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https://issues.apache.org/jira/browse/FLINK-5544?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15930169#comment-15930169
]
ASF GitHub Bot commented on FLINK-5544:
---------------------------------------
Github user StefanRRichter commented on a diff in the pull request:
https://github.com/apache/flink/pull/3359#discussion_r106672829
--- Diff:
flink-contrib/flink-timerserivce-rocksdb/src/main/java/org/apache/flink/contrib/streaming/api/operators/RocksDBInternalTimerService.java
---
@@ -0,0 +1,797 @@
+/*
+ * 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.
+ */
+
+package org.apache.flink.contrib.streaming.api.operators;
+
+
+import org.apache.flink.api.common.typeutils.base.IntSerializer;
+import org.apache.flink.api.common.typeutils.base.LongSerializer;
+import org.apache.flink.api.java.tuple.Tuple4;
+import org.apache.flink.core.fs.FileSystem;
+import org.apache.flink.core.fs.Path;
+import org.apache.flink.core.memory.DataInputViewStreamWrapper;
+import org.apache.flink.core.memory.DataOutputViewStreamWrapper;
+import org.apache.flink.runtime.state.KeyGroupRange;
+import org.apache.flink.runtime.state.KeyGroupRangeAssignment;
+import org.apache.flink.streaming.api.operators.InternalTimer;
+import org.apache.flink.streaming.api.operators.InternalTimerService;
+import org.apache.flink.streaming.api.operators.KeyContext;
+import org.apache.flink.streaming.runtime.tasks.ProcessingTimeService;
+import org.apache.flink.util.Preconditions;
+import org.rocksdb.ColumnFamilyDescriptor;
+import org.rocksdb.ColumnFamilyHandle;
+import org.rocksdb.ColumnFamilyOptions;
+import org.rocksdb.CompactionStyle;
+import org.rocksdb.DBOptions;
+import org.rocksdb.RocksDB;
+import org.rocksdb.RocksDBException;
+import org.rocksdb.RocksIterator;
+import org.rocksdb.StringAppendOperator;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+/**
+ * {@link InternalTimerService} that stores timers in RocksDB.
+ */
+public class RocksDBInternalTimerService<K, N> extends
InternalTimerService<K, N> {
+
+ private static Logger LOG =
LoggerFactory.getLogger(RocksDBInternalTimerService.class);
+
+ /** The data base where stores all timers */
+ private final RocksDB db;
--- End diff --
I think that we should avoid creating more instances of RocksDB if we can.
This makes native memory consumption more unpredictable and creates more files
on snapshots. My suggestion is do do a refactoring that timer services must be
requested through a keyed state backend. The RocksDB backend could then re-use
the same database instance as in the keyed backend for the timer service, to
reduce all the overheads. I think this request should still allow for asking a
RocksDB based timer service, even though using a `HeapKeyedStateBackend` and
vice-versa.
> Implement Internal Timer Service in RocksDB
> -------------------------------------------
>
> Key: FLINK-5544
> URL: https://issues.apache.org/jira/browse/FLINK-5544
> Project: Flink
> Issue Type: New Feature
> Components: State Backends, Checkpointing
> Reporter: Xiaogang Shi
> Assignee: Xiaogang Shi
>
> Now the only implementation of internal timer service is
> HeapInternalTimerService which stores all timers in memory. In the cases
> where the number of keys is very large, the timer service will cost too much
> memory. A implementation which stores timers in RocksDB seems good to deal
> with these cases.
> It might be a little challenging to implement a RocksDB timer service because
> the timers are accessed in different ways. When timers are triggered, we need
> to access timers in the order of timestamp. But when performing checkpoints,
> we must have a method to obtain all timers of a given key group.
> A good implementation, as suggested by [~StephanEwen], follows the idea of
> merge sorting. We can store timers in RocksDB with the format
> {{KEY_GROUP#TIMER#KEY}}. In this way, the timers under a key group are put
> together and are sorted.
> Then we can deploy an in-memory heap which keeps the first timer of each key
> group to get the next timer to trigger. When a key group's first timer is
> updated, we can efficiently update the heap.
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