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Status
Current state: Under DiscussionAccepted
Discussion thread: Thread
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Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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/** * 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 #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 * #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 highlyrequired, * recommended, if possible, to ensure that custom StateStores provide the consistency guarantees that Kafka Streams expects when operating * expects when operating* under thean {@code exactly-once} {@code processing.modeguarantee}. * <p> * @returnNew Whetherimplementations thisare StateStorerequired managesto implement itsthis ownmethod offsets. and return {@code true}. Existing */implementations default boolean* managesOffsets() { return false; } /**should upgrade to managing their own offsets as soon as possible, as the legacy offset management is deprecated * Flush any cached dataand will be removed in a future version. * * @deprecated Use {@link org.apache.kafka.streams.processor.api.ProcessingContext#commit() ProcessorContext#commit()} New implementations should always return {@code true} and manage their own offsets. In the future, * instead. this method will */ be removed and it @Deprecated will be assumed to defaultalways void flush() {return {@code true}. * @return Whether // no-op } this StateStore manages its own offsets. */** @Deprecated * Commit all written records to this StateStore.default boolean managesOffsets() { return false; } /** * <p> Flush any cached data * This method <b>MUST NOT<b> be called* by@deprecated usersUse from {@link org.apache.kafka.streams.processor.api.Processor ProcessingContext#commit() ProcessorContext#commit()} * processors}, as doing so may violate the consistency guarantees provided by this store, and expected by Kafkainstead. */ * Streams. @Deprecated default void *flush() <p>{ * Instead, users should call {@link org.apache.kafka.streams.processor.api.ProcessingContext#commit() // no-op } /** * ProcessorContext#commit()} to request a Task commit Commit all written records to this StateStore. * <p> * IfThis {@link #managesOffsets()} returns {@code true}, the given {@code changelogOffsets} will be guaranteed to be method <b>CANNOT<b> be called by users from {@link org.apache.kafka.streams.processor.api.Processor * persistedprocessors}. toDoing diskso alongwill withthrow thean written records{@link java.lang.UnsupportedOperationException}. * <p> * {@code changelogOffsets} will usually contain a single partition, in the case of a regular StateStore. However,Instead, users should call {@link org.apache.kafka.streams.processor.api.ProcessingContext#commit() * ProcessorContext#commit()} to request a Task commit. * they<p> may contain multiple partitions in* theIf case of a Global StateStore with multiple partitions. All provided * partitions <em>MUST</em> be{@link #managesOffsets()} returns {@code true}, the given {@code changelogOffsets} will be guaranteed to be * persisted to disk along with the written records. * <p> * Implementations <em>SHOULD</em> ensure that {@code changelogOffsets} arewill committedusually contain toa disksingle atomicallypartition, within the case of a regular * records they represent, if possible.StateStore. However, * they may contain multiple partitions *in @paramthe changelogOffsetscase Theof changelog offset(s) corresponding to the most recently written records.a Global StateStore with multiple partitions. All provided * partitions <em>MUST</ em> be persisted to defaultdisk. void commit(final Map<TopicPartition, Long> changelogOffsets)* {<p> * Implementations flush(); } /**<em>SHOULD</em> ensure that {@code changelogOffsets} are committed to disk atomically with the * Returnsrecords thethey mostrepresent, recently {@link #commit(Map) committed} offset for the given {@link TopicPartition}if possible. * <p> * @param changelogOffsets IfThe {@linkchangelog #managesOffsetsoffset(s)} andcorresponding {@link #persistent()} both return {@code true}, this method will return theto the most recently written records. */ default *void offset that corresponds to the changelog record most recently written to this store, for the given {@codecommit(final Map<TopicPartition, Long> changelogOffsets) { flush(); } * partition}./** * <p> Returns the most recently * This method <i>MUST</i> return the correct{@link #commit(Map) committed} offset even iffor the store is not currently given {@link #isOpen() openTopicPartition}. * <p> * @paramIf 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()}{@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}. * * @param partition The partition to get the committed return {@code false}offset for. */ @return The default Long committedOffset(final TopicPartition partition) { last {@link #commit(Map) committed} offset for the {@code partition}; or {@code null} if no offset * returnhas null; been committed for } |
Metrics
New
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the partition, or if either {@link #persistent()} or {@link #managesOffsets()}
* return {@code false}.
