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[9/9] flink git commit: [docs] Add "concepts" documentation page
[docs] Add "concepts" documentation page
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/9aa207c0
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/9aa207c0
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/9aa207c0
Branch: refs/heads/master
Commit: 9aa207c073f878d711ca8107627c1f9b2ac17507
Parents: 0331882
Author: Stephan Ewen <se...@apache.org>
Authored: Thu Mar 31 19:25:35 2016 +0200
Committer: Stephan Ewen <se...@apache.org>
Committed: Fri Apr 1 18:39:03 2016 +0200
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docs/concepts/concepts.md | 246 ++++++
docs/concepts/fig/checkpoints.svg | 249 ++++++
.../fig/event_ingestion_processing_time.svg | 375 ++++++++++
docs/concepts/fig/parallel_dataflow.svg | 487 ++++++++++++
docs/concepts/fig/processes.svg | 749 +++++++++++++++++++
docs/concepts/fig/program_dataflow.svg | 546 ++++++++++++++
docs/concepts/fig/slot_sharing.svg | 721 ++++++++++++++++++
docs/concepts/fig/state_partitioning.svg | 291 +++++++
docs/concepts/fig/tasks_chains.svg | 463 ++++++++++++
docs/concepts/fig/tasks_slots.svg | 395 ++++++++++
docs/concepts/fig/windows.svg | 193 +++++
docs/page/css/flink.css | 9 +
12 files changed, 4724 insertions(+)
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http://git-wip-us.apache.org/repos/asf/flink/blob/9aa207c0/docs/concepts/concepts.md
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diff --git a/docs/concepts/concepts.md b/docs/concepts/concepts.md
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+---
+title: "Concepts"
+---
+
+<!--
+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.
+-->
+
+<script type="text/javascript">
+$( document ).ready(function() {
+ // Handler for .ready() called.
+ $('.ga-track').click( function () {
+ console.log("tracking " + $(this).attr('id'))
+ // we just use the element id for tracking with google analytics
+ ga('send', 'event', 'button', 'click', $(this).attr('id'));
+ });
+
+});
+</script>
+
+* This will be replaced by the TOC
+{:toc}
+
+
+## Programs and Dataflows
+
+The basic building blocks of Flink programs are **streams** and **transformations** (note that a DataSet is internally
+also a stream). A *stream* is an intermediate result, and a *transformation* is an operation that takes one or more streams
+as input, and computes one or more result streams from them.
+
+When executed, Flink programs are mapped to **streaming dataflows**, consisting of **streams** and transformation **operators**.
+Each dataflow starts with one or more **sources** and ends in one or more **sinks**. The dataflows may resemble
+arbitrary **directed acyclic graphs** *(DAGs)*. (Special forms of cycle is permitted via *iteration* constructs, we
+omit this here for simplicity).
+
+In most cases, there is a one-to-one correspondence between the transformations in the programs and the operators
+in the dataflow. Sometimes, however, one transformation may consist of multiple transformation operators.
+
+<img src="fig/program_dataflow.svg" alt="A DataStream program, and its dataflow." class="offset" width="80%" />
+
+
+**Parallel Dataflows**
+
+Programs in Flink are inherently parallel and distributed. *Streams* are split into **stream partitions** and
+*operators* are split into **operator subtasks**. The operator subtasks execute independently from each other,
+in different threads and on different machines or containers.
+
+The number of operator subtasks is the **parallelism** of that particular operator. The parallelism of a stream
+is always that of its producing operator. Different operators of the program may have a different parallelism.
+
+<img src="fig/parallel_dataflow.svg" alt="A parallel dataflow" class="offset" width="80%" />
+
+Streams can transport data between two operators in a *one-to-one* (or *forwarding*) pattern, or in a *redistributing* pattern:
+
+ - **One-to-one** streams (for example between the *source* and the *map()* operators) preserves partitioning and order of
+ elements. That means that subtask[1] of the *map()* operator will see the same elements in the same order, as they
+ were produced by subtask[1] of the *source* operator.
+
+ - **Redistributing** streams (between *map()* and *keyBy/window*, as well as between *keyBy/window* and *sink*) change
+ the partitioning of streams. Each *stream partition* splits itself up and sends data to different target subtasks,
+ depending on the selected transformation. Examples are *keyBy()* (re-partitions by hash code), *broadcast()*, or
+ *rebalance()* (random redistribution).
+ In a *redistributing* exchange, order among elements is only preserved for each pair of sending- and receiving
+ task (for example subtask[1] of *map()* and subtask[2] of *keyBy/window*).
