Choosing a Pagination Strategy for Kafka Streams State Stores

Decision: Pagination for Kafka Streams State Stores

You need to expose paginated reads from a Kafka Streams application’s state store (e.g., for a downstream API or batch job). The decision must balance latency, memory usage, persistence, and operational complexity while staying within the capabilities of your Kafka Streams version.

Constraints and Requirements

• Low‑latency reads for interactive consumers.
• State size may exceed available heap, so disk‑backed options are preferable.
• The solution must work with Kafka Streams 3.x (compatible with Kafka 3.4+).
• Security: state store access should be restricted to authorized clients.
• Operational simplicity: minimal custom code and clear failure modes.

Comparison of Options

Option	Persistence	Memory Footprint	Query Latency	Implementation Complexity	Notes
NoopStore	None (discards updates)	Negligible	N/A (no data)	Low	Useful only when you do not need to retain state.
InmemStore	None (lost on restart)	Proportional to state size	Low (local heap)	Low	Simple but risks OOM with large state.
Windowed Store (Time/Session)	Depends on underlying store (often RocksDB)	Moderate (windows add overhead)	Low‑Medium	Medium	Natural fit for time‑based pagination; requires window configuration.
RocksDB‑backed KeyValueStore	Persistent on local disk	Low‑Medium (cache configurable)	Medium (disk I/O)	Low‑Medium	Scales to large state; range scans are efficient.
Processor API (custom punctuator)	Same as underlying store	Depends on store	Variable	High	Full control; you can implement cursor‑style pagination manually.

Trade‑offs

If your state fits comfortably in memory and you can tolerate loss on restart, InmemStore gives the lowest latency and simplest code. For larger datasets that must survive restarts, a RocksDB‑backed store provides durable storage with configurable cache to keep hot data in memory, trading a bit of latency for scalability. Windowed stores add a time dimension that can simplify pagination when your use case is naturally time‑bounded (e.g., last‑hour aggregates). The Processor API offers the most flexibility but requires you to manage iteration, state restoration, and error handling yourself, increasing development and testing effort.

Example Implementation: Interactive Queries with Range Query

The following Java snippet shows how to page through a RocksDB‑backed KeyValueStore using Interactive Queries. It assumes you have enabled Interactive Queries in your Streams application and that the store is queryable (either locally or via remote RPC).

// Configuration (set when building the Streams application)
Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_ID_CONFIG, "my-paginated-app");
props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker:9092");
// Enable Interactive Queries (default in 3.x)
props.put(StreamsConfig.STATE_DIR_CONFIG, "/var/lib/kafka-streams");

KStream source = builder.stream("input-topic");
// Example: store the latest value per key
source.toTable(Materialized.>as("latest-store")
        .withKeySerde(Serdes.String())
        .withValueSerde(Serdes.String()))
        .toStream();

KafkaStreams streams = new KafkaStreams(builder.build(), props);
streams.start();

// ----- Pagination client (could be a separate service) -----
// Wait until the store is ready (optional but recommended)
streams.storeStore(StoreQueryParameters.fromNameAndType("latest-store", QueryableStoreTypes.keyValueStore()))
        .whenReady(() -> {
            ReadOnlyKeyValueStore store = streams.store(
                    StoreQueryParameters.fromNameAndType("latest-store", QueryableStoreTypes.keyValueStore()));
            
            String fromKey = "user:1000";   // inclusive start key
            String toKey   = "user:2000";   // exclusive end key
            int pageSize = 50;
            
            try (KeyValueIterator it = store.range(fromKey, toKey)) {
                int count = 0;
                while (it.hasNext() && count < pageSize) {
                    KeyValue kv = it.next();
                    // emit kv.key, kv.value as one page of results
                    System.out.printf("Key: %s, Value: %s%n", kv.key, kv.value);
                    count++;
                }
                // To get the next page, set fromKey = kv.key (last returned key) + epsilon
            } catch (Exception e) {
                // Handle exceptions (e.g., store not yet queryable)
                e.printStackTrace();
            }
        });

Where to run: The Streams application runs on any host with access to the Kafka cluster. The pagination client can be a separate service or a temporary admin tool; it only needs read‑only access to the state store via Interactive Queries.

Required permissions: The client must be allowed to connect to the Streams application’s Interactive Queries port (default 9999) and have the ReadOnly privilege on the store (no write ACL needed). If TLS/SASL is enabled, configure the client accordingly.

Expected checks: After starting the Streams app, verify that the store is queryable (streams.store(...) != null) and that the range iterator returns keys in lexicographic order matching your key scheme. Monitor latency (e.g., System.nanoTime() before/after the iterator) to ensure it stays within your SLA.

Risks: Exposing the Interactive Queries endpoint can leak state data; restrict network access with firewalls or authentication. Very large page sizes may cause OOM on the client side if you materialize the whole range; keep pageSize reasonable or process items incrementally.

Validation Steps

1. Deploy the Streams application with the RocksDB‑backed store and confirm it starts without errors.
2. Enable Interactive Queries (default in 3.x) and note the exposed port.
3. Run the pagination client against a known key range and verify that the number of returned records matches pageSize (or fewer at the end of the range).
4. Measure end‑to‑end latency for a page request; ensure it stays within your target (e.g., < 100 ms for hot data).
5. Restart the Streams application and confirm that the store is restored and pagination continues to work correctly.
6. Review logs for any OutOfMemoryError or excessive disk I/O; adjust the RocksDB cache size if needed.