Status

Current state: Under Discussion

Discussion thread: Thread

JIRA: Unable to render Jira issues macro, execution error.

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

As described in KIP-844, under EOS, crash failures cause all Task state to be wiped out on restart. This is because, currently, data is written to the StateStore before the commit to its changelog has completed, so it's possible that records are written to disk that were not committed to the store changelog.

In KIP-844, it was proposed to create an alternative type of StateStore, which would enable users to opt-in to "transactional" behaviour, that ensured data was only persisted once the changelog commit has succeeded. However, the design and approach outlined in KIP-844 unfortunately did not perform well when tested (with a write throughput that was approximately only 4% of the regular RocksDB StateStore!).

This KIP explores an alternative design that should have little/no performance impact, potentially performing better than the status quo, and can thus be enabled for all stores.

Public Interfaces

New configuration

Changed Interfaces

• org.apache.kafka.streams.processor.StateStore
• org.apache.kafka.streams.processor.StateStoreContext

Changes:

   
    /**
     * Flush any cached data
     * 
     * @deprecated Use {@link org.apache.kafka.streams.processor.api.ProcessingContext#commit() ProcessorContext#commit()}
     *             instead.
     */
    @Deprecated
    default void flush() {
        // no-op
    }

    /**
     * Commit all written records to this StateStore.
     * <p>
     * This method MUST NOT be called by users from {@link org.apache.kafka.streams.processor.api.Processor processors},
     * as doing so may violate the consistency guarantees provided by this store, and expected by Kafka Streams.
     * Instead, users should call {@link org.apache.kafka.streams.processor.api.ProcessingContext#commit() 
     * ProcessorContext#commit()} to request a Task commit.
     * <p>
     * When called, every write written since the last call to {@link #commit(Map)}, or since this store was {@link
     * #init(StateStoreContext, StateStore) opened} will be made available to readers using the {@link
     * org.apache.kafka.common.IsolationLevel#READ_COMMITTED READ_COMMITTED} {@link
     * org.apache.kafka.common.IsolationLevel IsolationLevel}.
     * <p>
     * If {@link #persistent()} returns {@code true}, after this method returns, all records written since the last call
     * to {@link #commit(Map)} are guaranteed to be persisted to disk, and available to read, even if this {@link
     * StateStore} is {@link #close() closed} and subsequently {@link #init(StateStoreContext, StateStore) re-opened}.
     * <p>
     * If {@link #managesOffsets()} <em>also</em> returns {@code true}, the given {@code changelogOffsets} will be
     * guaranteed to be persisted to disk along with the written records.
     * <p>
     * {@code changelogOffsets} will usually contain a single partition, in the case of a regular StateStore. However,
     * they may contain multiple partitions in the case of a Global StateStore with multiple partitions. All provided
     * partitions <em>MUST</em> be persisted to disk.
     * <p>
     * Implementations <em>SHOULD</em> ensure that {@code changelogOffsets} are committed to disk atomically with the
     * records they represent.
     * 
     * @param changelogOffsets The changelog offset(s) corresponding to the most recently written records.
     */
    default void commit(final Map<TopicPartition, Long> changelogOffsets) {
        flush();
    }

    /**
     * Returns the most recently {@link #commit(Map) committed} offset for the given {@link TopicPartition}.
     * <p>
     * If {@link #managesOffsets()} and {@link #persistent()} both return {@code true}, this method will return the
     * offset that corresponds to the changelog record most recently written to this store, for the given {@code
     * partition}.
     * <p>
     * This method provides readers using the {@link org.apache.kafka.common.IsolationLevel#READ_COMMITTED} {@link
     * org.apache.kafka.common.IsolationLevel} a means to determine the point in the changelog that this StateStore
     * currently represents.
     * 
     * @param partition The partition to get the committed offset for.
     * @return The last {@link #commit(Map) committed} offset for the {@code partition}; or {@code null} if no offset
     *         has been committed for the partition, or if either {@link #persistent()} or {@link #managesOffsets()}
     *         return {@code false}.
     */
    default Long getCommittedOffset(final TopicPartition partition) {
        return null;
    }

