CL Chile Ministry of Finance CL Chile NON POOL Fully Repaid 3.7500 P006577 FOMENTO AGRIC CREDIT 2500000.00 0.00 0.00 2500000.00 755000.00 0.00 0.00 0.00 1745000.00 1745000.00 0.00 0.00 1950-07-01T00:00:00 1955-01-01T00:00:00 1948-03-25T00:00:00 1948-03-25T00:00:00 1949-04-07T00:00:00 1950-01-01T00:00:00
2018-10-31T00:00:00 IBRD00070 EUROPE AND CENTRAL ASIA NL Netherlands NON POOL Fully Repaid 3.5625 P037453 SHIPPING I 2000000.00 0.00 0.00 2000000.00 0.00 0.00 0.00 0.00 2000000.00 2000000.00 0.00 0.00 1949-01-15T00:00:00 1958-07-15T00:00:00 1948-07-15T00:00:00 1948-05-21T00:00:00 1948-08-03T00:00:00 1948-08-03T00:00:00
2018-10-31T00:00:00 IBRD00071 EUROPE AND CENTRAL ASIA NL Netherlands NON POOL Fully Repaid 3.5625 P037453 SHIPPING I 2000000.00 0.00 0.00 2000000.00 0.00 0.00 0.00 0.00 2000000.00 2000000.00 0.00 0.00 1949-01-15T00:00:00 1958-07-15T00:00:00 1948-07-15T00:00:00 1948-05-21T00:00:00 1948-08-03T00:00:00 1948-08-03T00:00:00

view raw
ibrd-statement-of-loans-latest-available-snapshot-snippet.csv
hosted with ❤ by GitHub

In this demonstration, both the Java and Python jobs will perform the same simple analysis of the larger historic dataset. For the analysis, we will ascertain the top 25 historic IBRD borrower, we will determine their total loan disbursements, current loan obligations, and the average interest rates they were charged for all loans. This simple analysis will be performed using Spark’s SQL capabilities. The results of the analysis, a Spark DataFrame containing 25 rows, will be saved as a CSV-format data file.

SELECT country, country_code,
       Format_number(total_disbursement, 0) AS total_disbursement,
       Format_number(total_obligation, 0) AS total_obligation,
       Format_number(avg_interest_rate, 2) AS avg_interest_rate
FROM   (SELECT country,
               country_code,
               Sum(disbursed) AS total_disbursement,
               Sum(obligation) AS total_obligation,
               Avg(interest_rate) AS avg_interest_rate
        FROM   loans
        GROUP  BY country, country_code
        ORDER  BY total_disbursement DESC
        LIMIT  25)

Google Cloud Storage

First, we need a location to store our Spark jobs, data files, and results, which will be accessible to Dataproc. Although there are a number of choices, the simplest and most convenient location for Dataproc is a Google Cloud Storage bucket. According to Google, Cloud Storage offers the highest level of availability and performance within a single region and is ideal for compute, analytics, and ML workloads in a particular region. Cloud Storage buckets are nearly identical to Amazon Simple Storage Service (Amazon S3), their object storage service.

Using the Google Cloud Console, Google’s Web Admin UI, create a new, uniquely named Cloud Storage bucket. Our Dataproc clusters will eventually be created in a single regional location. We need to ensure our new bucket is created in the same regional location as the clusters; I chose us-east1.

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We will need the new bucket’s link, to use within the Java and Python code as well from the command line with gsutil. The gsutil tool is a Python application that lets you access Cloud Storage from the command line. The bucket’s link may be found on the Storage Browser Console’s Overview tab. A bucket’s link is always in the format, gs://bucket-name.

screen_shot_2018-12-04_at_7.06.06_pm

Alternatively, we may also create the Cloud Storage bucket using gsutil with the make buckets (mb) command, as follows:

# Always best practice since features are updated frequently
gcloud components update
  
export PROJECT=your_project_name
export REGION=us-east1
export BUCKET_NAME=gs://your_bucket_name
  
# Make sure you are creating resources in the correct project
gcloud config set project $PROJECT
  
gsutil mb -p $PROJECT -c regional -l $REGION $BUCKET_NAME

Cloud Dataproc Cluster

Next, we will create two different Cloud Dataproc clusters for demonstration purposes. If you have not used Cloud Dataproc previously in your GCP Project, you will first need to enable the API for Cloud Dataproc.

screen_shot_2018-12-04_at_7.15.05_pm

Single Node Cluster

We will start with a single node cluster with no worker nodes, suitable for development and testing Spark and Hadoop jobs, using small datasets. Create a single-node Dataproc cluster using the Single Node Cluster mode option. Create the cluster in the same region as the new Cloud Storage bucket. This will allow the Dataproc cluster access to the bucket without additional security or IAM configuration. I used the n1-standard-1 machine type, with 1 vCPU and 3.75 GB of memory. Observe the resources assigned to Hadoop YARN for Spark job scheduling and cluster resource management.

screen_shot_2018-12-04_at_7.19.37_pm

The new cluster, consisting of a single node and no worker nodes, should be ready for use in a few minutes or less.

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Note the Image version, 1.3.16-deb9. According to Google, Dataproc uses image versions to bundle operating system, big data components, and Google Cloud Platform connectors into one package that is deployed on a cluster.  This image, released in November 2018, is the latest available version at the time of this post. The image contains:

  • Apache Spark 2.3.1
  • Apache Hadoop 2.9.0
  • Apache Pig 0.17.0
  • Apache Hive 2.3.2
  • Apache Tez 0.9.0
  • Cloud Storage connector 1.9.9-hadoop2
  • Scala 2.11.8
  • Python 2.7

To avoid lots of troubleshooting, make sure your code is compatible with the image’s versions. It is important to note the image does not contain a version of Python 3. You will need to ensure your Python code is built to run with Python 2.7. Alternatively, use Dataproc’s --initialization-actions flag along with bootstrap and setup shell scripts to install Python 3 on the cluster using pip or conda. Tips for installing Python 3 on Datapoc be found on Stack Overflow and elsewhere on the Internet.

