Posts Tagged Amazon EMR
Stream Processing with Apache Spark, Kafka, Avro, and Apicurio Registry on Amazon EMR and Amazon MSK
Posted by Gary A. Stafford in Analytics, AWS, Big Data, Cloud, Python, Software Development on September 30, 2021
Using a registry to decouple schemas from messages in an event streaming analytics architecture
Introduction
In the last post, Getting Started with Spark Structured Streaming and Kafka on AWS using Amazon MSK and Amazon EMR, we learned about Apache Spark and Spark Structured Streaming on Amazon EMR (fka Amazon Elastic MapReduce) with Amazon Managed Streaming for Apache Kafka (Amazon MSK). We consumed messages from and published messages to Kafka using both batch and streaming queries. In that post, we serialized and deserialized messages to and from JSON using schemas we defined as a StructType (pyspark.sql.types.StructType) in each PySpark script. Likewise, we constructed similar structs for CSV-format data files we read from and wrote to Amazon S3.
schema = StructType([
StructField("payment_id", IntegerType(), False),
StructField("customer_id", IntegerType(), False),
StructField("amount", FloatType(), False),
StructField("payment_date", TimestampType(), False),
StructField("city", StringType(), True),
StructField("district", StringType(), True),
StructField("country", StringType(), False),
])
In this follow-up post, we will read and write messages to and from Amazon MSK in Apache Avro format. We will store the Avro-format Kafka message’s key and value schemas in Apicurio Registry and retrieve the schemas instead of hard-coding the schemas in the PySpark scripts. We will also use the registry to store schemas for CSV-format data files.
Video Demonstration
In addition to this post, there is now a video demonstration available on YouTube.
Technologies
In the last post, Getting Started with Spark Structured Streaming and Kafka on AWS using Amazon MSK and Amazon EMR, we learned about Apache Spark, Apache Kafka, Amazon EMR, and Amazon MSK.
In a previous post, Hydrating a Data Lake using Log-based Change Data Capture (CDC) with Debezium, Apicurio, and Kafka Connect on AWS, we explored Apache Avro and Apicurio Registry.
Apache Spark
Apache Spark, according to the documentation, is a unified analytics engine for large-scale data processing. Spark provides high-level APIs in Java, Scala, Python (PySpark), and R. Spark provides an optimized engine that supports general execution graphs (aka directed acyclic graphs or DAGs). In addition, Spark supports a rich set of higher-level tools, including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.
Spark Structured Streaming
Spark Structured Streaming, according to the documentation, is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will run it incrementally and continuously and update the final result as streaming data continues to arrive. In short, Structured Streaming provides fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing without the user having to reason about streaming.
Apache Avro
Apache Avro describes itself as a data serialization system. Apache Avro is a compact, fast, binary data format similar to Apache Parquet, Apache Thrift, MongoDB’s BSON, and Google’s Protocol Buffers (protobuf). However, Apache Avro is a row-based storage format compared to columnar storage formats like Apache Parquet and Apache ORC.
Avro relies on schemas. When Avro data is read, the schema used when writing it is always present. According to the documentation, schemas permit each datum to be written with no per-value overheads, making serialization fast and small. Schemas also facilitate use with dynamic scripting languages since data, together with its schema, is fully self-describing.
Apicurio Registry
We can decouple the data from its schema by using schema registries such as Confluent Schema Registry or Apicurio Registry. According to Apicurio, in a messaging and event streaming architecture, data published to topics and queues must often be serialized or validated using a schema (e.g., Apache Avro, JSON Schema, or Google Protocol Buffers). Of course, schemas can be packaged in each application. Still, it is often a better architectural pattern to register schemas in an external system [schema registry] and then reference them from each application.
It is often a better architectural pattern to register schemas in an external system and then reference them from each application.
Amazon EMR
According to AWS documentation, Amazon EMR (fka Amazon Elastic MapReduce) is a cloud-based big data platform for processing vast amounts of data using open source tools such as Apache Spark, Hadoop, Hive, HBase, Flink, Hudi, and Presto. Amazon EMR is a fully managed AWS service that makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.
Amazon EMR on EKS, a deployment option for Amazon EMR since December 2020, allows you to run Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). With the EKS deployment option, you can focus on running analytics workloads while Amazon EMR on EKS builds, configures, and manages containers for open-source applications.
If you are new to Amazon EMR for Spark, specifically PySpark, I recommend a recent two-part series of posts, Running PySpark Applications on Amazon EMR: Methods for Interacting with PySpark on Amazon Elastic MapReduce.
Apache Kafka
According to the documentation, Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Amazon MSK
Apache Kafka clusters are challenging to set up, scale, and manage in production. According to AWS documentation, Amazon MSK is a fully managed AWS service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications.
Prerequisites
Similar to the previous post, this post will focus primarily on configuring and running Apache Spark jobs on Amazon EMR. To follow along, you will need the following resources deployed and configured on AWS:
- Amazon S3 bucket (holds all Spark/EMR resources);
- Amazon MSK cluster (using IAM Access Control);
- Amazon EKS container or an EC2 instance with the Kafka APIs installed and capable of connecting to Amazon MSK;
- Amazon EKS container or an EC2 instance with Apicurio Registry installed and capable of connecting to Amazon MSK (if using Kafka for backend storage) and being accessed by Amazon EMR;
- Ensure the Amazon MSK Configuration has
auto.create.topics.enable=true; this setting isfalseby default;
The architectural diagram below shows that the demonstration uses three separate VPCs within the same AWS account and AWS Region us-east-1, for Amazon EMR, Amazon MSK, and Amazon EKS. The three VPCs are connected using VPC Peering. Ensure you expose the correct ingress ports and the corresponding CIDR ranges within your Amazon EMR, Amazon MSK, and Amazon EKS Security Groups. For additional security and cost savings, use a VPC endpoint for private communications between Amazon EMR and Amazon S3.
Source Code
All source code for this post and the three previous posts in the Amazon MSK series, including the Python and PySpark scripts demonstrated herein, are open-sourced and located on GitHub.
Objective
We will run a Spark Structured Streaming PySpark job to consume a simulated event stream of real-time sales data from Apache Kafka. Next, we will enrich (join) that sales data with the sales region and aggregate the sales and order volumes by region within a sliding event-time window. Next, we will continuously stream those aggregated results back to Kafka. Finally, a batch query will consume the aggregated results from Kafka and display the sales results in the console.
Kafka messages will be written in Apache Avro format. The schemas for the Kafka message keys and values and the schemas for the CSV-format sales and sales regions data will all be stored in Apricurio Registry. The Python and PySpark scripts will use Apricurio Registry’s REST API to read, write, and manage the Avro schema artifacts.
We are writing the Kafka message keys in Avro format and storing an Avro key schema in the registry. This is only done for demonstration purposes and not a requirement. Kafka message keys are not required, nor is it necessary to store both the key and the value in a common format of Avro in Kafka.
Schema evolution, compatibility, and validation are important considerations, but out of scope for this post.
PySpark Scripts
PySpark, according to the documentation, is an interface for Apache Spark in Python. PySpark allows you to write Spark applications using the Python API. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core. There are three PySpark scripts and one new helper Python script covered in this post:
- 10_create_schemas.py: Python script creates all Avro schemas in Apricurio Registry using the REST API;
- 11_incremental_sales_avro.py: PySpark script simulates an event stream of sales data being published to Kafka over 15–20 minutes;
- 12_streaming_enrichment_avro.py: PySpark script uses a streaming query to read messages from Kafka in real-time, enriches sales data, aggregates regional sales results, and writes results back to Kafka as a stream;
- 13_batch_read_results_avro.py: PySpark script uses a batch query to read aggregated regional sales results from Kafka and display them in the console;
Preparation
To prepare your Amazon EMR resources, review the instructions in the previous post, Getting Started with Spark Structured Streaming and Kafka on AWS using Amazon MSK and Amazon EMR. Here is a recap, with a few additions required for this post.