    /**
     * Determines if this StateStore manages its own offsets.
     * <p>
     * If this method returns {@code true}, then offsets provided to {@link #commit(Map)} will be retrievable using
     * {@link #getCommittedOffset(TopicPartition)}, even if the store is {@link #close() closed} and later re-opened.
     * <p>
     * If this method returns {@code false}, offsets provided to {@link #commit(Map)} will be ignored, and {@link
     * #getCommittedOffset(TopicPartition)} will be expected to always return {@code null}.
     * <p>
     * This method is provided to enable custom StateStores to opt-in to managing their own offsets. This is highly
     * recommended, if possible, to ensure that custom StateStores provide the consistency guarantees that Kafka Streams
     * expects when operating under the {@code exactly-once} {@code processing.mode}.
     * 
     * @return Whether this StateStore manages its own offsets.
     */
    default boolean managesOffsets() {
        return false;
    }

   /**      
     * Return an approximate count of memory used by records not yet committed to this StateStore.
     * <p>
     * This method will return an approximation of the memory that would be freed by the next call to {@link
     * #commit(Map)}.
     * <p>
     * If no records have been written to this store since {@link #init(StateStoreContext, StateStore) opening}, or
     * since the last {@link #commit(Map)}; or if this store does not support atomic transactions, it will return {@code
     * 0}, as no records are currently being buffered.
     *
     * @return The approximate size of all records awaiting {@link #commit(Map)}; or {@code 0} if this store does not
     *         support transactions, or has not been written to since {@link #init(StateStoreContext, StateStore)} or
     *         last {@link #commit(Map)}.
     */
    @Evolving
    default long approximateNumUncommittedBytes() {
        return 0;
    }

    /**
     * The {@link IsolationLevel} that every transaction created by {@link StateStore#newTransaction()} should use.
     * <p>
     * In the context of a {@link org.apache.kafka.streams.processor.StateStore} transaction, these Isolation Levels
     * adhere to the <a href="https://en.wikipedia.org/wiki/Isolation_(database_systems)#Read_phenomena">ANSI SQL 92
     * definitions</a>.
     * <p>
     * All isolation levels guarantee "read-your-own-writes", i.e. that writes in the transaction will be seen by
     * subsequent reads <em>from within the same transaction</em>. Other guarantees vary by isolation level:
     * <p>
     * <table>
     *     <tr>
     *         <th>Isolation Level</th>
     *         <th>Description</th>
     *         <th>Permitted Read Phenomena</th>
     *     </tr>
     *     <tr>
     *         <td>{@link IsolationLevel#READ_UNCOMMITTED}</td> // todo: ALOS
     *         <td>Allows queries to read writes from all ongoing transactions that have not-yet been committed.</td>
     *         <td>dirty reads, non-repeatable reads, phantom reads</td>
     *     </tr>
     *     <tr>
     *         <td>{@link IsolationLevel#READ_COMMITTED}</td> // todo: EOS
     *         <td>Allows queries to only read writes that have been committed to the StateStore. Writes by an ongoing
     *         transaction are not visible <em>until that transaction commits</em>.</td>
     *         <td>non-repeatable reads, phantom reads</td>
     *     </tr>
     * </table>
     * <p>
     * Under {@link IsolationLevel#READ_UNCOMMITTED}, there are no guarantees on when records from other transactions
     * become visible, therefore implementations <em>may</em> refrain from making uncommitted records visible to other
     * transactions until they're committed, if doing so would improve performance.
     * <p>
     * The default implementation of this method will use {@link IsolationLevel#READ_COMMITTED READ_COMMITTED} if the
     * app is {@link #appConfigs() configured} to use an {@link StreamsConfig#EXACTLY_ONCE_V2 exactly-once} {@link
     * StreamsConfig#PROCESSING_GUARANTEE_CONFIG processing guarantee}. Otherwise, it will be {@link
     * IsolationLevel#READ_UNCOMMITTED READ_UNCOMMITTED}. 
     */
    default IsolationLevel isolationLevel() {
        return StreamsConfigUtils.eosEnabled(new StreamsConfig(appConfigs())) ?
                IsolationLevel.READ_COMMITTED : IsolationLevel.READ_UNCOMMITTED;
    }

Changed Metrics

• stream-state-metrics 
  • flush-rate is renamed to commit-rate 
  • flush-latency-avg  is renamed to commit-latency-avg 
  • flush-latency-max is renamed to commit-latency-max 

These changes are necessary to ensure these metrics are not confused with orthogonal operations, like RocksDB memtable flushes or cache flushes. They will be measuring the invocation of StateStore#commit, which replaces StateStore#flush.

