################################################################################ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ from abc import ABC, abstractmethod from enum import Enum from typing import TypeVar, Generic, Iterable, List, Iterator, Dict, Tuple, Optional from pyflink.common.time import Duration, Time from pyflink.common.typeinfo import TypeInformation, Types __all__ = [ 'ValueStateDescriptor', 'ValueState', 'ListStateDescriptor', 'ListState', 'MapStateDescriptor', 'MapState', 'ReducingStateDescriptor', 'ReducingState', 'AggregatingStateDescriptor', 'AggregatingState', 'ReadOnlyBroadcastState', 'BroadcastState', 'StateTtlConfig', 'OperatorStateStore', ] T = TypeVar('T') K = TypeVar('K') V = TypeVar('V') IN = TypeVar('IN') OUT = TypeVar('OUT') class OperatorStateStore(ABC): """ Interface for getting operator states. Currently, only :class:`~state.BroadcastState` is supported. .. versionadded:: 1.16.0 """ @abstractmethod def get_broadcast_state(self, state_descriptor: 'MapStateDescriptor') -> 'BroadcastState': """ Fetches the :class:`~state.BroadcastState` described by :class:`~state.MapStateDescriptor`, which has read/write access to the broadcast operator state. """ pass class State(ABC): """ Interface that different types of partitioned state must implement. """ @abstractmethod def clear(self) -> None: """ Removes the value mapped under the current key. """ pass class ValueState(State, Generic[T]): """ :class:`State` interface for partitioned single-value state. The value can be retrieved or updated. The state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. """ @abstractmethod def value(self) -> T: """ Returns the current value for the state. When the state is not partitioned the returned value is the same for all inputs in a given operator instance. If state partitioning is applied, the value returned depends on the current operator input, as the operator maintains an independent state for each partition. """ pass @abstractmethod def update(self, value: T) -> None: """ Updates the operator state accessible by :func:`value` to the given value. The next time :func:`value` is called (for the same state partition) the returned state will represent the updated value. When a partitioned state is updated with null, the state for the current key will be removed and the default value is returned on the next access. """ pass class AppendingState(State, Generic[IN, OUT]): """ Base interface for partitioned state that supports adding elements and inspecting the current state. Elements can either be kept in a buffer (list-like) or aggregated into one value. This state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. The state is only accessible by functions applied on a KeyedStream. The key is automatically supplied by the system, so the function always sees the value mapped to the key of the current element. That way, the system can handle stream and state partitioning consistently together. """ @abstractmethod def get(self) -> OUT: """ Returns the elements under the current key. """ pass @abstractmethod def add(self, value: IN) -> None: """ Adding the given value to the tail of this list state. """ pass class MergingState(AppendingState[IN, OUT]): """ Extension of AppendingState that allows merging of state. That is, two instance of MergingState can be combined into a single instance that contains all the information of the two merged states. """ pass class ReducingState(MergingState[T, T]): """ :class:`State` interface for reducing state. Elements can be added to the state, they will be combined using a reduce function. The current state can be inspected. The state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. The state is only accessible by functions applied on a KeyedStream. The key is automatically supplied by the system, so the function always sees the value mapped to the key of the current element. That way, the system can handle stream and state partitioning consistently together. """ pass class AggregatingState(MergingState[IN, OUT]): """ :class:`State` interface for aggregating state, based on an :class:`~pyflink.datastream.functions.AggregateFunction`. Elements that are added to this type of state will be eagerly pre-aggregated using a given AggregateFunction. The state holds internally always the accumulator type of the AggregateFunction. When accessing the result of the state, the