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Posted to commits@flink.apache.org by se...@apache.org on 2015/07/03 13:01:13 UTC
flink git commit: [docs] Add description and illustration of
checkpointing mechanism.
Repository: flink
Updated Branches:
refs/heads/master f428aa7a7 -> 728a5261e
[docs] Add description and illustration of checkpointing mechanism.
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/728a5261
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/728a5261
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/728a5261
Branch: refs/heads/master
Commit: 728a5261e40e75b9f2d6a12f03aed34a09b7c4c5
Parents: f428aa7
Author: Stephan Ewen <se...@apache.org>
Authored: Fri Jul 3 12:52:46 2015 +0200
Committer: Stephan Ewen <se...@apache.org>
Committed: Fri Jul 3 12:58:38 2015 +0200
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docs/internals/stream_checkpointing.md | 146 ++++++++++++++++++++++++++++
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<li class="divider"></li>
<li role="presentation" class="dropdown-header"><strong>Internals</strong></li>
<li><a href="{{ internals }}/general_arch.html">Architecture & Process Model</a></li>
+ <li><a href="{{ internals }}/stream_checkpointing.html">Fault Tolerance for Data Streaming</a></li>
<li><a href="{{ internals }}/types_serialization.html">Type Extraction & Serialization</a></li>
<li><a href="{{ internals }}/job_scheduling.html">Jobs & Scheduling</a></li>
<li><a href="{{ internals }}/add_operator.html">How-To: Add an Operator</a></li>
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http://git-wip-us.apache.org/repos/asf/flink/blob/728a5261/docs/internals/stream_checkpointing.md
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+---
+title: "Data Streaming Fault Tolerance"
+---
+<!--
+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.
+-->
+
+This document describes Flink' fault tolerance mechanism for streaming data flows.
+
+* This will be replaced by the TOC
+{:toc}
+
+
+## Introduction
+
+Apache Flink offers a fault tolerance mechanism to consistently recover the state of data streaming applications.
+The mechanism ensures that even in the presence of failures, the program's state will eventually reflect every
+record from the data stream **exactly once**.
+
+The fault tolerance mechanism continuously draws snapshots of the distributed streaming data flow. For streaming applications
+with small state, these snapshots are very light-weight and can be drawn frequently without impacting the performance much.
+The state of the streaming applications is stored at a configurable place (such as the master node, or HDFS).
+
+In case of a program failure (due to machine-, network-, or software failure), Flink stops the distributed streaming dataflow.
+The system then restarts the operators and resets them to the latest successful checkpoint. The input streams are reset to the
+point of the state snapshot. Any records that are processed as part of the restarted parallel dataflow are guaranteed to not
+have been part of the checkpointed state before.
+
+*Note:* For this mechanism to realize its full guarantees, the data stream source (such as message queue or broker) needs to be able
+to rewind the stream to a defined recent point. [Apache Kafka](http://kafka.apache.org) has this ability and Flink's connector to
+Kafka exploits this ability.
+
+*Note:* Because Flink's checkpoints are realized through distributed snapshots, we use the words *snapshot* and *checkpoint* interchangeably.
+
+
+## Checkpointing
+
+The central part of Flink's fault tolerance mechanism is drawing consistent snapshots of the distributed data stream and operator state.
+These shapshots act as consistent checkpoints to which the system can fall back in case of a failure. Flink's mechanism for drawing these
+snapshots is described in "[Lightweight Asynchronous Snapshots for Distributed Dataflows](http://arxiv.org/abs/1506.08603)". It is a modification of the
+[Chandy-Lamport algorithm](http://research.microsoft.com/en-us/um/people/lamport/pubs/chandy.pdf) for distributed snapshots, simplified
+and tailored to Flink's needs.
+
+
+
+### Barriers
+
+A core element in Flink's distributed snapshotting are the *stream barriers*. These barriers are injected into the data stream and flow
+with the records as part of the data stream. Barriers never overtake records, the flow strictly in line.
+A barrier separates the records in the data stream into the set of records that goes into the
+current snapshot, and the records that go into the next snapshot. Each barrier carries the ID of the snapshot whose records it pushed in front
+of it. Barriers do not interrupt the flow of the stream and are hence very lightweight. Multiple barriers from different snapshots can be in
+the stream at the same time, which means that various snapshots may happen concurrently.
+
+<div style="text-align: center">
+ <img src="{{ site.baseurl }}/internals/fig/stream_barriers.png" alt="Checkpoint barriers in data streams" style="width:60%; padding-top:10px; padding-bottom:10px;" />
+</div>
+
+Stream barriers are injected into the parallel data flow at the stream sources. The point where the barriers for snapshot *n* are injected
+(let's call it <i>S<sub>n</sub></i>) is the position in the source stream up to which the snapshot covers the data. For example, in Apache Kafka, this
+position would be the last record's offset in the partition. This position <i>S<sub>n</sub></i> is reported to the *checkpoint coordinator* (Flink's JobManager).
+
+The barriers then flow downstream. When an intermediate operator has received a barrier for snapshot *n* from all of its input streams, it emits itself a barrier
+for snapshot *n* into all of its outgoing streams. Once a sink operator (the end of a streaming DAG) has received the barrier *n* from all of its
+input streams, it acknowledges that snapshot *n* to the checkpoint coordinator. After all sinks acknowledged a snapshot, it is considered completed.
