Spark Streaming vs Apache Flink®: Modern Stream Processing Compared

Spark Streaming vs Flink at a Glance

Spark’s massive, batch-based ecosystem and dominance in the batch world of extract-transform-load (ETL) pipelines has made it the default choice for many analytics and AI/ML use cases. Spark Streaming (based on Discretized Streams or DStreams) and more recently Spark Structured Streaming have enabled users to process data streams while staying within the Spark ecosystem.

On the other hand, Flink's native streaming capabilities are rapidly gaining ground, especially for mission-critical, low-latency applications for use cases like fraud detection and prevention, real-time personalization, and predictive maintenance.

Evaluating these processing frameworks isn’t about which one is “better” overall—it’s about answering the critical questions for your specific architecture:

• When does it make sense to use Spark Streaming instead of Flink? Or Flink over Spark Streaming?

• What existing or future use cases should I use to build my list of requirements and business objectives?

• Which engine fits best in my hybrid or multicloud environment and my organization’s long-term IT strategy?

This guide will help you understand Flink vs Spark Streaming in terms of their architectural design, core use cases, cloud-native features, and performance metrics.

Summary of Spark Streaming vs. Apache Flink Comparison

NOTE: For the rest of the guide, Spark Streaming refers to the Structured Streaming API unless otherwise specified.

How Do Spark Streaming and Flink Differ Architecturally?

The core architectural differences between Spark Streaming and Flink stem from their original design philosophies: Spark as a batch processing engine that’s been adapted to streaming, and Flink as a native stream processor.

Execution Model: Micro-Batching vs. Native Streaming

The most immediate impact of these design philosophies is visible in how data traverses the engine. 

• Spark’s model treats an unbounded data stream as a sequence of small, bounded batch jobs, known as micro-batches.

  • Batch-First Roots: Spark’s robust optimization engine and execution model are inherited directly from its powerful batch capabilities.

  • How it Works: Data is collected from the source over a short, predefined interval (e.g., 500ms to a few seconds).Once the interval ends, the entire collection is processed as a single, discrete batch.

  • Latency: This inherent wait time for the batch boundary creates a periodic delay, leading to near real-time latency (typically in the seconds range) rather than true real-time.

• Flink was designed to handle continuous, unbounded streams natively.

  • Streaming-Native Design: Flink treats data as an endless flow, processing records event-at-a-time (or in a continuous, pipelined fashion).

  • How it Works: Data is immediately consumed and flows through a directed acyclic graph (DAG) of operators without waiting for a time boundary.

  • Latency: This continuous pipeline approach achieves ultra-low latency (often sub-second or millisecond) because there is no enforced waiting time.

What’s the difference between micro-batching and true streaming? 

Micro-batching sacrifices the immediate processing of an event for the efficiency and parallelism of batch processing. True streaming, or native streaming, processes an event as soon as it arrives, eliminating the periodic delay imposed by the batch boundary.

So how does Flink process events differently from Spark? 

Flink executes a single, long-running stream job that continuously updates state and output, whereas Spark executes a sequence of short-lived batch jobs.

Time Semantics: Event-Time and Watermarks in Flink vs. Event-Time Adaption in Spark Streaming

Correctly handling time is crucial for accurate stream processing, especially when events arrive out of order.

Flink excels in event-time processing, which is the most accurate way to handle distributed streams.

• Event Time: The timestamp assigned by the source when the event actually occurred (e.g., when a user clicked a button).

• Watermarks: Flink uses Watermarks—a logical clock advancement mechanism—to signal when the system believes it has received all events for a particular time window. This allows Flink to correctly process late-arriving data and accurately calculate time-windowed aggregations.

Spark also supports event-time processing and Watermarks, though this feature was integrated later into its micro-batch model.

• Processing Time: Spark's initial focus was often on processing time (when the event is processed by the engine), which is simpler but prone to inaccuracies if network latency or system load varies.

• Watermarks in Spark: Structured Streaming now uses watermarks to handle late data, but these checks often occur at the boundary of each micro-batch, tying the event-time logic to the underlying batch execution cadence.

Take the Apache Flink 101 course on Confluent Developer to learn more.

Fault Tolerance and State Management – Asynchronous Checkpointing vs. 

Both engines guarantee robust streaming durability and recovery, but their mechanisms are architecturally distinct.

Flink's fault tolerance is based on the Chandy-Lamport distributed snapshot algorithm, leading to efficient state management.

• Mechanism: Flink takes global, consistent snapshots of the entire application state—including the position in the source stream (offsets), user-defined function (UDF) state, and internal operator state—at regular intervals. These are called Checkpoints.

