Working with Apache Kafka® topics using PySpark jobs in Yandex Data Processing
Yandex Data Processing clusters support integration with Managed Service for Apache Kafka® clusters. You can write and read messages to and from Apache Kafka® topics using PySpark jobs. Reading supports both batch processing and stream processing.
To configure integration between Managed Service for Apache Kafka® and Yandex Data Processing clusters:
If you no longer need the resources you created, delete them.
Prepare the infrastructure
-
Create a cloud network named
dataproc-network
without subnets. -
Create a subnet named
dataproc-subnet-b
in theru-central1-b
availability zone. -
Set up a NAT gateway for the
dataproc-subnet-b
subnet. -
Create a security group named
dataproc-security-group
indataproc-network
. -
Create a service account named
dataproc-sa
with the following roles:storage.viewer
storage.uploader
dataproc.agent
dataproc.user
-
Create a bucket named
dataproc-bucket
. -
Grant the
dataproc-sa
service account theFULL_CONTROL
permission fordataproc-bucket
. -
Create a Yandex Data Processing cluster with the following parameters:
-
Cluster name:
dataproc-cluster
. -
Version:
2.1
. -
Services:
HDFS
LIVY
SPARK
TEZ
YARN
-
Service account:
dataproc-sa
. -
Availability zone:
ru-central1-b
. -
Bucket name:
dataproc-bucket
. -
Network:
dataproc-network
. -
Security groups:
dataproc-security-group
. -
Subclusters: Master with one
Data
subcluster and oneCompute
subcluster.
-
-
Create a Managed Service for Apache Kafka® cluster with the following parameters:
- Cluster name:
dataproc-kafka
. - Environment:
PRODUCTION
. - Version:
3.5
. - Availability zone:
ru-central1-b
. - Network:
dataproc-network
. - Security groups:
dataproc-security-group
. - Subnet:
dataproc-subnet-b
.
- Cluster name:
-
Create a Apache Kafka® topic with the following parameters:
- Name:
dataproc-kafka-topic
. - Number of partitions:
1
- Replication factor:
1
- Name:
-
Create a Apache Kafka® user with the following parameters:
- Name:
user1
. - Password:
password1
. - Topics to grant the user permissions for:
*
(all topics). - Permissions for topics:
ACCESS_ROLE_CONSUMER
,ACCESS_ROLE_PRODUCER
, andACCESS_ROLE_ADMIN
.
- Name:
-
If you do not have Terraform yet, install it.
-
Get the authentication credentials. You can add them to environment variables or specify them later in the provider configuration file.
-
Configure and initialize a provider. There is no need to create a provider configuration file manually, you can download it
. -
Place the configuration file in a separate working directory and specify the parameter values. If you did not add the authentication credentials to environment variables, specify them in the configuration file.
-
Download the kafka-and-data-proc.tf
configuration file to the same working directory.This file describes:
- Network.
- NAT gateway and route table required for Yandex Data Processing.
- Subnet.
- Security group required for the Yandex Data Processing and Managed Service for Apache Kafka® clusters.
- Service account required for the Yandex Data Processing cluster.
- Yandex Object Storage bucket.
- Static access key required to grant the service account the required permissions for the bucket.
- Yandex Data Processing cluster.
- Managed Service for Apache Kafka® cluster.
- Apache Kafka® user.
- Apache Kafka® topic.
-
In the
kafka-and-data-proc.tf
file, specify:folder_id
: Cloud folder ID, same as in the provider settings.dp_ssh_key
: Absolute path to the public key for the Yandex Data Processing cluster. For more information, see Connecting to a Yandex Data Processing host via SSH.
-
Make sure the Terraform configuration files are correct using this command:
terraform validate
If there are any errors in the configuration files, Terraform will point them out.
-
Create the required infrastructure:
-
Run the command to view planned changes:
terraform plan
If the resource configuration descriptions are correct, the terminal will display a list of the resources to modify and their parameters. This is a test step. No resources are updated.
-
If you are happy with the planned changes, apply them:
-
Run the command:
terraform apply
-
Confirm the update of resources.
-
Wait for the operation to complete.
