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create mode 100644 doc/ch-time-window/img/lifecycle-of-a-window.png create mode 100644 doc/ch-time-window/img/parallel-watermark.png create mode 100644 doc/ch-time-window/img/session-window.png create mode 100644 doc/ch-time-window/img/sliding-window.png create mode 100644 doc/ch-time-window/img/three-types-of-time.png create mode 100644 doc/ch-time-window/img/tumbling-window-join.png create mode 100644 doc/ch-time-window/img/tumbling-window.png create mode 100644 doc/ch-time-window/index.md create mode 100644 doc/ch-time-window/join.md create mode 100644 doc/ch-time-window/late-elements.md create mode 100644 doc/ch-time-window/process-function.md create mode 100644 doc/ch-time-window/time.md create mode 100644 doc/ch-time-window/window.md diff --git a/doc/_toc.yml b/doc/_toc.yml index 968c062..05f62d7 100644 --- a/doc/_toc.yml +++ b/doc/_toc.yml @@ -1,10 +1,41 @@ root: index subtrees: -- numbered: 2 - entries: - - file: ch-system-design/index + - numbered: 2 entries: - - file: ch-system-design/dataflow - - file: ch-system-design/flink-core - - file: ch-system-design/task-resource - - file: ch-system-design/exercise-wordcount + - file: ch-system-design/index + entries: + - file: ch-system-design/dataflow + - file: ch-system-design/flink-core + - file: ch-system-design/task-resource + - file: ch-system-design/exercise-wordcount + - file: ch-datastream-api/index + entries: + - file: ch-datastream-api/data-types + - file: ch-datastream-api/skeleton + - file: ch-datastream-api/transformations + - file: ch-datastream-api/user-define-functions + - file: ch-datastream-api/exercise-stock-basic + - file: ch-state-checkpoint/index + entries: + - file: ch-state-checkpoint/checkpoint + - file: ch-state-checkpoint/savepoint + - file: ch-state-checkpoint/state + - file: ch-state-checkpoint/exercise-state + - file: ch-table-sql/index + entries: + - file: ch-table-sql/table-overview + - file: ch-table-sql/catalog-function + - file: ch-table-sql/dynamic-table + - file: ch-table-sql/sql-join + - file: ch-table-sql/sql-ddl + - file: ch-table-sql/system-function + - file: ch-table-sql/sql-window + - file: ch-table-sql/exercise-iot + - file: ch-time-window/index + entries: + - file: ch-time-window/join + - file: ch-time-window/late-elements + - file: ch-time-window/process-function + - file: ch-time-window/time + - file: ch-time-window/window + - file: ch-time-window/exercise-stock \ No newline at end of file diff --git a/doc/ch-datastream-api/data-types.md b/doc/ch-datastream-api/data-types.md new file mode 100644 index 0000000..b693af3 --- /dev/null +++ b/doc/ch-datastream-api/data-types.md @@ -0,0 +1,316 @@ +(data-types)= +# 数据类型和序列化 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +几乎所有的大数据框架都要面临分布式计算、数据传输和持久化问题。数据传输过程前后要进行数据的序列化和反序列化:序列化就是将一个内存对象转换成二进制串,形成可网络传输或者可持久化的数据流。反序列化将二进制串转换为内存对象,这样就可以直接在编程语言中读写和操作这个对象。一种最简单的序列化方法就是将复杂数据结构转化成JSON格式。序列化和反序列化是很多大数据框架必须考虑的问题,在Java和大数据生态圈中,已有不少序列化工具,比如Java自带的序列化工具、Kryo等。一些RPC框架也提供序列化功能,比如最初用于Hadoop的Apache Avro、Facebook开发的Apache Thrift和Google开发的Protobuf,这些工具在速度和压缩比等方面比JSON有明显的优势。 + +但是Flink依然选择了重新开发了自己的序列化框架,因为序列化和反序列化将关乎整个流处理框架各方面的性能,对数据类型了解越多,可以更早地完成数据类型检查,节省数据存储空间。 + +## Flink支持的数据类型 + +![Flink支持的数据类型](./img/data-type.png) + +Flink支持上图所示的几种数据类型:基础类型、数组、复合类型、辅助类型。其中,Kryo是最后的备选方案,如果能够优化,尽量不要使用Kryo,否则会有大量的性能损失。 + +### 基础类型 + +所有Java和Scala基础数据类型,诸如Int、Double、Long(包括Java原生类型int和装箱后的类型Integer)、String,以及Date、BigDecimal和BigInteger。 + +### 数组 + +基础类型或其他对象类型组成的数组,如`String[]`。 + +### 复合类型 {#composite-types} + +#### Scala case class + +Scala case class是Scala的特色,用这种方式定义一个数据结构非常简洁。例如股票价格的数据结构: + +```scala +case class StockPrice(symbol: String = "", + price: Double = 0d, + ts: Long = 0) +``` + +这样定义的数据结构,所有的子字段都是`public`,可以直接读取。另外,我们可以不用`new`即可获取一个新的对象。 + +```scala +val stock = StockPrice("AAPL", 300d, 1582819200000L) +``` + +#### Java POJO + +Java的话,需要定义POJO(Plain Old Java Object)类,定义POJO类有一些注意事项: + +* 该类必须用`public`修饰。 +* 该类必须有一个`public`的无参数的构造函数。 +* 该类的所有非静态(non-static)、非瞬态(non-transient)字段必须是`public`,如果字段不是`public`则必须有标准的getter和setter方法,比如对于字段`A a`有`A getA()`和`setA(A a)`。 +* 所有子字段也必须是Flink支持的数据类型。 + +下面三个例子中,只有第一个是POJO,其他两个都不是POJO,非POJO类将使用Kryo序列化工具。 + +```java +public class StockPrice { + public String symbol; + public double price; + public long ts; + + public StockPrice() {} + public StockPrice(String symbol, Long timestamp, Double price){ + this.symbol = symbol; + this.ts = timestamp; + this.price = price; + } +} + +// NOT POJO +public class StockPriceNoGeterSeter { + + // LOGGER 无getter和setter + private Logger LOGGER = LoggerFactory.getLogger(StockPriceNoGeterSeter.class); + + public String symbol; + public double price; + public long ts; + + public StockPriceNoGeterSeter() {} + + public StockPriceNoGeterSeter(String symbol, long timestamp, Double price){ + this.symbol = symbol; + this.ts = timestamp; + this.price = price; + } +} + +// NOT POJO +public class StockPriceNoConstructor { + + public String symbol; + public double price; + public long ts; + + // 缺少无参数构造函数 + + public StockPriceNoConstructor(String symbol, Long timestamp, Double price){ + this.symbol = symbol; + this.ts = timestamp; + this.price = price; + } +} +``` + +如果不确定是否是POJO,可以使用下面的代码检查: + +```java +System.out.println(TypeInformation.of(StockPrice.class).createSerializer(new ExecutionConfig())); +``` + +返回的结果中,如果这个类在使用`KryoSerializer`,说明不是POJO类。 + +此外,使用Avro生成的类可以被Flink识别为POJO。 + +#### Tuple + +Tuple可被翻译为元组,比如我们可以将刚刚定义的股票价格抽象为一个三元组。Scala用括号来定义元组,比如一个三元组:`(String, Long, Double)`。 + +:::info +Scala访问元组中的元素时,要使用下划线。与其他地方从0开始计数不同,这里是从1开始计数,_1为元组中的第一个元素。下面的代码是一个Scala Tuple的例子。 +::: + +```scala +// Scala Tuple Example +def main(args: Array[String]): Unit = { + + val senv: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment + + val dataStream: DataStream[(String, Long, Double)] = + senv.fromElements(("0001", 0L, 121.2), ("0002" ,1L, 201.8), + ("0003", 2L, 10.3), ("0004", 3L, 99.6)) + + dataStream.filter(item => item._3 > 100) + + senv.execute("scala tuple") +} +``` + +Flink为Java专门准备了元组类型,比如3元组为`Tuple3`,最多支持到25元组`Tuple25`。访问元组中的元素时,要使用Tuple类准备好的公共字段:`f0`、`f1`...或者使用`getField(int pos)`方法,并注意进行类型转换。这里的元组是从0开始计数。 + +```java +// Java Tuple Example +public static void main(String[] args) throws Exception { + + StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment(); + + DataStream> dataStream = senv.fromElements( + Tuple3.of("0001", 0L, 121.2), + Tuple3.of("0002" ,1L, 201.8), + Tuple3.of("0003", 2L, 10.3), + Tuple3.of("0004", 3L, 99.6) + ); + + dataStream.filter(item -> item.f2 > 100).print(); + + dataStream.filter(item -> ((Double)item.getField(2) > 100)).print(); + + senv.execute("java tuple"); +} +``` + +Scala的Tuple中所有元素都不可变,如果想改变元组中的值,一般需要创建一个新的对象并赋值。Java的Tuple中的元素是可以被更改和赋值的,因此在Java中使用Tuple可以充分利用这一特性,可以减少垃圾回收的压力。 + +```java +// stock是一个Tuple3 +// 获取Tuple3中第三个位置的值 +Double price = stock.getField(2); +// 给第三个位置赋值 +stock.setField(70, 2); +``` + +### 辅助类型 + +Flink还支持Java的`ArrayList`、`HashMap`和`Enum`,Scala的`Either`和`Option`。 + +## 泛型和其他类型 + +当以上任何一个类型均不满足时,Flink认为该数据结构是一种泛型(GenericType),使用Kryo来进行序列化和反序列化。但Kryo在有些流处理场景效率非常低,有可能造成流数据的积压。我们可以使用`senv.getConfig.disableGenericTypes()`来禁用Kryo,禁用后,Flink遇到无法处理的数据类型将抛出异常,这种方法对于调试非常有效。 + +## TypeInformation + +以上如此多的类型,在Flink中,统一使用`TypeInformation`类表示。比如,POJO在Flink内部使用`PojoTypeInfo`来表示,`PojoTypeInfo`继承自`CompositeType`,`CompositeType`继承自`TypeInformation`。下图展示了`TypeInformation`的继承关系,可以看到,前面提到的诸多数据类型,在Flink中都有对应的类型。`TypeInformation`的一个重要的功能就是创建`TypeSerializer`序列化器,为该类型的数据做序列化。每种类型都有一个对应的序列化器来进行序列化。 + +![TypeInformation继承关系](./img/typeinformation.png)) + +使用前面介绍的各类数据类型时,Flink会自动探测传入的数据类型,生成对应的`TypeInformation`,调用对应的序列化器,因此用户其实无需关心类型推测。比如,Flink的`map`函数Scala签名为:`def map[R: TypeInformation](fun: T => R): DataStream[R]`,传入`map`的数据类型是T,生成的数据类型是R,Flink会推测T和R的数据类型,并使用对应的序列化器进行序列化。 + +![Flink数据类型推断和序列化](./img/type-inference-process.png) + +上图展示了Flink的类型推断和序列化过程,以一个字符串`String`类型为例,Flink首先推断出该类型,并生成对应的`TypeInformation`,然后在序列化时调用对应的序列化器,将一个内存对象写入内存块。 + +## 注册类 + +如果传递给Flink算子的数据类型是父类,实际运行过程中使用的是子类,子类中有一些父类没有的数据结构和特性,将子类注册可以提高性能。在执行环境上调用`env.registerType(clazz) `来注册类。`registerType`方法的源码如下所示,其中`TypeExtractor`对数据类型进行推断,如果传入的类型是POJO,则可以被Flink识别和注册,否则将使用Kryo。 + +```java +// Flink registerType java源码 +public void registerType(Class type) { + if (type == null) { + throw new NullPointerException("Cannot register null type class."); + } + + TypeInformation typeInfo = TypeExtractor.createTypeInfo(type); + + if (typeInfo instanceof PojoTypeInfo) { + config.registerPojoType(type); + } else { + config.registerKryoType(type); + } +} +``` + +## 注册序列化器 + +如果你的数据类型不是Flink支持的上述类型,这时Flink会使用Kryo序列化。我们需要对数据类型和序列化器进行注册,以便Flink对该数据类型进行序列化。 + +```java +// 使用对TestClassSerializer对TestClass进行序列化 +env.registerTypeWithKryoSerializer(TestClass.class, new TestClassSerializer()); +``` + +其中`TestClassSerializer`要继承`com.esotericsoftware.kryo.Serializer`。下面的代码是一个序列化示意案例。 + +```java +static class TestClassSerializer extends Serializer implements Serializable { + + private static final long serialVersionUID = -3585880741695717533L; + + @Override + public void write(Kryo kryo, Output output, TestClass testClass) { + ... + } + + @Override + public TestClass read(Kryo kryo, Input input, Class aClass) { + ... + } +} +``` + +相应的包需要添加到pom中: + +``` + + com.esotericsoftware.kryo + kryo + 2.24.0 + +``` + +对于Apache Thrift和Protobuf的用户,已经有人将序列化器编写好,我们可以直接拿来使用: + +```java +// Google Protobuf +env.getConfig().registerTypeWithKryoSerializer(MyCustomType.class, ProtobufSerializer.class); + +// Apache Thrift +env.getConfig().addDefaultKryoSerializer(MyCustomType.class, TBaseSerializer.class); +``` + +Google Protobuf的pom: + +``` + + com.twitter + chill-protobuf + 0.7.6 + + + com.esotericsoftware.kryo + kryo + + + + + com.google.protobuf + protobuf-java + 3.7.0 + +``` + +Apache Thrift的pom: + +``` + + com.twitter + chill-thrift + 0.7.6 + + + com.esotericsoftware.kryo + kryo + + + + + org.apache.thrift + libthrift + 0.11.0 + + + javax.servlet + servlet-api + + + org.apache.httpcomponents + httpclient + + + +``` \ No newline at end of file diff --git a/doc/ch-datastream-api/exercise-stock-basic.md b/doc/ch-datastream-api/exercise-stock-basic.md new file mode 100644 index 0000000..76f5a78 --- /dev/null +++ b/doc/ch-datastream-api/exercise-stock-basic.md @@ -0,0 +1,127 @@ +(exercise-stock-basic)= +# 习题 股票数据流处理 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +经过本章的学习,读者应该对Flink的DataStream API有了初步的认识,本节以股票价格这个场景来实践所学内容。 + +## 实验目的 + +针对具体的业务场景,学习如何定义相关数据结构,如何自定义Source,如何使用各类算子。 + +## 实验内容 + +我们虚构了一个股票交易数据集,如下所示,该数据集中有每笔股票的交易时间、价格和交易量。数据集放置在了`src/main/resource/stock`文件夹里。 + +``` +股票代号,交易日期,交易时间(秒),价格,交易量 +US2.AAPL,20200108,093003,297.260000000,100 +US2.AAPL,20200108,093003,297.270000000,100 +US2.AAPL,20200108,093003,297.310000000,100 +``` + +在[数据类型和序列化](./data-types.md)章节,我们曾介绍Flink所支持的数据结构。对于这个股票价格的业务场景,首先要做的是对该业务进行建模,读者需要设计一个`StockPrice`类,能够表征一次交易数据。这个类至少包含以下字段: + +```java +/* + * symbol 股票代号 + * ts 时间戳 + * price 价格 + * volume 交易量 + */ +``` + +接下来,我们自定义Source,这个自定义的类继承`SourceFunction`,读取数据集中的元素,并将数据写入`DataStream`中。为了模拟不同交易之间的时间间隔,我们使用`Thread.sleep()`方法,等待一定的时间。下面的代码展示了如何自定义Source,读者可以直接拿来借鉴。 + +```java +public class StockSource implements SourceFunction { + + // Source是否正在运行 + private boolean isRunning = true; + // 数据集文件名 + private String path; + private InputStream streamSource; + + public StockSource(String path) { + this.path = path; + } + + // 读取数据集中的元素,每隔时间发送一次股票数据 + // 使用SourceContext.collect(T element)发送数据 + @Override + public void run(SourceContext sourceContext) throws Exception { + DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyyMMdd HHmmss"); + // 从项目的resources目录获取输入 + streamSource = this.getClass().getClassLoader().getResourceAsStream(path); + BufferedReader br = new BufferedReader(new InputStreamReader(streamSource)); + String line; + boolean isFirstLine = true; + long timeDiff = 0; + long lastEventTs = 0; + while (isRunning && (line = br.readLine()) != null) { + String[] itemStrArr = line.split(","); + LocalDateTime dateTime = LocalDateTime.parse(itemStrArr[1] + " " + itemStrArr[2], formatter); + long eventTs = Timestamp.valueOf(dateTime).getTime(); + if (isFirstLine) { + // 从第一行数据提取时间戳 + lastEventTs = eventTs; + isFirstLine = false; + } + StockPrice stock = StockPrice.of(itemStrArr[0], Double.parseDouble(itemStrArr[3]), eventTs, Integer.parseInt(itemStrArr[4])); + // 输入文件中的时间戳是从小到大排列的 + // 新读入的行如果比上一行大,sleep,这样来模拟一个有时间间隔的输入流 + timeDiff = eventTs - lastEventTs; + if (timeDiff > 0) + Thread.sleep(timeDiff); + sourceContext.collect(stock); + lastEventTs = eventTs; + } + } + + // 停止发送数据 + @Override + public void cancel() { + try { + streamSource.close(); + } catch (Exception e) { + System.out.println(e.toString()); + } + isRunning = false; + } +} +``` + +对于我们自定义的这个Source,我们可以使用下面的时间语义: + +```java +env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime) +``` + +接下来,基于DataStream API,按照股票代号分组,对股票数据流进行分析和处理。 + +## 实验要求 + +完成数据结构定义和数据流处理部分的代码编写。其中数据流分析处理部分需要完成: + +* 程序1:价格最大值 + +实时计算某支股票的价格最大值。 + +* 程序2:汇率转换 + +数据中股票价格以美元结算,假设美元和人民币的汇率为7,使用`map`进行汇率转换,折算成人民币。 + +* 程序3:大额交易过滤 + +数据集中有交易量字段,给定某个阈值,过滤出交易量大于该阈值的,生成一个大额交易数据流。 + +## 实验报告 + +将思路和程序撰写成实验报告。 \ No newline at end of file diff --git a/doc/ch-datastream-api/img/batch-streaming-api-import.png b/doc/ch-datastream-api/img/batch-streaming-api-import.png new file mode 100644 index 0000000000000000000000000000000000000000..104bfd59debf19ac3e9c85d5b23aa1d0a12438fa GIT binary patch literal 91380 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zhmLX+x_=)R@B^(mC$_iW#oWo zzyHC{>&k!rp?t@PIm^CJq3GmQIotmEavN2B6S*|nuj=d27{3oP6_1_Nea2Z2`%cgD z{YJlZIkR9mu}AtA%D>Gx@cS42IgS$rcfkbze9_TJ`llXoKcQWaUi<&xxNm6^%-8*2 zpBw)FKS;c`|Kmg2|CCwUFKfel`|q#ZtNY)ZTa!P^%}5wKjx+3s_?TMxxH$T_PIO-D z%6}wdb;jyw>*#6g7%b8mKXHuCL>--xI^y@XmberD;{vZ$E*@^{{`VK`KBG983nVj5 L=bFSDEe-xJ)u$D! literal 0 HcmV?d00001 diff --git a/doc/ch-datastream-api/index.md b/doc/ch-datastream-api/index.md new file mode 100644 index 0000000..ea90f50 --- /dev/null +++ b/doc/ch-datastream-api/index.md @@ -0,0 +1,4 @@ +# DataStream API + +```{tableofcontents} +``` \ No newline at end of file diff --git a/doc/ch-datastream-api/skeleton.md b/doc/ch-datastream-api/skeleton.md new file mode 100644 index 0000000..8cd025e --- /dev/null +++ b/doc/ch-datastream-api/skeleton.md @@ -0,0 +1,94 @@ +(skeleton)= +# Flink程序的骨架结构 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +在进行详细的API介绍前,我们先回顾一下WordCount的案例,通过WordCount的代码结构,读者可以了解一个Flink程序的骨架结构。 + +我们知道,一个Java或Scala的程序入口一般是一个静态(static)的main函数。在main函数中,还需要定义下面几个核心步骤: + +1. 初始化运行环境。 + +2. 读取一到多个数据源Source。 + +3. 根据业务逻辑对数据流进行Transformation转换。 + +4. 将结果输出到Sink。 + +5. 调用作业执行函数。 + +接下来我们对这五个步骤拆解分析。 + +## 设置执行环境 + +一个Flink作业必须依赖一个执行环境: + +```java +// 创建Flink执行环境 +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +``` + +这行代码可以获取一个Flink流处理执行环境。Flink一般运行在一个集群上,执行环境是Flink程序运行的上下文,它提供了一系列作业与集群交互的方法,比如作业如何与外部世界交互。当调用`getExecutionEnvironment()`方法时,假如我们是在一个集群上提交作业,则返回集群的上下文,假如我们是在本地执行,则返回本地的上下文。本例中我们是进行流处理,在批处理场景则要获取DataSet API中批处理执行环境。 + +:::info +本例中我们是进行流处理,在批处理场景则要获取DataSet API中批处理执行环境。流处理和批处理的执行环境不同,流处理的执行环境名为`org.apache.flink.streaming.api.environment.StreamExecutionEnvironment`,批处理的执行环境名为`org.apache.flink.api.java.ExecutionEnvironment`。 +::: + +Scala和Java所需要引用的包也不相同,Scala需要调用`org.apache.flink.streaming.api.scala.StreamExecutionEnvironment`和`org.apache.flink.api.scala.ExecutionEnvironment`。 + +下图是批处理和流处理两种场景下,Java和Scala两种编程语言所需要引用的包。刚刚接触Flink的朋友很可能因为错误地引用导致出现莫名其妙的错误,一定要注意是否引用正确的包。 + +![流处理和批处理不同场景下,相关API的引用](./img/batch-streaming-api-import.png) + +另外,使用Scala API时,应该按照下面的方式引用,否则会出现一些问题。 + +```scala +import org.apache.flink.streaming.api.scala._ +``` + +Scala中的`_`就像Java中的`*`,是一种通配符。在这里使用`_`会引用`org.apache.flink.streaming.api.scala`下面的所有内容。 + +回到执行环境上,我们可以通过执行环境做很多设置。比如,`env.setParallelism(2)`告知执行环境整个作业的并行度为2;`env.disableOperatorChaining()`关闭算子链功能。 + +使用下面的设置可以创建一个基于本地的执行环境,这样我们使用IntelliJ Idea运行程序时,可以直接打开浏览器进入Flink Web UI查看运行的任务,本地的调试。 + +```java +Configuration conf = new Configuration(); +// 访问http://localhost:8082 可以看到Flink Web UI +conf.setInteger(RestOptions.PORT, 8082); +// 创建本地执行环境,并行度为2 +StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironment(2, conf); +``` + +此外,我们还可以在执行环境中设置一些时间属性等,配置Checkpoint等,我们将在后续章节中介绍这些功能。总之,执行环境是开发者和Flink交互的一个重要入口。 + +## 读取数据源 + +接着我们需要使用执行环境提供的方法读取数据源,读取数据源的部分统称为Source。数据源一般是消息队列或文件,我们也可以根据业务需求重写数据源,比如定时爬取网络中某处的数据。在本例中,我们使用`DataStream stream = env.addSource(consumer);`来读取数据源,其中`consumer`是一个Kafka消费者,我们消费Kafka中的数据作为Flink的输入数据。绝大多数流处理实战场景可能都是消费其他消息队列作为Source。 + +我们将在第七章介绍如何使用各类Source。 + +## 进行转换操作 + +此时,我们已经获取了一个文本数据流,接下来我们就可以在数据流上进行有状态的计算了。我们一般调用Flink提供的各类算子,使用链式调用的方式,对一个数据流进行操作。经过各算子的处理,`DataStream`可能被转换为`KeyedStream`、`WindowedStream`、`JoinedStream`等不同的数据流结构。相比Spark RDD的数据结构,Flink的数据流结构确实更加复杂。 + +本例中,我们先对一行文本进行分词,形成`(word, 1)`这样的二元组,然后以单词为Key进行分组,并开启一个时间窗口,统计该窗口中某个单词出现的次数。在这个过程中,涉及到对数据流的分组、窗口和聚合操作。其中,窗口相关操作涉及到如何将数据流中的元素划分到不同的窗口,聚合操作涉及到使用一个状态来记录单词出现的次数,不断维护更新状态来对数据进行实时处理。本章我们重点介绍一些DataStream API,第五章将介绍时间上的操作,第六章将介绍如何使用状态以及如何做失败恢复。 + +## 结果输出 + +然后我们需要将前面的计算结果输出到外部系统。目的地可能是一个消息队列、文件系统或数据库,或其他自定义的输出方式。输出结果的部分统称为Sink。 + +本例中,我们的结果是窗口内的词频统计,它是一个`DataStream>`的数据结构。我们调用`print`函数将这个数据流打印到标准输出(Standard Output)上。`print`主要是为调试使用的,在实战场景中,计算结果会输出到一个外部的数据库或下一个流处理作业。 + +## 执行 + +当定义好程序的Source、Transformation和Sink的业务逻辑后,程序并不会立即执行这些计算,我们还需要调用执行环境`execute()`方法来明确通知Flink去执行。Flink是延迟执行(Lazy Evaluation)的,即当程序明确调用`execute()`方法时,Flink才会将数据流图转化为一个`JobGraph`,提交给JobManager,JobManager根据当前的执行环境来执行这个作业。如果没有`execute()`方法,我们无法得到输出结果。 + +综上,一个Flink程序的核心业务逻辑主要包括:初始化执行环境、进行Source、Transformation和Sink操作,最后要调用执行环境的`execute()`方法。 \ No newline at end of file diff --git a/doc/ch-datastream-api/transformations.md b/doc/ch-datastream-api/transformations.md new file mode 100644 index 0000000..5db9e61 --- /dev/null +++ b/doc/ch-datastream-api/transformations.md @@ -0,0 +1,669 @@ +(transformations)= +# Transformations + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +Flink的Transformation转换主要包括四种:单数据流基本转换、基于Key的分组转换、多数据流转换和数据重分布转换。Transformation各算子可以对Flink数据流进行处理和转化,多个Transformation算子共同组成一个数据流图,DataStream Transformation是Flink流处理非常核心的API。下图展示了数据流上的几类操作,本章主要介绍四种Transformation:单数据流转换、基于Key的分组转换、多数据流转换和数据重分布转换,时间窗口部分将在第五章介绍。 + +![DataStream上的Transformaton操作分类](./img/transformations.png) + +Flink的Transformation是对数据流进行操作,其中数据流涉及到的最常用数据结构是`DataStream`,`DataStream`由多个相同的元素组成,每个元素是一个单独的事件。在Java中,我们使用泛型`DataStream`来定义这种组成关系,在Scala中,这种泛型对应的数据结构为`DataStream[T]`,`T`是数据流中每个元素的数据类型。在WordCount的例子中,数据流中每个元素的类型是字符串`String`,整个数据流的数据类型为`DataStream`。 + +在使用这些算子时,需要在算子上进行用户自定义操作,一般使用Lambda表达式或者继承类并重写函数两种方式完成这个用户自定义的过程。接下来,我们将对Flink Transformation中各算子进行详细介绍,并使用大量例子展示具体使用方法。 + +## 单数据流转换 + +单数据流基本转换主要对单个数据流上的各元素进行处理。 + +### map + +`map`算子对一个`DataStream`中的每个元素使用用户自定义的Mapper函数进行处理,每个输入元素对应一个输出元素,最终整个数据流被转换成一个新的`DataStream`。输出的数据流`DataStream`类型可能和输入的数据流`DataStream`不同。 + +![map](./img/map.png) + +我们可以重写`MapFunction`或`RichMapFunction`来自定义map函数,`MapFunction`在源码的定义为:`MapFunction`,其内部有一个`map`虚函数,我们需要对这个虚函数重写。下面的代码重写了`MapFunction`中的`map`函数,将输入结果乘以2,转化为字符串后输出。 + +```java +// 函数式接口类 +// T为输入类型,O为输出类型 +@FunctionalInterface +public interface MapFunction extends Function, Serializable { + // 调用这个API就是继承并实现这个虚函数 + O map(T value) throws Exception; +} +``` + +第二章中我们曾介绍过,对于这样一个虚函数,可以继承接口类并实现虚函数: + +```java +// 继承并实现MapFunction +// 第一个泛型是输入类型,第二个泛型是输出类型 +public static class DoubleMapFunction implements MapFunction { + @Override + public String map(Integer input) { + return "function input : " + input + ", output : " + (input * 2); + } +} +``` + +然后在主逻辑中调用这个类: + +```java +DataStream functionDataStream = dataStream.map(new DoubleMapFunction()); +``` + +这段的代码清单重写了`MapFunction`中的`map`函数,将输入结果乘以2,转化为字符串后输出。我们也可以不用显式定义`DoubleMapFunction`这个类,而是像下面的代码一样使用匿名类: + +```java +// 匿名类 +DataStream anonymousDataStream = dataStream.map(new MapFunction() { + @Override + public String map(Integer input) throws Exception { + return "anonymous function input : " + input + ", output : " + (input * 2); + } +}); +``` + +自定义map函数最简便的操作是使用Lambda表达式。 + +```java +// 使用Lambda表达式 +DataStream lambdaStream = dataStream + .map(input -> "lambda input : " + input + ", output : " + (input * 2)); +``` + +Scala的API相对更加灵活,可以使用下划线来构造Lambda表达式: + +```scala +// 使用 _ 构造Lambda表达式 +val lambda2 = dataStream.map { _.toDouble * 2 } +``` + +:::info +使用Scala时,Lambda表达式可以可以放在圆括号()中,也可以使用花括号{}中。使用Java时,只能使用圆括号。 +::: + +对上面的几种方式比较可见,Lambda表达式更为简洁。重写函数的方式代码更为臃肿,但定义更清晰。 + +此外,`RichMapFunction`是一种RichFunction,它除了`MapFunction`的基础功能外,还提供了一系列其他方法,包括`open`、`close`、`getRuntimeContext`和`setRuntimeContext`等虚函数方法,重写这些方法可以创建状态数据、对数据进行广播,获取累加器和计数器等,这部分内容将在后面介绍。 + +### filter + +`filter`算子对每个元素进行过滤,过滤的过程使用一个Filter函数进行逻辑判断。对于输入的每个元素,如果filter函数返回True,则保留,如果返回False,则丢弃,如下图所示。 + +![filter](./img/filter.png) + +我们可以使用Lambda表达式过滤掉小于等于0的元素: + +```java +DataStream dataStream = senv.fromElements(1, 2, -3, 0, 5, -9, 8); + +// 使用 -> 构造Lambda表达式 +DataStream lambda = dataStream.filter ( input -> input > 0 ); +``` + +也可以继承`FilterFunction`或`RichFilterFunction`,然后重写`filter`方法,我们还可以将参数传递给继承后的类。如下面的代码所示,`MyFilterFunction`增加一个构造函数参数`limit`,并在`filter`方法中使用这个参数。 + +```java +public static class MyFilterFunction extends RichFilterFunction { + + // limit参数可以从外部传入 + private Integer limit; + + public MyFilterFunction(Integer limit) { + this.limit = limit; + } + + @Override + public boolean filter(Integer input) { + return input > this.limit; + } +} + +// 继承RichFilterFunction +DataStream richFunctionDataStream = dataStream.filter(new MyFilterFunction(2)); +``` + +### flatMap + +`flatMap`算子和`map`有些相似,输入都是数据流中的每个元素,与之不同的是,`flatMap`的输出可以是零个、一个或多个元素,当输出元素是一个列表时,`flatMap`会将列表展平。