Proposed Changes

To ensure that data is not written to a state store until it has been committed to the changelog, we need to isolate writes from the underlying database until changelog commit. To achieve this, introduce the concept of transaction Isolation Levels, that dictate the visibility of records written by different threads.

Internally, we enable configuration of the level of isolation provided by StateStores via a context-wide IsolationLevel, which can be configured to either:

In Kafka Streams, all StateStore s are written to by a single StreamThread  (this is the Single Writer principle). However, multiple other threads may concurrently read from StateStore s, principally to service Interactive Queries. In practice, this means that under READ_COMMITTED, writes by the StreamThread  that owns the StateStore  will only become visible to Interactive Query threads once flush()  has been called.

StateStores are only flushed once the Kafka transaction for their changelog entries have been committed. This ensures that, under exactly-once, the records written to disk always reflect the state of the store's changelog.

The at-least-once processing mode does not need the isolation guarantees that READ_COMMITTED provides, and indeed, doing so could cause problems as the default commit.interval.ms under at-least-once is 30 seconds, which would require buffering records for up to 30 seconds, unnecessarily. For this reason, under at-least-once, Streams will automatically configure all StateStores to use the READ_UNCOMMITTED isolation level, which provides no isolation guarantees, and ensures that records are immediately written to disk.

In-memory Transaction Buffers

Many StateStore implementations, including RocksDB, will buffer records written to a transaction entirely in-memory, which could cause issues, either with JVM heap or native memory. To mitigate this, we will automatically force a Task commit if the total memory used for buffering uncommitted records returned by StateStore#approximateNumUncommittedBytes() exceeds the threshold configured by statestore.uncommitted.max.bytes. This will roughly bound the memory required for buffering uncommitted records, irrespective of the commit.interval.ms, and will effectively bound the number of records that will need to be restored in the event of a failure. Each StreamThread will be given 1/num.stream.threads of the configured limits, dividing it fairly between them.

It's possible that some Topologies can generate many more new StateStore entries than the records they process, in which case, it would be possible for such a Topology to cross the configured record/memory thresholds mid-processing, potentially causing an OOM error if these thresholds are exceeded by a lot. To mitigate this, the StreamThread will measure the increase in records/bytes written on each iteration, and pre-emptively commit if the next iteration is likely to cross the threshold.

Note that this new method provide default implementations that ensure existing custom stores and non-transactional stores (e.g. InMemoryKeyValueStore) do not force any early commits.

Interactive Queries

Interactive queries currently see every record, as soon as they are written to a StateStore. This can cause some consistency issues, as interactive queries can read records before they're committed to the Kafka changelog, which may be rolled-back. To address this, interactive queries will query the underlying StateStore, irrespective of the configured IsolationLevel. This ensures that interactive queries see a consistent view of the store, as they will not be able to read any uncommitted records. This is safe to do, because Interactive Queries have a read-only view of the StateStore, so it only needs to worry about avoiding reading writes that have not yet been flushed.

When operating under IsolationLevel.READ_COMMITTED (i.e. EOS), the maximum time for records to become visible to interactive queries will be commit.interval.ms, however, this is automatically set to a low value, and it's not recommended for users to use high intervals when operating under EOS.

When operating under IsolationLevel.READ_UNCOMMITTED, (i.e. ALOS), all records will be immediately visible to interactive queries, so the high default commit.interval.ms of 30s will have no impact on interactive query latency.

Error Handling

Kafka Streams currently generates a TaskCorruptedException when a Task needs to have its state wiped (under EOS) and be re-initialized. There are currently several different situations that generate this exception:

1. No offsets for the store can be found when opening it under EOS.
2. OutOfRangeException during restoration, usually caused by the changelog being wiped on application reset.
3. TimeoutException under EOS, when writing to or committing a Kafka transaction.