Amazon S3
We will start by gathering and copying the necessary files to your Amazon S3 bucket. The bucket will serve as the location for the Amazon EMR bootstrap script, additional JAR files required by Spark, PySpark scripts, and CSV-format data files.
There are a set of additional JAR files required by the Spark jobs we will be running. Download the JARs from Maven Central and GitHub, and place them in the emr_jars project directory. The JARs will include AWS MSK IAM Auth, AWS SDK, Kafka Client, Spark SQL for Kafka, Spark Streaming, and other dependencies. Compared to the last post, there is one additional JAR for Avro.
Update the SPARK_BUCKET environment variable, then upload the JARs, PySpark scripts, sample data, and EMR bootstrap script from your local copy of the GitHub project repository to your Amazon S3 bucket using the AWS s3 API.
Amazon EMR
The GitHub project repository includes a sample AWS CloudFormation template and an associated JSON-format CloudFormation parameters file. The CloudFormation template, stack.yml, accepts several environment parameters. To match your environment, you will need to update the parameter values such as SSK key, Subnet, and S3 bucket. The template will build a minimally-sized Amazon EMR cluster with one master and two core nodes in an existing VPC. You can easily modify the template and parameters to meet your requirements and budget.
aws cloudformation deploy \
--stack-name spark-kafka-demo-dev \
--template-file ./cloudformation/stack.yml \
--parameter-overrides file://cloudformation/dev.json \
--capabilities CAPABILITY_NAMED_IAM
The CloudFormation template has two essential Spark configuration items — the list of applications to install on EMR and the bootstrap script deployment.
Below, we see the EMR bootstrap shell script, bootstrap_actions.sh.
The bootstrap script performed several tasks, including deploying the additional JAR files we copied to Amazon S3 earlier to EMR cluster nodes.
Parameter Store
The PySpark scripts in this demonstration will obtain configuration values from the AWS Systems Manager (AWS SSM) Parameter Store. Configuration values include a list of Amazon MSK bootstrap brokers, the Amazon S3 bucket that contains the EMR/Spark assets, and the Apicurio Registry REST API base URL. Using the Parameter Store ensures that no sensitive or environment-specific configuration is hard-coded into the PySpark scripts. Modify and execute the ssm_params.sh script to create the AWS SSM Parameter Store parameters.
Create Schemas in Apricurio Registry
To create the schemas necessary for this demonstration, a Python script is included in the project, 10_create_schemas.py. The script uses Apricurio Registry’s REST API to create six new Avro-based schema artifacts.
Apricurio Registry supports several common artifact types, including AsyncAPI specification, Apache Avro schema, GraphQL schema, JSON Schema, Apache Kafka Connect schema, OpenAPI specification, Google protocol buffers schema, Web Services Definition Language, and XML Schema Definition. We will use the registry to store Avro schemas for use with Kafka and CSV data sources and sinks.
Although Apricurio Registry does not support CSV Schema, we can store the schemas for the CSV-format sales and sales region data in the registry as JSON-format Avro schemas.
{
"name": "Sales",
"type": "record",
"doc": "Schema for CSV-format sales data",
"fields": [
{
"name": "payment_id",
"type": "int"
},
{
"name": "customer_id",
"type": "int"
},
{
"name": "amount",
"type": "float"
},
{
"name": "payment_date",
"type": "string"
},
{
"name": "city",
"type": [
"string",
"null"
]
},
{
"name": "district",
"type": [
"string",
"null"
]
},
{
"name": "country",
"type": "string"
}
]
}
We can then retrieve the JSON-format Avro schema from the registry, convert it to PySpark StructType, and associate it to the DataFrame used to persist the sales data from the CSV files.
root
|-- payment_id: integer (nullable = true)
|-- customer_id: integer (nullable = true)
|-- amount: float (nullable = true)
|-- payment_date: string (nullable = true)
|-- city: string (nullable = true)
|-- district: string (nullable = true)
|-- country: string (nullable = true)
Using the registry allows us to avoid hard-coding the schema as a StructType in the PySpark scripts in advance.
Add the PySpark script as an EMR Step. EMR will run the Python script the same way it runs PySpark jobs.
The Python script creates six schema artifacts in Apricurio Registry, shown below in Apricurio Registry’s browser-based user interface. Schemas include two key/value pairs for two Kafka topics and two for CSV-format sales and sales region data.
You have the option of enabling validation and compatibility rules for each schema with Apricurio Registry.
Each Avro schema artifact is stored as a JSON object in the registry.
Simulate Sales Event Stream
Next, we will simulate an event stream of sales data published to Kafka over 15–20 minutes. The PySpark script, 11_incremental_sales_avro.py, reads 1,800 sales records into a DataFrame (pyspark.sql.DataFrame) from a CSV file located in S3. The script then takes each Row (pyspark.sql.Row) of the DataFrame, one row at a time, and writes them to the Kafka topic, pagila.sales.avro, adding a slight delay between each write.
The PySpark scripts first retrieve the JSON-format Avro schema for the CSV data from Apricurio Registry using the Python requests module and Apricurio Registry’s REST API (get_schema()).
{
"name": "Sales",
"type": "record",
"doc": "Schema for CSV-format sales data",
"fields": [
{
"name": "payment_id",
"type": "int"
},
{
"name": "customer_id",
"type": "int"
},
{
"name": "amount",
"type": "float"
},
{
"name": "payment_date",
"type": "string"
},
{
"name": "city",
"type": [
"string",
"null"
]
},
{
"name": "district",
"type": [
"string",
"null"
]
},
{
"name": "country",
"type": "string"
}
]
}
The script then creates a StructType from the JSON-format Avro schema using an empty DataFrame (struct_from_json()). Avro column types are converted to Spark SQL types. The only apparent issue is how Spark mishandles the nullable value for each column. Recognize, column nullability in Spark is an optimization statement, not an enforcement of the object type.
root
|-- payment_id: integer (nullable = true)
|-- customer_id: integer (nullable = true)
|-- amount: float (nullable = true)
|-- payment_date: string (nullable = true)
|-- city: string (nullable = true)
|-- district: string (nullable = true)
|-- country: string (nullable = true)
The resulting StructType is used to read the CSV data into a DataFrame (read_from_csv()).
For Avro-format Kafka key and value schemas, we use the same method, get_schema(). The resulting JSON-format schemas are then passed to the to_avro() and from_avro() methods to read and write Avro-format messages to Kafka. Both methods are part of the pyspark.sql.avro.functions module. Avro column types are converted to and from Spark SQL types.
We must run this PySpark script, 11_incremental_sales_avro.py, concurrently with the PySpark script, 12_streaming_enrichment_avro.py, to simulate an event stream. We will start both scripts in the next part of the post.
Stream Processing with Structured Streaming
The PySpark script, 12_streaming_enrichment_avro.py, uses a streaming query to read sales data messages from the Kafka topic, pagila.sales.avro, in real-time, enriches the sales data, aggregates regional sales results, and writes the results back to Kafka in micro-batches every two minutes.
The PySpark script performs a stream-to-batch join between the streaming sales data from the Kafka topic, pagila.sales.avro, and a CSV file that contains sales regions based on the common country column. Schemas for the CSV data and the Kafka message keys and values are retrieved from Apicurio Registry using the REST API identically to the previous PySpark script.
The PySpark script then performs a streaming aggregation of the sale amount and order quantity over a sliding 10-minute event-time window, writing results to the Kafka topic, pagila.sales.summary.avro, every two minutes. Below is a sample of the resulting streaming DataFrame, written to external storage, Kafka in this case, using a DataStreamWriter interface (pyspark.sql.streaming.DataStreamWriter).