+
+
+**Tasks & Operator Chains**
+
+For the distributed execution, Flink *chains* operator subtasks together into *tasks*. Each task is executed by one thread.
+Chaining operators together into tasks is a useful optimization: it reduces the overhead of thread-to-thread
+handover and buffering, and increases overall throughput while decreasing latency.
+The chaining behavior can be configured in the APIs.
+
+The sample dataflow in the figure below is executed with five subtasks, and hence with five parallel threads.
+
+<img src="fig/tasks_chains.svg" alt="Operator chaining into Tasks" class="offset" width="80%" />
+
+
+## Distributed Execution
+
+**Master, Worker, Client**
+
+The Flink runtime consists of two types of processes:
+
+ - The **master** processes (also called *JobManagers*) coordinate the distributed execution. They schedule tasks, coordinate
+ checkpoints, coordinate recovery on failures, etc.
+
+ There is always at least one master process. A high-availability setup will have multiple master processes, out of
+ which one is always the *leader*, and the others are *standby*.
+
+ - The **worker** processes (also called *TaskManagers*) execute the *tasks* (or more specifically, the subtasks) of a dataflow,
+ and buffer and exchange the data *streams*.
+
+ There must always be at least one worker process.
+
+The master and worker processes can be started in an arbitrary fashion: Directly on the machines, via containers, or via
+resource frameworks like YARN. Workers connect to masters, announcing themselves as available, and get work assigned.
+
+The **client** is not part of the runtime and program execution, but is used to prepare and send to dataflow to the master.
+After that, the client can disconnect, or stay connected to receive progress reports. The client runs either as part of the
+Java/Scala program that triggers the execution, or in the command line process `./bin/flink run ...`.
+
+<img src="fig/processes.svg" alt="The processes involved in executing a Flink dataflow" class="offset" width="80%" />
+
+
+**Workers, Slots, Resources**
+
+Each worker (TaskManager) is a *JVM process*, and may execute one or more subtasks in separate threads.
+To control how many tasks a worker accepts, a worker has so called **task slots** (at least one).
+
+Each *task slot* is a fix subset of resources of the TaskManager. A TaskManager with three slots, for example,
+will dedicate 1/3 of its managed memory to each slot. Slotting the resources means that a subtask will not
+compete with subtasks from other jobs for managed memory, but that the subtask a certain amount of reserved
+managed memory. Note that no CPU isolation happens here, slots currently only separate managed memory of tasks.
+
+Adjusting the number of task slots thus allows users to define how subtasks are isolated against each other.
+Having one slot per TaskManager means each task group runs in a separate JVM (which can be started in a
+separate container, for example). Having multiple slots
+means more subtasks share the same JVM. Tasks in the same JVM share TCP connections (via multiplexing) and
+heartbeats messages, or may shared data sets and data structures, thus reducing the per-task overhead.
+
+<img src="fig/tasks_slots.svg" alt="A TaskManager with Task Slots and Tasks" class="offset" width="80%" />
+
+By default, Flink allows subtasks to share slots, if they are subtasks of different tasks, but from the same
+job. The result is that one slot may hold an entire pipeline of the job. Allowing this *slot sharing*
+has two main benefits:
+
+ - A Flink cluster needs exactly as many tasks slots, as the highest parallelism used in the job.
+ No need to calculate how many tasks (with varying parallelism) a program contains in total.
+
+ - It is easier to get better resource utilization. Without slot sharing, the non-intensive
+ *source/map()* subtasks would block as many resources as the resource intensive *window* subtasks.
+ With slot sharing, increasing the base parallelism from two to six yields full utilization of the
+ slotted resources, while still making sure that each TaskManager gets only a fair share of the
+ heavy subtasks.
+
+The slot sharing behavior can be controlled in the APIs, to prevent sharing where it is undesirable.
+The mechanism for that are the *resource groups*, which define what (sub)tasks may share slots.
+
+As a rule-of-thumb, a good default number of task slots would be the number of CPU cores.
+With hyper threading, each slot then takes 2 or more hardware thread contexts.
+
+<img src="fig/slot_sharing.svg" alt="TaskManagers with shared Task Slots" class="offset" width="80%" />
+
+
+## Time and Windows
+
+Aggregating events (e.g., counts, sums) work slightly differently on streams than in batch processing.
+For example, it is impossible to first count all elements in the stream and then return the count,
+because streams are in general infinite (unbounded). Instead, aggregates on streams (counts, sums, etc),
+are scoped by **windows**, such as *"count over the last 5 minutes"*, or *"sum of the last 100 elements"*.