The first two of these are extremely rare, and make sense to keep. However, timeouts are much more frequent. They currently require the store to be wiped under EOS because when a timeout occurs, the data in the local StateStore will have been written, but the data in the Kafka changelog will have failed to be written, causing a mismatch in consistency.

With Transactional StateStores, we can guarantee that the local state is consistent with the changelog, therefore, it will no longer be necessary to reset the local state on a TimeoutException.

Atomic Checkpointing

Kafka Streams currently stores the changelog offsets for a StateStore in a per-Task on-disk file, .checkpoint, which under EOS, is written only when Streams shuts down successfully. There are two major problems with this approach:

• To ensure that the data on-disk matches the checkpoint offsets in the .checkpoint file, we must flush the StateStores whenever we update the offsets in .checkpoint. This is a performance regression, as it causes a significant increase in the frequency of RocksDB memtable flushes, which increases load on RocksDB's compaction threads.
• There's a race condition, where it's possible the application exits after data has been committed to RocksDB, but before the checkpoint file has been updated, causing a consistency violation.

To resolve this, we move the responsibility for offset management to the StateStore itself. The new commit method takes a map of all the changelog offsets that correspond to the state of the transaction buffer being committed.

RocksDBStore will store these offsets in a separate Column Family, and will be configured to atomically flush all its Column Families. This guarantees that the changelog offsets will always be flushed to disk together with the data they represent, irrespective of how that flush is triggered. This allows us to remove the explicit memtable flush(), enabling RocksDB to dictate when memtables are flushed to disk.

The existing .checkpoint files will be retained for any StateStore that does not set managesOffsets()  to true , and to ensure managed offsets are available when the store is closed. Existing offsets will be automatically migrated into StateStores that manage their own offsets, iff there is no offset returned by StateStore#getCommittedOffset.

Required interface changes:

• Add methods void commit(Map<TopicPartition, Long> changelogOffsets), boolean managesOffsets() and Long getCommittedOffset(TopicPartition) to StateStore .
• Deprecate method flush() on StateStore.

Offsets for Consumer Rebalances

Kafka Streams directly reads from the Task .checkpoint file during Consumer rebalance, in order to optimize assignments of stateful Tasks by assigning them to the instance with the most up-to-date copy of the data, which minimises restoration. To allow this to continue functioning, Kafka Streams will continue to write the changelog offsets to the .checkpoint file, even for stores that manage their own offsets.

Offsets will be written to .checkpoint at the following times:

1. During StateStore initialization, in order to synchronize the offsets in .checkpoint  with the offsets returned by StateStore#getCommittedOffset(TopicPartition), which are the source of truth for stores that manage their own offsets.
2. When the StateStore is closed, in order to ensure that the offsets used for Task assignment reflect the state persisted to disk.
3. At the end of every Task commit, if-and-only-if at least one StateStore in the Task is persistent and does not manage its own offsets. This ensures that stores that don't manage their offsets continue to have their offsets persisted to disk whenever the StateStore data itself is committed.
   • Avoiding writing .checkpoint when every persistent store manages its own offsets ensures we don't pay a significant performance penalty when the commit interval is short, as it is by default under EOS.
   • Since all persistent StateStores provided by Kafka Streams will manage their own offsets, the common case is that the .checkpoint file will not be updated on commit(Map) 

Tasks that are already assigned to an instance, already use the in-memory offsets when calculating partition assignments, so no change is necessary here.

Interactive Query .position Offsets

Input partition "Position" offsets, introduced by KIP-796: Interactive Query v2, are currently stored in a .position file by the RocksDBStore implementation. To ensure consistency with the committed data and changelog offsets, these position offsets will be stored in RocksDB, in the same column family as the changelog offsets, instead of the .position file. When a StateStore that manages its own offsets is first initialized, if a .position file exists in the store directory, its offsets will be automatically migrated into the store, and the file will be deleted.

