[SOLVED] How to Read Avro from Kafka Using Pyspark 2.4.0 with Read Stream? - Python - kafka

[SOLVED] A Simple Guide on Reading Avro Data from Kafka Using Pyspark 2.4.0 with Read Stream

In this guide, we will show how to read Avro data from Kafka using Pyspark 2.4.0 with the Read Stream feature. This process is important for developers and data engineers. They want to use Kafka’s strong streaming features with Avro data. We will go through the steps, settings, and code examples we need to do this easily.

What We Will Discuss:

• Part 1 - Setting Up Your Environment: We need to make sure we have all the tools and libraries installed for Pyspark and Kafka.
• Part 2 - Configuring Kafka for Avro Serialization: We will set up Kafka to handle Avro serialization. This helps in moving data efficiently.
• Part 3 - Establishing a Spark Session with Avro Support: We will create a Spark session that supports Avro. This is important to process Avro data.
• Part 4 - Reading from Kafka Stream in Pyspark: We will learn how to read data streams from Kafka topics with Pyspark.
• Part 5 - Deserializing Avro Data in Pyspark: We will look at ways to deserializing the Avro data we get from Kafka. This makes the data ready for analysis.
• Part 6 - Writing Processed Data to Sink: We will store the processed data back into a sink. This can be a database or another Kafka topic.

By following this guide, we will understand how to read Avro from Kafka using Pyspark 2.4.0 with Read Stream. This is very useful for people who work with data processing and real-time analytics.

For more reading, we can check these resources:

• Learn how to create a custom serializer for Kafka.
• Understand how to fix Kafka consumer issues.
• Discover the difference between Kafka and other messaging systems.

Let’s embrace real-time data processing with Pyspark and Kafka, and improve our data engineering skills today!

Part 1 - Setting Up Your Environment

To read Avro data from Kafka with PySpark 2.4.0, we need to set up our environment right. This means we have to install the needed packages and check our settings. Let’s follow these steps to set up our environment:

1. Install Required Packages: First, we need to install these packages:

   • Apache Kafka
   • Confluent Kafka libraries for Avro
   • Apache Spark 2.4.0
   • PySpark

   We can install the Python libraries using pip:

   pip install pyspark confluent_kafka avro-python3

2. Download and Setup Confluent Schema Registry: The Schema Registry is important for working with Avro schemas. We can download it from the Confluent website. After that, we start the Schema Registry using:

   ./bin/schema-registry-start ./etc/schema-registry/schema-registry.properties

3. Kafka Broker Configuration: We need to make sure our Kafka broker is running. We can start it with:

   ./bin/kafka-server-start.sh ./config/server.properties

4. Setup Environment Variables: We have to set some environment variables that point to our Kafka and Spark installations:

   export KAFKA_HOME=/path/to/kafka
   export SPARK_HOME=/path/to/spark
   export PATH=$PATH:$KAFKA_HOME/bin:$SPARK_HOME/bin

5. Verify Installations: Finally, we check if Kafka and Spark are installed correctly by running:

   kafka-topics.sh --list --zookeeper localhost:2181
   spark-submit --version

By following these steps, we will get our environment ready for reading Avro data from Kafka with PySpark 2.4.0. For more info on Kafka setup, we can look at the Kafka documentation.

Part 2 - Configuring Kafka for Avro Serialization

To read Avro data from Kafka using Pyspark, we need to configure Kafka for Avro serialization. This means we have to set up a schema registry. We also need to configure the Kafka producer and consumer to use Avro.

1. Install Dependencies:
   First, we need to make sure we have the right libraries installed. We can do this with:

   pip install confluent-kafka avro-python3

2. Set Up Schema Registry:
   We need a schema registry to keep our Avro schemas. The Confluent Schema Registry is a good choice. We should install and run it with our Kafka broker.