function's :func:`~pyflink.datastream.functions.AggregateFunction.get_result` method. The state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. The state is only accessible by functions applied on a KeyedStream. The key is automatically supplied by the system, so the function always sees the value mapped to the key of the current element. That way, the system can handle stream and state partitioning consistently together. """ pass class ListState(MergingState[T, Iterable[T]]): """ :class:`State` interface for partitioned list state in Operations. The state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. Currently only keyed list state is supported. When it is a keyed list state, the state key is automatically supplied by the system, so the user function always sees the value mapped to the key of the current element. That way, the system can handle stream and state partitioning consistently together. """ @abstractmethod def update(self, values: List[T]) -> None: """ Updating existing values to the given list of values. """ pass @abstractmethod def add_all(self, values: List[T]) -> None: """ Adding the given values to the tail of this list state. """ pass def __iter__(self) -> Iterator[T]: return iter(self.get()) class MapState(State, Generic[K, V]): """ :class:`State` interface for partitioned key-value state. The key-value pair can be added, updated and retrieved. The state is accessed and modified by user functions, and checkpointed consistently by the system as part of the distributed snapshots. The state key is automatically supplied by the system, so the function always sees the value mapped to the key of the current element. That way, the system can handle stream and state partitioning consistently together. """ @abstractmethod def get(self, key: K) -> V: """ Returns the current value associated with the given key. """ pass @abstractmethod def put(self, key: K, value: V) -> None: """ Associates a new value with the given key. """ pass @abstractmethod def put_all(self, dict_value: Dict[K, V]) -> None: """ Copies all of the mappings from the given map into the state. """ pass @abstractmethod def remove(self, key: K) -> None: """ Deletes the mapping of the given key. """ pass @abstractmethod def contains(self, key: K) -> bool: """ Returns whether there exists the given mapping. """ pass @abstractmethod def items(self) -> Iterable[Tuple[K, V]]: """ Returns all the mappings in the state. """ pass @abstractmethod def keys(self) -> Iterable[K]: """ Returns all the keys in the state. """ pass @abstractmethod def values(self) -> Iterable[V]: """ Returns all the values in the state. """ pass @abstractmethod def is_empty(self) -> bool: """ Returns true if this state contains no key-value mappings, otherwise false. """ pass def __getitem__(self, key: K) -> V: return self.get(key) def __setitem__(self, key: K, value: V) -> None: self.put(key, value) def __delitem__(self, key: K) -> None: self.remove(key) def __contains__(self, key: K) -> bool: return self.contains(key) def __iter__(self) -> Iterator[K]: return iter(self.keys()) class ReadOnlyBroadcastState(State, Generic[K, V]): """ A read-only view of the :class:`BroadcastState`. Although read-only, the user code should not modify the value returned by the :meth:`get` or the items returned by :meth:`items`, as this can lead to inconsistent states. The reason for this is that we do not create extra copies of the elements for performance reasons. """ @abstractmethod def get(self, key: K) -> V: """ Returns the current value associated with the given key. """ pass @abstractmethod def contains(self, key: K) -> bool: """ Returns whether there exists the given mapping. """ pass @abstractmethod def items(self) -> Iterable[Tuple[K, V]]: """ Returns all the mappings in the state. """ pass @abstractmethod def keys(self) -> Iterable[K]: """ Returns all the keys in the state. """ pass @abstractmethod def values(self) -> Iterable[V]: """ Returns all the values in the state. """ pass @abstractmethod def is_empty(self) -> bool: """ Returns true if this state contains no key-value mappings, otherwise false. """ pass def __getitem__(self, key: K) -> V: return self.get(key) def __contains__(self, key: K) -> bool: return self.contains(key) def __iter__(self) -> Iterator[K]: return iter(self.keys()) class BroadcastState(ReadOnlyBroadcastState[K, V]): """ A type of state that can be