When writing data to a RocksDBStore (via put, delete, etc.), the input partition offsets will be read from the changelog record metadata (as before), and these offsets will be added to the current transactions WriteBatch. When the StateStore is committed, the position offsets in the current WriteBatch will be written to RocksDB, alongside the records they correspond to. Alongside this, RocksDBStore will maintain two Position maps in-memory, one containing the offsets pending in the current transaction's WriteBatch, and the other containing committed offsets. On commit(Map), the uncommitted Position map will be merged into the committed Position map such that both maps contain the same offsets. In this sense, the two Position maps will diverge during writes, and re-converge on-commit.

When an interactive query is made under the READ_COMMITTED IsolationLevel the PositionBound will constrain the committed Position map, whereas under READ_UNCOMMITTED, the PositionBound will constrain the uncommitted Position map.

RocksDB Transactions

When the isolation level is READ_COMMITTED, we will use RocksDB's WriteBatchWithIndex as a means to accomplishing atomic writes when not using the RocksDB WAL. When reading records from the StreamThread, we will use the WriteBatchWithIndex#getFromBatchAndDB and WriteBatchWithIndex#newIteratorWithBase utilities in order to ensure that uncommitted writes are available to query. When reading records from Interactive Queries, we will use the regular RocksDB#get and RocksDB#newIterator methods, to ensure we see only records that have been flushed (see above). The performance of this is expected to actually be better than the existing, non-batched write path. The main performance concern is that the buffer must reside completely in-memory until it is committed, which is addressed by statestore.uncommitted.max.bytes, see above.

Compatibility, Deprecation, and Migration Plan

The above changes will retain compatibility for all existing StateStores, including user-defined custom implementations. Any StateStore that extends RocksDBStore will automatically inherit its behaviour, although its internals will change, potentially requiring users that depend on internal behaviour to update their code.

All new methods on existing classes will have defaults set to ensure compatibility.

Kafka Streams will automatically migrate offsets found in an existing .checkpoint file, and/or an existing .position file, to store those offsets directly in the StateStore, if managesOffsets returns true. Users of the in-built store types will not need to make any changes. See Upgrading.

Users may notice a change in the performance/behaviour of Kafka Streams. Most notably, under EOS Kafka Streams will now regularly "commit" StateStores, where it would have only done so when the store was closing in the past. While the performance should not be a concern, this change will be observable in metrics, potentially causing thresholds users may have set in monitoring systems to require updating.

Upgrading

When upgrading to a version of Kafka Streams that includes the changes outlined in this KIP, users will not be required to take any action. Kafka Streams will automatically upgrade any RocksDB stores to manage offsets directly in the RocksDB database, by importing the offsets from any existing .checkpoint and/or .position files.

Downgrading

When downgrading from a version of Kafka Streams that includes the changes outlined in this KIP to a version that does not contain these changes, users will not be required to take any action. The older Kafka Streams version will be unable to open any RocksDB stores that were upgraded to store offsets (see Upgrading), which will cause Kafka Streams to wipe the state for those Tasks and restore the state, using an older RocksDB store format, from the changelogs.

Since downgrading is a low frequency event, and since restoring state from scratch is already an existing failure mode for older versions of Kafka Streams, we deem this an acceptable automatic downgrade strategy.

Test Plan

Testing will be accomplished by both the existing tests and by writing some new unit tests that verify atomicity, durability and consistency guarantees that this KIP provides.

Rejected Alternatives

Dual-Store Approach (KIP-844)

The design outlined in KIP-844, sadly, does not perform well (as described above), and requires users to opt-in to transactionality, instead of being a guarantee provided out-of-the-box.

Replacing RocksDB memtables with ThreadCache

It was pointed out on the mailing list that Kafka Streams fronts all RocksDB StateStores with a configurable record cache, and that this cache duplicates the function requests for recently written records provided by RocksDB memtables. A suggestion was made to utilize this record cache (the ThreadCache class) as a replacement for memtables, by directly flushing them to SSTables using the RocksDB SstFileWriter.

This is out of scope of this KIP, as its goal would be reducing the duplication (and hence, memory usage) of RocksDB StateStores; whereas this KIP is tasked with improving the consistency of StateStores to reduce the frequency and impact of state restoration, improving their scalability.

It has been recommended to instead pursue this idea in a subsequent KIP, as the interface changes outlined in this KIP should be compatible with this idea.