3. Producer Configuration:
   Now, we configure our Kafka producer to use Avro serialization. We do this by setting the important properties:

   from confluent_kafka import avro
   from confluent_kafka.avro import AvroProducer
   
   producer_conf = {
       'bootstrap.servers': 'localhost:9092',
       'schema.registry.url': 'http://localhost:8081',
       'key.serializer': 'org.apache.kafka.common.serialization.StringSerializer',
       'value.serializer': 'io.confluent.kafka.serializers.KafkaAvroSerializer',
       'key.deserializer': 'org.apache.kafka.common.serialization.StringDeserializer',
       'value.deserializer': 'io.confluent.kafka.serializers.KafkaAvroDeserializer',
   }
   
   producer = AvroProducer(producer_conf, default_value_schema=avro.load("path/to/your/schema.avsc"))

4. Consumer Configuration:
   Also, we need to set up our Kafka consumer to read messages that are serialized with Avro:

   from confluent_kafka.avro import AvroConsumer
   
   consumer_conf = {
       'bootstrap.servers': 'localhost:9092',
       'group.id': 'your-group-id',
       'schema.registry.url': 'http://localhost:8081',
       'key.deserializer': 'org.apache.kafka.common.serialization.StringDeserializer',
       'value.deserializer': 'io.confluent.kafka.serializers.KafkaAvroDeserializer',
       'auto.offset.reset': 'earliest',
   }
   
   consumer = AvroConsumer(consumer_conf)
   consumer.subscribe(['your-topic'])

5. Schema Registration:
   We must register our schema in the schema registry. We can do this using the REST API or the Confluent command-line tools.

6. Testing:
   We should produce messages with our Avro producer and then consume them with our Avro consumer. This will help us check if everything is working as it should. For more help on setting up your Kafka environment, we can look at the guide on how to create custom serializers.

We should change the configurations based on our Kafka cluster setup and network settings. This setup helps us serialize and deserialize Avro data in Kafka. It allows smooth data streaming with Pyspark.

Part 3 - Establishing a Spark Session with Avro Support

To read Avro data from Kafka using PySpark, we need to set up a Spark session with Avro support. This means we have to configure the Spark session to include the Avro package. Here is the code to create a Spark session with Avro support in PySpark 2.4.0.

from pyspark.sql import SparkSession

# Create Spark session with Avro support
spark = SparkSession.builder \
    .appName("Kafka Avro Reader") \
    .config("spark.sql.avro.compression.codec", "snappy") \
    .config("spark.jars.packages", "org.apache.spark:spark-avro_2.11:2.4.0") \
    .getOrCreate()

Key Configurations:

• spark.sql.avro.compression.codec: This sets the Avro compression codec like snappy or deflate.
• spark.jars.packages: This includes the Avro library for Spark. We need to make sure the version matches our Spark version.

We have to make sure we have the Spark environment and PySpark installed. For more info on how to use Avro with Spark, we can look at this article on using Spark with Avro.

This setup helps us to process Avro data from Kafka topics efficiently.

Part 4 - Reading from Kafka Stream in Pyspark

To read from a Kafka stream in Pyspark, we can use the

from pyspark.sql import SparkSession
from pyspark.sql.functions import expr

spark = SparkSession \
    .builder \
    .appName("KafkaExample") \
    .getOrCreate()

df = spark \
    .readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "topic_name") \
    .load()

In this code, we create a Spark session. This is important for running our application. Then, we read from Kafka. We set the server address and the topic we want to read.

After that, we can process the data. We can use various functions to change the data. Here is an example to select the value from the Kafka stream.

value_df = df.selectExpr("CAST(value AS STRING)")

Now we have the data in a string format. We can work with it easily.

Next, we write the stream to the console. This helps us to see the output.

query = value_df \
    .writeStream \
    .outputMode("append") \
    .format("console") \
    .start()

query.awaitTermination()

In this code, we set the output mode to append. This means we will add new data to the output. We then write to the console. Finally, we wait for the process to finish.

This is how we can read from a Kafka stream in Pyspark. It is simple and useful for many applications.

Part 5 - Deserializing Avro Data in Pyspark

To deserialize Avro data in Pyspark, we need to set up the right schema and use the correct methods from the pyspark.sql module. Here is how we can do it:

1. Install Required Libraries:
   First, we need to install the necessary libraries for Avro and Kafka.

   pip install pyspark
   pip install avro-python3

2. Define the Avro Schema:
   Next, we create a schema for our Avro data. We can use a JSON file or a string for this.

   Example schema (schema.avsc):

   {
     "type": "record",
     "name": "User",
     "fields": [
       { "name": "name", "type": "string" },
       { "name": "age", "type": "int" }
     ]
   }

