Archive for category Big Data
GTM Stack: Exploring IoT Data Analytics at the Edge with Grafana, Mosquitto, and TimescaleDB on ARM-based Architectures
Posted by Gary A. Stafford in Big Data, IoT, Python, Raspberry Pi, Software Development on October 12, 2020
In the following post, we will explore the integration of several open-source software applications to build an IoT edge analytics stack, designed to operate on ARM-based edge nodes. We will use the stack to collect, analyze, and visualize IoT data without first shipping the data to the Cloud or other external systems.
The Edge
Edge computing is a fast-growing technology trend, which involves pushing compute capabilities to the edge. Wikipedia describes edge computing as a distributed computing paradigm that brings computation and data storage closer to the location needed to improve response times and save bandwidth. The term edge commonly refers to a compute node at the edge of a network (edge device), sitting in close proximity to the source a data and between that data source and external system such as the Cloud.
In his recent post, 3 Advantages (And 1 Disadvantage) Of Edge Computing, well-known futurist Bernard Marr argues reduced bandwidth requirements, reduced latency, and enhanced security and privacy as three primary advantages of edge computing. Due to techniques like data downsampling, Marr advises one potential disadvantage of edge computing is that important data could end up being overlooked and discarded in the quest to save bandwidth and reduce latency.
David Ricketts, Head of Marketing at Quiss Technology PLC, estimates in his post, Cloud and Edge Computing — The Stats You Need to Know for 2018, the global edge computing market is expected to reach USD 6.72 billion by 2022 at a compound annual growth rate of a whopping 35.4 percent. Realizing the market potential, many major Cloud providers, edge device manufacturers, and integrators are rapidly expanding their edge compute capabilities. AWS, for example, currently offers more than a dozen services in the edge computing category.
Internet of Things
Edge computing is frequently associated with the Internet of Things (IoT). IoT devices, industrial equipment, and sensors generate data, which is transmitted to other internal and external systems, often by way of edge nodes, such as an IoT Gateway. IoT devices typically generate time-series data. According to Wikipedia, a time series is a set of data points indexed in time order — a sequence taken at successive equally spaced points in time. IoT devices typically generate continuous high-volume streams of time-series data, often on the scale of millions of data points per second. IoT data characteristics require IoT platforms to minimally support temporal accuracy, high-volume ingestion and processing, efficient data compression and downsampling, and real-time querying capabilities.
The IoT devices and the edge devices, such as IoT Gateways, which aggregate and transmit IoT data from these devices to external systems, are generally lower-powered, with limited processor, memory, and storage capabilities. Accordingly, IoT platforms must satisfy all the requirements of IoT data while simultaneously supporting resource-constrained environments.
IoT Analytics at the Edge
Leading Cloud providers AWS, Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud all offer IoT services. Many offer IoT services with edge computing capabilities. AWS offers AWS IoT Greengrass. Greengrass provides local compute, messaging, data management, sync, and ML inference capabilities to edge devices. Azure offers Azure IoT Edge. Azure IoT Edge provides the ability to run AI, Azure and third-party services, and custom business logic on edge devices using standard containers. Google Cloud offers Edge TPU. Edge TPU (Tensor Processing Unit) is Google’s purpose-built application-specific integrated circuit (ASIC), designed to run AI at the edge.
IoT Analytics
Many Cloud providers also offer IoT analytics as part of their suite of IoT services, although not at the edge. AWS offers AWS IoT Analytics, while Azure has Azure Time Series Insights. Google provides IoT analytics, indirectly, through downstream analytic systems and ad hoc analysis using Google BigQuery or advanced analytics and machine learning with Cloud Machine Learning Engine. These services generally all require data to be transmitted to the Cloud for analytics.
The ability to analyze IoT data at the edge, as data is streamed in real-time, is critical to a rapid feedback loop. IoT edge analytics can accelerate anomaly detection, improve predictive maintenance capabilities, and expedite proactive inventory replenishment.
The IoT Edge Analytics Stack
In my opinion, the ideal IoT edge analytics stack is comprised of lightweight, purpose-built, easily deployable and manageable, platform- and programming language-agnostic, open-source software components. The minimal IoT edge analytics stack should include a lightweight message broker, a time-series database, an ANSI-standard ad-hoc query engine, and a data visualization tool. Each component should be purpose-built for IoT.
Lightweight Message Broker
We will use Eclipse Mosquitto as our message broker. According to the project’s description, Mosquitto is an open-source message broker that implements the Message Queuing Telemetry Transport (MQTT) protocol versions 5.0, 3.1.1, and 3.1. Mosquitto is lightweight and suitable for use on all devices from low power single board computers (SBCs) to full servers.
MQTT Client Library
We will interact with Mosquitto using Eclipse Paho. According to the project, the Eclipse Paho project provides open-source, mainly client-side implementations of MQTT and MQTT-SN in a variety of programming languages. MQTT and MQTT for Sensor Networks (MQTT-SN) are lightweight publish/subscribe messaging transports for TCP/IP and connectionless protocols, such as UDP, respectively.
We will be using Paho’s Python Client. The Paho Python Client provides a client class with support for both MQTT v3.1 and v3.1.1 on Python 2.7 or 3.x. The client also provides helper functions to make publishing messages to an MQTT server straightforward.