created to store the state of a :class:`BroadcastStream`. This state assumes that the same elements are sent to all instances of an operator. CAUTION: the user has to guarantee that all task instances store the same elements in this type of state. Each operator instance individually maintains and stores elements in the broadcast state. The fact that the incoming stream is a broadcast one guarantees that all instances see all the elements. Upon recovery or re-scaling, the same state is given to each of the instances. To avoid hotspots, each task reads its previous partition, and if there are more tasks (scale up ), then the new instances read from the old instances in a round-robin fashion. This is why each instance has to guarantee that it stores the same elements as the rest. If not, upon recovery or rescaling you may have unpredictable redistribution of the partitions, thus unpredictable results. """ @abstractmethod def put(self, key: K, value: V) -> None: """ Associates a new value with the given key. """ pass @abstractmethod def put_all(self, dict_value: Dict[K, V]) -> None: """ Copies all of the mappings from the given map into the state. """ pass @abstractmethod def remove(self, key: K) -> None: """ Deletes the mapping of the given key. """ pass def __setitem__(self, key: K, value: V) -> None: self.put(key, value) def __delitem__(self, key: K) -> None: self.remove(key) class StateDescriptor(ABC): """ Base class for state descriptors. A StateDescriptor is used for creating partitioned State in stateful operations. """ def __init__(self, name: str, type_info: TypeInformation): """ Constructor for StateDescriptor. :param name: The name of the state :param type_info: The type information of the value. """ self.name = name self.type_info = type_info self._ttl_config = None # type: Optional[StateTtlConfig] def get_name(self) -> str: """ Get the name of the state. :return: The name of the state. """ return self.name def enable_time_to_live(self, ttl_config: 'StateTtlConfig'): """ Configures optional activation of state time-to-live (TTL). State user value will expire, become unavailable and be cleaned up in storage depending on configured StateTtlConfig. :param ttl_config: Configuration of state TTL """ self._ttl_config = ttl_config class ValueStateDescriptor(StateDescriptor): """ StateDescriptor for ValueState. This can be used to create partitioned value state using RuntimeContext.get_state(ValueStateDescriptor). """ def __init__(self, name: str, value_type_info: TypeInformation): """ Constructor of the ValueStateDescriptor. :param name: The name of the state. :param value_type_info: the type information of the state. """ super(ValueStateDescriptor, self).__init__(name, value_type_info) class ListStateDescriptor(StateDescriptor): """ StateDescriptor for ListState. This can be used to create state where the type is a list that can be appended and iterated over. """ def __init__(self, name: str, elem_type_info: TypeInformation): """ Constructor of the ListStateDescriptor. :param name: The name of the state. :param elem_type_info: the type information of the state element. """ super(ListStateDescriptor, self).__init__(name, Types.LIST(elem_type_info)) class MapStateDescriptor(StateDescriptor): """ StateDescriptor for MapState. This can be used to create state where the type is a map that can be updated and iterated over. """ def __init__(self, name: str, key_type_info: TypeInformation, value_type_info: TypeInformation): """ Constructor of the MapStateDescriptor. :param name: The name of the state. :param key_type_info: The type information of the key. :param value_type_info: the type information of the value. """ super(MapStateDescriptor, self).__init__(name, Types.MAP(key_type_info, value_type_info)) class ReducingStateDescriptor(StateDescriptor): """ StateDescriptor for ReducingState. This can be used to create partitioned reducing state using RuntimeContext.get_reducing_state(ReducingStateDescriptor). """ def __init__(self, name: str, reduce_function, type_info: TypeInformation): """ Constructor of the ReducingStateDescriptor. :param name: The name of the state. :param reduce_function: The ReduceFunction used to aggregate the state. :param type_info: The type of the values in the state. """ super(ReducingStateDescriptor, self).