| # |
| # 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. |
| # |
| |
| """A module for caching state reads/writes in Beam applications.""" |
| # pytype: skip-file |
| |
| from __future__ import absolute_import |
| |
| import collections |
| import logging |
| import threading |
| |
| from apache_beam.metrics import monitoring_infos |
| |
| _LOGGER = logging.getLogger(__name__) |
| |
| |
| class Metrics(object): |
| """Metrics container for state cache metrics.""" |
| |
| # A set of all registered metrics |
| ALL_METRICS = set() |
| PREFIX = "beam:metric:statecache:" |
| |
| def __init__(self): |
| self._context = threading.local() |
| |
| def initialize(self): |
| """Needs to be called once per thread to initialize the local metrics cache. |
| """ |
| if hasattr(self._context, 'metrics'): |
| return # Already initialized |
| self._context.metrics = collections.defaultdict(int) |
| |
| def count(self, name): |
| self._context.metrics[name] += 1 |
| |
| def hit_miss(self, total_name, hit_miss_name): |
| self._context.metrics[total_name] += 1 |
| self._context.metrics[hit_miss_name] += 1 |
| |
| def get_monitoring_infos(self, cache_size, cache_capacity): |
| """Returns the metrics scoped to the current bundle.""" |
| metrics = self._context.metrics |
| if len(metrics) == 0: |
| # No metrics collected, do not report |
| return [] |
| # Add all missing metrics which were not reported |
| for key in Metrics.ALL_METRICS: |
| if key not in metrics: |
| metrics[key] = 0 |
| # Gauges which reflect the state since last queried |
| gauges = [monitoring_infos.int64_gauge(self.PREFIX + name, val) |
| for name, val in metrics.items()] |
| gauges.append(monitoring_infos.int64_gauge(self.PREFIX + 'size', |
| cache_size)) |
| gauges.append(monitoring_infos.int64_gauge(self.PREFIX + 'capacity', |
| cache_capacity)) |
| # Counters for the summary across all metrics |
| counters = [monitoring_infos.int64_counter(self.PREFIX + name + '_total', |
| val) |
| for name, val in metrics.items()] |
| # Reinitialize metrics for this thread/bundle |
| metrics.clear() |
| return gauges + counters |
| |
| @staticmethod |
| def counter_hit_miss(total_name, hit_name, miss_name): |
| """Decorator for counting function calls and whether |
| the return value equals None (=miss) or not (=hit).""" |
| Metrics.ALL_METRICS.update([total_name, hit_name, miss_name]) |
| |
| def decorator(function): |
| |
| def reporter(self, *args, **kwargs): |
| value = function(self, *args, **kwargs) |
| if value is None: |
| self._metrics.hit_miss(total_name, miss_name) |
| else: |
| self._metrics.hit_miss(total_name, hit_name) |
| return value |
| |
| return reporter |
| |
| return decorator |
| |
| @staticmethod |
| def counter(metric_name): |
| """Decorator for counting function calls.""" |
| Metrics.ALL_METRICS.add(metric_name) |
| |
| def decorator(function): |
| |
| def reporter(self, *args, **kwargs): |
| self._metrics.count(metric_name) |
| return function(self, *args, **kwargs) |
| |
| return reporter |
| |
| return decorator |
| |
| |
| class StateCache(object): |
| """ Cache for Beam state access, scoped by state key and cache_token. |
| Assumes a bag state implementation. |
| |
| For a given state_key, caches a (cache_token, value) tuple and allows to |
| a) read from the cache (get), |
| if the currently stored cache_token matches the provided |
| a) write to the cache (put), |
| storing the new value alongside with a cache token |
| c) append to the currently cache item (extend), |
| if the currently stored cache_token matches the provided |
| c) empty a cached element (clear), |
| if the currently stored cache_token matches the provided |
| d) evict a cached element (evict) |
| |
| The operations on the cache are thread-safe for use by multiple workers. |
| |
| :arg max_entries The maximum number of entries to store in the cache. |
| TODO Memory-based caching: https://issues.apache.org/jira/browse/BEAM-8297 |
| """ |
| |
| def __init__(self, max_entries): |
| _LOGGER.info('Creating state cache with size %s', max_entries) |
| self._cache = self.LRUCache(max_entries, (None, None)) |
| self._lock = threading.RLock() |
| self._metrics = Metrics() |
| |
| @Metrics.counter_hit_miss("get", "hit", "miss") |
| def get(self, state_key, cache_token): |
| assert cache_token and self.is_cache_enabled() |
| with self._lock: |
| token, value = self._cache.get(state_key) |
| return value if token == cache_token else None |
| |
| @Metrics.counter("put") |
| def put(self, state_key, cache_token, value): |
| assert cache_token and self.is_cache_enabled() |
| with self._lock: |
| return self._cache.put(state_key, (cache_token, value)) |
| |
| @Metrics.counter("extend") |
| def extend(self, state_key, cache_token, elements): |
| assert cache_token and self.is_cache_enabled() |
| with self._lock: |
| token, value = self._cache.get(state_key) |
| if token in [cache_token, None]: |
| if value is None: |
| value = [] |
| value.extend(elements) |
| self._cache.put(state_key, (cache_token, value)) |
| else: |
| # Discard cached state if tokens do not match |
| self.evict(state_key) |
| |
| @Metrics.counter("clear") |
| def clear(self, state_key, cache_token): |
| assert cache_token and self.is_cache_enabled() |
| with self._lock: |
| token, _ = self._cache.get(state_key) |
| if token in [cache_token, None]: |
| self._cache.put(state_key, (cache_token, [])) |
| else: |
| # Discard cached state if tokens do not match |
| self.evict(state_key) |
| |
| @Metrics.counter("evict") |
| def evict(self, state_key): |
| assert self.is_cache_enabled() |
| with self._lock: |
| self._cache.evict(state_key) |
| |
| def evict_all(self): |
| with self._lock: |
| self._cache.evict_all() |
| |
| def initialize_metrics(self): |
| self._metrics.initialize() |
| |
| def is_cache_enabled(self): |
| return self._cache._max_entries > 0 |
| |
| def size(self): |
| return len(self._cache) |
| |
| def get_monitoring_infos(self): |
| """Retrieves the monitoring infos and resets the counters.""" |
| with self._lock: |
| size = len(self._cache) |
| capacity = self._cache._max_entries |
| return self._metrics.get_monitoring_infos(size, capacity) |
| |
| class LRUCache(object): |
| |
| def __init__(self, max_entries, default_entry): |
| self._max_entries = max_entries |
| self._default_entry = default_entry |
| self._cache = collections.OrderedDict() |
| |
| def get(self, key): |
| value = self._cache.pop(key, self._default_entry) |
| if value != self._default_entry: |
| self._cache[key] = value |
| return value |
| |
| def put(self, key, value): |
| self._cache[key] = value |
| while len(self._cache) > self._max_entries: |
| self._cache.popitem(last=False) |
| |
| def evict(self, key): |
| self._cache.pop(key, self._default_entry) |
| |
| def evict_all(self): |
| self._cache.clear() |
| |
| def __len__(self): |
| return len(self._cache) |