Once again, schemas for the second Kafka topic’s message key and value are retrieved from Apicurio Registry using its REST API. The key schema:
{
"name": "Key",
"type": "int",
"doc": "Schema for pagila.sales.summary.avro Kafka topic key"
}
And, the value schema:
{
"name": "Value",
"type": "record",
"doc": "Schema for pagila.sales.summary.avro Kafka topic value",
"fields": [
{
"name": "region",
"type": "string"
},
{
"name": "sales",
"type": "float"
},
{
"name": "orders",
"type": "int"
},
{
"name": "window_start",
"type": "long",
"logicalType": "timestamp-millis"
},
{
"name": "window_end",
"type": "long",
"logicalType": "timestamp-millis"
}
]
}
The schema as applied to the streaming DataFrame utilizing the to_avro() method.
root
|-- region: string (nullable = false)
|-- sales: float (nullable = true)
|-- orders: integer (nullable = false)
|-- window_start: long (nullable = true)
|-- window_end: long (nullable = true)
Submit this streaming PySpark script, 12_streaming_enrichment_avro.py, as an EMR Step.
Wait about two minutes to give this third PySpark script time to start its streaming query fully.
Then, submit the second PySpark script, 11_incremental_sales_avro.py, as an EMR Step. Both PySpark scripts will run concurrently on your Amazon EMR cluster or using two different clusters.
The PySpark script, 11_incremental_sales_avro.py, should run for approximately 15–20 minutes.
During that time, every two minutes, the script, 12_streaming_enrichment_avro.py, will write micro-batches of aggregated sales results to the second Kafka topic, pagila.sales.summary.avroin Avro format. An example of a micro-batch recorded in PySpark’s stdout log is shown below.
Once this script completes, wait another two minutes, then stop the streaming PySpark script, 12_streaming_enrichment_avro.py.
Review the Results
To retrieve and display the results of the previous PySpark script’s streaming computations from Kafka, we can use the final PySpark script, 13_batch_read_results_avro.py.
Run the final script PySpark as EMR Step.
This final PySpark script reads all the Avro-format aggregated sales messages from the Kafka topic, using schemas from Apicurio Registry, using a batch read. The script then summarizes the final sales results for each sliding 10-minute event-time window, by sales region, to the stdout job log.
Conclusion
In this post, we learned how to get started with Spark Structured Streaming on Amazon EMR using PySpark, the Apache Avro format, and Apircurio Registry. We decoupled Kafka message key and value schemas and the schemas of data stored in S3 as CSV, storing those schemas in a registry.
This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.
Getting Started with Spark Structured Streaming and Kafka on AWS using Amazon MSK and Amazon EMR
Posted by Gary A. Stafford in Analytics, AWS, Big Data, Build Automation, Cloud, Software Development on September 9, 2021
Exploring Apache Spark with Apache Kafka using both batch queries and Spark Structured Streaming
Introduction
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. Using Structured Streaming, you can express your streaming computation the same way you would express a batch computation on static data. In this post, we will learn how to use Apache Spark and Spark Structured Streaming with Apache Kafka. Specifically, we will utilize Structured Streaming on Amazon EMR (fka Amazon Elastic MapReduce) with Amazon Managed Streaming for Apache Kafka (Amazon MSK). We will consume from and publish to Kafka using both batch and streaming queries. Spark jobs will be written in Python with PySpark for this post.
Apache Spark
According to the documentation, Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python (PySpark), and R, and an optimized engine that supports general execution graphs. In addition, Spark supports a rich set of higher-level tools, including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.
Spark Structured Streaming
According to the documentation, Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will run it incrementally and continuously and update the final result as streaming data continues to arrive. In short, Structured Streaming provides fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing without the user having to reason about streaming.
Amazon EMR
According to the documentation, Amazon EMR (fka Amazon Elastic MapReduce) is a cloud-based big data platform for processing vast amounts of data using open source tools such as Apache Spark, Hadoop, Hive, HBase, Flink, and Hudi, and Presto. Amazon EMR is a fully managed AWS service that makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.
A deployment option for Amazon EMR since December 2020, Amazon EMR on EKS, allows you to run Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). With the EKS deployment option, you can focus on running analytics workloads while Amazon EMR on EKS builds, configures, and manages containers for open-source applications.
If you are new to Amazon EMR for Spark, specifically PySpark, I recommend an earlier two-part series of posts, Running PySpark Applications on Amazon EMR: Methods for Interacting with PySpark on Amazon Elastic MapReduce.
Apache Kafka
According to the documentation, Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Amazon MSK
Apache Kafka clusters are challenging to set up, scale, and manage in production. According to the documentation, Amazon MSK is a fully managed AWS service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications.
Prerequisites
This post will focus primarily on configuring and running Apache Spark jobs on Amazon EMR. To follow along, you will need the following resources deployed and configured on AWS:
- Amazon S3 bucket (holds Spark resources and output);
- Amazon MSK cluster (using IAM Access Control);
- Amazon EKS container or an EC2 instance with the Kafka APIs installed and capable of connecting to Amazon MSK;
- Connectivity between the Amazon EKS cluster or EC2 and Amazon MSK cluster;
- Ensure the Amazon MSK Configuration has
auto.create.topics.enable=true; this setting isfalseby default;
As shown in the architectural diagram above, the demonstration uses three separate VPCs within the same AWS account and AWS Region, us-east-1, for Amazon EMR, Amazon MSK, and Amazon EKS. The three VPCs are connected using VPC Peering. Ensure you expose the correct ingress ports and the corresponding CIDR ranges within your Amazon EMR, Amazon MSK, and Amazon EKS Security Groups. For additional security and cost savings, use a VPC endpoint for private communications between Amazon EMR and Amazon S3.
Source Code
All source code for this post and the two previous posts in the Amazon MSK series, including the Python/PySpark scripts demonstrated here, are open-sourced and located on GitHub.
PySpark Scripts
According to the Apache Spark documentation, PySpark is an interface for Apache Spark in Python. It allows you to write Spark applications using Python API. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core.
There are nine Python/PySpark scripts covered in this post:
- Initial sales data published to Kafka
01_seed_sales_kafka.py - Batch query of Kafka
02_batch_read_kafka.py - Streaming query of Kafka using grouped aggregation
03_streaming_read_kafka_console.py - Streaming query using sliding event-time window
04_streaming_read_kafka_console_window.py - Incremental sales data published to Kafka
05_incremental_sales_kafka.py - Streaming query from/to Kafka using grouped aggregation
06_streaming_read_kafka_kafka.py - Batch query of streaming query results in Kafka
07_batch_read_kafka.py - Streaming query using static join and sliding window
08_streaming_read_kafka_join_window.py - Streaming query using static join and grouped aggregation
09_streaming_read_kafka_join.py
Amazon MSK Authentication and Authorization
Amazon MSK provides multiple authentication and authorization methods to interact with the Apache Kafka APIs. For this post, the PySpark scripts use Kafka connection properties specific to IAM Access Control. You can use IAM to authenticate clients and to allow or deny Apache Kafka actions. Alternatively, you can use TLS or SASL/SCRAM to authenticate clients and Apache Kafka ACLs to allow or deny actions. In a recent post, I demonstrated the use of SASL/SCRAM and Kafka ACLs with Amazon MSK:Securely Decoupling Applications on Amazon EKS using Kafka with SASL/SCRAM.
Language Choice
According to the latest Spark 3.1.2 documentation, Spark runs on Java 8/11, Scala 2.12, Python 3.6+, and R 3.5+. The Spark documentation contains code examples written in all four languages and provides sample code on GitHub for Scala, Java, Python, and R. Spark is written in Scala.