• Exactly-Once: This mechanism, combined with appropriate sources and sinks, enables robust, guaranteed exactly-once semantics for stateful operations.

• Recovery: When a failure occurs, the job is restarted from the last successful checkpoint, which is stored externally (e.g., in HDFS or S3). The lightweight, distributed nature of the snapshot allows for very fast recovery.

Spark's fault tolerance is based on recovering and re-executing its micro-batches.

• Mechanism: Spark uses checkpointing to save the metadata required for recovery (like stream offsets and the structure of the computation graph) and relies heavily on a Write-Ahead Log (WAL) for reliability. The state is typically saved incrementally per micro-batch.

• Exactly-Once: Structured Streaming achieves exactly-once semantics by ensuring that it can re-read data from the source (which must be replayable, like Apache Kafka^®) and by using transactional/idempotent sinks.

• Recovery: When a failure occurs, Spark restores the job metadata from the checkpoint and re-processes the lost input data by re-executing the last few micro-batches. This process can be generally slower than Flink's direct state restoration, depending on the micro-batch size.

What Are the Key Use Cases for Flink vs. Spark Streaming?

Choosing between Apache Spark Structured Streaming and Apache Flink often comes down to aligning the engine's core architectural strengths (micro-batching vs. native streaming) with your specific application requirements for latency, state complexity, and ecosystem integration.

Flink: The Low-Latency and Event-Driven Specialist 

Flink is the fit-for-purpose choice when you need to manage complex application state over time in order to build cloud-native, event-driven pipelines and highly accurate, time-sensitive applications. Its native, event-at-a-time execution model eliminates the periodic delay associated with micro-batching, making it the superior choice for applications where latency must be measured in milliseconds.

Spark Structured Streaming: The Ecosystem Workhorse

Spark is ideal for organizations already invested in the broader Spark ecosystem and for workflows where near real-time latency (measured in seconds) is sufficient. It thrives in batch-centric workflows that are incrementally modernized to include a streaming component.

If you are already operating within a managed Spark environment like Databricks or utilizing the full capabilities of the Spark ecosystem (MLlib, Spark SQL, Delta Lake), using Structured Streaming drastically lowers the operational overhead and provides a cohesive platform for all data processing tasks, from batch ETL to streaming.

State Handling and Durability

The efficient management of application state is the defining feature of a cloud-native stream processor, influencing cost and recovery time.

Flink is arguably more cloud-native in its state management due to its:

• Externalized State: Flink's architecture is built around managing complex, application-specific state (e.g., session windows, aggregations) using external state backends like RocksDB (which can spill to disk), making the state size independent of the executor memory.

• Incremental Checkpointing: Flink supports efficient incremental checkpointing—persisting only the changes since the last snapshot—which drastically reduces I/O and recovery time, a major cost and performance benefit in cloud storage (like S3 or GCS).

• Savepoints: Flink uses Savepoints (manually triggered, consistent snapshots) for planned operations like upgrades, debugging, or migration, enhancing Data pipeline modernization by decoupling application changes from infrastructure.

Spark Streaming uses state management that is fundamentally tied to its micro-batch model:

• Versioned Key-Value Store: Spark manages state using a versioned key-value store, typically stored in memory and checkpointed to cloud storage. While efficient for batch-like state, complex, massive state can be more challenging without careful tuning.

• State Store Providers: Structured Streaming now supports RocksDB as an external state store provider, moving its capabilities closer to Flink's to handle larger states that don't fit in JVM memory.

Scalability and Elasticity

The ability to scale resources dynamically based on load is essential for cost-efficiency in the cloud.

Flink is highly elastic—its architecture, coupled with cloud orchestrators like the Flink Kubernetes Operator, allows for fine-grained dynamic scaling. 

• Because the state is externalized and consistent, Flink jobs can be scaled up or down (add or remove task slots) using Savepoints with minimal disruption and rapid recovery from the last consistent state. 

• This is ideal for optimizing costs on bursty stream workloads.

Spark Streaming is designed for elastic scaling, leveraging cloud environments and Spark's built-in dynamic allocation to automatically adjust compute resources (executors) up or down based on data volume and processing load

• Spark's elasticity is often managed by the underlying cloud platform (e.g., Databricks' auto-scaling, EMR, or Kubernetes).

• Spark also benefits from managing a single cluster that handles both batch and streaming, allowing resources to be shifted quickly between different workload types.

• While effective, the scaling mechanism is typically applied at the Executor/Cluster level, tied to the micro-batch cadence.