sjwiesman commented on a change in pull request #8903: [FLINK-12747][docs] Getting Started - Table API Example Walkthrough URL: https://github.com/apache/flink/pull/8903#discussion_r303138794
########## File path: docs/getting-started/tutorials/table_api.md ########## @@ -0,0 +1,423 @@ +--- +title: "Table API" +nav-id: tableapitutorials +nav-title: 'Table API' +nav-parent_id: apitutorials +nav-pos: 1 +--- +<!-- +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. +--> + +Apache Flink offers a Table API as a unified, relational API for batch and stream processing, i.e., queries are executed with the same semantics on unbounded, real-time streams or bounded, recorded streams and produce the same results. +The Table API in Flink is commonly used to ease the definition of data analytics, data pipelining, and ETL applications. + +* This will be replaced by the TOC +{:toc} + +## What Are We Building? + +In this tutorial, we'll show how to build a continuous ETL pipeline for tracking financial transactions by account over time. +We will start by building our report as a nightly batch job, and then migrate to a streaming pipeline to see how batch is just a special case of streaming. + +## Prerequisites + +We'll assume that you have some familiarity with Java or Scala, but you should be able to follow along even if you're coming from a different programming language. +We'll also assume that you're familiar with basic relational concepts such as `SELECT` and `GROUP BY` clauses. + +If you want to follow along, you will require a computer with: + +* Java 8 +* Maven + +## Help, I’m Stuck! + +If you get stuck, check out the [community support resources](https://flink.apache.org/community.html). +In particular, Apache Flink's [user mailing list](https://flink.apache.org/community.html#mailing-lists) is consistently ranked as one of the most active of any Apache project and a great way to get help quickly. + +## How To Follow Along + +If you would like to follow along this walkthrough provides a Flink Maven Archetype to create a skeleton project with all the necessary dependencies quickly. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight bash %} +$ mvn archetype:generate \ + -DarchetypeGroupId=org.apache.flink \ + -DarchetypeArtifactId=flink-walkthrough-table-java \{% unless site.is_stable %} + -DarchetypeCatalog=https://repository.apache.org/content/repositories/snapshots/ \{% endunless %} + -DarchetypeVersion={{ site.version }} \ + -DgroupId=spend-report \ + -DartifactId=spend-report \ + -Dversion=0.1 \ + -Dpackage=spendreport \ + -DinteractiveMode=false +{% endhighlight %} +</div> +<div data-lang="scala" markdown="1"> +{% highlight bash %} +$ mvn archetype:generate \ + -DarchetypeGroupId=org.apache.flink \ + -DarchetypeArtifactId=flink-walkthrough-table-scala \{% unless site.is_stable %} + -DarchetypeCatalog=https://repository.apache.org/content/repositories/snapshots/ \{% endunless %} + -DarchetypeVersion={{ site.version }} \ + -DgroupId=spend-report \ + -DartifactId=spend-report \ + -Dversion=0.1 \ + -Dpackage=spendreport \ + -DinteractiveMode=false +{% endhighlight %} +</div> +</div> + +{% unless site.is_stable %} +<p style="border-radius: 5px; padding: 5px" class="bg-danger"> + <b>Note</b>: For Maven 3.0 or higher, it is no longer possible to specify the repository (-DarchetypeCatalog) via the commandline. If you wish to use the snapshot repository, you need to add a repository entry to your settings.xml. For details about this change, please refer to <a href="http://maven.apache.org/archetype/maven-archetype-plugin/archetype-repository.html";>Maven official document</a> +</p> +{% endunless %} + +You can edit the `groupId`, `artifactId` and `package` if you like. With the above parameters, +Maven will create a project with all the dependencies to complete this tutorial. +After importing the project into your editor, you will see a file following code. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); +BatchTableEnvironment tEnv = BatchTableEnvironment.create(env); + +tEnv.registerTableSource("transactions", new TransactionTableSource()); +tEnv.registerTableSink("spend_report", new SpendReportTableSink()); +tEnv.registerFunction("truncateDateToHour", new TruncateDateToHour()); + +tEnv + .scan("transactions") + .insertInto("spend_report"); + +env.execute("Spend Report"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val env = ExecutionEnvironment.getExecutionEnvironment() +val tEnv = BatchTableEnvironment.create(env) + +tEnv.registerTableSource("transactions", new TransactionTableSource()) +tEnv.registerTableSink("spend_report", new SpendReportTableSink()) + +val truncateDateToHour = new TruncateDateToHour + +tEnv + .scan("transactions") + .insertInto("spend_report") + +env.execute("Spend Report") +{% endhighlight %} +</div> +</div> + +Let's break down this code by component. + +## Breaking Down The Code + +#### The Execution Environment + +The first two lines set up our `ExecutionEnvironment`. +The execution environment is how we set properties for our deployments, specify whether we are writing a batch or streaming application, and create our sources. +Here we have chosen to use the batch environment since we are building a periodic batch report. +We then wrap it in a `BatchTableEnvironment` to have full access to the Table API. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); +BatchTableEnvironment tEnv = BatchTableEnvironment.create(env); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val env = ExecutionEnvironment.getExecutionEnvironment(); +val tEnv = BatchTableEnvironment.create(env); +{% endhighlight %} +</div> +</div> + + +#### Registering Tables + +Next, we register tables that we can use to connect to external systems for reading and writing both batch and streaming data. +A table source provides access to data which is stored in external systems (such as a database, key-value store, message queue, or file system). +A table sink emits a table to an external storage system. +Depending on the type of source and sink, they support different formats such as CSV, JSON, Avro, or Parquet. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +tEnv.registerTableSource("transactions", new TransactionTableSource()); +tEnv.registerTableSink("spend_report", new SpendReportTableSink()); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +tEnv.registerTableSource("transactions", new TransactionTableSource()) +tEnv.registerTableSink("spend_report", new SpendReportTableSink()) +{% endhighlight %} +</div> +</div> + +We register two tables, a transaction input table, and a spend report output table. +The transactions (`transactions`) table lets us read credit card transactions, which contain account ID's (`accountId`), timestamps (`timestamp`), and US$ amounts (`amount`). +The spend report (`spend_report`) table writes the output of a job to standard output so we can easily see our results. +Both tables support running batch and streaming applications. + +#### Registering A UDF + +Along with tables, we include a user-defined function for working with timestamps. Our function takes a timestamp and rounds it down to the nearest hour. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +tEnv.registerFunction("truncateDateToHour", new TruncateDateToHour()); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +val truncateDateToHour = new TruncateDateToHour +{% endhighlight %} +</div> +</div> + +#### The Query + +With our environment configured and tables registered, we are ready to build our first application. +From the `TableEnvironment` we can `scan` an input table to read its rows and then write those results into an output table using `insertInto`. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +tEnv + .scan("transactions") + .insertInto("spend_report"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +tEnv + .scan("transactions") + .insertInto("spend_report") +{% endhighlight %} +</div> +</div> + +Initially, the job reads all transactions and writes them to standard output. + +#### Execute + +Flink applications are built lazily and shipped to the cluster for execution only once fully formed. +We call `ExecutionEnvironment#execute` to begin the execution of our job. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +env.execute("Spend Report"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +env.execute("Spend Report") +{% endhighlight %} +</div> +</div> + +## Attempt One + +Now that we have the skeleton of a job let's add some business logic. +We want a report that shows the total spend for each account across each hour of the day. +Just like a SQL query, we can select the required fields and group by our keys. +Because the timestamp field has millisecond granularity, we will use our UDF to round it down to the nearest hour. +Finally, we will select all the fields, summing the total spend per account-hour pair. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +tEnv + .scan("transactions") + .select("accountId, timestamp.truncateDateToHour as timestamp, amount") + .groupBy("accountId, timestamp") + .select("accountId, timestamp, amount.sum as total") + .insertInto("spend_report"); +{% endhighlight %} +</div> + +<div data-lang="scala" markdown="1"> +{% highlight scala %} +tEnv + .scan("transactions") + .select('accountId, truncateDateToHour('timestamp) as 'timestamp, 'amount) + .groupBy('accountId, 'timestamp) + .select('accountId, 'timestamp, 'amount.sum as 'total) + .insertInto("spend_report") +{% endhighlight %} +</div> +</div> + +## Adding Windows + +While this works, we can do better. +The `timestamp` column represents the [event time]({{ site.baseurl }}/dev/event_time.html) of each row, where event time is the logical time when an event took place in the real world. +Flink understands the concept of event time, which we can leverage to clean up our code. + +Grouping data based on time is a typical operation in data processing, especially when working with infinite streams. +A grouping based on time is called a [window]({{ site.baseurl }} /dev/stream/operators/windows.html) and Flink offers flexible windowing semantics. +The most basic type of window is called a `Tumble` window, which has a fixed size and whose buckets do not overlap. + +<div class="codetabs" markdown="1"> +<div data-lang="java" markdown="1"> +{% highlight java %} +tEnv + .scan("transactions") + .window(Tumble.over("1.hour").on("timestamp").as("w")) Review comment: I was thinking code snippets throughout with a full runnable project (with imports) at the end. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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