There are countless posts and industry opinions on choosing the best language for Spark. Taking no sides, I have selected the language I use most frequently for data analytics, Python using PySpark. Compared to Scala, these two languages exhibit some of the significant differences: compiled versus interpreted, statically-typed versus dynamically-typed, JVM- versus non-JVM-based, Scala’s support for concurrency and true multi-threading, and Scala’s 10x raw performance versus the perceived ease-of-use, larger community, and relative maturity of Python.
Preparation
Amazon S3
We will start by gathering and copying the necessary files to your Amazon S3 bucket. The bucket will serve as the location for the Amazon EMR bootstrap script, additional JAR files required by Spark, PySpark scripts, CSV-format data files, and eventual output from the Spark jobs.
There are a small set of additional JAR files required by the Spark jobs we will be running. Download the JARs from Maven Central and GitHub, and place them in the emr_jars project directory. The JARs will include AWS MSK IAM Auth, AWS SDK, Kafka Client, Spark SQL for Kafka, Spark Streaming, and other dependencies.
cd ./pyspark/emr_jars/
wget https://github.com/aws/aws-msk-iam-auth/releases/download/1.1.0/aws-msk-iam-auth-1.1.0-all.jar
wget https://repo1.maven.org/maven2/software/amazon/awssdk/bundle/2.17.28/bundle-2.17.28.jar
wget https://repo1.maven.org/maven2/org/apache/commons/commons-pool2/2.11.0/commons-pool2-2.11.0.jar
wget https://repo1.maven.org/maven2/org/apache/kafka/kafka-clients/2.8.0/kafka-clients-2.8.0.jar
wget https://repo1.maven.org/maven2/org/apache/spark/spark-sql-kafka-0-10_2.12/3.1.2/spark-sql-kafka-0-10_2.12-3.1.2.jar
wget https://repo1.maven.org/maven2/org/apache/spark/spark-streaming_2.12/3.1.2/spark-streaming_2.12-3.1.2.jar
wget https://repo1.maven.org/maven2/org/apache/spark/spark-tags_2.12/3.1.2/spark-tags_2.12-3.1.2.jar
wget https://repo1.maven.org/maven2/org/apache/spark/spark-token-provider-kafka-0-10_2.12/3.1.2/spark-token-provider-kafka-0-10_2.12-3.1.2.jar
Next, update the SPARK_BUCKET environment variable, then upload the JARs and all necessary project files from your copy of the GitHub project repository to your Amazon S3 bucket using the AWS s3 API.
cd ./pyspark/
export SPARK_BUCKET="<your-bucket-111222333444-us-east-1>"
aws s3 cp emr_jars/ \
"s3://${SPARK_BUCKET}/jars/" --recursive
aws s3 cp pyspark_scripts/ \
"s3://${SPARK_BUCKET}/spark/" --recursive
aws s3 cp emr_bootstrap/ \
"s3://${SPARK_BUCKET}/spark/" --recursive
aws s3 cp data/ \
"s3://${SPARK_BUCKET}/spark/" --recursive
Amazon EMR
The GitHub project repository includes a sample AWS CloudFormation template and an associated JSON-format CloudFormation parameters file. The template, stack.yml, accepts several parameters. To match your environment, you will need to update the parameter values such as SSK key, Subnet, and S3 bucket. The template will build a minimally-sized Amazon EMR cluster with one master and two core nodes in an existing VPC. The template can be easily modified to meet your requirements and budget.
aws cloudformation deploy \
--stack-name spark-kafka-demo-dev \
--template-file ./cloudformation/stack.yml \
--parameter-overrides file://cloudformation/dev.json \
--capabilities CAPABILITY_NAMED_IAM
Whether you decide to use the CloudFormation template, two essential Spark configuration items in the EMR template are the list of applications to install and the bootstrap script deployment.
Below, we see the EMR bootstrap shell script, bootstrap_actions.sh, deployed and executed on the cluster’s nodes.
The script performed several tasks, including deploying the additional JAR files we copied to Amazon S3 earlier.
AWS Systems Manager Parameter Store
The PySpark scripts in this demonstration will obtain two parameters from the AWS Systems Manager (AWS SSM) Parameter Store. They include the Amazon MSK bootstrap brokers and the Amazon S3 bucket that contains the Spark assets. Using the Parameter Store ensures that no sensitive or environment-specific configuration is hard-coded into the PySpark scripts. Modify and execute the ssm_params.sh script to create two AWS SSM Parameter Store parameters.
aws ssm put-parameter \
--name /kafka_spark_demo/kafka_servers \
--type String \
--value "<b-1.your-brokers.kafka.us-east-1.amazonaws.com:9098,b-2.your-brokers.kafka.us-east-1.amazonaws.com:9098>" \
--description "Amazon MSK Kafka broker list" \
--overwrite
aws ssm put-parameter \
--name /kafka_spark_demo/kafka_demo_bucket \
--type String \
--value "<your-bucket-111222333444-us-east-1>" \
--description "Amazon S3 bucket" \
--overwrite
Spark Submit Options with Amazon EMR
Amazon EMR provides multiple options to run Spark jobs. The recommended method for PySpark scripts is to use Amazon EMR Steps from the EMR console or AWS CLI to submit work to Spark installed on an EMR cluster. In the console and CLI, you do this using a Spark application step, which runs the spark-submit script as a step on your behalf. With the API, you use a Step to invoke spark-submit using command-runner.jar. Alternately, you can SSH into the EMR cluster’s master node and run spark-submit. We will employ both techniques to run the PySpark jobs.
Securely Accessing Amazon MSK from Spark
Each of the PySpark scripts demonstrated in this post uses a common pattern for accessing Amazon MSK from Amazon EMR using IAM Authentication. Whether producing or consuming messages from Kafka, the same security-related options are used to configure Spark (starting at line 10, below). The details behind each option are outlined in the Security section of the Spark Structured Streaming + Kafka Integration Guide and the Configure clients for IAM access control section of the Amazon MSK IAM access control documentation.
Data Source and Analysis Objective
For this post, we will continue to use data from PostgreSQL’s sample Pagila database. The database contains simulated movie rental data. The dataset is fairly small, making it less than ideal for ‘big data’ use cases but small enough to quickly install and minimize data storage and analytical query costs.
According to mastersindatascience.org, data analytics is “…the process of analyzing raw data to find trends and answer questions…” Using Spark, we can analyze the movie rental sales data as a batch or in near-real-time using Structured Streaming to answer different questions. For example, using batch computations on static data, we could answer the question, how do the current total all-time sales for France compare to the rest of Europe? Or, what were the total sales for India during August? Using streaming computations, we can answer questions like, what are the sales volumes for the United States during this current four-hour marketing promotional period? Or, are sales to North America beginning to slow as the Olympics are aired during prime time?
Data analytics — the process of analyzing raw data to find trends and answer questions. (mastersindatascience.org)
Batch Queries
Before exploring the more advanced topic of streaming computations with Spark Structured Streaming, let’s first use a simple batch query and a batch computation to consume messages from the Kafka topic, perform a basic aggregation, and write the output to both the console and Amazon S3.
PySpark Job 1: Initial Sales Data
Kafka supports Protocol Buffers, JSON Schema, and Avro. However, to keep things simple in this first post, we will use JSON. We will seed a new Kafka topic with an initial batch of 250 JSON-format messages. This first batch of messages represents previous online movie rental sale transaction records. We will use these sales transactions for both batch and streaming queries.
The PySpark script, 01_seed_sales_kafka.py, and the seed data file, sales_seed.csv, are both read from Amazon S3 by Spark, running on Amazon EMR. The location of the Amazon S3 bucket name and the Amazon MSK’s broker list values are pulled from AWS SSM Parameter Store using the parameters created earlier. The Kafka topic that stores the sales data, pagila.sales.spark.streaming, is created automatically by the script the first time it runs.
