Kafka Streams vs ksqlDB: Choosing Right

Both process data from Kafka in real-time. Choosing wrong wastes engineering time and creates operational headaches.

ksqlDB is built on Kafka Streams. Every query compiles to a Streams topology. The question is whether SQL abstraction helps or limits you.

We started with ksqlDB because the team knew SQL. When we needed external API calls, we switched to Kafka Streams for that pipeline. Now we use both.

Data Engineer at a retail company

The Core Difference

Kafka Streams is a Java library you embed in your application. ksqlDB is a standalone server with SQL interface.

Kafka Streams:

KTable<Windowed<String>, Double> hourlyRevenue = orders
    .groupBy((key, order) -> order.getRegion())
    .windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofHours(1)))
    .aggregate(() -> 0.0, (region, order, total) -> total + order.getAmount());

ksqlDB:

CREATE TABLE hourly_revenue AS
  SELECT region, SUM(amount) AS total
  FROM orders
  WINDOW TUMBLING (SIZE 1 HOUR)
  GROUP BY region;

Same result. Different tradeoffs.

When to Use Kafka Streams

Complex logic: ksqlDB handles standard SQL. When you need conditional routing with external validation, Kafka Streams wins.

transactions
    .filter((key, tx) -> tx.getAmount() > 10000)
    .mapValues(tx -> {
        FraudScore score = fraudService.evaluate(tx);  // External call
        tx.setFraudScore(score.getValue());
        return tx;
    })
    .split()
    .branch((key, tx) -> tx.getFraudScore() > 0.8, Branched.withConsumer(s -> s.to("fraud-review")))
    .defaultBranch(Branched.withConsumer(s -> s.to("approved")));

ksqlDB cannot call external services. HTTP calls, database lookups, ML inference—use Kafka Streams.

Custom state stores: Direct access to RocksDB, custom serializers, TTL policies.

Embedded in microservices: No additional infrastructure. Deploy as standard JAR. Scale by running more instances.

Processor API: When DSL isn't enough, raw access to stream processor lifecycle.

When to Use ksqlDB

Rapid prototyping: Explore data without writing code.

SELECT * FROM orders EMIT CHANGES LIMIT 10;

SQL-native teams: If your team knows SQL but not Java, ksqlDB removes the learning curve.

Connect integration: Manage connectors from SQL.

CREATE SOURCE CONNECTOR postgres_source WITH (
  'connector.class' = 'io.debezium.connector.postgresql.PostgresConnector',
  'database.hostname' = 'postgres'
);

Simple aggregations: Straightforward windowed operations without business logic.

Decision Matrix

Criteria	Kafka Streams	ksqlDB
Team skills	Java developers	SQL analysts
External API calls	Supported	Not supported
Testing	Standard unit/integration	Limited
Deployment	JAR in your app	Dedicated cluster
Debugging	Full stack traces	Query analysis

Operational Differences

Deployment: Kafka Streams is a library—no cluster to manage. ksqlDB requires dedicated server instances.

Scaling: Both limited by partition count. Maximum parallelism = number of partitions. A unified console helps track consumer lag across both Kafka Streams and ksqlDB applications.

Performance: ksqlDB has SQL parsing overhead. For high-volume, latency-sensitive workloads, measure before committing.

State restoration: Both maintain local state stores backed by changelog topics. After crashes:

State Size	Recovery Time
1 GB	~30 seconds
10 GB	2-5 minutes
100 GB+	30-60 minutes

During recovery, the instance can't process new records. Use num.standby.replicas=1 for faster failover.

Hybrid Approach

Use both. ksqlDB for quick transformations. Kafka Streams for complex business logic.

[Source] → [ksqlDB] → [Intermediate Topics] → [Kafka Streams] → [Output]
           filtering    simple enrichment       external calls    complex logic

Common in mature organizations. Use ksqlDB for the 80% that fits SQL. Use Kafka Streams for the 20% that requires code.

The best choice depends on your team and constraints. Neither is universally better.

Book a demo to see how Conduktor Console shows Kafka Streams and ksqlDB consumer lag side-by-side, with state store metrics and topology visualization.