*/
default Long committedOffset(final TopicPartition partition) {
return null;
}
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Metrics
New
Each added metric will be on store-level and have the following tags:
type
=stream-state-metrics
thread-id
= [thread ID]task-id
= [task ID]- [store type]
-state-id
= [store ID] for key-value stores - [store type]
-session-state-id
= [store ID] for session stores - [store type]
-window-state-id
= [store ID] for window stores
Name | RecordingLevel | Metric Type | Description |
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commit-rate | DEBUG | Rate | The average number of calls to commit per second |
commit-latency-avg | DEBUG | Avg | The average latency of calls to commit |
commit-latency-max | DEBUG | Max | The maximum latency of calls to commit |
...
Deprecated
stream-state-metrics
flush-rate
flush-latency-avg
flush-latency-max
...
3 new methods will be added to StateStore, each defined with a default
implementation that ensures existing custom StateStores continue to function as they do today, to guarantee compatibility. All internal StateStore implementations will be updated with implementations of each of these methods that best suits that implementation..
For persistent stores (i.e. StateStore#persistent()
returns true
) that do not manage their own offsets (i.e. StateStore#managedOffsets()
returns false
), the existing checkpointing behaviour A new, WrappedStateStore
implementation will be provided internally, which will provide the legacy .checkpoint
file offset management behaviour used today. When opening state stores, the StateManager
will automatically wrap the store with this legacy wrapper iff it's both persistent()
and does not managesOffsets()
by continuing to store checkpoint offsets in the .checkpoint
file. This will ensure that existing, compatibility for custom stores that have not been upgraded to support the new API will function as they do today, without polluting the StateManager
with a large amount of legacy code, which would make it difficult to testmanage their own offsets yet.
Calling StateStore#commit
from inside a Processor
would be dangerous, because it would cause the StateStore
to believe that processed records have been committed when they have not (yet). Therefore, we will prevent this method from being called in this context by implementing it in AbstractReadWriteDecorator
, which wraps all stores provided to Processor
s, and throwing an UnsupportedOperationException
.
StateStore Implementations
...
RocksDBStore
will manage its own offsets (i.e. managesOffsets
will return true
), by storing changelog 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, instead of coupling it to Task commit.
To ensure that offsets can be returned by committedOffset
, even with the store is closed (i.e. after close()
or before init()
), we will write out the latest value for all changelog offsets to a separate file on-disk in the close()
method, which will be read from when isOpen()
returns false
. This file will be deleted during init
, and the offsets will be read directly from the column family while the database is open.
Interactive Query .position Offsets
Input partition "Position
" offsets, introduced by KIP-796: Interactive Query v2, are currently stored in a .position
file directly 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 written to the offsets column family.
InMemoryKeyValueStore
Since InMemoryKeyValueStore
is not persistent
, there is no state to synchronise the changelog offsets with on-commit. Therefore, InMemoryKeyValueStore
will not manage offsets (i.e. managesOffsets
will return false
), and therefore, no changes to its implementation is required.
Custom Persistent Stores
Any existing, persistent StateStore that has not been updated to manage its own offsets, will inherit the default
implementation of all three new methods. This will indicate that Kafka Streams should continue to checkpoint its offsets using the legacy .checkpoint
file. Maintainers of custom, persistent stores may add offset management after upgrading.
Consumer Rebalance Metadata
When determining the partition assignment, StreamsPartitionsAssignor
considers the checkpoint offsets on-disk for all StateStores on each instance, even for Tasks they have not (yet) been assigned. This is done via TaskManager#getTaskOffsetSums()
, which directly reads from the per-Task .checkpoint
file.
...
Interactive Query .position Offsets
Input partition "Position
" offsets, introduced by KIP-796: Interactive Query v2, are currently stored in a .position
file directly 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 written to the offsets column family.
InMemoryKeyValueStore
Since InMemoryKeyValueStore
is not persistent
, there is no state to synchronise the changelog offsets with on-commit. Therefore, InMemoryKeyValueStore
will not manage offsets (i.e. managesOffsets
will return false
), and therefore, no changes to its implementation is required.
Custom Persistent Stores
Any existing, persistent StateStore that has not been updated to manage its own offsets, will inherit the default
implementation of all three new methods. This will indicate that Kafka Streams should continue to checkpoint its offsets using the legacy .checkpoint
file. Maintainers of custom, persistent stores may add offset management after upgrading.
