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Memory Management

You can specify the total memory (RAM) size used for internal caching and compacting of records. This caching happens before the records are written to state stores or forwarded downstream to other nodes.

The record caches are implemented slightly different in the DSL and Processor API.

Record caches in the DSL

You can specify the total memory (RAM) size of the record cache for an instance of the processing topology. It is leveraged by the following KTable instances:

  • Source KTable: KTable instances that are created via StreamsBuilder#table() or StreamsBuilder#globalTable().
  • Aggregation KTable: instances of KTable that are created as a result of aggregations.

For such KTable instances, the record cache is used for:

  • Internal caching and compacting of output records before they are written by the underlying stateful processor node to its internal state stores.
  • Internal caching and compacting of output records before they are forwarded from the underlying stateful processor node to any of its downstream processor nodes.

Use the following example to understand the behaviors with and without record caching. In this example, the input is a KStream<String, Integer> with the records <K,V>: <A, 1>, <D, 5>, <A, 20>, <A, 300>. The focus in this example is on the records with key == A.

  • An aggregation computes the sum of record values, grouped by key, for the input and returns a KTable<String, Integer>.

    • Without caching: a sequence of output records is emitted for key A that represent changes in the resulting aggregation table. The parentheses (()) denote changes, the left number is the new aggregate value and the right number is the old aggregate value: <A, (1, null)>, <A, (21, 1)>, <A, (321, 21)>.
    • With caching: a single output record is emitted for key A that would likely be compacted in the cache, leading to a single output record of <A, (321, null)>. This record is written to the aggregation’s internal state store and forwarded to any downstream operations.

The cache size is specified through the cache.max.bytes.buffering parameter, which is a global setting per processing topology:

// Enable record cache of size 10 MB.
Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 10 * 1024 * 1024L);

This parameter controls the number of bytes allocated for caching. Specifically, for a processor topology instance with T threads and C bytes allocated for caching, each thread will have an even C/T bytes to construct its own cache and use as it sees fit among its tasks. This means that there are as many caches as there are threads, but no sharing of caches across threads happens.

The basic API for the cache is made of put() and get() calls. Records are evicted using a simple LRU scheme after the cache size is reached. The first time a keyed record R1 = <K1, V1> finishes processing at a node, it is marked as dirty in the cache. Any other keyed record R2 = <K1, V2> with the same key K1 that is processed on that node during that time will overwrite <K1, V1>, this is referred to as “being compacted”. This has the same effect as Kafka’s log compaction, but happens earlier, while the records are still in memory, and within your client-side application, rather than on the server-side (i.e. the Apache Kafka® broker). After flushing, R2 is forwarded to the next processing node and then written to the local state store.

The semantics of caching is that data is flushed to the state store and forwarded to the next downstream processor node whenever the earliest of or cache.max.bytes.buffering (cache pressure) hits. Both and cache.max.bytes.buffering are global parameters. As such, it is not possible to specify different parameters for individual nodes.

Here are example settings for both parameters based on desired scenarios.

  • To turn off caching the cache size can be set to zero:

    // Disable record cache
    Properties streamsConfiguration = new Properties();
    streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);

    Turning off caching might result in high write traffic for the underlying RocksDB store. With default settings caching is enabled within Kafka Streams but RocksDB caching is disabled. Thus, to avoid high write traffic it is recommended to enable RocksDB caching if Kafka Streams caching is turned off.

    For example, the RocksDB Block Cache could be set to 100MB and Write Buffer size to 32 MB. For more information, see the RocksDB config.

  • To enable caching but still have an upper bound on how long records will be cached, you can set the commit interval. In this example, it is set to 1000 milliseconds:

    Properties streamsConfiguration = new Properties();
    // Enable record cache of size 10 MB.
    streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 10 * 1024 * 1024L);
    // Set commit interval to 1 second.
    streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000);

The effect of these two configurations is described in the figure below. The records are shown using 4 keys: blue, red, yellow, and green. Assume the cache has space for only 3 keys.

  • When the cache is disabled (a), all of the input records will be output.

  • When the cache is enabled (b):

    • Most records are output at the end of commit intervals (e.g., at t1 a single blue record is output, which is the final over-write of the blue key up to that time).
    • Some records are output because of cache pressure (i.e. before the end of a commit interval). For example, see the red record before t2. With smaller cache sizes we expect cache pressure to be the primary factor that dictates when records are output. With large cache sizes, the commit interval will be the primary factor.
    • The total number of records output has been reduced from 15 to 8.

Record caches in the Processor API

You can specify the total memory (RAM) size of the record cache for an instance of the processing topology. It is used for internal caching and compacting of output records before they are written from a stateful processor node to its state stores.

The record cache in the Processor API does not cache or compact any output records that are being forwarded downstream. This means that all downstream processor nodes can see all records, whereas the state stores see a reduced number of records. This does not impact correctness of the system, but is a performance optimization for the state stores. For example, with the Processor API you can store a record in a state store while forwarding a different value downstream.

Following from the example first shown in section State Stores, to enable caching, you can add the withCachingEnabled call.

StoreBuilder countStoreBuilder =

Other memory usage

There are other modules inside Kafka that allocate memory during runtime. They include the following:

  • Producer buffering, managed by the producer config buffer.memory.
  • Consumer buffering, currently not strictly managed, but can be indirectly controlled by fetch size, i.e., fetch.max.bytes and
  • Both producer and consumer also have separate TCP send / receive buffers that are not counted as the buffering memory. These are controlled by the send.buffer.bytes / receive.buffer.bytes configs.
  • Deserialized objects buffering: after consumer.poll() returns records, they will be deserialized to extract timestamp and buffered in the streams space. Currently this is only indirectly controlled by buffered.records.per.partition.
  • RocksDB’s own memory usage, both on-heap and off-heap; critical configs (for RocksDB version 4.1.0) include block_cache_size, write_buffer_size and max_write_buffer_number. These can be specified through the rocksdb.config.setter configuration.


Iterators should be closed explicitly to release resources: Store iterators (e.g., KeyValueIterator and WindowStoreIterator) must be closed explicitly upon completeness to release resources such as open file handlers and in-memory read buffers, or use try-with-resources statement (available since JDK7) for this Closeable class.

Otherwise, stream application’s memory usage keeps increasing when running until it hits an OOM.