Real-Time Streaming Architectures With Kafka, Flink, and Pinot

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Real-time streaming architectures are essential for modern data-driven applications. They allow organizations to process, analyze, and act upon data as it is generated. Apache Kafka, Apache Flink, and Apache Pinot are three powerful tools that can be combined to create robust real-time streaming architectures. This article explores how to leverage these technologies together, with coding examples, to build an effective real-time streaming pipeline.

Introduction to Real-Time Streaming

Real-time streaming involves continuous data processing as new data arrives. This is opposed to batch processing, where data is collected and processed in chunks. Real-time streaming architectures are crucial for applications such as fraud detection, recommendation systems, and real-time analytics.

Components of a Real-Time Streaming Architecture

Apache Kafka: The Distributed Streaming Platform

Apache Kafka is a distributed streaming platform capable of handling high-throughput, low-latency data streams. It is used for building real-time data pipelines and streaming applications.

Key Features:

  • Scalability: Kafka can scale horizontally by adding more brokers.
  • Durability: Kafka ensures data durability through replication.
  • Fault Tolerance: Kafka is resilient to node failures.

Example: Setting Up Kafka

bash

# Start ZooKeeper
bin/zookeeper-server-start.sh config/zookeeper.properties# Start Kafka Broker
bin/kafka-server-start.sh config/server.properties# Create a Kafka Topic
bin/kafka-topics.sh –create –topic test-topic –bootstrap-server localhost:9092 –partitions 1 –replication-factor 1

Apache Flink: The Stream Processing Framework

Apache Flink is a stream processing framework for high-performance, scalable, and accurate real-time applications. It supports event time processing and stateful computations.

Key Features:

  • Low Latency: Flink processes data with very low latency.
  • Stateful Computations: Flink manages state effectively, even for complex computations.
  • Event Time Processing: Flink handles out-of-order events efficiently.

Example: Flink Job for Processing Kafka Streams

java

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>(“test-topic”, new SimpleStringSchema(), properties);
DataStream<String> stream = env.addSource(consumer);

DataStream<String> processedStream = stream
.map(value -> “Processed: “ + value);

processedStream.print();

env.execute(“Flink Kafka Stream Processing”);

Apache Pinot: The Real-Time OLAP Store

Apache Pinot is a real-time distributed OLAP datastore designed for low-latency analytics on event-driven data. It enables real-time analytics with near-instantaneous query responses.

Key Features:

  • Real-Time Ingestion: Pinot can ingest data from streams like Kafka in real-time.
  • Low-Latency Queries: Optimized for sub-second query performance.
  • Scalability: Pinot can scale out to handle large volumes of data.

Example: Setting Up Pinot

bash

# Start Pinot Controller
bin/pinot-admin.sh StartController -configFileName conf/pinot-controller.conf# Start Pinot Broker
bin/pinot-admin.sh StartBroker -configFileName conf/pinot-broker.conf# Start Pinot Server
bin/pinot-admin.sh StartServer -configFileName conf/pinot-server.conf# Create a Pinot Table
bin/pinot-admin.sh AddTable -tableConfigFile /path/to/tableConfig.json -schemaFile /path/to/schema.json

Building a Real-Time Streaming Pipeline

Step 1: Data Ingestion with Kafka

Kafka acts as the first layer in the pipeline, collecting and buffering data from various sources.

Kafka Producer Example:

java

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");KafkaProducer<String, String> producer = new KafkaProducer<>(props);
producer.send(new ProducerRecord<>(“test-topic”, “key”, “value”));
producer.close();

Step 2: Stream Processing with Flink

Flink processes the incoming data in real-time. This can include transformations, aggregations, and enrichments.

Flink Streaming Example:

java

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>(“test-topic”, new SimpleStringSchema(), properties);
DataStream<String> stream = env.addSource(consumer);

DataStream<String> processedStream = stream
.map(value -> “Processed: “ + value)
.keyBy(value -> value.split(“:”)[1])
.timeWindow(Time.seconds(10))
.reduce((value1, value2) -> value1 + “, “ + value2);

processedStream.print();

env.execute(“Flink Stream Processing”);

Step 3: Real-Time Analytics with Pinot

Pinot ingests processed data from Kafka and provides low-latency OLAP queries.

Pinot Ingestion from Kafka:

json

{
"tableName": "myTable",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "timestamp",
"schemaName": "mySchema",
"replication": "1"
},
"tableIndexConfig": {
"loadMode": "MMAP"
},
"ingestionConfig": {
"streamIngestionConfig": {
"streamConfigMaps": {
"streamType": "kafka",
"stream.kafka.topic.name": "test-topic",
"stream.kafka.broker.list": "localhost:9092",
"stream.kafka.consumer.type": "lowlevel",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest"
}
}
}
}

Pinot Query Example:

sql

SELECT COUNT(*), AVG(metric)
FROM myTable
WHERE timestamp > CURRENT_TIMESTAMP - INTERVAL '1' HOUR

Advantages of Using Kafka, Flink, and Pinot Together

  1. Seamless Integration: Kafka, Flink, and Pinot are designed to work together, ensuring smooth data flow from ingestion to analytics.
  2. Scalability: Each component can scale independently, allowing the entire architecture to handle large volumes of data.
  3. Low Latency: The combination ensures low-latency data processing and querying, making it suitable for real-time applications.
  4. Flexibility: This architecture can be adapted for various use cases, including fraud detection, real-time monitoring, and user behavior analytics.

Conclusion

Real-time streaming architectures are essential for businesses that require immediate insights and actions based on data. Apache Kafka, Apache Flink, and Apache Pinot form a powerful trio for building such architectures. Kafka handles reliable data ingestion, Flink provides real-time processing with low latency, and Pinot enables interactive and fast analytics on the processed data.

By integrating these technologies, you can build scalable and robust real-time data pipelines that cater to a wide range of use cases, from monitoring and alerting systems to real-time recommendation engines and financial tickers. The examples provided in this article offer a foundation to start building your real-time streaming solutions, enabling your organization to harness the full potential of real-time data.