Time-Series Database
Time-series databases are optimal for storing IoT data. According to InfluxData, makers of a leading time-series database, InfluxDB, a time-series database (TSDB), is a database optimized for time-stamped or time-series data. Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time. Jiao Xian, of Alibaba Cloud, has authored an insightful post on the time-series database ecosystem, What Are Time Series Databases? A few leading Cloud providers offer purpose-built time-series databases, though they are not available at the edge. AWS offers Amazon Timestream and Alibaba Cloud offers Time Series Database.
InfluxDB is an excellent choice for a time-series database. It was my first choice, along with TimescaleDB, when developing this stack. However, InfluxDB Flux’s apparent incompatibilities with some ARM-based architectures ruled it out for inclusion in the stack for this particular post.
We will use TimescaleDB as our time-series database. TimescaleDB is the leading open-source relational database for time-series data. Described as ‘PostgreSQL for time-series,’ TimescaleDB is based on PostgreSQL, which provides full ANSI SQL, rock-solid reliability, and a massive ecosystem. TimescaleDB claims to achieve 10–100x faster queries than PostgreSQL, InfluxDB, and MongoDB, with native optimizations for time-series analytics.
TimescaleDB claims to achieve 10–100x faster queries than PostgreSQL, InfluxDB, and MongoDB, with native optimizations for time-series analytics.
TimescaleDB is designed for performing analytical queries, both through its native support for PostgreSQL’s full range of SQL functionality, as well as additional functions native to TimescaleDB. These time-series optimized functions include Median/Percentile, Cumulative Sum, Moving Average, Increase, Rate, Delta, Time Bucket, Histogram, and Gap Filling.
Ad-hoc Data Query Engine
We have the option of using psql, the terminal-based front-end to PostgreSQL, to execute ad-hoc queries against TimescaleDB. The psql front-end enables you to enter queries interactively, issue them to PostgreSQL, and see the query results.
We also have the option of using pgAdmin, specifically the biarms/pgadmin4 Docker version, to execute ad-hoc queries and perform most other database tasks. pgAdmin is the most popular open-source administration and development platform for PostgreSQL. While several popular Docker versions of pgAdmin only support Linux AMD64 architectures, the biarms/pgadmin4 Docker version supports ARM-based devices.
Data Visualization
For data visualization, we will use Grafana. Grafana allows you to query, visualize, alert on, and understand metrics no matter where they are stored. With Grafana, you can create, explore, and share dashboards, fostering a data-driven culture. Grafana allows you to define thresholds visually and get notified via Slack, PagerDuty, and more. Grafana supports dozens of data sources, including MySQL, PostgreSQL, Elasticsearch, InfluxDB, TimescaleDB, Graphite, Prometheus, Google BigQuery, GraphQL, and Oracle. Grafana is extensible through a large collection of plugins.
Edge Deployment and Management Platform
Docker introduced the current industry standard for containers in 2013. Docker containers are a standardized unit of software that allows developers to isolate apps from their environment. We will use Docker to deploy the IoT edge analytics stack, referred to herein as the GTM Stack, composed of containerized versions of Eclipse Mosquitto, TimescaleDB, and Grafana, and pgAdmin, to an ARM-based edge node. The acronym, GTM, comes from the three primary OSS projects composing the stack. The acronym also suggests Greenwich Mean Time, relating to the precise time-series nature of IoT data.
Running Docker Engine in swarm mode, we can use Docker to deploy the complete IoT edge analytics stack to the swarm, running on the edge node. The deploy command accepts a stack description in the form of a Docker Compose file, a YAML file used to configure the application’s services. With a single command, we can create and start all the services from the configuration file.
Source Code
All source code for this post is available on GitHub. Use the following command to git clone a local copy of the project.
IoT Devices
For this post, I have deployed three Linux ARM-based IoT devices, each connected to a sensor array. Each sensor array contains multiple analog and digital sensors. The sensors record temperature, humidity, air quality (liquefied petroleum gas (LPG), carbon monoxide (CO), and smoke), light, and motion. For more information on the IoT device and sensor hardware involved, please see my previous post: Getting Started with IoT Analytics on AWS.
Each ARM-based IoT device is running a small Python3-based script, sensor_data_to_mosquitto.py, shown below.
The IoT devices’ script implements the Eclipse Paho MQTT Python client library. An MQTT message containing simultaneous readings from each sensor is sent to a Mosquitto topic on the edge node, at a configurable frequency.
IoT Edge Node
For this post, I have deployed a single Linux ARM-based edge node. The three IoT devices, containing sensor arrays, communicate with the edge node over Wi-Fi. The IoT devices could easily use an alternative communication protocol, such as BLE, LoRaWAN, or Ethernet. For more information on BLE and LoRaWAN, please see some of my previous posts: LoRa and LoRaWAN for IoT: Getting Started with LoRa and LoRaWAN Protocols for Low Power, Wide Area Networking of IoT and BLE and GATT for IoT: Getting Started with Bluetooth Low Energy (BLE) and Generic Attribute Profile (GATT) Specification for IoT.
The edge node is also running a small Python3-based script, mosquitto_to_timescaledb.py, shown below.
