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Stream Processing in Real-time Systems

Published: November 2025 | 18 min read

Understanding Stream Processing

Stream processing enables real-time data analysis and processing as it flows through the system. Unlike batch processing, which handles data in large chunks, stream processing deals with continuous data streams, making it ideal for time-sensitive applications.

Key Components

  1. Stream Sources
  2. Message queues (Kafka, Pulsar, Kinesis)
  3. IoT device data
  4. Clickstream data

  5. Log files

  6. Processing Frameworks

  7. Apache Flink
  8. Apache Kafka Streams
  9. Spark Streaming

  10. Apache Beam

  11. State Management

  12. Local state
  13. Distributed state
  14. Checkpointing
  15. Exactly-once processing

Implementation Example

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

# Set up the execution environment
env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# Define source table
t_env.execute_sql("""
    CREATE TABLE sensor_readings (
        sensor_id STRING,
        temperature DOUBLE,
        humidity DOUBLE,
        event_time TIMESTAMP(3),
        WATERMARK FOR event_time AS event_time - INTERVAL '5' SECOND
    ) WITH (
        'connector' = 'kafka',
        'topic' = 'sensor_data',
        'properties.bootstrap.servers' = 'localhost:9092',
        'properties.group.id' = 'sensor_processor',
        'format' = 'json',
        'scan.startup.mode' = 'latest-offset'
    )
""")

# Process the stream
result = t_env.sql_query("""
    SELECT 
        sensor_id,
        TUMBLE_START(event_time, INTERVAL '1' HOUR) as window_start,
        AVG(temperature) as avg_temp,
        MAX(temperature) as max_temp,
        MIN(temperature) as min_temp
    FROM sensor_readings
    GROUP BY 
        TUMBLE(event_time, INTERVAL '1' HOUR),
        sensor_id
""")

# Sink the results
t_env.execute_sql("""
    CREATE TABLE sensor_aggregates (
        sensor_id STRING,
        window_start TIMESTAMP(3),
        avg_temp DOUBLE,
        max_temp DOUBLE,
        min_temp DOUBLE
    ) WITH (
        'connector' = 'jdbc',
        'url' = 'jdbc:postgresql://db:5432/iot',
        'table-name' = 'sensor_aggregates',
        'username' = 'postgres',
        'password' = 'password'
    )
""")

# Execute the job
result.execute_insert("sensor_aggregates").wait()

Best Practices

  1. Fault Tolerance
  2. Implement checkpointing
  3. Handle out-of-order events

  4. Manage backpressure

  5. Performance Optimization

  6. Parallel processing
  7. Event time vs processing time

  8. State management

  9. Monitoring

  10. Latency metrics
  11. Throughput metrics
  12. Error rates

Common Challenges

  • Event Time Processing: Handling late-arriving data
  • State Management: Scaling stateful operations
  • Resource Allocation: Balancing latency and throughput
  • Testing: Validating stream processing logic

Tools Comparison

Tool Best For Processing Model Language Support
Apache Flink Complex event processing Event time processing Java, Scala, Python, SQL
Kafka Streams Kafka-native processing At-least-once Java, Scala
Spark Streaming Micro-batch processing Micro-batch Java, Scala, Python, R
Apache Beam Unified batch/streaming Both Java, Python, Go

Next Steps

  1. Set up monitoring for stream processing jobs
  2. Implement exactly-once processing
  3. Optimize for low-latency requirements
  4. Plan for scalability and fault tolerance