__init__(name, type_info) from pyflink.datastream.functions import ReduceFunction, ReduceFunctionWrapper if not isinstance(reduce_function, ReduceFunction): if callable(reduce_function): reduce_function = ReduceFunctionWrapper(reduce_function) # type: ignore else: raise TypeError("The input must be a ReduceFunction or a callable function!") self._reduce_function = reduce_function def get_reduce_function(self): return self._reduce_function class AggregatingStateDescriptor(StateDescriptor): """ A StateDescriptor for AggregatingState. The type internally stored in the state is the type of the Accumulator of the :func:`~pyflink.datastream.functions.AggregateFunction`. """ def __init__(self, name: str, agg_function, state_type_info): super(AggregatingStateDescriptor, self).__init__(name, state_type_info) from pyflink.datastream.functions import AggregateFunction if not isinstance(agg_function, AggregateFunction): raise TypeError("The input must be a pyflink.datastream.functions.AggregateFunction!") self._agg_function = agg_function def get_agg_function(self): return self._agg_function class StateTtlConfig(object): class UpdateType(Enum): """ This option value configures when to update last access timestamp which prolongs state TTL. """ Disabled = 0 """ TTL is disabled. State does not expire. """ OnCreateAndWrite = 1 """ Last access timestamp is initialised when state is created and updated on every write operation. """ OnReadAndWrite = 2 """ The same as OnCreateAndWrite but also updated on read. """ def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor return getattr(StateDescriptor.StateTTLConfig.UpdateType, self.name) @staticmethod def _from_proto(proto): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor update_type_name = StateDescriptor.StateTTLConfig.UpdateType.Name(proto) return StateTtlConfig.UpdateType[update_type_name] class StateVisibility(Enum): """ This option configures whether expired user value can be returned or not. """ ReturnExpiredIfNotCleanedUp = 0 """ Return expired user value if it is not cleaned up yet. """ NeverReturnExpired = 1 """ Never return expired user value. """ def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor return getattr(StateDescriptor.StateTTLConfig.StateVisibility, self.name) @staticmethod def _from_proto(proto): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor state_visibility_name = StateDescriptor.StateTTLConfig.StateVisibility.Name(proto) return StateTtlConfig.StateVisibility[state_visibility_name] class TtlTimeCharacteristic(Enum): """ This option configures time scale to use for ttl. """ ProcessingTime = 0 """ Processing time """ def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor return getattr(StateDescriptor.StateTTLConfig.TtlTimeCharacteristic, self.name) @staticmethod def _from_proto(proto): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor ttl_time_characteristic_name = \ StateDescriptor.StateTTLConfig.TtlTimeCharacteristic.Name(proto) return StateTtlConfig.TtlTimeCharacteristic[ttl_time_characteristic_name] def __init__(self, update_type: UpdateType, state_visibility: StateVisibility, ttl_time_characteristic: TtlTimeCharacteristic, ttl: Time, cleanup_strategies: 'StateTtlConfig.CleanupStrategies'): self._update_type = update_type self._state_visibility = state_visibility self._ttl_time_characteristic = ttl_time_characteristic self._ttl = ttl self._cleanup_strategies = cleanup_strategies @staticmethod def new_builder(ttl: Time): return StateTtlConfig.Builder(ttl) def get_update_type(self) -> 'StateTtlConfig.UpdateType': return self._update_type def get_state_visibility(self) -> 'StateTtlConfig.StateVisibility': return self._state_visibility def get_ttl(self) -> Time: return self._ttl def get_ttl_time_characteristic(self) -> 'StateTtlConfig.TtlTimeCharacteristic': return self._ttl_time_characteristic def is_enabled(self) -> bool: return self._update_type.value != StateTtlConfig.UpdateType.Disabled.value def get_cleanup_strategies(self) -> 'StateTtlConfig.CleanupStrategies': return self._cleanup_strategies def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor state_ttl_config = StateDescriptor.StateTTLConfig() state_ttl_config.update_type = self._update_type._to_proto() state_ttl_config.state_visibility = self._state_visibility._to_proto() state_ttl_config.ttl_time_characteristic = self._ttl_time_characteristic._to_proto() state_ttl_config.ttl = self._ttl.to_milliseconds() state_ttl_config.cleanup_strategies.CopyFrom(self._cleanup_strategies._to_proto()) return