Similar to the IoT devices, the edge node’s script implements the Eclipse Paho MQTT Python client library. The script pulls MQTT messages off a Mosquitto topic(s), serializes the message payload to JSON, and writes the payload’s data to the TimescaleDB database. The edge node’s script accepts several arguments, which allow you to configure necessary Mosquitto and TimescaleDB variables.
Why not use Telegraf?
Telegraf is a plugin-driven agent that collects, processes, aggregates, and writes metrics. There is a Telegraf output plugin, the PostgreSQL and TimescaleDB Output Plugin for Telegraf, produced by TimescaleDB. The plugin could replace the need to manage and maintain the above script. However, I chose not to use it because it is not yet an official Telegraf plugin. If the plugin was included in a Telegraf release, I would certainly encourage its use.
Script Management
Both the Linux-based IoT devices and the edge node run systemd system and service manager. To ensure the Python scripts keep running in the case of a system restart, we define a systemd unit. Units are the objects that systemd knows how to manage. These are basically a standardized representation of system resources that can be managed by the suite of daemons and manipulated by the provided utilities. Each script has a systemd unit files. Below, we see the gtm_stack_mosquitto unit file, gtm_stack_mosquitto.service.
The gtm_stack_mosq_to_tmscl unit file, gtm_stack_mosq_to_tmscl.service, is nearly identical.
To install the gtm_stack_mosquitto.service systemd unit file on each IoT device, use the following commands.
Installing the gtm_stack_mosq_to_tmscl.service unit file on the edge node is nearly identical.
Docker Stack
The edge node runs the GTM Docker stack, stack.yml, in a swarm. As discussed earlier, the stack contains four containers: Eclipse Mosquitto, TimescaleDB, and Grafana, along with pgAdmin. The Mosquitto, TimescaleDB, and Grafana containers have paths within the containers, bind-mounted to directories on the edge device. With bind-mounting, the container’s data will persist if the containers are removed and re-created. The containers are running on their own isolated overlay network.
The GTM Docker stack is installed using the following commands on the edge node. We will assume Docker and git are pre-installed on the edge node for this post.
First, we create the proper local directories on the edge device, which will be used to bind-mount to the container’s directories. Below, we see the bind-mounted local directories with the eventual container’s contents stored within them.
Next, we copy the custom Mosquitto configuration file, mosquitto.conf, included in the project, to the correct location on the edge device. Lastly, we initialize the Docker swarm and deploy the stack.
docker container ls command, showing the running GTM Stack containers
docker stats command, showing the resource consumption of GTM Stack containersTimescaleDB Setup
With the GTM stack running, we need to create a single Timescale hypertable, sensor_data, in the TimescaleDB demo_iot database, to hold the incoming IoT sensor data. Hypertables, according to TimescaleDB, are designed to be easy to manage and to behave like standard PostgreSQL tables. Hypertables are comprised of many interlinked “chunk” tables. Commands made to the hypertable automatically propagate changes down to all of the chunks belonging to that hypertable.
I suggest using psql to execute the required DDL statements, which will create the hypertable, as well as the proceeding views and database user permissions. All SQL statements are included in the project’s statements.sql file. One way to use psql is to install it on your local workstation, then use psql to connect to the remote edge node. I prefer to instantiate a local PostgreSQL Docker container instance running psql. I then use the local container’s psql client to connect to the edge node’s TimescaleDB database. For example, from my local machine, I run the following docker run command to connect to the edge node’s TimescaleDB database, on the edge node, located locally at 192.168.1.12.
Although not always practical, could also access psql from within the TimescaleDB Docker container, running on the actual edge node, using the following docker exec command.
TimescaleDB Continuous Aggregates
For this post’s demonstration, we need to create four TimescaleDB database views, which will be queried from an eventual Grafana Dashboard. The views are TimescaleDB Continuous Aggregates. According to Timescale, aggregate queries that touch large swathes of time-series data can take a long time to compute because the system needs to scan large amounts of data on every query execution. TimescaleDB continuous aggregates automatically calculate the results of a query in the background and materialize the results.
For example, in this post, we generate sensor data every five seconds from the three IoT devices. When visualizing a 24-hour period in Grafana, using continuous aggregates with an interval of one minute, we would reduce the total volume of data queried from ~51,840 rows to ~4,320 rows, a reduction of over 91%. The larger the time period or the number of IoT devices being analyzed, the more significant these savings will positively impact query performance.
A time_bucket on the time partitioning column of the hypertable is required in all continuous aggregate views. The time_bucket function, in this case, has a bucket width (interval) of 1 minute. The interval is configurable.
Limiting Grafana’s Access to IoT Data
Following the Grafana recommendation for database user permissions, we create a grafanareader PostgresSQL user, and limit the user’s access to the sensor_data table and the four views we created. Grafana will use this user’s credentials to perform SELECT queries of the TimescaleDB demo_iot database.
Grafana Dashboards
Using the TimescaleDB continuous aggregates we have created, we can quickly build a richly-featured dashboard in Grafana. Below we see a typical IoT Dashboard you might build to monitor the post’s IoT sensor data in near real-time. An exported version, dashboard_external_export.json, is included in the GitHub project.
Grafana’s documentation includes a comprehensive set of instructions for Using PostgreSQL in Grafana. To connect to the TimescaleDB database from Grafana, we use a PostgreSQL data source.