如下图所示,输入是包含圆形或正方形的列表,`flatMap`过滤掉圆形,正方形列表被展平,以单个元素的形式输出。 + +![flatMap](./img/flatmap.png) + +我们可以用切水果的例子来理解map和flatMap的区别。map会对每个输入元素生成一个对应的输出元素: + +``` +{苹果,梨,香蕉}.map(去皮) => {去皮苹果, 去皮梨,去皮香蕉} +``` + +`flatMap`先对每个元素进行相应的操作,生成一个相应的集合,再将集合展平: + +``` +{苹果,梨,香蕉}.flMap(切碎) +=> +{[苹果碎片1, 苹果碎片2], [梨碎片1,梨碎片2, 梨碎片3],[香蕉碎片1]} +=> +{苹果碎片1, 苹果碎片2, 梨碎片1,梨碎片2, 梨碎片3,香蕉碎片1} +``` + +下面的代码对字符串进行切词处理: + +```java +DataStream dataStream = + senv.fromElements("Hello World", "Hello this is Flink"); + +// split函数的输入为 "Hello World" 输出为 "Hello" 和 "World" 组成的列表 ["Hello", "World"] +// flatMap将列表中每个元素提取出来 +// 最后输出为 ["Hello", "World", "Hello", "this", "is", "Flink"] +DataStream words = dataStream.flatMap ( + (String input, Collector collector) -> { + for (String word : input.split(" ")) { + collector.collect(word); + } + }).returns(Types.STRING); +``` + +因为`flatMap`可以输出零到多个元素,我们可以将其看做是`map`和`filter`更一般的形式。如果我们只想对长度大于15的句子进行处理,可以先在程序判断处理,再输出,如下所示。 + +```java +// 只对字符串数量大于15的句子进行处理 +// 使用匿名函数 +DataStream longSentenceWords = dataStream.flatMap(new FlatMapFunction() { + @Override + public void flatMap(String input, Collector collector) throws Exception { + if (input.length() > 15) { + for (String word: input.split(" ")) + collector.collect(word); + } + } +}); +``` + +:::tip +虽然`flatMap`可以完全替代`map`和`filter`,但Flink仍然保留了这三个API,主要因为`map`和`filter`的语义更明确:`map`可以表示一对一的转换,代码阅读者能够确认对于一个输入,肯定能得到一个输出;`filter`则明确表示发生了过滤操作。更明确的语义有助于提高代码的可读性。 +::: + +Scala的API相对更简单一些: + +```scala +val dataStream: DataStream[String] = +senv.fromElements("Hello World", "Hello this is Flink") + +val words = dataStream.flatMap ( input => input.split(" ") ) + +val words2 = dataStream.map { _.split(" ") } +``` + +## 基于Key的分组转换 + +对数据分组主要是为了进行后续的聚合操作,即对同组数据进行聚合分析。如下图所示,`keyBy`会将一个`DataStream`转化为一个`KeyedStream`,聚合操作会将`KeyedStream`转化为`DataStream`。如果聚合前每个元素数据类型是T,聚合后的数据类型仍为T。 + +![DataStream和KeyedStream的转换关系](./img/keyedstream-datastream.png) + +### keyBy + +绝大多数情况,我们要根据事件的某种属性或数据的某个字段进行分组,然后对一个分组内的数据进行处理。如下图所示,`keyBy`算子根据元素的形状对数据进行分组,相同形状的元素被分到了一起,可被后续算子统一处理。比如,对股票数据流处理时,可以根据股票代号进行分组,然后对同一支股票统计其价格变动。又如,电商用户行为日志把所有用户的行为都记录了下来,如果要分析某一个用户行为,需要先按用户ID进行分组。 + +`keyBy`算子将`DataStream`转换成一个`KeyedStream`。`KeyedStream`是一种特殊的`DataStream`,事实上,`KeyedStream`继承了`DataStream`,`DataStream`的各元素随机分布在各算子实例中,`KeyedStream`的各元素按照Key分组,相同Key的数据会被分配到同一算子实例中。我们需要向`keyBy`算子传递一个参数,以告知Flink以什么作为Key进行分组。 + +![keyBy](./img/keyBy.png) + +我们可以使用数字位置来指定Key: + +```java +DataStream> dataStream = senv.fromElements( + Tuple2.of(1, 1.0), Tuple2.of(2, 3.2), Tuple2.of(1, 5.5), + Tuple2.of(3, 10.0), Tuple2.of(3, 12.5)); + +// 使用数字位置定义Key 按照第一个字段进行分组 +DataStream> keyedStream = dataStream.keyBy(0).sum(1); +``` + +也可以使用字段名来指定Key。比如,我们有一个`Word`类: + +```java +public class Word { + + public String word; + public int count; + + public Word() {} + + public Word(String word, int count) { + this.word = word; + this.count = count; + } + + public static Word of(String word, int count) { + return new Word(word, count); + } + + @Override + public String toString() { + return this.word + ": " + this.count; + } +} +``` +我们可以直接用`Word`中的字段名`word`来选择Key。 +```java +DataStream fieldNameStream = wordStream.keyBy("word").sum("count"); +``` + +:::info +这种方法只适用于[数据类型和序列化](data-types.md#composite-types)章节中提到的Scala case class或Java POJO类型的数据。 +::: + +指定Key本质上是实现一个`KeySelector`,在Flink源码中,它是这么定义的: + +```java +// IN为数据流元素,KEY为所选择的Key +@FunctionalInterface +public interface KeySelector extends Function, Serializable { + // 选择一个字段作为Key + KEY getKey(IN value) throws Exception; +} +``` + +我们可以重写`getKey()`方法,如下所示: + +```java +DataStream wordStream = senv.fromElements( + Word.of("Hello", 1), Word.of("Flink", 1), + Word.of("Hello", 2), Word.of("Flink", 2) +); + +// 使用KeySelector +DataStream keySelectorStream = wordStream.keyBy(new KeySelector () { + @Override + public String getKey(Word in) { + return in.word; + } + }).sum("count"); +``` + +一旦按照Key分组后,我们后续可以对每组数据进行时间窗口的处理以及状态的创建和更新。数据流里相同Key的数据都可以访问和修改相同的状态,如何使用时间和状态将在后续章节中分别介绍。 + +### Aggregation + +常见的聚合操作有`sum`、`max`、`min`等,这些聚合操作统称为聚合(Aggregation)。与批处理不同,这些聚合函数是对流数据进行统计,流数据是依次进入Flink的,聚合操作是对流入的数据进行实时统计,并不断输出到下游。 + +使用聚合函数时,我们需要一个参数来指定按照哪个字段进行聚合。跟`keyBy`相似,我们可以使用数字位置来指定对哪个字段进行聚合,也可以实现一个`KeySelector`。 + +`sum`算子对该字段进行加和,并将结果保存在该字段上,它无法确定其他字段的数值,或者说无法保证其他字段的计算结果。下面的例子中,`sum`对第二个字段求和,他只保证了第二个字段的求和结果的正确性,第三个字段是不确定的。 + +```java +DataStream> tupleStream = + senv.fromElements( + Tuple3.of(0, 0, 0), Tuple3.of(0, 1, 1), Tuple3.of(0, 2, 2), + Tuple3.of(1, 0, 6), Tuple3.of(1, 1, 7), Tuple3.of(1, 0, 8)); + +// 按第一个字段分组,对第二个字段求和,打印出来的结果如下: +// (0,0,0) +// (0,1,0) +// (0,3,0) +// (1,0,6) +// (1,1,6) +// (1,1,6) +DataStream> sumStream = tupleStream.keyBy(0).sum(1); +``` + +`max`算子对该字段求最大值,并将结果保存在该字段上。对于其他字段,该操作并不能保证其数值的计算结果。下面的例子对第三个字段求最大值,第二个字段是不确定的。 + +```java +// 按第一个字段分组,对第三个字段求最大值max,打印出来的结果如下: +// (0,0,0) +// (0,0,1) +// (0,0,2) +// (1,0,6) +// (1,0,7) +// (1,0,8) +DataStream> maxStream = tupleStream.keyBy(0).max(2); +``` + +`maxBy`算子对该字段求最大值,`maxBy`与`max`的区别在于,`maxBy`同时保留其他字段的数值,即`maxBy`返回数据流中最大的整个元素,包括其他字段。以下面的输入中Key为1的数据为例,我们要求第三个字段的最大值,Flink首先接收到`(1,0,6)`,当接收到`(1,1,7)`时,最大值发生变化,Flink将`(1,1,7)`这整个元组返回,当`(1,0,8)`到达时,最大值再次发生变化,Flink将`(1,0,8)`这整个元组返回。反观`max`,它只负责所求的字段,其他字段概不负责,无法保证其他字段的结果。因此,`maxBy`保证的是最大值的整个元素,`max`只保证最大值的字段。 + +```java +// 按第一个字段分组,对第三个字段求最大值maxBy,打印出来的结果如下: +// (0,0,0) +// (0,1,1) +// (0,2,2) +// (1,0,6) +// (1,1,7) +// (1,0,8) +DataStream> maxByStream = tupleStream.keyBy(0).maxBy(2); +``` + +同样,`min`和`minBy`的区别在于,`min`算子对某字段求最小值,`minBy`返回具有最小值的整个元素。 + +其实,这些聚合操作里已经使用了状态数据,比如,`sum`算子内部记录了当前的和,`max`算子内部记录了当前的最大值。算子的计算过程其实就是不断更新状态数据的过程。由于内部使用了状态数据,而且状态数据并不会被清理,因此一定要慎重地在一个无限数据流上使用这些聚合操作。 + +:::info +对于一个`KeyedStream`,一次只能使用一个Aggregation聚合操作,无法链式使用多个。 +::: + +### reduce + +前面几个Aggregation是几个较为常用的操作,对分组数据进行处理更为通用的方法是使用`reduce`算子。 + +![reduce](./img/reduce.png) + +上图展示了`reduce`算子的原理:`reduce`在分组的数据流上生效,它接受两个输入,生成一个输出,即两两合一地进行汇总操作,生成一个同类型的新元素。 + +例如,我们定义一个学生分数类: + +```java +public static class Score { + public String name; + public String course; + public int score; + + public Score(){} + + public Score(String name, String course, int score) { + this.name = name; + this.course = course; + this.score = score; + } + + public static Score of(String name, String course, int score) { + return new Score(name, course, score); + } + + @Override + public String toString() { + return "(" + this.name + ", " + this.course + ", " + Integer.toString(this.score) + ")"; + } +} +``` + +在这个类上进行`reduce`: + +```java +DataStream dataStream = senv.fromElements( + Score.of("Li", "English", 90), Score.of("Wang", "English", 88), + Score.of("Li", "Math", 85), Score.of("Wang", "Math", 92), + Score.of("Liu", "Math", 91), Score.of("Liu", "English", 87)); + +// 实现ReduceFunction +DataStream sumReduceFunctionStream = dataStream + .keyBy("name") + .reduce(new MyReduceFunction()); +``` + +其中`MyReduceFunction`继承并实现了`ReduceFunction`: + +```java +public static class MyReduceFunction implements ReduceFunction { + @Override + public Score reduce(Score s1, Score s2) { + return Score.of(s1.name, "Sum", s1.score + s2.score); + } +} +``` + +使用Lambda表达式更简洁一些: + +```java +// 使用 Lambda 表达式 +DataStream sumLambdaStream = dataStream + .keyBy("name") + .reduce((s1, s2) -> Score.of(s1.name, "Sum", s1.score + s2.score)); +``` + +## 多数据流转换 + +很多情况下,我们需要对多个数据流进行整合处理。 + +### union + +在`DataStream`上使用`union`算子可以合并多个同类型的数据流,或者说,可以将多个`DataStream`合并为一个新的`DataStream`。数据将按照先进先出(First In First Out)的模式合并,且不去重。下图中,`union`对白色和深色两个数据流进行合并,生成一个数据流。 + +![union示意图](./img/union.png) + +假设股票价格数据流来自不同的交易所,我们将其合并成一个数据流: + +```java +DataStream shenzhenStockStream = ... +DataStream hongkongStockStream = ... +DataStream shanghaiStockStream = ... +DataStream unionStockStream = shenzhenStockStream.union(hongkongStockStream, shanghaiStockStream); +``` + +### connect + +`union`虽然可以合并多个数据流,但有一个限制:多个数据流的数据类型必须相同。`connect`提供了和`union`类似的功能,用来连接两个数据流,它与`union`的区别在于: + +1. `connect`只能连接两个数据流,`union`可以连接多个数据流。 +2. `connect`所连接的两个数据流的数据类型可以不一致,`union`所连接的两个数据流的数据类型必须一致。 +3. 两个`DataStream`经过`connect`之后被转化为`ConnectedStreams`,`ConnectedStreams`会对两个流的数据应用不同的处理方法,且双流之间可以共享状态。 + +如下图所示,`connect`经常被应用在使用控制流对数据流进行控制处理的场景上。控制流可以是阈值、规则、机器学习模型或其他参数。 + +![对一个数据流进行控制处理](./img/connect-control.png) + +两个`DataStream`经过`connect`之后被转化为`ConnectedStreams`。对于`ConnectedStreams`,我们需要重写`CoMapFunction`或`CoFlatMapFunction`。这两个接口都提供了三个泛型,这三个泛型分别对应第一个输入流的数据类型、第二个输入流的数据类型和输出流的数据类型。在重写函数时,对于`CoMapFunction`,`map1`处理第一个流的数据,`map2`处理第二个流的数据;对于`CoFlatMapFunction`,`flatMap1`处理第一个流的数据,`flatMap2`处理第二个流的数据。下面是`CoFlatMapFunction`在源码中的签名。 + +```java +// IN1为第一个输入流的数据类型 +// IN2为第二个输入流的数据类型 +// OUT为输出类型 +public interface CoFlatMapFunction extends Function, Serializable { + + // 处理第一个流的数据 + void flatMap1(IN1 value, Collector out) throws Exception; + + // 处理第二个流的数据 + void flatMap2(IN2 value, Collector out) throws Exception; +} +``` + +Flink并不能保证两个函数调用顺序,两个函数的调用依赖于两个数据流中数据的流入先后顺序,即第一个数据流有数据到达时,`map1`或`flatMap1`会被调用,第二个数据流有数据到达时,`map2`或`flatMap2`会被调用。下面的代码对一个整数流和一个字符串流进行了`connect`操作。 + +```java +DataStream intStream = senv.fromElements(1, 0, 9, 2, 3, 6); +DataStream stringStream = senv.fromElements("LOW", "HIGH", "LOW", "LOW"); + +ConnectedStreams connectedStream = intStream.connect(stringStream); +DataStream mapResult = connectedStream.map(new MyCoMapFunction()); + +// CoMapFunction三个泛型分别对应第一个流的输入、第二个流的输入,map之后的输出 +public static class MyCoMapFunction implements CoMapFunction { + @Override + public String map1(Integer input1) { + return input1.toString(); + } + + @Override + public String map2(String input2) { + return input2; + } +} +``` + +两个数据流`connect`之后,可以使用`FlatMapFunction`也可以使用`ProcessFunction`继续处理,可以做到类似SQL中的连接(Join)的效果,我们将在[ProcessFunction](../chapter-time-window/process-function.md#process-on-two-streams)部分讲解如何对两个数据流使用`connect`实现Join效果。Flink也提供了`join`算子,`join`主要作用在时间窗口上,`connect`相比而言更广义一些,关于`join`的介绍将在第五章时间相关章节中介绍。 + +## 并行度与数据重分布 + +### 并行度 + +第二章中我们曾经提到,Flink使用并行度来定义某个算子被切分为多少个算子子任务,或者说多少个算子实例、分区。我们编写的大部分Transformation转换操作能够形成一个逻辑视图,当实际运行时,逻辑视图中的算子会被并行切分为一到多个算子子任务,每个算子子任务处理一部分数据,各个算子并行地在多个子任务上执行。假如算子的并行度为2,那么它有两个子任务。 + +并行度可以在一个Flink作业的执行环境层面统一设置,这样将影响该作业所有算子并行度,也可以对某个算子单独设置其并行度。如果不进行任何设置,默认情况下,一个作业所有算子的并行度会依赖于这个作业的执行环境。如果一个作业在本地执行,那么并行度默认是本机CPU核心数。当我们将作业提交到Flink集群时,需要使用提交作业的Client,并指定一系列参数,其中一个参数就是并行度。 + +下面的代码展示了如何获取执行环境的默认并行度,如何更改执行环境的并行度。 + +```java +StreamExecutionEnvironment senv = + StreamExecutionEnvironment.getExecutionEnvironment(); + +// 获取当前执行环境的默认并行度 +int defaultParalleism = senv.getParallelism(); + +// 设置所有算子的并行度为4,表示所有算子的并行执行的实例数为4 +senv.setParallelism(4); +``` + +也可以对某个算子设置并行度: + +```java +dataStream.map(new MyMapper()).setParallelism(defaultParallelism * 2); +``` + +### 数据重分布 + +默认情况下,数据是自动分配到多个实例(或者称之为分区)上的。