3. Read Avro Data from Kafka:
   We can use Pyspark to read Avro data from the Kafka stream.

   from pyspark.sql import SparkSession
   from pyspark.sql.functions import from_avro
   
   spark = SparkSession.builder \
       .appName("Kafka Avro Deserialization") \
       .getOrCreate()
   
   kafka_df = spark.readStream \
       .format("kafka") \
       .option("kafka.bootstrap.servers", "localhost:9092") \
       .option("subscribe", "your_topic") \
       .load()

4. Deserialize the Avro Data:
   We will use the from_avro function to deserialize our Avro data with the schema we defined.

   avro_schema = '''{
     "type": "record",
     "name": "User",
     "fields": [
       {"name": "name", "type": "string"},
       {"name": "age", "type": "int"}
     ]
   }'''
   
   deserialized_df = kafka_df.selectExpr("CAST(value AS BINARY) as value") \
       .select(from_avro("value", avro_schema).alias("data")) \
       .select("data.*")

5. Start the Streaming Query:
   Finally we start the streaming query to show the deserialized data.

   query = deserialized_df.writeStream \
       .outputMode("append") \
       .format("console") \
       .start()
   
   query.awaitTermination()

This way we can effectively deserialize Avro data in Pyspark while getting it from Kafka. For more details on Kafka and Avro serialization, we can check the article on writing custom Kafka serializer. If we face problems with our Kafka consumer, we should look at this guide on fixing Kafka consumer issues.

Part 6 - Writing Processed Data to Sink

We can write processed data to a sink in Pyspark after we read Avro data from Kafka. We use the writeStream operation for this. Below is a simple example that shows how to write the processed DataFrame to a sink like the console or a file.

Writing to Console

We can write the output to the console. This is useful for debugging:

processed_df.writeStream \
    .outputMode("append") \
    .format("console") \
    .start() \
    .awaitTermination()

Writing to File

If we want to write the processed data to a file, we need to choose the file format. For example, we can use Parquet or JSON. We also need to set the output path:

processed_df.writeStream \
    .outputMode("append") \
    .format("parquet") \
    .option("path", "/path/to/output/directory") \
    .option("checkpointLocation", "/path/to/checkpoint/directory") \
    .start() \
    .awaitTermination()

Writing to Kafka

If we want to send the resulting DataFrame back to Kafka, we can use this method:

processed_df.selectExpr("CAST(key AS STRING)", "to_json(struct(*)) AS value") \
    .writeStream \
    .outputMode("append") \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("topic", "output_topic") \
    .start() \
    .awaitTermination()

Make sure we have the right Kafka server address and topic name. We use the to_json function to convert the DataFrame rows to JSON format. Then we send them to Kafka.

For more help with Avro data or to fix common problems, we can visit this guide. If we want to learn more about Kafka configurations, we should check out Kafka Server Configuration.

Frequently Asked Questions

1. How can we read Avro data from Kafka using PySpark?

To read Avro data from Kafka with PySpark, we need to set up our Spark session for Avro. Also, we have to configure the Kafka source correctly. This means we need to tell the Kafka topic and give the right settings for Avro. For more help, look at this article on reading Avro from Kafka using PySpark.

2. What do we need before using PySpark with Kafka and Avro?

Before we use PySpark with Kafka and Avro, we must have a working Kafka cluster. We also need the PySpark libraries and Avro tools installed. Plus, we should know some basics about Spark Streaming. For more details on setting up Kafka, check this guide on Kafka server configuration.

3. How do we handle deserialization of Avro messages in PySpark?

To deserialize Avro messages in PySpark, we need to use the Avro schema. This helps us change binary data to a readable format. We can do this by using the avro package in our Spark app. For more detailed instructions, see this resource on decoding and deserializing Avro with PySpark.

4. What are some common issues when reading from Kafka streams in PySpark?

Some common problems when we read from Kafka streams in PySpark are configuration mistakes, serialization problems, and network issues. We need to make sure our Kafka broker is running and we can access it. For help with problems, look at this article on fixing Kafka consumer issues.

5. Can we write processed data back to Kafka using PySpark?

Yes, we can write processed data back to Kafka with PySpark. We usually use the DataFrame API for our changes. Then we use the writeStream method to set Kafka as the output. For a step-by-step guide, check this article on writing streaming data in PySpark.