The data displayed in each Panel in the Grafana Dashboard is based on a SQL query. For example, the Average Temperature Panel might use a query similar to the example below. This particular query also converts Celcius to Fahrenheit. Note the use of Grafana Macros (e.g., $__time(), $__timeFilter()). Macros can be used within a query to simplify syntax and allow for dynamic parts.
SELECT
$__time(bucket),
device_id AS metric,
((avg_temp * 1.9) + 32) AS avg_temp
FROM temperature_humidity_summary_minute
WHERE
$__timeFilter(bucket)
ORDER BY 1,2
Below, we see another example from the Average Humidity Panel. In this particular query, we might choose to validate the humidity data is within an acceptable range of 0%–100%.
SELECT
$__time(bucket),
device_id AS metric,
avg_humidity
FROM temperature_humidity_summary_minute
WHERE
$__timeFilter(bucket)
AND avg_humidity >= 0.0
AND avg_humidity <= 100.0
ORDER BY 1,2
Mobile Friendly
Grafana dashboards are mobile-friendly. Below we see two views of the dashboard, using the Chrome mobile browser on an Apple iPhone.
Grafana Alerts
Grafana allows Alerts to be created based on Rules you define in each Panel. If data values match the Rule’s conditions, which you pre-define, such as a temperature reading above a certain threshold for a set amount of time, an alert is sent to your choice of destinations. According to the Rule shown below, If the average temperature exceeds 75°F for a period of 5 minutes, an alert is sent.
As demonstrated below, when the temperature in the laboratory began to exceed 75°F, the alert entered a ‘Pending’ state. If the temperature exceeded 75°F for the pre-determined period of 5 minutes, the alert status changed to ‘Alerting’, and an alert was sent. When the temperature dropped back below 75°F for the pre-determined period of 5 minutes, the alert status changed from ‘Alerting’ to ‘OK’, and a subsequent notification was sent.
There are currently 18 destinations available out-of-the-box with Grafana, including Slack, email, PagerDuty, webhooks, HipChat, and Microsoft Teams. We can use Grafana Alerts to notify the proper resources, in near real-time, if an issue is detected, based on the data. Below, we see an actual series of high-temperature alerts sent by Grafana to a Slack channel, followed by subsequent notifications as the temperature returned to normal.
Ad-hoc Queries
The ability to perform ad-hoc queries on the time-series IoT data is an essential feature of the IoT edge analytics stack. We can use psql or pgAdmin to perform ad-hoc queries against the TimescaleDB database. Below are examples of typical ad-hoc queries we might perform on the IoT sensor data. These example queries demonstrate TimescaleDB’s advanced analytical capabilities for working with time-series data, including Moving Average, Delta, Time Bucket, and Histogram.
Conclusion
In this post, we have explored the development of an IoT edge analytics stack, comprised of lightweight, purpose-built, easily deployable and manageable, platform- and programming language-agnostic, open-source software components. These components included Docker containerized versions of Eclipse Mosquitto, TimescaleDB, Grafana, and pgAdmin, referred to as the GTM Stack. Using the GTM stack, we collected, analyzed, and visualized IoT data, without first shipping the data to Cloud or other external systems.
This blog represents my own viewpoints and not of my employer, Amazon Web Services. All product names, logos, and brands are the property of their respective owners.
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.
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.
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).
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.
AWS managed policy to the RolePart 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.
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
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;"
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;.
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.
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”.
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.
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.
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.
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.
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.
Each device includes the following sensors:
- MQ135 Air Quality Sensor Hazardous Gas Detection Sensor: CO, LPG, Smoke (link)
(requires an MCP3008 – 8-Channel 10-Bit ADC w/ SPI Interface (link)) - DHT22/AM2302 Digital Temperature and Humidity Sensor (link)
- Onyehn IR Pyroelectric Infrared PIR Motion Sensor (link)
- Anmbest Light Intensity Detection Photosensitive Sensor (link)
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 | |
| } |
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.
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.
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.
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.
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.
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 |
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 |
Below, we see a successful deployment of the IoT Analytics Demo CloudFormation 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.
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 |
If successful, you should see the five messages appear in the Test tab, shown above. Example output from the script is shown below.
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 |
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.
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.
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.
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.
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 | |
| } |
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 | |
| } |
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.
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.
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.
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.
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.
IoT Analytics Data sets also support sending content results, which are materialized views of your IoT Analytics data, to an Amazon S3 bucket.
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.
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.
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.
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.
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.
For the demonstration, we will create an Amazon QuickSight Data set that will use our IoT Analytics Data set as a data source.
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.
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.
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.
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.
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.
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.
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.
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.
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 |
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.
Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight: Part 2
Posted by Gary A. Stafford in AWS, Big Data, Cloud, Software Development, SQL on January 14, 2020
Introduction
In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, 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 data sources, stored in the data lake, to create compelling visualizations using Amazon QuickSight.
High-level AWS architecture diagram of the demonstration.
Background
If you recall the demonstration from part one of the post, we had adopted the persona of a large, US-based electric energy provider. The energy provider had developed and sold its next-generation Smart Electrical Monitoring Hub (Smart Hub) to residential customers. Customers can analyze their electrical usage with a fine level of 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.
