Introduction

Stream processing has become an essential component in modern data engineering, with real-time analytics and event-driven applications gaining popularity. Two of the most widely used frameworks for real-time stream processing are Apache Spark’s Structured Streaming and Apache Flink. Both of these frameworks provide powerful tools for processing high-throughput, low-latency data streams, but they differ in architecture, performance, and use cases.

In this article, we’ll perform a deep dive into both frameworks, compare their features, and help you decide which one is best suited for your use case.

What is Apache Spark’s Structured Streaming?

Apache Spark’s Structured Streaming is a scalable, fault-tolerant, and high-throughput stream processing engine built on top of Spark’s unified batch processing engine. It allows you to process streaming data as if it were a continuous batch, making it easier to write stream-processing logic using the same API.

Key Features:

  • Unified Batch and Streaming: Allows you to use the same code for batch and streaming workloads.
  • Fault Tolerance: Based on Spark’s checkpointing mechanism, ensuring reliable data processing.
  • Exactly Once Semantics: Provides strong guarantees about the exact once processing model.
  • Integration with Spark Ecosystem: Structured Streaming integrates seamlessly with other Spark components, such as Spark SQL, MLlib, and GraphX.

Apache Flink is an open-source, stream processing framework designed for stateful computations over unbounded data streams. It is known for its low-latency, high-throughput, and strong consistency guarantees.

Key Features:

  • Event-Time Processing: Flink allows processing based on event time rather than ingestion time, which is useful for time-sensitive applications.
  • Stateful Stream Processing: Flink’s stateful stream processing capabilities allow you to maintain application state across time windows and handle out-of-order data.
  • Exactly Once Semantics: Like Structured Streaming, Flink also provides strong consistency guarantees for stream processing.
  • Advanced Windowing: Supports complex windowing operations, including session windows, sliding windows, and tumbling windows.

1. Programming Model

  • Spark Structured Streaming: Uses a declarative API where the user specifies the computation on the streaming data, which Spark then converts into an optimized execution plan.
  • Flink: Provides a more low-level approach with detailed control over state, time, and windowing. The API is more flexible but may require more code for certain operations.

2. Latency

  • Spark Structured Streaming: While designed for low-latency processing, Spark typically performs better with batch-like workloads and has higher latency in high-throughput scenarios due to its micro-batching approach.
  • Flink: Designed for ultra-low-latency stream processing, Flink is optimized for event-driven applications that require sub-second latency.

3. State Management

  • Spark Structured Streaming: Spark maintains state in a distributed manner, with a strong emphasis on fault tolerance through checkpointing. However, Spark’s state management is not as flexible as Flink’s.
  • Flink: Flink excels in stateful stream processing, offering more sophisticated state management capabilities. Flink’s ability to handle large states across time windows makes it ideal for complex use cases.

4. Windowing

  • Spark Structured Streaming: Supports event-time and processing-time windows but lacks the advanced flexibility found in Flink.
  • Flink: Flink supports a wide variety of window types, including tumbling, sliding, and session windows, providing more control over how streams are processed.

5. Fault Tolerance

  • Spark Structured Streaming: Achieves fault tolerance via checkpointing, where the state is saved to a reliable storage system like HDFS or S3. In case of failures, the system can recover.
  • Flink: Uses checkpointing and savepoints, which are more flexible and enable fine-grained control over the recovery process, making Flink a better choice for applications that require fine-tuned fault tolerance.

6. Stream Processing Guarantees

  • Spark Structured Streaming: Provides Exactly-Once semantics in streaming, which guarantees that each record is processed only once, even in the event of failures.
  • Flink: Offers Exactly-Once semantics as well but also provides more fine-grained control over state consistency, which can be advantageous for complex workflows.

When to Use Apache Spark Structured Streaming?

Apache Spark Structured Streaming is ideal for:

  • Batch and Streaming Unified Workloads: If your pipeline needs to handle both batch and stream data using a unified framework, Spark is the better choice.
  • Integration with the Spark Ecosystem: If you’re already using Spark for batch processing or machine learning, leveraging Spark’s Structured Streaming makes sense.
  • Ease of Use: The declarative API and higher-level abstractions make it easier to implement simple stream processing jobs.

Apache Flink shines in the following scenarios:

  • Low Latency Requirements: For applications requiring sub-second latency and high throughput, Flink’s streaming-first architecture is better suited.
  • Complex Stateful Processing: Flink’s stateful stream processing capabilities are unmatched when it comes to maintaining complex application state.
  • Event-Time Processing: If your stream processing requires accurate time-based computations, Flink’s event-time processing model is highly beneficial.
  • Advanced Windowing: If you need flexible and advanced windowing logic (e.g., session windows, sliding windows), Flink offers a much richer set of features.

Performance Comparison

While both Spark Structured Streaming and Flink offer high-performance stream processing, their architectures and optimizations can affect performance in different use cases:

  • Throughput: Flink generally outperforms Spark in scenarios requiring very high throughput due to its event-driven, stream-first model.
  • Latency: Flink provides lower latency due to its native support for real-time stream processing. Spark, with its micro-batching, typically introduces higher latency but can still handle large-scale data at reasonably low latencies.

Conclusion

Choosing between Apache Spark Structured Streaming and Apache Flink depends on your specific requirements. Apache Spark Structured Streaming offers a powerful unified model for batch and stream processing, making it ideal for simpler use cases where integration with Spark’s ecosystem is a priority. On the other hand, Apache Flink is the better choice for applications with low-latency needs, complex stateful processing, and advanced time-based or windowing operations.

Both frameworks are exceptional at handling real-time data streams, but understanding their differences will help you make an informed decision based on your stream processing needs.