+
+When snapshot *n* is completed, it is certain that no records from before <i>S<sub>n</sub></i> will be needed any more from the source, because these records (and
+their descendant records) have passed through the entire data flow topology.
+
+<div style="text-align: center">
+ <img src="{{ site.baseurl }}/internals/fig/stream_aligning.png" alt="Aligning data streams at operators with multiple inputs" style="width:100%; padding-top:10px; padding-bottom:10px;" />
+</div>
+
+Operators that receive more than one input stream need to *align* the input streams on the snapshot barriers. The figure above illutrates this:
+
+ - As soon as the operator received snapshot barrier *n* from an incoming stream, it cannot process any further records from that stream until it has received the
+the barrier *n* from the other inputs as well. Otherwise, it would have mixed records that belong to snapshot *n* and with records that belong to snapshot *n+1*.
+ - Streams that report barrier *n* are temporarily set aside. Records that are received from these streams are not processed, but put into an input buffer.
+ - Once the last stream has received barrier *n*, the operator emits all pending outgoing records, and then emits snapshot *n* barriers itself.
+ - After that, it resumes processing records from all input streams, processing records from the input buffers before processing the records from the streams.
+
+
+### State
+
+When operators contain any form of *state*, this state must be part of the snapshots as well. Operator state comes in different forms:
+
+ - *User-defined state*: This is state that is created and modified directly by the transformation functions (like `map()` or `filter()`). User-defined state can either be a simple variable in the function's java object, or the associated key/value state of a function (see [State in Streaming Applications]({{ site.baseurl }}/apis/streaming_guide.html#stateful-computation) for details).
+ - *System state*: This state refers to data buffers that are part of the operator's computation. A typical example for this state are the *window buffers*, inside which the system collects (and aggregates) records for windows until the window is evaluated and evicted.
+
+Operators snapshot their state at the point in time when they received all snapshot barriers from their input streams, before emitting the barriers to their output streams. At that point, all updates to the state from records before the barriers will have been made, and no updates that depend on records from after the barriers have been applied. Because the state of a snapshot may be potentially large, it is stored in a configurable *state backend*. By default, this is the JobManager's memory, but for serious setups, a distributed reliable storage should be configured (such as HDFS). After the state has been stored, the operator acknowledges the checkpoint, emity the snapshot barrier into the output streams, and proceeds.
+
+The resulting snapshot now contains:
+ - For each parallel stream data source, the offset/position in the stream when the snapshot was started
+ - For each operator, a pointer to the state that was stored as part of the snapshot
+
+<div style="text-align: center">
+ <img src="{{ site.baseurl }}/internals/fig/checkpointing.png" alt="Illustration of the Checkpointing Mechanism" style="width:100%; padding-top:10px; padding-bottom:10px;" />
+</div>
+
+<!--
+
+### Asynchronous State Snapshots
+
+Note that the above described mechanism implies that operators stop processing input records while they are storing a snapshot of their state in the *state backend*. This *synchronous* state snapshot introduces a delay every time a snapshot is taken.
+
+It is possible to let an operator continue processing while it stores its state snapshot, effectively letting the state snapshots happen *asynchronously* in the background. To do that, the operator must be able to produce a state object that should be stored in a way such that further modifications to the operator state do not affect that state object.
+
+After receiving the checkpoint barriers on its inputs, the operator starts the asynchronous snapshot copying of its state. It immediately emits the barrier to its outputs and continues with the regular stream processing. Once the background copy process has completed, it acknowledges the checkpoint to the checkpoint coordinator (the JobManager). The checkpoint is now only complete after all sinks received the barriers and all stateful operators acknowledged their completed backup (which may be later than the barriers reaching the sinks).
+
+User-defined state that is used through the key/value state abstraction can be snapshotted *asynchronously*.
+User functions that implement the interface {% gh_link /flink-staging/flink-streaming/flink-streaming-core/src/main/java/org/apache/flink/streaming/api/checkpoint/Checkpointed.java "Checkpointed" %} will be snapshotted *synchronously*, while functions that implement {% gh_link /flink-staging/flink-streaming/flink-streaming-core/src/main/java/org/apache/flink/streaming/api/checkpoint/CheckpointedAsynchronously.java "CheckpointedAsynchronously" %} will be snapshotted *asynchronously*. Note that for the latter, the user function must guarantee that any future modifications to its state to not affect the state object returned by the `snapshotState()` method.
+
+
+
+### Incremental State Snapshots
+
+For large state, taking a snapshot copy of the entire state can be costly, and may prohibit very frequent checkpoints. This problem can be solved by drawing *incremental state snapshots*.
+For incremental snapshots, only the changes since the last snapshot are stored in the current snapshot. The state can then be reconstructed by taking the latest full snapshot and applying the incremental changes to the state.
+
+-->
+
+
+## Recovery
+
+Recovery under this mechanism is straightforward: Upon a failure, Flink selects the latest completed checkpoint *k*. The system then re-deploys the
+entire distributed dataflow, and gives each operator the state that was snapshotted as part of checkpoint *k*. The sources are set to start reading the
+stream from position <i>S<sub>k</sub></i>. For example in Apache Kafka, that means telling the consumer to start fetching from offset <i>S<sub>k</sub></i>.
+
+If state was snapshotted incrementally, the operators start with the state of the latest full snapshot and then apply a series of incremental snapshot updates to that state.
+
+