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Glossary

Kafka Streams vs Apache Flink: When to Use What

Compare Kafka Streams and Apache Flink for stream processing. Learn architectural differences, operational trade-offs, and when to choose each framework.

Kafka Streams vs Apache Flink: When to Use What

When building real-time data pipelines, two names dominate the conversation: Kafka Streams and Apache Flink. Both are mature, production-ready stream processing frameworks, but they take fundamentally different approaches to solving similar problems. Understanding these differences is crucial for choosing the right tool for your use case.

What Are Kafka Streams and Apache Flink?

Kafka Streams is a client library for building stream processing applications that read from and write to Apache Kafka. Released in 2016 as part of Apache Kafka, it's designed to be lightweight and embedded directly into your Java or Scala applications. There's no separate cluster to manage—your application is the stream processor.

Apache Flink is a distributed stream processing framework with its own cluster runtime. Originally developed in academia and open-sourced in 2014, Flink provides a complete execution environment with advanced features for complex event processing, batch processing, and machine learning. Applications are submitted to a Flink cluster, which handles scheduling, fault tolerance, and resource management.

The fundamental difference: Kafka Streams is a library you embed in your application, while Flink is a framework that runs your application.

Architecture and Design Philosophy

Kafka Streams: The Library Approach

Kafka Streams applications are standard Java applications that happen to process streams. You package your application as a JAR file and deploy it like any other microservice. Scaling is horizontal—run more instances of your application, and Kafka Streams automatically redistributes the work based on Kafka partition assignments.

Kafka Streams Architecture:
┌────────────────────────────────────────┐
│  Kafka Cluster                         │
│  ┌──────┐ ┌──────┐ ┌──────┐           │
│  │Topic │ │Topic │ │Topic │           │
│  │  A   │ │  B   │ │  C   │           │
│  └───┬──┘ └───┬──┘ └───┬──┘           │
└──────┼────────┼────────┼───────────────┘
       │        │        │
    ┌──▼────────▼────────▼───┐
    │ Consumer Group         │
    │  (Auto-rebalancing)    │
    └──┬────────┬────────┬───┘
       │        │        │
┌──────▼──┐ ┌──▼─────┐ ┌▼────────┐
│ App     │ │ App    │ │ App     │
│Instance │ │Instance│ │Instance │
│ 1       │ │ 2      │ │ 3       │
│┌───────┐│ │┌──────┐│ │┌───────┐│
││State  ││ ││State ││ ││State  ││
││Store  ││ ││Store ││ ││Store  ││
│└───────┘│ │└──────┘│ │└───────┘│
└─────────┘ └────────┘ └─────────┘
 (Kubernetes/Docker/VMs)

This architecture has significant implications. There's no separate cluster to operate, no JobManager or TaskManager to monitor. Your operations team uses the same tools they already know for deploying and monitoring Java applications. State is stored locally using RocksDB, with changelog topics in Kafka providing durability and recovery.

Apache Flink: The Framework Approach

Flink provides a complete distributed runtime. You submit jobs to a Flink cluster, which consists of a JobManager (coordinator) and one or more TaskManagers (workers). The JobManager handles scheduling, checkpointing, and failure recovery, while TaskManagers execute the actual data processing tasks.

Apache Flink Architecture:
┌────────────────────────────────┐
│    Flink Cluster               │
│                                │
│  ┌──────────────────────────┐  │
│  │     JobManager           │  │
│  │  - Scheduling            │  │
│  │  - Checkpointing         │  │
│  │  - Resource Management   │  │
│  └───────────┬──────────────┘  │
│              │                 │
│     ┌────────┼────────┐        │
│     │        │        │        │
│  ┌──▼─────┐┌─▼──────┐┌▼──────┐│
│  │TaskMgr ││TaskMgr ││TaskMgr││
│  │   1    ││   2    ││   3   ││
│  │┌──────┐││┌──────┐││┌─────┐││
│  ││Tasks ││││Tasks ││││Tasks│││
│  ││State ││││State ││││State│││
│  │└──────┘││└──────┘││└─────┘││
│  └────────┘└────────┘└───────┘│
└────────────────────────────────┘
         │
         ▼
┌────────────────┐
│ External State │
│ Backend        │
│ (HDFS/S3)      │
└────────────────┘

This architecture enables sophisticated features like dynamic resource allocation, savepoints for versioning application state, and unified batch and stream processing. However, it also means operating a separate cluster infrastructure with its own monitoring, scaling, and high availability requirements.

Deployment and Operations

Operational Complexity

Kafka Streams wins on operational simplicity. Deploy it as a standard application using Docker, Kubernetes, or traditional application servers. Scaling means starting more instances. Rolling updates are straightforward—just redeploy your application.

Flink requires cluster management. You need to provision JobManagers with high availability (typically using ZooKeeper or Kubernetes), manage TaskManager resources, and understand Flink-specific operational concepts like checkpoints and savepoints. Tools like Kubernetes operators and platforms such as Ververica Platform help, but there's inherently more to manage.

For teams already running Kafka and familiar with microservices deployment, Kafka Streams fits naturally into existing workflows. Streaming management tools can enhance this by providing visibility into Kafka topic health, consumer lag, and data quality—critical for monitoring Kafka Streams applications.