Update the two environment variables, then submit your first Spark job as an Amazon EMR Step using the AWS CLI and the emr API:
From the Amazon EMR console, we should observe the Spark job has been completed successfully in about 30–90 seconds.
The Kafka Consumer API allows applications to read streams of data from topics in the Kafka cluster. Using the Kafka Consumer API, from within a Kubernetes container running on Amazon EKS or an EC2 instance, we can observe that the new Kafka topic has been successfully created and that messages (initial sales data) have been published to the new Kafka topic.
export BBROKERS="b-1.your-cluster.kafka.us-east-1.amazonaws.com:9098,b-2.your-cluster.kafka.us-east-1.amazonaws.com:9098, ..."
bin/kafka-console-consumer.sh \
--topic pagila.sales.spark.streaming \
--from-beginning \
--property print.key=true \
--property print.value=true \
--property print.offset=true \
--property print.partition=true \
--property print.headers=true \
--property print.timestamp=true \
--bootstrap-server $BBROKERS \
--consumer.config config/client-iam.properties
PySpark Job 2: Batch Query of Amazon MSK Topic
The PySpark script, 02_batch_read_kafka.py, performs a batch query of the initial 250 messages in the Kafka topic. When run, the PySpark script parses the JSON-format messages, then aggregates the data by both total sales and order count, by country, and finally, sorts by total sales.
window = Window.partitionBy("country").orderBy("amount")
window_agg = Window.partitionBy("country")
.withColumn("row", F.row_number().over(window)) \
.withColumn("orders", F.count(F.col("amount")).over(window_agg)) \
.withColumn("sales", F.sum(F.col("amount")).over(window_agg)) \
.where(F.col("row") == 1).drop("row") \
The results are written to both the console as stdout and to Amazon S3 in CSV format.
Again, submit this job as an Amazon EMR Step using the AWS CLI and the emr API:
To view the console output, click on ‘View logs’ in the Amazon EMR console, then click on the stdout logfile, as shown below.
The stdout logfile should contain the top 25 total sales and order counts, by country, based on the initial 250 sales records.
+------------------+------+------+
|country |sales |orders|
+------------------+------+------+
|India |138.80|20 |
|China |133.80|20 |
|Mexico |106.86|14 |
|Japan |100.86|14 |
|Brazil |96.87 |13 |
|Russian Federation|94.87 |13 |
|United States |92.86 |14 |
|Nigeria |58.93 |7 |
|Philippines |58.92 |8 |
|South Africa |46.94 |6 |
|Argentina |42.93 |7 |
|Germany |39.96 |4 |
|Indonesia |38.95 |5 |
|Italy |35.95 |5 |
|Iran |33.95 |5 |
|South Korea |33.94 |6 |
|Poland |30.97 |3 |
|Pakistan |25.97 |3 |
|Taiwan |25.96 |4 |
|Mozambique |23.97 |3 |
|Ukraine |23.96 |4 |
|Vietnam |23.96 |4 |
|Venezuela |22.97 |3 |
|France |20.98 |2 |
|Peru |19.98 |2 |
+------------------+------+------+
only showing top 25 rows
The PySpark script also wrote the same results to Amazon S3 in CSV format.
The total sales and order count for 69 countries were computed, sorted, and coalesced into a single CSV file.
Streaming Queries
To demonstrate streaming queries with Spark Structured Streaming, we will use a combination of two PySpark scripts. The first script, 03_streaming_read_kafka_console.py, will perform a streaming query and computation of messages in the Kafka topic, aggregating the total sales and number of orders. Concurrently, the second PySpark script, 04_incremental_sales_kafka.py, will read additional Pagila sales data from a CSV file located on Amazon S3 and write messages to the Kafka topic at a rate of two messages per second. The first script, 03_streaming_read_kafka_console.py, will stream aggregations in micro-batches of one-minute increments to the console. Spark Structured Streaming queries are processed using a micro-batch processing engine, which processes data streams as a series of small, batch jobs.
Note that this first script performs grouped aggregations as opposed to aggregations over a sliding event-time window. The aggregated results represent the total, all-time sales at a point in time, based on all the messages currently in the topic when the micro-batch was computed.
To follow along with this part of the demonstration, you can run the two Spark jobs as concurrent steps on the existing Amazon EMR cluster, or create a second EMR cluster, identically configured to the existing cluster, to run the second PySpark script, 04_incremental_sales_kafka.py. Using a second cluster, you can use a minimally-sized single master node cluster with no core nodes to save cost.
PySpark Job 3: Streaming Query to Console
The first PySpark scripts, 03_streaming_read_kafka_console.py, performs a streaming query of messages in the Kafka topic. The script then aggregates the data by both total sales and order count, by country, and finally, sorts by total sales.
.groupBy("country") \
.agg(F.count("amount"), F.sum("amount")) \
.orderBy(F.col("sum(amount)").desc()) \
.select("country",
(F.format_number(F.col("sum(amount)"), 2)).alias("sales"),
(F.col("count(amount)")).alias("orders")) \
The results are streamed to the console using the processingTime trigger parameter. A trigger defines how often a streaming query should be executed and emit new data. The processingTime parameter sets a trigger that runs a micro-batch query periodically based on the processing time (e.g. ‘5 minutes’ or ‘1 hour’). The trigger is currently set to a minimal processing time of one minute for ease of demonstration.
.trigger(processingTime="1 minute") \
.outputMode("complete") \
.format("console") \
.option("numRows", 25) \
For demonstration purposes, we will run the Spark job directly from the master node of the EMR Cluster. This method will allow us to easily view the micro-batches and associated logs events as they are output to the console. The console is normally used for testing purposes. Submitting the PySpark script from the cluster’s master node is an alternative to submitting an Amazon EMR Step. Connect to the master node of the Amazon EMR cluster using SSH, as the hadoop user:
export EMR_MASTER=<your-emr-master-dns.compute-1.amazonaws.com>
export EMR_KEY_PATH=path/to/key/<your-ssk-key.pem>
ssh -i ${EMR_KEY_PATH} hadoop@${EMR_MASTER}
Submit the PySpark script, 03_streaming_read_kafka_console.py, to Spark:
export SPARK_BUCKET="<your-bucket-111222333444-us-east-1>"
spark-submit s3a://${SPARK_BUCKET}/spark/03_streaming_read_kafka_console.py
Before running the second PySpark script, 04_incremental_sales_kafka.py, let the first script run long enough to pick up the existing sales data in the Kafka topic. Within about two minutes, you should see the first micro-batch of aggregated sales results, labeled ‘Batch: 0’ output to the console. This initial micro-batch should contain the aggregated results of the existing 250 messages from Kafka. The streaming query’s first micro-batch results should be identical to the previous batch query results.
-------------------------------------------
Batch: 0
-------------------------------------------
+------------------+------+------+
|country |sales |orders|
+------------------+------+------+
|India |138.80|20 |
|China |133.80|20 |
|Mexico |106.86|14 |
|Japan |100.86|14 |
|Brazil |96.87 |13 |
|Russian Federation|94.87 |13 |
|United States |92.86 |14 |
|Nigeria |58.93 |7 |
|Philippines |58.92 |8 |
|South Africa |46.94 |6 |
|Argentina |42.93 |7 |
|Germany |39.96 |4 |
|Indonesia |38.95 |5 |
|Italy |35.95 |5 |
|Iran |33.95 |5 |
|South Korea |33.94 |6 |
|Poland |30.97 |3 |
|Pakistan |25.97 |3 |
|Taiwan |25.96 |4 |
|Mozambique |23.97 |3 |
|Ukraine |23.96 |4 |
|Vietnam |23.96 |4 |
|Venezuela |22.97 |3 |
|France |20.98 |2 |
|Peru |19.98 |2 |
+------------------+------+------+
only showing top 25 rows
Immediately below the batch output, there will be a log entry containing information about the batch. In the log entry snippet below, note the starting and ending offsets of the topic for the Spark job’s Kafka consumer group, 0 (null) to 250, representing the initial sales data.