state_ttl_config @staticmethod def _from_proto(proto): update_type = StateTtlConfig.UpdateType._from_proto(proto.update_type) state_visibility = StateTtlConfig.StateVisibility._from_proto(proto.state_visibility) ttl_time_characteristic = \ StateTtlConfig.TtlTimeCharacteristic._from_proto(proto.ttl_time_characteristic) ttl = Time.milliseconds(proto.ttl) cleanup_strategies = StateTtlConfig.CleanupStrategies._from_proto(proto.cleanup_strategies) builder = StateTtlConfig.new_builder(ttl) \ .set_update_type(update_type) \ .set_state_visibility(state_visibility) \ .set_ttl_time_characteristic(ttl_time_characteristic) builder._strategies = cleanup_strategies._strategies builder._is_cleanup_in_background = cleanup_strategies._is_cleanup_in_background return builder.build() def __repr__(self): return "StateTtlConfig<" \ "update_type={}," \ " state_visibility={}," \ "ttl_time_characteristic ={}," \ "ttl={}>".format(self._update_type, self._state_visibility, self._ttl_time_characteristic, self._ttl) class Builder(object): """ Builder for the StateTtlConfig. """ def __init__(self, ttl: Time): self._ttl = ttl self._update_type = StateTtlConfig.UpdateType.OnCreateAndWrite self._state_visibility = StateTtlConfig.StateVisibility.NeverReturnExpired self._ttl_time_characteristic = StateTtlConfig.TtlTimeCharacteristic.ProcessingTime self._is_cleanup_in_background = True self._strategies = {} # type: Dict def set_update_type(self, update_type: 'StateTtlConfig.UpdateType') -> 'StateTtlConfig.Builder': """ Sets the ttl update type. :param update_type: The ttl update type configures when to update last access timestamp which prolongs state TTL. """ self._update_type = update_type return self def update_ttl_on_create_and_write(self) -> 'StateTtlConfig.Builder': return self.set_update_type(StateTtlConfig.UpdateType.OnCreateAndWrite) def update_ttl_on_read_and_write(self) -> 'StateTtlConfig.Builder': return self.set_update_type(StateTtlConfig.UpdateType.OnReadAndWrite) def set_state_visibility( self, state_visibility: 'StateTtlConfig.StateVisibility') -> 'StateTtlConfig.Builder': """ Sets the state visibility. :param state_visibility: The state visibility configures whether expired user value can be returned or not. """ self._state_visibility = state_visibility return self def return_expired_if_not_cleaned_up(self) -> 'StateTtlConfig.Builder': return self.set_state_visibility( StateTtlConfig.StateVisibility.ReturnExpiredIfNotCleanedUp) def never_return_expired(self) -> 'StateTtlConfig.Builder': return self.set_state_visibility(StateTtlConfig.StateVisibility.NeverReturnExpired) def set_ttl_time_characteristic( self, ttl_time_characteristic: 'StateTtlConfig.TtlTimeCharacteristic') \ -> 'StateTtlConfig.Builder': """ Sets the time characteristic. :param ttl_time_characteristic: The time characteristic configures time scale to use for ttl. """ self._ttl_time_characteristic = ttl_time_characteristic return self def use_processing_time(self) -> 'StateTtlConfig.Builder': return self.set_ttl_time_characteristic( StateTtlConfig.TtlTimeCharacteristic.ProcessingTime) def cleanup_full_snapshot(self) -> 'StateTtlConfig.Builder': """ Cleanup expired state in full snapshot on checkpoint. """ self._strategies[ StateTtlConfig.CleanupStrategies.Strategies.FULL_STATE_SCAN_SNAPSHOT] = \ StateTtlConfig.CleanupStrategies.EMPTY_STRATEGY return self def cleanup_incrementally(self, cleanup_size: int, run_cleanup_for_every_record) -> 'StateTtlConfig.Builder': """ Cleanup expired state incrementally cleanup local state. Upon every state access this cleanup strategy checks a bunch of state keys for expiration and cleans up expired ones. It keeps a lazy iterator through all keys with relaxed consistency if backend supports it. This way all keys should be regularly checked and cleaned eventually over time if any state is constantly being accessed. Additionally to the incremental cleanup upon state access, it can also run per every record. Caution: if there are a lot of registered states using this option, they all will be iterated for every record to check if there is something to cleanup. if no access happens to this state or no records are processed in case of run_cleanup_for_every_record, expired state will persist. Time spent for the incremental cleanup increases record processing latency. Note: At the moment incremental cleanup is implemented