Data Visualization and BI
The data analysis process in the demonstration was divided into four logical stages: 1) Raw Data Ingestion, 2) Data Transformation, 3) Data Enrichment, and 4) Data Visualization and Business Intelligence (BI).
Full data analysis workflow diagram (click to enlarge…)
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 data visualization and business intelligence, which integrate with Amazon Athena. Amazon services include Amazon QuickSight, Amazon EMR, and Amazon SageMaker. Third-party solutions from AWS Partners, many available on the AWS Marketplace, include Tableau, Looker, Sisense, and Domo.
In this demonstration, we will focus on Amazon QuickSight. Amazon QuickSight is a fully managed business intelligence (BI) service. QuickSight lets you create and publish interactive dashboards that include ML Insights. Dashboards can be accessed from any device, and embedded into your applications, portals, and websites. QuickSight serverlessly scales automatically from tens of users to tens of thousands without any infrastructure management.
Using QuickSight
QuickSight APIs
Amazon recently added a full set of aws quicksight APIs for interacting with QuickSight. For example, to preview the three QuickSight data sets created for this part of the demo, with the AWS CLI, we would use the list-data-sets comand.
| aws quicksight list-data-sets –aws-account-id 123456789012 |
| { | |
| "Status": 200, | |
| "DataSetSummaries": [ | |
| { | |
| "Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/9eb88a69-20de-d8be-aefd-2c7ac4e23748", | |
| "DataSetId": "9eb88a69-20de-d8be-aefd-2c7ac4e23748", | |
| "Name": "etl_output_parquet", | |
| "CreatedTime": 1578028774.897, | |
| "LastUpdatedTime": 1578955245.02, | |
| "ImportMode": "SPICE" | |
| }, | |
| { | |
| "Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/78e81193-189c-6dd0-864fb-a33244c9654", | |
| "DataSetId": "78e81193-189c-6dd0-864fb-a33244c9654", | |
| "Name": "electricity_rates_parquet", | |
| "CreatedTime": 1578029224.996, | |
| "LastUpdatedTime": 1578945179.472, | |
| "ImportMode": "SPICE" | |
| }, | |
| { | |
| "Arn": "arn:aws:quicksight:us-east-1:123456789012:dataset/a474214d-c838-b384-bcca-ea1fcd2dd094", | |
| "DataSetId": "a474214d-c838-b384-bcca-ea1fcd2dd094", | |
| "Name": "smart_hub_locations_parquet", | |
| "CreatedTime": 1578029124.565, | |
| "LastUpdatedTime": 1578888788.135, | |
| "ImportMode": "SPICE" | |
| } | |
| ], | |
| "RequestId": "2524e80c-7c67-7fbd-c3f1-b700c521badc" | |
| } |
To examine details of a single data set, with the AWS CLI, we would use the describe-data-set command.
| aws quicksight describe-data-set \ | |
| –aws-account-id 123456789012 \ | |
| –data-set-id 9eb88a69-20de-d8be-aefd-2c7ac4e23748 |
QuickSight Console
However, for this final part of the demonstration, we will be working from the Amazon QuickSight Console, as opposed to the AWS CLI, AWS CDK, or CloudFormation templates.
Signing Up for QuickSight
To use Amazon QuickSight, you must sign up for QuickSight.
There are two Editions of Amazon QuickSight, Standard and Enterprise. For this demonstration, the Standard Edition will suffice.
QuickSight Data Sets
Amazon QuickSight uses Data Sets as the basis for all data visualizations. According to AWS, QuickSight data sets can be created from a wide variety of data sources, including Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon Athena, and Amazon S3. You can also upload Excel spreadsheets or flat files (CSV, TSV, CLF, ELF, and JSON), connect to on-premises databases like SQL Server, MySQL, and PostgreSQL and import data from SaaS applications like Salesforce. Below, we see a list of the latest data sources available in the QuickSight New Data Set Console.
Demonstration Data Sets
For the demonstration, I have created three QuickSight data sets, all based on Amazon Athena. You have two options when using Amazon Athena as a data source. The first option is to select a table from an AWS Glue Data Catalog database, such as the database we created in part one of the post, ‘smart_hub_data_catalog.’ The second option is to create a custom SQL query, based on one or more tables in an AWS Glue Data Catalog database.
Of the three data sets created for part two of this demonstration, two data sets use tables directly from the Data Catalog, including ‘etl_output_parquet’ and ‘electricity_rates_parquet.’ The third data set uses a custom SQL query, based on the single Data Catalog table, ‘smart_hub_locations_parquet.’ All three tables used to create the data sets represent the enriched, highly efficient Parquet-format data sources in the S3-based Data Lake.
Data Set Features
There are a large number of features available when creating and configuring data sets. We cannot possibly cover all of them in this post. Let’s look at three features: geospatial field types, calculated fields, and custom SQL.
Geospatial Data Types
QuickSight can intelligently detect common types of geographic fields in a data source and assign QuickSight geographic data type, including Country, County, City, Postcode, and State. QuickSight can also detect geospatial data, including Latitude and Longitude. We will take advantage of this QuickSight feature for our three data set’s data sources, including the State, Postcode, Latitude, and Longitude field types.