PySpark Job 4: Incremental Sales Data
As described earlier, the second PySpark script, 04_incremental_sales_kafka.py, reads 1,800 additional sales records from a second CSV file located on Amazon S3, sales_incremental_large.csv. The script then publishes messages to the Kafka topic at a deliberately throttled rate of two messages per second. Concurrently, the first PySpark job, still running and performing a streaming query, will consume the new Kafka messages and stream aggregated total sales and orders in micro-batches of one-minute increments to the console over a period of about 15 minutes.
Submit the second PySpark script as a concurrent Amazon EMR Step to the first EMR cluster, or submit as a step to the second Amazon EMR cluster.
The job sends a total of 1,800 messages to Kafka at a rate of two messages per second for 15 minutes. The total runtime of the job should be approximately 19 minutes, given a few minutes for startup and shutdown. Why run for so long? We want to make sure the job’s runtime will span multiple, overlapping, sliding event-time windows.
After about two minutes, return to the terminal output of the first Spark job, 03_streaming_read_kafka_console.py, running on the master node of the first cluster. As long as new messages are consumed every minute, you should see a new micro-batch of aggregated sales results stream to the console. Below we see an example of Batch 3, which reflects additional sales compared to Batch 0, shown previously. The results reflect the current all-time sales by country in real-time as the sales are published to Kafka.
-------------------------------------------
Batch: 5
-------------------------------------------
+------------------+------+------+
|country |sales |orders|
+------------------+------+------+
|China |473.35|65 |
|India |393.44|56 |
|Japan |292.60|40 |
|Mexico |262.64|36 |
|United States |252.65|35 |
|Russian Federation|243.65|35 |
|Brazil |220.69|31 |
|Philippines |191.75|25 |
|Indonesia |142.81|19 |
|South Africa |110.85|15 |
|Nigeria |108.86|14 |
|Argentina |89.86 |14 |
|Germany |85.89 |11 |
|Israel |68.90 |10 |
|Ukraine |65.92 |8 |
|Turkey |58.91 |9 |
|Iran |58.91 |9 |
|Saudi Arabia |56.93 |7 |
|Poland |50.94 |6 |
|Pakistan |50.93 |7 |
|Italy |48.93 |7 |
|French Polynesia |47.94 |6 |
|Peru |45.95 |5 |
|United Kingdom |45.94 |6 |
|Colombia |44.94 |6 |
+------------------+------+------+
only showing top 25 rows
If we fast forward to a later micro-batch, sometime after the second incremental sales job is completed, we should see the top 25 aggregated sales by country of 2,050 messages — 250 seed plus 1,800 incremental messages.
-------------------------------------------
Batch: 20
-------------------------------------------
+------------------+--------+------+
|country |sales |orders|
+------------------+--------+------+
|China |1,379.05|195 |
|India |1,338.10|190 |
|United States |915.69 |131 |
|Mexico |855.80 |120 |
|Japan |831.88 |112 |
|Russian Federation|723.95 |105 |
|Brazil |613.12 |88 |
|Philippines |528.27 |73 |
|Indonesia |381.46 |54 |
|Turkey |350.52 |48 |
|Argentina |298.57 |43 |
|Nigeria |294.61 |39 |
|South Africa |279.61 |39 |
|Taiwan |221.67 |33 |
|Germany |199.73 |27 |
|United Kingdom |196.75 |25 |
|Poland |182.77 |23 |
|Spain |170.77 |23 |
|Ukraine |160.79 |21 |
|Iran |160.76 |24 |
|Italy |156.79 |21 |
|Pakistan |152.78 |22 |
|Saudi Arabia |146.81 |19 |
|Venezuela |145.79 |21 |
|Colombia |144.78 |22 |
+------------------+--------+------+
only showing top 25 rows
Compare the informational output below for Batch 20 to Batch 0, previously. Note the starting offset of the Kafka consumer group on the topic is 1986, and the ending offset is 2050. This is because all messages have been consumed from the topic and aggregated. If additional messages were streamed to Kafka while the streaming job is still running, additional micro-batches would continue to be streamed to the console every one minute.
PySpark Job 5: Aggregations over Sliding Event-time Window
In the previous example, we analyzed total all-time sales in real-time (e.g., show me the current, total, all-time sales for France compared to the rest of Europe, at regular intervals). This approach is opposed to sales made during a sliding event-time window (e.g., are the total sales for the United States trending better during this current four-hour marketing promotional period than the previous promotional period). In many cases, real-time sales during a distinct period or event window is probably a more commonly tracked KPI than total all-time sales.
If we add a sliding event-time window to the PySpark script, we can easily observe the total sales and order counts made during the sliding event-time window in real-time.
.withWatermark("timestamp", "10 minutes") \
.groupBy("country",
F.window("timestamp", "10 minutes", "5 minutes")) \
.agg(F.count("amount"), F.sum("amount")) \
.orderBy(F.col("window").desc(),
F.col("sum(amount)").desc()) \
Windowed totals would not include sales (messages) present in the Kafka topic before the streaming query beginning, nor in previous sliding windows. Constructing the correct query always starts with a clear understanding of the question you are trying to answer.
Below, in the abridged console output of the micro-batch from the script, 05_streaming_read_kafka_console_window.py, we see the results of three ten-minute sliding event-time windows with a five-minute overlap. The sales and order totals represent the volume sold during that window, with this micro-batch falling within the active current window, 19:30 to 19:40 UTC.
Plotting the total sales over time using sliding event-time windows, we will observe the results do not reflect a running total. Total sales only accumulate within a sliding window.
Compare these results to the results of the previous script, whose total sales reflect a running total.
PySpark Job 6: Streaming Query from/to Amazon MSK
The PySpark script, 06_streaming_read_kafka_kafka.py, performs the same streaming query and grouped aggregation as the previous script, 03_streaming_read_kafka_console.py. However, instead of outputting results to the console, the results of this job will be written to a new Kafka topic on Amazon MSK.
.format("kafka") \
.options(**options_write) \
.option("checkpointLocation", "/checkpoint/kafka/") \
Repeat the same process used with the previous script. Re-run the seed data script, 01_seed_sales_kafka.py, but update the input topic to a new name, such as pagila.sales.spark.streaming.in. Next, run the new script, 06_streaming_read_kafka_kafka.py. Give the script time to start and consume the 250 seed messages from Kafka. Then, update the input topic name and re-run the incremental data PySpark script, 04_incremental_sales_kafka.py, concurrent to the new script on the same cluster or run on the second cluster.
When run, the script, 06_streaming_read_kafka_kafka.py, will continuously consume messages from the new pagila.sales.spark.streaming.in topic and publish grouped aggregation results to a new topic, pagila.sales.spark.streaming.out.
Use the Kafka Consumer API to view new messages as the Spark job publishes them in near real-time to Kafka.
export BBROKERS="b-1.your-cluster.kafka.us-east-1.amazonaws.com:9098,b-2.your-cluster.kafka.us-east-1.amazonaws.com:9098, ..."
bin/kafka-console-consumer.sh \
--topic pagila.sales.spark.streaming.out \
--from-beginning \
--property print.key=true \
--property print.value=true \
--property print.offset=true \
--property print.partition=true \
--property print.headers=true \
--property print.timestamp=true \
--bootstrap-server $BBROKERS \
--consumer.config config/client-iam.properties
PySpark Job 7: Batch Query of Streaming Results from MSK
When run, the previous script produces Kafka messages containing non-windowed sales aggregations to the Kafka topic every minute. Using the next PySpark script, 07_batch_read_kafka.py, we can consume those aggregated messages using a batch query and display the most recent sales totals to the console. Each country’s most recent all-time sales totals and order counts should be identical to the previous script’s results, representing the aggregation of all 2,050 Kafka messages — 250 seed plus 1,800 incremental messages.