+
+Windows can be *time driven* (example: every 30 seconds) or *data driven* (example: every 100 elements).
+One typically distinguishes different types of windows, such as *tumbling windows* (no overlap),
+*sliding windows* (with overlap), and *session windows* (gap of activity).
+
+<img src="fig/windows.svg" alt="Time- and Count Windows" class="offset" width="80%" />
+
+More window examples can be found in this [blog post](https://flink.apache.org/news/2015/12/04/Introducing-windows.html).
+
+
+**Time**
+
+When referring to time in a streaming program (for example to define windows), one can refer to different notions
+of time:
+
+ - **Event Time** is the time when an event was created. It is usually described by a timestamp in the events,
+ for example attached by the producing sensor, or the producing service. Flink accesses event timestamps
+ via [timestamp assigners]({{ site.baseurl }}/apis/streaming/event_timestamps_watermarks.html).
+
+ - **Ingestion time** is the time when an event enters the Flink dataflow at the source operator.
+
+ - **Processing Time** is the local time at each operator that performs a time-based operation.
+
+<img src="fig/event_ingestion_processing_time.svg" alt="Event Time, Ingestion Time, and Processing Time" class="offset" width="80%" />
+
+More details on how to handle time are in the [event time docs]({{ site.baseurl }}/apis/streaming/event_time.html).
+
+
+## State and Fault Tolerance
+
+While many operations in a dataflow simply look at one individual *event at a time* (for example an event parser),
+some operations remember information across individual events (for example window operators).
+These operations are called **stateful**.
+
+The state from stateful operation is maintained in what can be thought of as an embedded key/value store.
+The state is partitioned and distributed strictly together with the streams that are read by the
+stateful operators. Hence, access the key/value state is only possible on *keyed streams*, after a *keyBy()* function,
+and is restricted to the values of the current event's key. Aligning the keys of streams and state
+makes sure that all state updates are local operations, guaranteeing consistency without transaction overhead.
+This alignment also allows Flink to redistribute the state and adjust the stream partitioning transparently.
+
+<img src="fig/state_partitioning.svg" alt="State and Partitioning" class="offset" width="50%" />
+
+**Checkpoints for Fault Tolerance**
+
+Flink implements fault tolerance using a combination of **stream replay** and **checkpoints**. A checkpoint
+defines a consistent point in streams and state from which an streaming dataflow can resume, and maintain consistency
+*(exactly-once processing semantics)*. The events and state update since the last checkpoint are replayed from the input streams.
+
+Checkpoints interval is a means of trading off the overhead of fault tolerance during execution, with the recovery time (the amount
+of events that need to be replayed).
+
+More details on checkpoints and fault tolerance are in the [fault tolerance docs]({{ site.baseurl }}/internals/stream_checkpointing.html/).
+
+<img src="fig/checkpoints.svg" alt="checkpoints and snapshots" class="offset" width="60%" />
+
+**State backends**
+
+The exact data structures in which the key/values indexes are stored depend on the chosen **state backend**. One state backend
+stores data in an in-memory hash map, another state backend uses [RocksDB](http://rocksdb.org) as the key/value index.
+In addition to defining the data structure that holds the state, the state backends also implements the logic to
+take a point-in-time snapshot of the key/value state and store that snapshot as part of a checkpoint.
+
+
+## Batch on Streaming
+
+Flink executes batch programs as a special case of streaming programs, where the streams are bounded (finite number of elements).
+A *DataSet* is treated internally as a stream of data. The concepts above thus apply to batch programs in the
+same way as well as they apply to streaming programs, with minor exceptions:
+
+ - Programs in the DataSet API do not use checkpoints. Recovery happens by fully replaying the streams.
+ That is possible, because inputs are bounded. This pushes the cost more towards the recovery,
+ but makes the regular processing cheaper, because it avoids checkpoints.
+
+ - Stateful operation in the DataSet API use simplified in-memory/out-of-core data structures, rather than
+ key/value indexes.
+
+ - The DataSet API introduces special synchronized (superstep-based) iterations, which are only possible on
+ bounded streams. For details, check out the [iteration docs]({{ site.baseurl }}/apis/batch/iterations.html).
+
http://git-wip-us.apache.org/repos/asf/flink/blob/9aa207c0/docs/concepts/fig/checkpoints.svg
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diff --git a/docs/concepts/fig/checkpoints.svg b/docs/concepts/fig/checkpoints.svg
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+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
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