Calculated Fields
A commonly-used QuickSight data set feature is the ‘Calculated field.’ For the ‘etl_output_parquet’ data set, I have created a new field (column), cost_dollar.
The cost field is the electrical cost of the device, over a five minute time interval, in cents (¢). The calculated cost_dollar field is the quotient of the cost field divided by 100. This value represents the electrical cost of the device, over a five minute time interval, in dollars ($). This is a straightforward example. However, a calculated field can be very complex, built from multiple arithmetic, comparison, and conditional functions, string calculations, and data set fields.
Data set calculated fields can also be created and edited from the QuickSight Analysis Console (discussed later).
Custom SQL
The third QuickSight data set is based on an Amazon Athena custom SQL query.
| SELECT lon, lat, postcode, hash, tz, state | |
| FROM smart_hub_data_catalog.smart_hub_locations_parquet; |
Although you can write queries in the QuickSight Data Prep Console, I prefer to write custom Athena queries using the Athena Query Editor. Using the Editor, you can write, run, debug, and optimize queries to ensure they function correctly, first.
The Athena query can then be pasted into the Custom SQL window. Clicking ‘Finish’ in the window is the equivalent of ‘Run query’ in the Athena Query Editor Console. The query runs and returns data.
Similar to the Athena Query Editor, queries executed in the QuickSight Data Prep Console will show up in the Athena History tab, with a /* QuickSight */ comment prefix.
SPICE
You will notice the three QuickSight data sets are labeled, ‘SPICE.’ According to AWS, the acronym, SPICE, stands for ‘Super-fast, Parallel, In-memory, Calculation Engine.’ QuickSight’s in-memory calculation engine, SPICE, achieves blazing fast performance at scale. SPICE automatically replicates data for high availability allowing thousands of users to simultaneously perform fast, interactive analysis while shielding your underlying data infrastructure, saving you time and resources. With the Standard Edition of QuickSight, as the first Author, you get 1 GB of SPICE in-memory data for free.
QuickSight Analysis
The QuickSight Analysis Console is where Analyses are created. A specific QuickSight Analysis will contain a collection of data sets and data visualizations (visuals). Each visual is associated with a single data set.
Types of QuickSight Analysis visuals include: horizontal and vertical, single and stacked bar charts, line graphs, combination charts, area line charts, scatter plots, heat maps, pie and donut charts, tree maps, pivot tables, gauges, key performance indicators (KPI), geospatial diagrams, and word clouds. Individual visual titles, legends, axis, and other visual aspects can be easily modified. Visuals can contain drill-downs.
A data set’s fields can be modified from within the Analysis Console. Field types and formats, such as date, numeric, currency fields, can be customized for display. The Analysis can include a Title and subtitle. There are some customizable themes available to change the overall look of the Analysis.
Analysis Filters
Data displayed in the visuals can be further shaped using a combination of Filters, Conditional formatting, and Parameters. Below, we see an example of a typical filter based on a range of dates and times. The data set contains two full days’ worth of data. Here, we are filtering the data to a 14-hour peak electrical usage period, between 8 AM and 10 PM on the same day, 12/21/2019.
Drill-Down, Drill-Up, Focus, and Exclude
According to AWS, all visual types except pivot tables offer the ability to create a hierarchy of fields for a visual element. The hierarchy lets you drill down or up to see data at different levels of the hierarchy. Focus allows you to concentrate on a single element within a hierarchy of fields. Exclude allows you to remove an element from a hierarchy of fields. Below, we see an example of all four of these features, available to apply to the ‘Central Air Conditioner’. Since the AC unit is the largest consumer of electricity on average per day, applying these filters to understand its impact on the overall electrical usage may be useful to an analysis. We can also drill down to minutes from hours or up to days from hours.
Example QuickSight Analysis
A QuickSight Analysis is shared by the Analysis Author as a QuickSight Dashboard. Below, we see an example of a QuickSight Dashboard, built and shared for this demonstration. The ‘Residential Electrical Usage Analysis’ is built from the three data sets created earlier. From those data sets, we have constructed several visuals, including a geospatial diagram, donut chart, heat map, KPI, combination chart, stacked vertical bar chart, and line graph. Each visual’s title, layout, and field display has all customized. The data displayed in the visuals have been filtered differently, including by date and time, by customer id (loc_id), and by state. Conditional formatting is used to enhance the visual appearance of visuals, such as the ‘Total Electrical Cost’ KPI.
Conclusion
In part one, we learned how to ingest, transform, and enrich raw, semi-structured data, in multiple formats, 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 used 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.
Executing Amazon Athena Queries from JetBrains PyCharm
Posted by Gary A. Stafford in AWS, Big Data, Cloud, Python, Software Development, SQL on January 8, 2020
Amazon Athena
According to Amazon, Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Amazon Athena supports and works with a variety of popular data file formats, including CSV, JSON, Apache ORC, Apache Avro, and Apache Parquet.
The underlying technology behind Amazon Athena is Presto, the popular, open-source distributed SQL query engine for big data, created by Facebook. According to AWS, the Athena query engine is based on Presto 0.172. Athena is ideal for quick, ad-hoc querying, but it can also handle complex analysis, including large joins, window functions, and arrays. In addition to Presto, Athena also uses Apache Hive to define tables.
