alpinegizmo commented on a change in pull request #11826: URL: https://github.com/apache/flink/pull/11826#discussion_r412078154
########## File path: docs/tutorials/index.md ########## @@ -0,0 +1,186 @@ +--- +title: Hands-on Tutorials +nav-id: tutorials +nav-pos: 2 +nav-title: '<i class="fa fa-hand-paper-o title appetizer" aria-hidden="true"></i> Hands-on Tutorials' +nav-parent_id: root +nav-show_overview: true +always-expand: true +--- +<!-- +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 will be replaced by the TOC +{:toc} + +## Goals and Scope of these Tutorials + +These tutorials present an introduction to Apache Flink that includes just enough to get you started +writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of +(ultimately important) details. The focus is on providing straightforward introductions to Flink's +APIs for managing state and time, with the expectation that having mastered these fundamentals, +you'll be much better equipped to pick up the rest of what you need to know from the more detailed +reference documentation. The links at the end of each page will lead you to where you can learn +more. + +Specifically, you will learn: + +- how to implement streaming data processing pipelines +- how and why Flink manages state +- how to use event time to consistently compute accurate analytics +- how to build event-driven applications on continuous streams +- how Flink is able to provide fault-tolerant, stateful stream processing with exactly-once semantics + +These tutorials focus on four critical concepts: continuous processing of streaming data, event +time, stateful stream processing, and state snapshots. This page introduces these concepts. + +{% info Note %} Accompanying these tutorials are a set of hands-on exercises that will guide you +through learning how to work with the concepts being presented. + +{% top %} + +## Stream Processing + +Streams are data's natural habitat. Whether it's events from web servers, trades from a stock +exchange, or sensor readings from a machine on a factory floor, data is created as part of a stream. +But when you analyze data, you can either organize your processing around _bounded_ or _unbounded_ +streams, and which of these paradigms you choose has profound consequences. + +<img src="{{ site.baseurl }}/fig/bounded-unbounded.png" alt="Bounded and unbounded streams" class="offset" width="90%" /> + +**Batch processing** is the paradigm at work when you process a bounded data stream. In this mode of +operation you can choose to ingest the entire dataset before producing any results, which means that +it's possible, for example, to sort the data, compute global statistics, or produce a final report +that summarizes all of the input. + +**Stream processing**, on the other hand, involves unbounded data streams. Conceptually, at least, +the input may never end, and so you are forced to continuously process the data as it arrives. + +In Flink, applications are composed of **streaming dataflows** that may be transformed by +user-defined **operators**. These dataflows form directed graphs that start with one or more +**sources**, and end in one or more **sinks**. + +<img src="{{ site.baseurl }}/fig/program_dataflow.svg" alt="A DataStream program, and its dataflow." class="offset" width="80%" /> + +Often 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 operators. + +An application may consume real-time data from streaming sources such as message queues or +distributed logs, such as Apache Kafka or Kinesis. But flink can also consume bounded, historic data +from a variety of data sources. Similarly, the streams of results being produced by a Flink +application can be sent to a wide variety of systems, and the state held within Flink can be +accessed via a REST API. + +<img src="{{ site.baseurl }}/fig/flink-application-sources-sinks.png" alt="Flink application with sources and sinks" class="offset" width="90%" /> + +### Parallel Dataflows + +Programs in Flink are inherently parallel and distributed. During execution, a +*stream* has one or more **stream partitions**, and each *operator* has one or +more **operator subtasks**. The operator subtasks are independent of one +another, and execute in different threads and possibly 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 same program may have different levels of +parallelism. + +<img src="{{ site.baseurl }}/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 in the figure above) preserve the partitioning and ordering of + the 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 (as between *map()* and *keyBy/window* above, as + well as between *keyBy/window* and *Sink*) change the partitioning of + streams. Each *operator subtask* sends data to different target subtasks, + depending on the selected transformation. Examples are *keyBy()* (which + re-partitions by hashing the key), *broadcast()*, or *rebalance()* (which + re-partitions randomly). In a *redistributing* exchange the ordering among + the elements is only preserved within each pair of sending and receiving + subtasks (for example, subtask[1] of *map()* and subtask[2] of + *keyBy/window*). So, for example, the redistribution between the keyBy/window and + the Sink operators shown above introduces non-determinism regarding the + order in which the aggregated results for different keys arrive at the Sink. + +{% top %} + +## Timely Stream Processing + +For most streaming applications it is very valuable to be able re-process historic data with the +same code that is used to process live data -- and to produce deterministic, consistent results, +regardless. + +It can also be crucial to pay attention to the order in which events occurred, rather than the order +in which they are delivered for processing, and to be able to reason about when a set of events is +(or should be) complete. For example, consider the set of events involved in an e-commerce +transaction, or financial trade. + +These requirements for timely stream processing can be met by using event time timestamps that are +recorded in the data stream, rather than using the clocks of the machines processing the data. + +{% top %} + +## Stateful Stream Processing + +Flink's operations can be stateful. This means that how one event is handled can depend on the +accumulated effect of all the events that came before it. State may be used for something simple, +such as counting events per minute to display on a dashboard, or for something more complex, such as +computing features for a fraud detection model. + +A Flink application is run in parallel on a distributed cluster. The various parallel instances of a +given operator will execute independently, in separate threads, and in general will be running on +different machines. + +The set of parallel instances of a stateful operator is effectively a sharded key-value store. Each +parallel instance is responsible for handling events for a specific group of keys, and the state for +those keys is kept locally. + +The diagram below shows a job running with a parallelism of two across the first three operators in +the job graph, terminating in a sink that has a parallelism of one. The third operator is stateful, +and you can see that a fully connected network shuffle is occurring between the second and third +operators. This is being done to partition the stream by some key, so that all of the events that +need to be processed together, will be. + +<img src="{{ site.baseurl }}/fig/parallel-job.png" alt="State is sharded" class="offset" width="65%" /> + +State is always accessed locally, which helps Flink applications achieve high throughput and +low-latency. You can choose to keep state on the JVM heap, or if it is too large, in efficiently +organized on-disk data structures. Review comment: Double-checked with my wife, the editor. She says no hyphen in this case. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