only for Heap state backend. Setting it for RocksDB will have no effect. Note: If heap state backend is used with synchronous snapshotting, the global iterator keeps a copy of all keys while iterating because of its specific implementation which does not support concurrent modifications. Enabling of this feature will increase memory consumption then. Asynchronous snapshotting does not have this problem. :param cleanup_size: max number of keys pulled from queue for clean up upon state touch for any key :param run_cleanup_for_every_record: run incremental cleanup per each processed record """ self._strategies[StateTtlConfig.CleanupStrategies.Strategies.INCREMENTAL_CLEANUP] = \ StateTtlConfig.CleanupStrategies.IncrementalCleanupStrategy( cleanup_size, run_cleanup_for_every_record) return self def cleanup_in_rocksdb_compact_filter( self, query_time_after_num_entries, periodic_compaction_time=None) -> \ 'StateTtlConfig.Builder': """ Cleanup expired state while Rocksdb compaction is running. RocksDB compaction filter will query current timestamp, used to check expiration, from Flink every time after processing {@code queryTimeAfterNumEntries} number of state entries. Updating the timestamp more often can improve cleanup speed but it decreases compaction performance because it uses JNI call from native code. Periodic compaction could speed up expired state entries cleanup, especially for state entries rarely accessed. Files older than this value will be picked up for compaction, and re-written to the same level as they were before. It makes sure a file goes through compaction filters periodically. :param query_time_after_num_entries: number of state entries to process by compaction filter before updating current timestamp :param periodic_compaction_time: periodic compaction which could speed up expired state cleanup. 0 means turning off periodic compaction. :return: """ self._strategies[ StateTtlConfig.CleanupStrategies.Strategies.ROCKSDB_COMPACTION_FILTER] = \ StateTtlConfig.CleanupStrategies.RocksdbCompactFilterCleanupStrategy( query_time_after_num_entries, periodic_compaction_time if periodic_compaction_time else Duration.of_days(30)) return self def disable_cleanup_in_background(self) -> 'StateTtlConfig.Builder': """ Disable default cleanup of expired state in background (enabled by default). If some specific cleanup is configured, e.g. :func:`cleanup_incrementally` or :func:`cleanup_in_rocksdb_compact_filter`, this setting does not disable it. """ self._is_cleanup_in_background = False return self def set_ttl(self, ttl: Time) -> 'StateTtlConfig.Builder': """ Sets the ttl time. :param ttl: The ttl time. """ self._ttl = ttl return self def build(self) -> 'StateTtlConfig': return StateTtlConfig( self._update_type, self._state_visibility, self._ttl_time_characteristic, self._ttl, StateTtlConfig.CleanupStrategies(self._strategies, self._is_cleanup_in_background) ) class CleanupStrategies(object): """ TTL cleanup strategies. This class configures when to cleanup expired state with TTL. By default, state is always cleaned up on explicit read access if found expired. Currently cleanup of state full snapshot can be additionally activated. """ class Strategies(Enum): """ Fixed strategies ordinals in strategies config field. """ FULL_STATE_SCAN_SNAPSHOT = 0 INCREMENTAL_CLEANUP = 1 ROCKSDB_COMPACTION_FILTER = 2 def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor return getattr( StateDescriptor.StateTTLConfig.CleanupStrategies.Strategies, self.name) @staticmethod def _from_proto(proto): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor strategies_name = \ StateDescriptor.StateTTLConfig.CleanupStrategies.Strategies.Name(proto) return StateTtlConfig.CleanupStrategies.Strategies[strategies_name] class CleanupStrategy(ABC): """ Base interface for cleanup strategies configurations. """ pass class EmptyCleanupStrategy(CleanupStrategy): pass class IncrementalCleanupStrategy(CleanupStrategy): """ Configuration of cleanup strategy while taking the full snapshot. """ def __init__(self, cleanup_size: int, run_cleanup_for_every_record: bool): self._cleanup_size = cleanup_size self._run_cleanup_for_every_record = run_cleanup_for_every_record def get_cleanup_size(self) -> int: return self._cleanup_size def run_cleanup_for_every_record(self) -> bool: return self._run_cleanup_for_every_record class RocksdbCompactFilterCleanupStrategy(CleanupStrategy): """ Configuration of cleanup strategy using custom compaction filter in RocksDB. """ def __init__(self, query_time_after_num_entries: int, periodic_compaction_time=None): self._query_time_after_num_entries = query_time_after_num_entries self._periodic_compaction_time = periodic_compaction_time \ if periodic_compaction_time else Duration.of_days(30) def get_query_time_after_num_entries(self) -> int: return self._query_time_after_num_entries def get_periodic_compaction_time(self) -> Duration: return self._periodic_compaction_time EMPTY_STRATEGY = EmptyCleanupStrategy() def __init__(self, strategies: Dict[Strategies, CleanupStrategy], is_cleanup_in_background: bool): self._strategies = strategies self._is_cleanup_in_background = is_cleanup_in_background def is_cleanup_in_background(self) -> bool: return self._is_cleanup_in_background def in_full_snapshot(self) -> bool: return (StateTtlConfig.CleanupStrategies.Strategies.FULL_STATE_SCAN_SNAPSHOT in self._strategies) def get_incremental_cleanup_strategy(self) \ -> 'StateTtlConfig.CleanupStrategies.IncrementalCleanupStrategy': if self._is_cleanup_in_background: default_strategy = \ StateTtlConfig.CleanupStrategies.IncrementalCleanupStrategy(5, False) else: default_strategy = None return self._strategies.get( # type: ignore StateTtlConfig.CleanupStrategies.Strategies.INCREMENTAL_CLEANUP, default_strategy) def get_rocksdb_compact_filter_cleanup_strategy(self) \ -> 'StateTtlConfig.CleanupStrategies.RocksdbCompactFilterCleanupStrategy': if self._is_cleanup_in_background: default_strategy = \ StateTtlConfig.CleanupStrategies.RocksdbCompactFilterCleanupStrategy(1000) else: default_strategy = None return self._strategies.get( # type: ignore StateTtlConfig.CleanupStrategies.Strategies.ROCKSDB_COMPACTION_FILTER, default_strategy) def _to_proto(self): from pyflink.fn_execution.flink_fn_execution_pb2 import StateDescriptor DescriptorCleanupStrategies = StateDescriptor.StateTTLConfig.CleanupStrategies CleanupStrategies = StateTtlConfig.CleanupStrategies cleanup_strategies = StateDescriptor.StateTTLConfig.CleanupStrategies() cleanup_strategies.is_cleanup_in_background = self._is_cleanup_in_background for k, v in self._strategies.items(): cleanup_strategy = cleanup_strategies.strategies.add() cleanup_strategy.strategy = k._to_proto() if isinstance(v, CleanupStrategies.EmptyCleanupStrategy): empty_strategy = DescriptorCleanupStrategies.EmptyCleanupStrategy.EMPTY_STRATEGY cleanup_strategy.empty_strategy = empty_strategy elif isinstance(v, CleanupStrategies.IncrementalCleanupStrategy): incremental_cleanup_strategy = \ DescriptorCleanupStrategies.IncrementalCleanupStrategy() incremental_cleanup_strategy.cleanup_size = v._cleanup_size incremental_cleanup_strategy.run_cleanup_for_every_record = \ v._run_cleanup_for_every_record cleanup_strategy.incremental_cleanup_strategy.CopyFrom( incremental_cleanup_strategy) elif isinstance(v, CleanupStrategies.RocksdbCompactFilterCleanupStrategy): rocksdb_compact_filter_cleanup_strategy = \ DescriptorCleanupStrategies.RocksdbCompactFilterCleanupStrategy() rocksdb_compact_filter_cleanup_strategy.query_time_after_num_entries = \ v._query_time_after_num_entries cleanup_strategy.rocksdb_compact_filter_cleanup_strategy.CopyFrom( rocksdb_compact_filter_cleanup_strategy) return cleanup_strategies @staticmethod def _from_proto(proto): CleanupStrategies = StateTtlConfig.CleanupStrategies strategies = {} is_cleanup_in_background = proto.is_cleanup_in_background for strategy_entry in proto.strategies: strategy = CleanupStrategies.Strategies._from_proto(strategy_entry.strategy) if strategy_entry.HasField('empty_strategy'): strategies[strategy] = CleanupStrategies.EmptyCleanupStrategy elif strategy_entry.HasField('incremental_cleanup_strategy'): incremental_cleanup_strategy = strategy_entry.incremental_cleanup_strategy strategies[strategy] = CleanupStrategies.IncrementalCleanupStrategy( incremental_cleanup_strategy.cleanup_size, incremental_cleanup_strategy.run_cleanup_for_every_record) elif strategy_entry.HasField('rocksdb_compact_filter_cleanup_strategy'): rocksdb_compact_filter_cleanup_strategy = \ strategy_entry.rocksdb_compact_filter_cleanup_strategy strategies[strategy] = CleanupStrategies.RocksdbCompactFilterCleanupStrategy( rocksdb_compact_filter_cleanup_strategy.query_time_after_num_entries) return CleanupStrategies(strategies, is_cleanup_in_background)