State Management

Both frameworks support stateful processing with fault tolerance, but with different approaches.

Kafka Streams uses local state stores backed by changelog topics. State is partitioned alongside data, keeping related data and computation together. Recovery involves replaying the changelog topic. This works well for moderate state sizes (gigabytes per instance).

Flink uses distributed snapshots (checkpoints) to external storage like S3, HDFS, or distributed filesystems. Checkpoints are coordinated across all parallel tasks, providing consistent global snapshots. This enables larger state (terabytes) and features like savepoints for application versioning and migration.

Feature Comparison

Processing Capabilities

Both frameworks support core stream processing operations: filtering, mapping, aggregations, joins, and windowing. However, differences emerge in advanced scenarios.

Windowing: Both support tumbling, sliding, and session windows. Flink additionally offers custom window logic and more flexible trigger mechanisms.

Joins: Kafka Streams excels at stream-table and stream-stream joins over Kafka topics. Flink supports similar joins plus more complex patterns like interval joins and temporal table joins.

Exactly-Once Semantics: Both provide exactly-once processing guarantees. Kafka Streams achieves this through Kafka transactions. Flink uses distributed checkpoints with two-phase commit for external systems.

Complex Event Processing: Flink's CEP library enables sophisticated pattern matching over event streams—detecting sequences, timeouts, and complex conditions. Kafka Streams handles simpler patterns but requires custom logic for advanced CEP scenarios.

Language Support

Kafka Streams is Java-native with Scala support through wrapper libraries. Your application must run on the JVM.

Flink provides native APIs for Java, Scala, and Python. The DataStream API offers fine-grained control, while the Table API and SQL layer provide higher-level abstractions for analytics use cases.

When to Use Kafka Streams

Choose Kafka Streams when:

Your data lives in Kafka: If you're already using Kafka for event streaming, Kafka Streams is the natural choice. No data movement required, and you leverage Kafka's partitioning and consumer groups for scaling.

You prefer microservices architecture: Kafka Streams applications deploy like any other microservice. They fit cleanly into containerized environments and don't require separate cluster infrastructure.

State requirements are moderate: For applications with gigabytes of state per instance, Kafka Streams' local storage model works well. Think fraud detection with recent transaction history, real-time aggregations over short windows, or event-driven microservices.

Team expertise favors application development over cluster operations: If your team is comfortable with Java application development but less experienced with distributed systems operations, Kafka Streams reduces operational burden.

Example: An e-commerce platform processing clickstream data to generate real-time product recommendations. Each Kafka Streams instance processes a subset of users, maintaining recent browsing history in local state stores. Scaling happens by adding instances, and the existing Kafka infrastructure handles message distribution.

When to Use Apache Flink

Choose Apache Flink when:

You need advanced processing capabilities: Complex event processing, ML model serving, iterative algorithms, or sophisticated windowing logic favor Flink's richer API surface.

State size is large or unbounded: Applications with terabytes of state, or state that grows without bound, benefit from Flink's distributed state backend and checkpoint mechanism.

Multiple data sources: While Flink works excellently with Kafka, it also connects to databases, filesystems, message queues, and custom sources. If your pipeline integrates diverse systems, Flink's broad connector ecosystem helps.

Batch and stream unification matters: Flink's unified runtime lets you write logic once and execute it on both streaming and batch data, useful for backfilling or reprocessing historical data.

You have cluster operations expertise: Teams already running Kubernetes, YARN, or other cluster managers can leverage that expertise for Flink deployments.

Example: A financial services firm detecting complex fraud patterns across multiple event types (transactions, logins, device changes) with pattern matching over days of history. Flink's CEP library expresses multi-step fraud scenarios naturally, while distributed state handles millions of customer profiles.

Monitoring and Development Tools

Both frameworks benefit from proper tooling for development and operations.

For Kafka Streams, monitoring happens through application metrics, Kafka consumer lag, and state store health. Streaming management tools provide visual insights into topic consumption patterns, data flow, and schema evolution—essential context when debugging stream processing issues or understanding data quality problems.

For Flink, the web UI provides job graphs, checkpoint statistics, and backpressure indicators. Third-party monitoring integrates with metrics systems like Prometheus. The challenge is correlating Flink metrics with source Kafka topics, where governance platforms like Conduktor providing unified visibility across both systems prove valuable.

Summary

Kafka Streams and Apache Flink both deliver production-grade stream processing, but serve different needs:

Choose Kafka Streams for Kafka-centric pipelines with moderate state, when you value operational simplicity and microservices deployment patterns. It excels at event-driven applications and real-time aggregations within the Kafka ecosystem.

Choose Apache Flink for complex processing requirements, large state, multiple data sources, or when you need advanced features like CEP, ML integration, or unified batch/stream processing. Accept the operational complexity of managing a separate cluster in exchange for greater flexibility.

Many organizations use both: Kafka Streams for microservices and simple transformations, Flink for complex analytics and ML pipelines. The key is matching the tool to the problem, not choosing one as the universal solution.

Start with your requirements: data sources, state size, processing complexity, and team capabilities. The right choice becomes clear when you prioritize what matters most for your use case.