To get the latest total sales by country, we will consume all the messages from the output topic, group the results by country, find the maximum (max) value from the sales column for each country, and finally, display the results sorted sales in descending order.
window = Window.partitionBy("country") \
.orderBy(F.col("timestamp").desc())
.withColumn("row", F.row_number().over(window)) \
.where(F.col("row") == 1).drop("row") \
.select("country", "sales", "orders") \
Writing the top 25 results to the console, we should see the same results as we saw in the final micro-batch (Batch 20, shown above) of the PySpark script, 03_streaming_read_kafka_console.py.
+------------------+------+------+
|country |sales |orders|
+------------------+------+------+
|India |948.63|190 |
|China |936.67|195 |
|United States |915.69|131 |
|Mexico |855.80|120 |
|Japan |831.88|112 |
|Russian Federation|723.95|105 |
|Brazil |613.12|88 |
|Philippines |528.27|73 |
|Indonesia |381.46|54 |
|Turkey |350.52|48 |
|Argentina |298.57|43 |
|Nigeria |294.61|39 |
|South Africa |279.61|39 |
|Taiwan |221.67|33 |
|Germany |199.73|27 |
|United Kingdom |196.75|25 |
|Poland |182.77|23 |
|Spain |170.77|23 |
|Ukraine |160.79|21 |
|Iran |160.76|24 |
|Italy |156.79|21 |
|Pakistan |152.78|22 |
|Saudi Arabia |146.81|19 |
|Venezuela |145.79|21 |
|Colombia |144.78|22 |
+------------------+------+------+
only showing top 25 rows
PySpark Job 8: Streaming Query with Static Join and Sliding Window
The PySpark script, 08_streaming_read_kafka_join_window.py, performs the same streaming query and computations over sliding event-time windows as the previous script, 05_streaming_read_kafka_console_window.py. However, instead of totaling sales and orders by country, the script totals by sales and orders sales region. A sales region is composed of multiple countries in the same geographical area. The PySpark script reads in a static list of sales regions and countries from Amazon S3, sales_regions.csv.
The script then performs a join operation between the results of the streaming query and the static list of regions, joining on country. Using the join, the streaming sales data from Kafka is enriched with the sales category. Any sales record whose country does not have an assigned sales region is categorized as ‘Unassigned.’
.join(df_regions, on=["country"], how="leftOuter") \
.na.fill("Unassigned") \
Sales and orders are then aggregated by sales region, and the top 25 are output to the console every minute.
To run the job, repeat the previous process of renaming the topic (e.g., pagila.sales.spark.streaming.region), then running the initial sales data job, this script, and finally, concurrent with this script, the incremental sales data job. Below, we see a later micro-batch output to the console from the Spark job. We see three sets of sales results, by sales region, from three different ten-minute sliding event-time windows with a five-minute overlap.
PySpark Script 9: Static Join with Grouped Aggregations
As a comparison, we can exclude the sliding event-time window operations from the previous streaming query script, 08_streaming_read_kafka_join_window.py, to obtain the current, total, all-time sales by sales region. See the script, 09_streaming_read_kafka_join.py, in the project repository for details.
-------------------------------------------
Batch: 20
-------------------------------------------
+--------------+--------+------+
|sales_region |sales |orders|
+--------------+--------+------+
|Asia & Pacific|5,780.88|812 |
|Europe |3,081.74|426 |
|Latin America |2,545.34|366 |
|Africa |1,029.59|141 |
|North America |997.57 |143 |
|Middle east |541.23 |77 |
|Unassigned |352.47 |53 |
|Arab States |244.68 |32 |
+--------------+--------+------+
Conclusion
In this post, we learned how to get started with Spark Structured Streaming on Amazon EMR. First, we explored how to run jobs written in Python with PySpark on Amazon EMR as Steps and directly from the EMR cluster’s master node. Next, we discovered how to produce and consume messages with Apache Kafka on Amazon MSK, using batch and streaming queries. Finally, we learned about aggregations over a sliding event-time window compared to grouped aggregations and how Structured Streaming queries are processed using a micro-batch.
In a subsequent post, we will learn how to use Apache Avro and the Apicurio Registry with PySpark on Amazon EMR to read and write Apache Avro format messages to Amazon MSK.
This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.
Running Spark Jobs on Amazon EMR with Apache Airflow: Using the new Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Service on AWS
Posted by Gary A. Stafford in AWS, Bash Scripting, Big Data, Build Automation, Cloud, DevOps, Python, Software Development on December 24, 2020
Introduction
In the first post of this series, we explored several ways to run PySpark applications on Amazon EMR using AWS services, including AWS CloudFormation, AWS Step Functions, and the AWS SDK for Python. This second post in the series will examine running Spark jobs on Amazon EMR using the recently announced Amazon Managed Workflows for Apache Airflow (Amazon MWAA) service.
Amazon EMR
According to AWS, Amazon Elastic MapReduce (Amazon EMR) is a Cloud-based big data platform for processing vast amounts of data using common open-source tools such as Apache Spark, Hive, HBase, Flink, Hudi, and Zeppelin, Jupyter, and Presto. Using Amazon EMR, data analysts, engineers, and scientists are free to explore, process, and visualize data. EMR takes care of provisioning, configuring, and tuning the underlying compute clusters, allowing you to focus on running analytics.
Users interact with EMR in a variety of ways, depending on their specific requirements. For example, you might create a transient EMR cluster, execute a series of data analytics jobs using Spark, Hive, or Presto, and immediately terminate the cluster upon job completion. You only pay for the time the cluster is up and running. Alternatively, for time-critical workloads or continuously high volumes of jobs, you could choose to create one or more persistent, highly available EMR clusters. These clusters automatically scale compute resources horizontally, including the use of EC2 Spot instances, to meet processing demands, maximizing performance and cost-efficiency.
AWS currently offers 5.x and 6.x versions of Amazon EMR. Each major and minor release of Amazon EMR offers incremental versions of nearly 25 different, popular open-source big-data applications to choose from, which Amazon EMR will install and configure when the cluster is created. The latest Amazon EMR releases are Amazon EMR Release 6.2.0 and Amazon EMR Release 5.32.0.
Amazon MWAA
Apache Airflow is a popular open-source platform designed to schedule and monitor workflows. According to Wikipedia, Airflow was created at Airbnb in 2014 to manage the company’s increasingly complex workflows. From the beginning, the project was made open source, becoming an Apache Incubator project in 2016 and a top-level Apache Software Foundation project (TLP) in 2019.
Many organizations build, manage, and maintain Apache Airflow on AWS using compute services such as Amazon EC2 or Amazon EKS. Amazon recently announced Amazon Managed Workflows for Apache Airflow (Amazon MWAA). With the announcement of Amazon MWAA in November 2020, AWS customers can now focus on developing workflow automation, while leaving the management of Airflow to AWS. Amazon MWAA can be used as an alternative to AWS Step Functions for workflow automation on AWS.
Apache recently announced the release of Airflow 2.0.0 on December 17, 2020. The latest 1.x version of Airflow is 1.10.14, released December 12, 2020. However, at the time of this post, Amazon MWAA was running Airflow 1.10.12, released August 25, 2020. Ensure that when you are developing workflows for Amazon MWAA, you are using the correct Apache Airflow 1.10.12 documentation.
The Amazon MWAA service is available using the AWS Management Console, as well as the Amazon MWAA API using the latest versions of the AWS SDK and AWS CLI.
