Table of Contents |
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Status
Current state: Under DiscussionAdopted
Discussion thread: Thread
JIRA:
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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 ensures consistency of local stores with their changelog topics, but can cause long delays in processing while it rebuilds the local state from the changelog. These delays are proprotional to the number of records in the changelog topic, which for highly active tables, or those with a very high cardinality, can be very large. Real-world use-cases have been observed where these delays can span multiple days, where both processing, and interactive queries, are paused.
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 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. This should bound state restore under EOS to less than 1 second, irrespective of the size of the changelogs.
Public InterfacesPublic Interfaces
New configuration
Name | Default | Description |
---|---|---|
default.state.isolation.level | READ_UNCOMMITTED | The default isolation level for Interactive Queries against StateStores. Supported values are |
statestore.uncommitted.max.bytes | 67108864 (64 MB) | Maximum number of memory bytes to be used to buffer uncommitted state-store records. If this limit is exceeded, a task commit will be requested. No limit: -1. Note: if this is too high or unbounded, it's possible for RocksDB to trigger out-of-memory errors. |
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Code Block | ||||||||
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/** * 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 getCommittedOffsetcommittedOffset(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#committedOffset(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#committedOffset(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; } |
Metrics
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language | java |
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title | org.apache.kafka.streams.processor.StateStoreContext |
...
/**
* 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;
}
New
stream-state-metrics
commit-rate
- the number of calls toStateStore#commit(Map)
commit-latency-avg
- the average time taken to callStateStore#commit(Map)
commit-latency-max
- the maximum time taken to callStateStore#commit(Map)
Deprecated
stream-state-metrics
flush-rate
flush-latency-avg
flush-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
.
While the flush
metrics are only deprecated, they will no longer record any data under normal use, as Kafka Streams will no longer call 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, we introduce the concept of transaction Isolation Levels, that dictate the visibility of records, written by processing threads, to Interactive Query threads.
We enable configuration of the level of isolation provided by StateStores via a default.state.isolation.level
, which can be configured to either:
default.state.isolation.level | Description |
---|---|
READ_UNCOMMITTED | Records written by the StreamThread are visible to all Interactive Query threads immediately. This level provides no atomicity, consistency, isolation or durability guarantees. Under this Isolation Level, Streams behaves as it currently does, wiping state stores on-error when the |
READ_COMMITTED | Records written by the StreamThread are only visible to Interactive Query threads once they have been committed. Under this Isolation Level, Streams will isolate writes from state stores until commit. This guarantees consistency of the on-disk data with the store changelog, so Streams will not need to wipe stores on-error. |
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 commit()
has been called.
The default value for default.state.isolation.level will be READ_UNCOMMITTED
, to mirror the behaviour we have today; but this will be automatically set to READ_COMMITTED
if the processing.mode has been set to an EOS mode, and the user has not explicitly set deafult.state.isolation.level
to READ_UNCOMMITTED
. This will provide EOS users with the most useful behaviour out-of-the-box, but ensures that they may choose to sacrifice the benefits of transactionality to ensure that Interactive Queries can read records before they are committed, which is required by a minority of use-cases.
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 provides 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, we have introduced configurable isolation levels, configured globally via default.state.isolation.level
(see above).
When operating under the READ_COMMITTED
isolation level, the maximum time for records to become visible to interactive queries will be commit.interval.ms
. Under EOS, this is by default a low value (100 ms
), but under at-least-once
, the default is 30 seconds. Users may need to adjust their commit.interval.ms
to meet the visibility latency goals for their use-case.
When operating under the READ_UNCOMMITTED
isolation level, (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:
- No offsets for the store can be found when opening it under EOS.
OutOfRangeException
during restoration, usually caused by the changelog being wiped on application reset.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
when operating under the READ_COMMITTED
isolation level.
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#committedOffset
.
Required interface changes:
- Add methods
void commit(Map<TopicPartition, Long> changelogOffsets)
,boolean managesOffsets()
andLong committedOffset(TopicPartition)
toStateStore
. - Deprecate method
flush()
onStateStore.
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:
- During StateStore initialization, in order to synchronize the offsets in
.checkpoint
with the offsets returned byStateStore#committedOffset(TopicPartition)
, which are the source of truth for stores that manage their own offsets. - When the StateStore is closed, in order to ensure that the offsets used for Task assignment reflect the state persisted to disk.
- 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 oncommit(Map)
- Avoiding writing
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. 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
isolation level 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 committed (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 WriteBatch 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. The overall performance of this should be at least as good as before, but the profile will be different, with write latency being substantially faster, and commit latency being a bit higher.
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.
Users that currently use processing.mode: exactly-once(-v2|-beta)
and who wish to continue to read uncommitted records from their Interactive Queries will need to explicitly set default.state.isolation.level: READ_UNCOMMITTED
.
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.
Transactional support under READ_UNCOMMITTED
When query isolation level is READ_UNCOMMITTED, Interactive Query threads need to read records from the ongoing transaction buffer. Unfortunately, the RocksDB WriteBatch is not thread-safe, causing Iterators created by Interactive Query threads to produce invalid results/throw unexpected errors as the WriteBatch is modified/closed during iteration.
Ideally, we would build an implementation of a transaction buffer that is thread-safe, enabling Interactive Query threads to query it safely. One approach would be to "chain together" WriteBatches, creating a new WriteBatch every time a new Iterator is created by an Interactive Query thread and "freezing" the previous WriteBatch.
It was decided to defer tackling this problem to a later KIP, in order to realise the benefits of transactional state stores to users as quickly as possible.
Query-time Isolation Levels
It was requested that users be able to select the isolation level of queries on a per-query basis. This would require some additional API changes (to the Interactive Query APIs). Such an API would require that state stores are always transactional, and that the transaction buffers can be read from by READ_UNCOMMITTED queries. Due to the problems outlined in the previous section, it was decided to also defer this to a subsequent KIP.
The new configuration option default.state.isolation.level
was deliberately named to enable query-time isolation levels in the future, whereby any query that didn't explicitly choose an isolation level would use the configured default. Until then, this configuration option will globally control the isolation level of all queries, with no way to override it per-query
Metrics
New
stream-state-metrics
commit-rate
- the number of calls toStateStore#commit(Map)
commit-latency-avg
- the average time taken to callStateStore#commit(Map)
commit-latency-max
- the maximum time taken to callStateStore#commit(Map)
Deprecated
stream-state-metrics
flush-rate
flush-latency-avg
flush-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
.
While the flush
metrics are only deprecated, they will no longer record any data under normal use, as Kafka Streams will no longer call 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:
- No offsets for the store can be found when opening it under EOS.
OutOfRangeException
during restoration, usually caused by the changelog being wiped on application reset.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()
andLong getCommittedOffset(TopicPartition)
toStateStore
. - Deprecate method
flush()
onStateStore.
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:
- During StateStore initialization, in order to synchronize the offsets in
.checkpoint
with the offsets returned byStateStore#getCommittedOffset(TopicPartition)
, which are the source of truth for stores that manage their own offsets. - When the StateStore is closed, in order to ensure that the offsets used for Task assignment reflect the state persisted to disk.
- 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 oncommit(Map)
- Avoiding writing
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.