-
All the required resources will be created in the specified folder. You can check resource availability and their settings in the management console
. -
Create PySpark jobs
-
On a local computer, save the following scripts:
kafka-write.py
Script for writing messages to an Apache Kafka® topic:
#!/usr/bin/env python3 from pyspark.sql import SparkSession, Row from pyspark.sql.functions import to_json, col, struct def main(): spark = SparkSession.builder.appName("dataproc-kafka-write-app").getOrCreate() df = spark.createDataFrame([ Row(msg="Test message #1 from dataproc-cluster"), Row(msg="Test message #2 from dataproc-cluster") ]) df = df.select(to_json(struct([col(c).alias(c) for c in df.columns])).alias('value')) df.write.format("kafka") \ .option("kafka.bootstrap.servers", "<host_FQDN>:9091") \ .option("topic", "dataproc-kafka-topic") \ .option("kafka.security.protocol", "SASL_SSL") \ .option("kafka.sasl.mechanism", "SCRAM-SHA-512") \ .option("kafka.sasl.jaas.config", "org.apache.kafka.common.security.scram.ScramLoginModule required " "username=user1 " "password=password1 " ";") \ .save() if __name__ == "__main__": main()
kafka-read-batch.py
Script for reading from a topic and batch processing:
#!/usr/bin/env python3 from pyspark.sql import SparkSession, Row from pyspark.sql.functions import to_json, col, struct def main(): spark = SparkSession.builder.appName("dataproc-kafka-read-batch-app").getOrCreate() df = spark.read.format("kafka") \ .option("kafka.bootstrap.servers", "<host_FQDN>:9091") \ .option("subscribe", "dataproc-kafka-topic") \ .option("kafka.security.protocol", "SASL_SSL") \ .option("kafka.sasl.mechanism", "SCRAM-SHA-512") \ .option("kafka.sasl.jaas.config", "org.apache.kafka.common.security.scram.ScramLoginModule required " "username=user1 " "password=password1 " ";") \ .option("startingOffsets", "earliest") \ .load() \ .selectExpr("CAST(value AS STRING)") \ .where(col("value").isNotNull()) df.write.format("text").save("s3a://dataproc-bucket/kafka-read-batch-output") if __name__ == "__main__": main()
kafka-read-stream.py
Script for reading from a topic and stream processing:
#!/usr/bin/env python3 from pyspark.sql import SparkSession, Row from pyspark.sql.functions import to_json, col, struct def main(): spark = SparkSession.builder.appName("dataproc-kafka-read-stream-app").getOrCreate() query = spark.readStream.format("kafka")\ .option("kafka.bootstrap.servers", "<host_FQDN>:9091") \ .option("subscribe", "dataproc-kafka-topic") \ .option("kafka.security.protocol", "SASL_SSL") \ .option("kafka.sasl.mechanism", "SCRAM-SHA-512") \ .option("kafka.sasl.jaas.config", "org.apache.kafka.common.security.scram.ScramLoginModule required " "username=user1 " "password=password1 " ";") \ .option("startingOffsets", "earliest")\ .load()\ .selectExpr("CAST(value AS STRING)")\ .where(col("value").isNotNull())\ .writeStream\ .trigger(once=True)\ .queryName("received_messages")\ .format("memory")\ .start() query.awaitTermination() df = spark.sql("select value from received_messages") df.write.format("text").save("s3a://dataproc-bucket/kafka-read-stream-output") if __name__ == "__main__": main()
-
Get the Apache Kafka® host FQDN and specify it in each script.
-
Upload the prepared scripts to the bucket root.
-
Create a PySpark job for writing a message to the Apache Kafka® topic. In the Main python file field, specify the
s3a://dataproc-bucket/kafka-write.py
script path. -
Wait for the job status to change to
Done
. -
Make sure the data is successfully written to the topic. To do this, create a new PySpark job for reading data from the topic and batch processing. In the Main python file field, specify the
s3a://dataproc-bucket/kafka-read-batch.py
script path. -
Wait for the new job status to change to
Done
. -
Download the file with the read data from the bucket:
part-00000
{"msg":"Test message #1 from dataproc-cluster"} {"msg":"Test message #2 from dataproc-cluster"}
The file resides in the new
kafka-read-batch-output
folder in the bucket. -
Read messages from the topic during stream processing. To do this, create another PySpark job. In the Main python file field, specify the
s3a://dataproc-bucket/kafka-read-stream.py
script path. -
Wait for the new job status to change to
Done
. -
Download the files with the read data from the bucket:
part-00000
{"msg":"Test message #1 from dataproc-cluster"}
part-00001
{"msg":"Test message #2 from dataproc-cluster"}
The files reside in the new
kafka-read-stream-output
folder in the bucket.
Note
You can view the job logs and search data in them using Yandex Cloud Logging. For more information, see Working with logs.
Delete the resources you created
Some resources are not free of charge. Delete the resources you no longer need to avoid paying for them:
Delete:
-
Delete the objects from the buckets.
-
In the terminal window, go to the directory containing the infrastructure plan.
-
Delete the
kafka-and-data-proc.tf
configuration file. -
Make sure the Terraform configuration files are correct using this command:
terraform validate
If there are any errors in the configuration files, Terraform will point them out.
-
Confirm updating the resources.
-
Run the command to view planned changes:
terraform plan
If the resource configuration descriptions are correct, the terminal will display a list of the resources to modify and their parameters. This is a test step. No resources are updated.
-
If you are happy with the planned changes, apply them:
-
Run the command:
terraform apply
-
Confirm the update of resources.
-
Wait for the operation to complete.
-
This will delete all the resources described in the
kafka-and-data-proc.tf
configuration file. -