Athena Query Editor
In the previous post, Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight, we used the Athena Query Editor to construct and test SQL queries against semi-structured data in an S3-based Data Lake. The Athena Query Editor has many of the basic features Data Engineers and Analysts expect, including SQL syntax highlighting, code auto-completion, and query formatting. Queries can be run directly from the Editor, saved for future reference, and query results downloaded. The Editor can convert SELECT queries to CREATE TABLE AS (CTAS) and CREATE VIEW AS statements. Access to AWS Glue data sources is also available from within the Editor.
Full-Featured IDE
Although the Athena Query Editor is fairly functional, many Engineers perform a majority of their software development work in a fuller-featured IDE. The choice of IDE may depend on one’s predominant programming language. According to the PYPL Index, the ten most popular, current IDEs are:
- Microsoft Visual Studio
- Android Studio
- Eclipse
- Visual Studio Code
- Apache NetBeans
- JetBrains PyCharm
- JetBrains IntelliJ
- Apple Xcode
- Sublime Text
- Atom
Within the domains of data science, big data analytics, and data analysis, languages such as SQL, Python, Java, Scala, and R are common. Although I work in a variety of IDEs, my go-to choices are JetBrains PyCharm for Python (including for PySpark and Jupyter Notebook development) and JetBrains IntelliJ for Java and Scala (including Apache Spark development). Both these IDEs also support many common SQL-based technologies, out-of-the-box, and are easily extendable to add new technologies.
Athena Integration with PyCharm
Utilizing the extensibility of the JetBrains suite of professional development IDEs, it is simple to add Amazon Athena to the list of available database drivers and make JDBC (Java Database Connectivity) connections to Athena instances on AWS.
Downloading the Athena JDBC Driver
To start, download the Athena JDBC Driver from Amazon. There are two versions, based on your choice of Java JDKs. Considering Java 8 was released six years ago (March 2014), most users will likely want the AthenaJDBC42-2.0.9.jar is compatible with JDBC 4.2 and JDK 8.0 or later.
Installation Guide
AWS also supplies a JDBC Driver Installation and Configuration Guide. The guide, as well as the Athena JDBC and ODBC Drivers, are produced by Simba Technologies (acquired by Magnitude Software). Instructions for creating an Athena Driver starts on page 23.
Creating a New Athena Driver
From PyCharm’s Database Tool Window, select the Drivers dialog box, select the downloaded Athena JDBC Driver JAR. Select com.simba.athena.jdbc.Driver in the Class dropdown. Name the Driver, ‘Amazon Athena.’
You can configure the Athena Driver further, using the Options and Advanced tabs.
Creating a New Athena Data Source
From PyCharm’s Database Tool Window, select the Data Source dialog box to create a new connection to your Athena instance. Choose ‘Amazon Athena’ from the list of available Database Drivers.
You will need four items to create an Athena Data Source:
- Your IAM User Access Key ID
- Your IAM User Secret Access Key
- The AWS Region of your Athena instance (e.g., us-east-1)
- An existing S3 bucket location to store query results
The Athena connection URL is a combination of the AWS Region and the S3 bucket, items 3 and 4, above. The format of the Athena connection URL is as follows.
jdbc:awsathena://AwsRegion=your-region;S3OutputLocation=s3://your-bucket-name/query-results-path
Give the new Athena Data Source a logical Name, input the User (Access Key ID), Password (Secret Access Key), and the Athena URL. To test the Athena Data Source, use the ‘Test Connection’ button.
You can create multiple Athena Data Sources using the Athena Driver. For example, you may have separate Development, Test, and Production instances of Athena, each in a different AWS Account.
Data Access
Once a successful connection has been made, switching to the Schemas tab, you should see a list of available AWS Glue Data Catalog databases. Below, we see the AWS Glue Catalog, which we created in the prior post. This Glue Data Catalog database contains ten metadata tables, each corresponding to a semi-structured, file-based data source in an S3-based data lake.
In the example below, I have chosen to limit the new Athena Data Source to a single Data Catalog database, to which the Data Source’s IAM User has access. Applying the core AWS security principle of granting least privilege, IAM Users should only have the permissions required to perform a specific set of approved tasks. This principle applies to the Glue Data Catalog databases, metadata tables, and the underlying S3 data sources.
Querying Athena from PyCharm
From within the PyCharm’s Database Tool Window, you should now see a list of the metadata tables defined in your AWS Glue Data Catalog database(s), as well as the individual columns within each table.
Similar to the Athena Query Editor, you can write SQL queries against the database tables in PyCharm. Like the Athena Query Editor, PyCharm has standard features SQL syntax highlighting, code auto-completion, and query formatting. Right-click on the Athena Data Source and choose New, then Console, to start.
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, 0.234, is more than 50 releases ahead of the current Athena version. Both Athena and Presto functionality continue to change and diverge. There are also additional considerations and limitations for SQL queries in Athena to be aware of.
Whereas the Athena Query Editor is limited to only one query per query tab, in PyCharm, we can write and run multiple SQL queries in the same console window and have multiple console sessions opened to Athena at the same time.
By default, PyCharm’s query results are limited to the first ten rows of data. The number of rows displayed, as well as many other preferences, can be changed in the PyCharm’s Database Preferences dialog box.