Airflow has a mechanism that allows you to expand its functionality and integrate with other systems. Given its integration capabilities, Airflow has extensive support for AWS, including Amazon EMR, Amazon S3, AWS Batch, Amazon RedShift, Amazon DynamoDB, AWS Lambda, Amazon Kinesis, and Amazon SageMaker. Outside of support for Amazon S3, most AWS integrations can be found in the Hooks, Secrets, Sensors, and Operators of Airflow codebase’s contrib section.
Getting Started
Source Code
Using this git clone command, download a copy of this post’s GitHub repository to your local environment.
git clone --branch main --single-branch --depth 1 --no-tags \
https://github.com/garystafford/aws-airflow-demo.git
Preliminary Steps
This post assumes the reader has completed the demonstration in the previous post, Running PySpark Applications on Amazon EMR Methods for Interacting with PySpark on Amazon Elastic MapReduce. This post will re-use many of the last post’s AWS resources, including the EMR VPC, Subnets, Security Groups, AWS Glue Data Catalog, Amazon S3 buckets, EMR Roles, EC2 key pair, AWS Systems Manager Parameter Store parameters, PySpark applications, and Kaggle datasets.
Configuring Amazon MWAA
The easiest way to create a new MWAA Environment is through the AWS Management Console. I strongly suggest that you review the pricing for Amazon MWAA before continuing. The service can be quite costly to operate, even when idle, with the smallest Environment class potentially running into the hundreds of dollars per month.
Using the Console, create a new Amazon MWAA Environment. The Amazon MWAA interface will walk you through the creation process. Note the current ‘Airflow version’, 1.10.12.
Amazon MWAA requires an Amazon S3 bucket to store Airflow assets. Create a new Amazon S3 bucket. According to the documentation, the bucket must start with the prefix airflow-. You must also enable Bucket Versioning on the bucket. Specify a dags folder within the bucket to store Airflow’s Directed Acyclic Graphs (DAG). You can leave the next two options blank since we have no additional Airflow plugins or additional Python packages to install.
With Amazon MWAA, your data is secure by default as workloads run within their own Amazon Virtual Private Cloud (Amazon VPC). As part of the MWAA Environment creation process, you are given the option to have AWS create an MWAA VPC CloudFormation stack.
For this demonstration, choose to have MWAA create a new VPC and associated networking resources.
The MWAA CloudFormation stack contains approximately 22 AWS resources, including a VPC, a pair of public and private subnets, route tables, an Internet Gateway, two NAT Gateways, and associated Elastic IPs (EIP). See the MWAA documentation for more details.
As part of the Amazon MWAA Networking configuration, you must decide if you want web access to Airflow to be public or private. The details of the network configuration can be found in the MWAA documentation. I am choosing public webserver access for this demonstration, but the recommended choice is private for greater security. With the public option, AWS still requires IAM authentication to sign in to the AWS Management Console in order to access the Airflow UI.
You must select an existing VPC Security Group or have MWAA create a new one. For this demonstration, choose to have MWAA create a Security Group for you.
Lastly, select an appropriately-sized Environment class for Airflow based on the scale of your needs. The mw1.small class will be sufficient for this demonstration.
Finally, for Permissions, you must select an existing Airflow execution service role or create a new role. For this demonstration, create a new Airflow service role. We will later add additional permissions.
Airflow Execution Role
As part of this demonstration, we will be using Airflow to run Spark jobs on EMR (EMR Steps). To allow Airflow to interact with EMR, we must increase the new Airflow execution role’s default permissions. Additional permissions include allowing the new Airflow role to assume the EMR roles using iam:PassRole. For this demonstration, we will include the two default EMR Service and JobFlow roles, EMR_DefaultRole and EMR_EC2_DefaultRole. We will also include the corresponding custom EMR roles created in the previous post, EMR_DemoRole and EMR_EC2_DemoRole. For this demonstration, the Airflow service role also requires three specific EMR permissions as shown below. Later in the post, Airflow will also read files from S3, which requires s3:GetObject permission.
Create a new policy by importing the project’s JSON file, iam_policy/airflow_emr_policy.json, and attach the new policy to the Airflow service role. Be sure to update the AWS Account ID in the file with your own Account ID.
The Airflow service role, created by MWAA, is shown below with the new policy attached.
Final Architecture
Below is the final high-level architecture for the post’s demonstration. The diagram shows the approximate route of a DAG Run request, in red. The diagram includes an optional S3 Gateway VPC endpoint, not detailed in the post, but recommended for additional security. According to AWS, a VPC endpoint enables you to privately connect your VPC to supported AWS services and VPC endpoint services powered by AWS PrivateLink without requiring an internet gateway. In this case a private connection between the MWAA VPC and Amazon S3. It is also possible to create an EMR Interface VPC Endpoint to securely route traffic directly to EMR from MWAA, instead of connecting over the Internet.
Amazon MWAA Environment
The new MWAA Environment will include a link to the Airflow UI.
Airflow UI
Using the supplied link, you should be able to access the Airflow UI using your web browser.
Our First DAG
The Amazon MWAA documentation includes an example DAG, which contains one of several sample programs, SparkPi, which comes with Spark. I have created a similar DAG that is included in the GitHub project, dags/emr_steps_demo.py. The DAG will create a minimally-sized single-node EMR cluster with no Core or Task nodes. The DAG will then use that cluster to submit the calculate_pi job to Spark. Once the job is complete, the DAG will terminate the EMR cluster.
Upload the DAG to the Airflow S3 bucket’s dags directory. Substitute your Airflow S3 bucket name in the AWS CLI command below, then run it from the project’s root.
aws s3 cp dags/spark_pi_example.py \
s3://<your_airflow_bucket_name>/dags/
The DAG, spark_pi_example, should automatically appear in the Airflow UI. Click on ‘Trigger DAG’ to create a new EMR cluster and start the Spark job.
The DAG has no optional configuration to input as JSON. Select ‘Trigger’ to submit the job, as shown below.
The DAG should complete all three tasks successfully, as shown in the DAG’s ‘Graph View’ tab below.
Switching to the EMR Console, you should see the single-node EMR cluster being created.
On the ‘Steps’ tab, you should see that the ‘calculate_pi’ Spark job has been submitted and is waiting for the cluster to be ready to be run.
Triggering DAGs Programmatically
The Amazon MWAA service is available using the AWS Management Console, as well as the Amazon MWAA API using the latest versions of the AWS SDK and AWS CLI. To automate the DAG Run, we could use the AWS CLI and invoke the Airflow CLI via an endpoint on the Apache Airflow Webserver. The Amazon MWAA documentation and Airflow’s CLI documentation explains how.
Below is an example of triggering the spark_pi_example DAG programmatically using Airflow’s trigger_dag CLI command. You will need to replace the WEB_SERVER_HOSTNAME variable with your own Airflow Web Server’s hostname. The ENVIROMENT_NAME variable assumes only one MWAA environment is returned by jq.
Analytics Job with Airflow
Next, we will submit an actual analytics job to EMR. If you recall from the previous post, we had four different analytics PySpark applications, which performed analyses on the three Kaggle datasets. For the next DAG, we will run a Spark job that executes the bakery_sales_ssm.py PySpark application. This job should already exist in the processed data S3 bucket.
The DAG, dags/bakery_sales.py, creates an EMR cluster identical to the EMR cluster created with the run_job_flow.py Python script in the previous post. All EMR configuration options available when using AWS Step Functions are available with Airflow’s airflow.contrib.operators and airflow.contrib.sensors packages for EMR.
Airflow leverages Jinja Templating and provides the pipeline author with a set of built-in parameters and macros. The Bakery Sales DAG contains eleven Jinja template variables. Seven variables will be configured in the Airflow UI by importing a JSON file into the ‘Admin’ ⇨ ‘Variables’ tab. These template variables are prefixed with var.value in the DAG. The other three variables will be passed as a DAG Run configuration as a JSON blob, similar to the previous DAG example. These template variables are prefixed with dag_run.conf.