Consumer Rebalance Metadata
When determining the partition assignment, StreamsPartitionsAssignor
considers the checkpoint offsets on-disk for all StateStores on each instance, even for Tasks they have not (yet) been assigned. This is done via TaskManager#getTaskOffsetSums()
, which, currently, directly reads from the per-Task .checkpoint
file.
Since stores will now be managing their offsets internally, we will need to read offsets from the stores themselves instead of the .checkpoint
file.
- On start-up, we will construct and initialize state stores for every corresponding Task store directory we find on-disk, and call
committedOffset
to determine their stored offset(s). We will then cache these offsets in-memory. This cache will be shared among allStreamThread
s, and will therefore be thread-safe. - On rebalance, we will consult this cache to compute our Task offset lags.
- After state restore completes for an assigned Task, we will update the offset cache to
Task.LATEST_OFFSET
, to indicate that the Task now has the currently latest offset. - When closing a StateStore, we will update the offset cache with the current changelog offset for the store.
- This will ensure that when a
Task
is reassigned to another instance, the Task lag for the local state matches what's on-disk, instead of using the sentinel valueTask.LATEST_OFFSET
, which is only valid for Tasks currently assigned to a thread on the local instance.
- This will ensure that when a
Compatibility, Deprecation, and Migration Plan
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.
...
flush
deprecation
All internal usage of the StateStore#flush
method has been removed/replaced. Therefore, the main concern is with end-users calling StateStore#flush
directly from custom Processor
s. Obviously, users cannot safely call StateStore#commit
, because they do not know the changelog offsets that correspond to the locally written state. Forcibly flushing/fsyncing recently written records to disk may also violate any transactional guarantees that StateStores are providing. Therefore, the default implementation of flush
has been updated to a no-op. Users are now advised (via JavaDoc) that should instead request an early commit via. ProcessingContext#commit()
.
It's highly unlikely that users are depending on the flush
method forcibly persisting state to disk, as it does today, because Kafka Streams guarantees that before the Processor
is initialized, its task state will correspond with its changelog. Calling flush
will also not impact restore performance in the event of an error, because under EOS, all task state will be wiped regardless, and under ALOS task state will be rebuilt from the last committed changelog offset irrespective of what state is already on disk.Therefore, it's likely that most/all user invocations of flush
today has no effect, other than to potentially cause performance problems (e.g. by forcing RocksDB to flush many small memtables prematurely)of an error, because under EOS, all task state will be wiped regardless, and under ALOS task state will be rebuilt from the last committed changelog offset irrespective of what state is already on disk.
Therefore, it's likely that most/all user invocations of flush
today has no effect, other than to potentially cause performance problems (e.g. by forcing RocksDB to flush many small memtables prematurely).
managesOffsets
deprecation
The newly introduced StateStore#managesOffsets
method will be immediately deprecated, to allow for its removal in a future release. It is being introduced as a transitional mechanism, to ensure that existing custom StateStores
continue to work as expected without managing their own offsets. However, to encourage maintainers of custom stores to add support for offset management, we will emit a WARN
level log message when initializing any StateStore
that is persistent, but does not manage its own offsets.
Upgrade
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.
The default
implementation of all three methods has been designed to ensure backwards-compatibility for any custom store that does not yet support these methods.
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.
Downgrade
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.
...
.
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.
Downgrade
RocksDBStore will not be compatible with older versions of Kafka Streams, due to the extra offsets column family that older versions of Kafka Streams do not recognize. If a user downgrades from a version containing this KIP, to a version without it, by default the on-disk state for any Task containing a RocksDBStore will be wiped and restored from their changelogs.
However, we will support using the upgrade.from
config to safely downgrade without losing local RocksDB state. When upgrade.from
is set to a version less than the version that introduced this KIP:
- On
RocksDBStore#close:
- All changelogs stored in the RocksDB offsets column family will be written to the Task
.checkpoint
file. - All source topic offsets stored in the RocksDB offsets column family will be written to the store
.position
file. - The RocksDB offsets column family will be deleted from the RocksDB database, ensuring that older versions of Kafka Streams will be able to open the database successfully.
- All changelogs stored in the RocksDB offsets column family will be written to the Task
Test Plan
The existing tests that depend on .checkpoint
files will either be removed (if they are no longer relevant), or modified to instead call StateStore#committedOffset()
.
...