Saving Queries and Exporting Results
In PyCharm, Athena queries can be saved as part of your PyCharm projects, as .sql files. Whereas the Athena Query Editor is limited to CSV, in PyCharm, query results can be exported in a variety of standard data file formats.
Athena Query History
All Athena queries ran from PyCharm are recorded in the History tab of the Athena Console. Although PyCharm shows query run times, the Athena History tab also displays the amount of data scanned. Knowing the query run time and volume of data scanned is useful when performance tuning queries.
Other IDEs
The technique shown for JetBrains PyCharm can also be applied to other JetBrains products, including GoLand, DataGrip, PhpStorm, and IntelliJ (shown below).
This blog represents my own view points and not of my employer, Amazon Web Services.
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.
Preview 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.
From 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.
High-level AWS architecture diagram of the demonstration.
Featured Technologies
The following AWS services and open-source technologies are featured prominently in this post.
Amazon S3-based Data Lake
An 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
AWS 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
The 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
An 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
An 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
Amazon 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
Amazon 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
AWS 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}} |
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 | |
| } | |
| } |
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.
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} |
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 |
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> |
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).
Full 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.
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.
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.
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.
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.
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 |
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 |
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 |
Confirm the contents of the DATA_BUCKET S3 bucket with the following command.
| aws s3 ls s3://${DATA_BUCKET}/ \ | |
| –recursive –human-readable –summarize |
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 |
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 |
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/ |
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 |
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' |
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 |
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 |
You can check the Glue Crawler Console to ensure the four Crawlers finished successfully.
Alternately, use another AWS CLI / jq command.
| aws glue get-crawler-metrics \ | |
| | jq -r '.CrawlerMetricsList[] | "\(.CrawlerName): \(.StillEstimating), \(.TimeLeftSeconds)"' \ | |
| | grep "^smart-hub-[A-Za-z-]*" |
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 |
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 + ";" |
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.
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 |
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 |
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.
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() |
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://${LogBucketName}/glue-etl-jobs/" | |
| } | |
| Description: "Convert electrical rates XML data to Parquet" | |
| ExecutionProperty: | |
| MaxConcurrentRuns: 2 | |
| MaxRetries: 0 | |
| Name: rates-xml-to-parquet | |
| Role: !GetAtt "CrawlerRole.Arn" | |
| DependsOn: | |
| – CrawlerRole | |
| – GlueDatabase | |
| – DataBucket | |
| – ScriptBucket | |
| – LogBucket |
First, copy the Glue ETL Job Python script to the SCRIPT_BUCKET S3 bucket.
| aws s3 cp glue-scripts/rates_xml_to_parquet.py \ | |
| s3://${SCRIPT_BUCKET}/glue_scripts/ |
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 |
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')" |
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" | |
| } | |
| } |
The job’s progress and the results are also visible in the AWS Glue Console’s ETL Jobs tab.
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.
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 |
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 |
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.
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) |
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; |
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 |
The ten INSERT INTO SQL statement’s result statuses (one per device sensor) are visible from the Athena Console’s History tab.
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 |
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 |
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 |
The resulting Parquet file is visible in the S3 Management Console.
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 |
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}/ |
| 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/ |
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.
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' |
| 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 |
‘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 |
The results of the fix may be seen from the AWS Glue Console. All ten tables are now classified correctly.
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.
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; |
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 |
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.
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.
(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
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.
(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.
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.
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 |
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
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 $NF}')
You should observe log output similar to the following. Retrieve the complete URL, for example, http://127.0.0.1:8888/?token=f78cbe..., to access the Jupyter web-based user interface.
From the Jupyter dashboard landing page, you should see all the files in the project’s work/ directory. Note the types of files you can create from the dashboard, including Python 3, R, and Scala (using Apache Toree or spylon-kernal) notebooks, and text. You can also open a Jupyter terminal or create a new Folder from the drop-down menu. At the time of this post (March 2020), the latest jupyter/all-spark-notebook Docker Image runs Spark 2.4.5, Scala 2.11.12, Python 3.7.6, and OpenJDK 64-Bit Server VM, Java 1.8.0 Update 242.
Bootstrap Environment
Included in the project is a bootstrap script, bootstrap_jupyter.sh. The script will install the required Python packages using pip, the Python package installer, and a requirement.txt file. The bootstrap script also installs the latest PostgreSQL driver JAR, configures Apache Log4j to reduce log verbosity when submitting Spark jobs, and installs htop. Although these tasks could also be done from a Jupyter terminal or from within a Jupyter notebook, using a bootstrap script ensures you will have a consistent work environment every time you spin up the Jupyter Docker stack. Add or remove items from the bootstrap script as necessary.
| #!/bin/bash | |
| set -ex | |
| # update/upgrade and install htop | |
| sudo apt-get update -y && sudo apt-get upgrade -y | |
| sudo apt-get install htop | |
| # install required python packages | |
| python3 -m pip install –user –upgrade pip | |
| python3 -m pip install -r requirements.txt –upgrade | |
| # download latest postgres driver jar | |
| POSTGRES_JAR="postgresql-42.2.17.jar" | |
| if [ -f "$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 |
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)) |
From a Jupyter terminal window, use the following command to run the script.
python3 01_simple_script.py
You should observe the following output.
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.
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 |