As as an alternative to the Google Cloud Console, we are able to create the cluster using a REST command. Google provides the Google Cloud Console’s equivalent REST request, as shown in the example below.

screen_shot_2018-12-04_at_7.20.07_pm

Additionally, we have the option of using the gcloud command line tool. This tool provides the primary command-line interface to Google Cloud Platform and is part of Google’s Cloud SDK, which also includes the aforementioned gsutil. Here again, Google provides the Google Cloud Console’s equivalent gcloud command. This is a great way to learn to use the command line.

screen_shot_2018-12-04_at_7.20.21_pm

Using the dataproc clusters create command, we are able to create the same cluster as shown above from the command line, as follows:

export PROJECT=your_project_name
export CLUSTER_1=your_single_node_cluster_name 
export REGION=us-east1
export ZONE=us-east1-b
export MACHINE_TYPE_SMALL=n1-standard-1
  
gcloud dataproc clusters create $CLUSTER_1 \
  --region $REGION \
  --zone $ZONE \
  --single-node \
  --master-machine-type $MACHINE_TYPE_SMALL \
  --master-boot-disk-size 500 \
  --image-version 1.3-deb9 \
  --project $PROJECT

There are a few useful commands to inspect your running Dataproc clusters. The dataproc clusters describe command, in particular, provides detailed information about all aspects of the cluster’s configuration and current state.

gcloud dataproc clusters list --region $REGION

gcloud dataproc clusters describe $CLUSTER_2 \
  --region $REGION --format json

Standard Cluster

In addition to the single node cluster, we will create a second three-node Dataproc cluster. We will compare the speed of a single-node cluster to that of a true cluster with multiple worker nodes. Create a new Dataproc cluster using the Standard Cluster mode option. Again, make sure to create the cluster in the same region as the new Storage bucket.

screen_shot_2018-12-04_at_10.15.05_pm

The second cluster contains a single master node and two worker nodes. All three nodes use the n1-standard-4 machine type, with 4 vCPU and 15 GB of memory. Although still considered a minimally-sized cluster, this cluster represents a significant increase in compute power over the first single-node cluster, which had a total of 2 vCPU, 3.75 GB of memory, and no worker nodes on which to distribute processing. Between the two workers in the second cluster, we have 8 vCPU and 30 GB of memory for computation.

screen_shot_2018-12-04_at_10.18.54_pm

Again, we have the option of using the gcloud command line tool to create the cluster:

export PROJECT=your_project_name
export CLUSTER_2=your_three_node_cluster_name 
export REGION=us-east1
export ZONE=us-east1-b
export NUM_WORKERS=2
export MACHINE_TYPE_LARGE=n1-standard-4
  
gcloud dataproc clusters create $CLUSTER_2 \
  --region $REGION \
  --zone $ZONE \
  --master-machine-type $MACHINE_TYPE_LARGE \
  --master-boot-disk-size 500 \
  --num-workers $NUM_WORKERS \
  --worker-machine-type $MACHINE_TYPE_LARGE \
  --worker-boot-disk-size 500 \
  --image-version 1.3-deb9 \
  --project $PROJECT

Cluster Creation Speed: Cloud Dataproc versus Amazon EMS?

In a series of rather unscientific tests, I found the three-node Dataproc cluster took less than two minutes on average to be created. Compare that time to a similar three-node cluster built with Amazon’s EMR service using their general purpose m4.4xlarge Amazon EC2 instance type. In a similar series of tests, I found the EMR cluster took seven minutes on average to be created. The EMR cluster took 3.5 times longer to create than the comparable Dataproc cluster. Again, although not a totally accurate comparison, since both services offer different features, it gives you a sense of the speed of Dataproc as compared to Amazon EMR.

Staging Buckets

According to Google, when you create a cluster, Cloud Dataproc creates a Cloud Storage staging bucket in your project or reuses an existing Cloud Dataproc-created bucket from a previous cluster creation request. Staging buckets are used to stage miscellaneous configuration and control files that are needed by your cluster. Below, we see the staging buckets created for the two Dataproc clusters.

screen_shot_2018-12-04_at_10.26.49_pm

Project Files

Before uploading the jobs and running them on the Cloud Dataproc clusters, we need to understand what is included in the two GitHub projects. If you recall from the Kaggle section of the post, both projects are basically the same but, written in different languages, Java and Python. The jobs they contain all perform the same basic analysis on the dataset.

Java Project

The dataproc-java-demo Java-based GitHub project contains three classes, each which are jobs to run by Spark. The InternationalLoansApp Java class is only intended to be run locally with the smaller 8.7K rows of data in the snapshot CSV file (gist).

package org.example.dataproc;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;
public class InternationalLoansApp {
public static void main(String[] args) {
InternationalLoansApp app = new InternationalLoansApp();
app.start();
}
private void start() {
SparkSession spark = SparkSession.builder()
.appName("dataproc-java-demo")
.master("local[*]")
.getOrCreate();
spark.sparkContext().setLogLevel("INFO"); // INFO by default
// Loads CSV file from local directory
Dataset<Row> dfLoans = spark.read()
.format("csv")
.option("header", "true")
.option("inferSchema", true)
.load("data/ibrd-statement-of-loans-latest-available-snapshot.csv");
// Basic stats
System.out.printf("Rows of data:%d%n", dfLoans.count());
System.out.println("Inferred Schema:");
dfLoans.printSchema();
//