有的时候,我们需要手动在多个实例上进行数据分配,例如,我们知道某个实例上的数据过多,其他实例上的数据稀疏,产生了数据倾斜,这时我们需要将数据均匀分布到各个实例上,以避免部分分区负载过重。数据倾斜问题会导致整个作业的计算时间过长或者内存不足等问题。 + +本节涉及到的各个数据重分布算子的输入是`DataStream`,输出也是`DataStream`。`keyBy`也有对数据进行分组和数据重分布的功能,但`keyBy`输出的是`KeyedStream`。 + +#### shuffle + +`shuffle`基于正态分布,将数据随机分配到下游各算子实例上。 + +```java +dataStream.shuffle(); +``` + +#### rebalance与rescale + +`rebalance`使用Round-ribon思想将数据均匀分配到各实例上。Round-ribon是负载均衡领域经常使用的均匀分配的方法,上游的数据会轮询式地分配到下游的所有的实例上。如下图所示,上游的算子会将数据依次发送给下游所有算子实例。 + +![rebalance将数据轮询式地分配到下游实例上](./img/rebalance.png) + +```java +dataStream.rebalance(); +``` + +`rescale`与`rebalance`很像,也是将数据均匀分布到各下游各实例上,但它的传输开销更小,因为`rescale`并不是将每个数据轮询地发送给下游每个实例,而是就近发送给下游实例。 + +```java +dataStream.rescale(); +``` + +![上游两个实例下游四个实例时进行rescale](./img/rescale1.png) + +如上图所示,当上游有两个实例时,上游第一个实例将数据发送给下游第一个和第二个实例,上游第二个实例将数据发送给下游第三个和第四个实例,相比`rebalance`将数据发送给下游每个实例,`rescale`的传输开销更小。下图则展示了当上游有四个实例,下游有两个实例,上游前两个实例将数据发送给下游第一个实例,上游后两个实例将数据发送给下游第二个实例。 + +![上游四个实例下游两个实例时进行rescale](./img/rescale2.png) + +#### broadcast + +英文单词"broadcast"翻译过来为广播,在Flink里,数据会被复制并广播发送给下游的所有实例上。 + +```java +dataStream.broadcast(); +``` + +#### global + +`global`会将所有数据发送给下游算子的第一个实例上,使用这个算子时要小心,以免造成严重的性能问题。 + +```java +dataStream.global(); +``` + +#### partitionCustom + +我们也可以在`DataStream`上使用`partitionCustom`来自定义数据重分布逻辑。下面是`partitionCustom`的源码,它有两个参数:第一个参数是自定义的`Partitioner`,我们需要重写里面的`partition`函数;第二个参数是对数据流哪个字段使用`partiton`逻辑。 + +```java +public class DataStream { + public DataStream partitionCustom(Partitioner partitioner, int field) { + ... + } + + public DataStream partitionCustom(Partitioner partitioner, String field) { + ... + } + + public DataStream partitionCustom(Partitioner partitioner, KeySelector keySelector) { + ... + } +} +``` + +下面为`Partitioner`的源码,`partition`函数的返回一个整数,表示该元素将被路由到下游第几个实例。 + +```java +@FunctionalInterface +public interface Partitioner extends java.io.Serializable, Function { + + // 根据key决定该数据分配到下游第几个分区(实例) + int partition(K key, int numPartitions); +} +``` + +`Partitioner[K]`中泛型K为根据哪个字段进行分区,比如我们要对一个`Score`数据流重分布,希望按照id均匀分配到下游各实例,那么泛型K就为id的数据类型`Long`。同时,泛型K也是`int partition(K key, int numPartitions)`函数的第一个参数的数据类型。 + +```java +public class Score { + public Long id; + public String name; + public Double score; +} +``` + +在调用`partitionCustom(partitioner, field)`时,第一个参数是我们重写的`Partitioner`,第二个参数表示按照id字段进行处理。 + +`partitionCustom`涉及的类型和函数有点多,使用例子解释更为直观。下面的代码按照数据流中的第二个字段进行数据重分布,当该字段中包含数字时,将被路由到下游算子的前半部分,否则被路由到后半部分。如果设置并行度为4,表示所有算子的实例总数为4,或者说共有4个分区,那么如果字符串包含数字时,该元素将被分配到第0个和第1个实例上,否则被分配到第2个和第3个实例上。 + +```java +public class PartitionCustomExample { + + public static void main(String[] args) throws Exception { + + StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment(); + + // 获取当前执行环境的默认并行度 + int defaultParalleism = senv.getParallelism(); + + // 设置所有算子的并行度为4,表示所有算子的并行执行的实例数为4 + senv.setParallelism(4); + + DataStream> dataStream = senv.fromElements( + Tuple2.of(1, "123"), Tuple2.of(2, "abc"), + Tuple2.of(3, "256"), Tuple2.of(4, "zyx"), + Tuple2.of(5, "bcd"), Tuple2.of(6, "666")); + + // 对(Int, String)中的第二个字段使用 MyPartitioner 中的重分布逻辑 + DataStream> partitioned = dataStream.partitionCustom(new MyPartitioner(), 1); + + partitioned.print(); + + senv.execute("partition custom transformation"); + } + + /** + * Partitioner 其中泛型T为指定的字段类型 + * 重写partiton函数,并根据T字段对数据流中的所有元素进行数据重分配 + * */ + public static class MyPartitioner implements Partitioner { + + private Random rand = new Random(); + private Pattern pattern = Pattern.compile(".*\\d+.*"); + + /** + * key 泛型T 即根据哪个字段进行数据重分配,本例中是Tuple2(Int, String)中的String + * numPartitons 为当前有多少个并行实例 + * 函数返回值是一个Int 为该元素将被发送给下游第几个实例 + * */ + @Override + public int partition(String key, int numPartitions) { + int randomNum = rand.nextInt(numPartitions / 2); + + Matcher m = pattern.matcher(key); + if (m.matches()) { + return randomNum; + } else { + return randomNum + numPartitions / 2; + } + } + } +} +``` \ No newline at end of file diff --git a/doc/ch-datastream-api/user-define-functions.md b/doc/ch-datastream-api/user-define-functions.md new file mode 100644 index 0000000..c71e3ea --- /dev/null +++ b/doc/ch-datastream-api/user-define-functions.md @@ -0,0 +1,232 @@ +(user-define-functions)= +# 用户自定义函数 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +我们在[Transformations](transformations.md)部分中介绍了常用的一些操作,可以发现,使用Flink的算子必须进行自定义,自定义时可以使用Lambda表达式,也可以继承并重写函数类。本节将从源码和案例两方面对用户自定义函数进行一个总结和梳理。 + +## 函数类 + + +对于`map`、`flatMap`、`reduce`等方法,我们可以实现`MapFunction`、`FlatMapFunction`、`ReduceFunction`等`interface`接口。这些函数类签名中都有泛型参数,用来定义该函数的输入或输出的数据类型。我们要继承这些类,并重写里面的自定义函数。以`flatMap`对应的`FlatMapFunction`为例,它在源码中的定义为: + +```scala +package org.apache.flink.api.common.functions; + +@FunctionalInterface +public interface FlatMapFunction extends Function, Serializable { + void flatMap(T value, Collector out) throws Exception; +} +``` + +这是一个函数式接口类,它继承了Flink的`Function`函数式接口。我们在第二章中提到函数式接口,这正是只有一个抽象函数方法的接口类,其目的是为了方便应用Java Lambda表达式。此外,它还继承了`Serializable`,以便进行序列化,这是因为这些函数在运行过程中要发送到各个实例上,发送前后要进行序列化和反序列化。需要注意的是,使用这些函数时,一定要保证函数内的所有内容都可以被序列化。如果有一些不能被序列化的内容,或者使用接下来介绍的RichFunction函数类,或者重写Java的序列化和反序列化方法。 + +进一步观察`FlatMapFunction`发现,这个函数类有两个泛型T和O,T是输入,O是输出。在继承这个接口类时,要设置好对应的输入和输出数据类型,否则会报错。我们最终其实是要重写虚函数`flatMap`,函数的两个参数也与输入输出的泛型类型对应。参数`value`是`flatMap`的输入,数据类型是T,参数`out`是`flatMap`的输出,它是一个`Collector`,从`Collector`命名可以看出它起着收集的作用,最终输出成一个数据流,我们需要将类型为O的数据写入`out`。 + +下面的例子继承`FlatMapFunction`,并实现`flatMap`,只对长度大于limit的字符串切词: + +```scala +// 使用FlatMapFunction实现过滤逻辑,只对字符串长度大于 limit 的内容进行词频统计 +public static class WordSplitFlatMap implements FlatMapFunction { + + private Integer limit; + + public WordSplitFlatMap(Integer limit) { + this.limit = limit; + } + + @Override + public void flatMap(String input, Collector collector) throws Exception { + if (input.length() > limit) { + for (String word: input.split(" ")) + collector.collect(word); + } + } +} + +DataStream dataStream = senv.fromElements("Hello World", "Hello this is Flink"); +DataStream functionStream = dataStream.flatMap(new WordSplitFlatMap(10)); +``` + +## Lambda表达式 + +当不需要处理非常复杂的业务逻辑时,使用Lambda表达式可能是更好的选择,Lambda表达式能让代码更简洁紧凑。Java和Scala都可以支持Lambda表达式。 + +### Scala的Lambda表达式 + +我们先看对Lambda表达式支持最好的Scala。对于`flatMap`,Flink的Scala源码有三种定义,我们先看一下第一种的定义: + +```scala +def flatMap[O: TypeInformation](fun: (T, Collector[O]) => Unit): DataStream[O] = {...} +``` + +`flatMap`输入是泛型T,输出是泛型O,接收一个名为`fun`的Lambda表达式,`fun`形如`(T, Collector[O] => {...})`。Lambda表达式要将数据写到`Collector[O]`中。 + +我们继续以切词为例,程序可以写成下面的样子,`flatMap`中的内容是一个Lambda表达式。其中的`foreach(out.collect)`本质上也是一个Lambda表达式。从这个例子可以看出,Scala的无所不在的函数式编程思想。 + +```scala +val lambda = dataStream.flatMap{ + (value: String, out: Collector[String]) => { + if (value.size > 10) { + value.split(" ").foreach(out.collect) + } + } +} +``` + +然后我们看一下源码中Scala的第二种定义: + +```scala +def flatMap[O: TypeInformation](fun: T => TraversableOnce[O]): DataStream[O] = {...} +``` + +与之前的不同,这里的Lambda表达式输入是泛型T,输出是一个`TraversableOnce[O]`,`TraversableOnce`表示这是一个O组成的列表。与之前使用`Collector`收集输出不同,这里直接输出一个列表,Flink帮我们将列表做了展平。使用`TraversableOnce`也导致我们无论如何都要返回一个列表,即使是一个空列表,否则无法匹配函数的定义。总结下来,这种场景的Lambda表达式输入是一个T,无论如何输出都是一个O的列表,即使是一个空列表。 + +```scala +// 只对字符串数量大于15的句子进行处理 +val longSentenceWords = dataStream.flatMap { + input => { + if (input.size > 15) { + // 输出是 TraversableOnce 因此返回必须是一个列表 + // 这里将Array[String]转成了Seq[String] + input.split(" ").toSeq + } else { + // 为空时必须返回空列表,否则返回值无法与TraversableOnce匹配! + Seq.empty + } + } +} +``` + +在使用Lambda表达式时,我们应该逐渐学会使用IntelliJ Idea的类型检查和匹配功能。比如在本例中,如果返回值不是一个`TraversableOnce`,那么IntelliJ Idea会将该行标红,告知我们输入或输出的类型不匹配。 + +此外,还有第三种只针对Scala的Lambda表达式使用方法。Flink为了保持Java和Scala API的一致性,一些Scala独有的特性没有被放入标准的API,而是集成到了一个扩展包中。这种API支持类型匹配的偏函数(Partial Function),结合case关键字,能够在语义上更好地描述数据类型: + +```scala +val data: DataStream[(String, Long, Double)] = ... +data.flatMapWith { + case (symbol, timestamp, price) => // ... +} +``` + +使用这种API时,需要添加引用: + +```scala +import org.apache.flink.streaming.api.scala.extensions._ +``` + +这种方式给输入定义了变量名和类型,方便阅读者阅读代码,同时也保留了函数式编程的简洁。Spark的大多数算子默认都支持此功能,Flink没有默认支持此功能,而是将这个功能放到了扩展包里,对于Spark用户来说,迁移到Flink时需要注意这个区别。此外`mapWith`、`filterWith`、`keyingBy`、`reduceWith`也分别是其他算子相对应的接口。 + +使用`flatMapWith`,之前的切词可以实现为: + +```scala +val flatMapWith = dataStream.flatMapWith { + case (sentence: String) => { + if (sentence.size > 15) { + sentence.split(" ").toSeq + } else { + Seq.empty + } + } +} +``` + +### Java + +再来看看Java,因为一些遗留问题,它的Lambda表达式使用起来有一些区别。 + +第二章中提到,Java有类型擦除问题,`void flatMap(IN value, Collector out)`编译成了`void flatMap(IN value, Collector out)`,擦除了泛型信息,Flink无法自动获取返回类型,如果不做其他操作,会抛出异常。 + +``` +org.apache.flink.api.common.functions.InvalidTypesException: The generic type parameters of 'Collector' are missing. + In many cases lambda methods don't provide enough information for automatic type extraction when Java generics are involved. + An easy workaround is to use an (anonymous) class instead that implements the 'org.apache.flink.api.common.functions.FlatMapFunction' interface. + Otherwise the type has to be specified explicitly using type information. +``` + +这种情况下,根据报错提示,或者使用一个类实现`FlatMapFunction`(包括匿名类),或者添加类型信息。这个类型信息,正是[数据类型和序列化](./data-types)章节中所介绍的数据类型。 + +```java +DataStream words = dataStream.flatMap ( + (String input, Collector collector) -> { + for (String word : input.split(" ")) { + collector.collect(word); + } + }) + // 提供类型信息以解决类型擦除问题 + .returns(Types.STRING); +``` + +通过这里对Scala和Java的对比不难发现,Scala更灵活,Java更严谨,各有优势。 + +## Rich函数类 + +在上面两种自定义方法的基础上,Flink还提供了RichFunction函数类。从名称上来看,这种函数类在普通的函数类上增加了Rich前缀,比如`RichMapFunction`、`RichFlatMapFunction`或`RichReduceFunction`等等。比起普通的函数类,Rich函数类增加了: + +* `open()`方法:Flink在算子调用前会执行这个方法,可以用来进行一些初始化工作。 +* `close()`方法:Flink在算子最后一次调用结束后执行这个方法,可以用来释放一些资源。 +* `getRuntimeContext()`方法:获取运行时上下文。每个并行的算子子任务都有一个运行时上下文,上下文记录了这个算子运行过程中的一些信息,包括算子当前的并行度、算子子任务序号、广播数据、累加器、监控数据。最重要的是,我们可以从上下文里获取状态数据。 + +我们可以看一下源码中的函数签名: + +```java +public abstract class RichFlatMapFunction extends AbstractRichFunction implements FlatMapFunction +``` + +它既实现了`FlatMapFunction`接口类,又继承了`AbstractRichFunction`。其中`AbstractRichFunction`是一个抽象类,有一个成员变量`RuntimeContext`,有`open`、`close`和`getRuntimeContext`等方法。 + +我们尝试继承并实现`RichFlatMapFunction`,并使用一个累加器。首先简单介绍累加器的概念:在单机环境下,我们可以用一个for循环做累加统计,但是在分布式计算环境下,计算是分布在多台节点上的,每个节点处理一部分数据,因此单纯循环无法满足计算,累加器是大数据框架帮我们实现的一种机制,允许我们在多节点上进行累加统计。 + +```java +// 实现RichFlatMapFunction类 +// 添加了累加器 Accumulator +public static class WordSplitRichFlatMap extends RichFlatMapFunction { + + private int limit; + + // 创建一个累加器 + private IntCounter numOfLines = new IntCounter(0); + + public WordSplitRichFlatMap(Integer limit) { + this.limit = limit; + } + + @Override + public void open(Configuration parameters) throws Exception { + super.open(parameters); + // 在RuntimeContext中注册累加器 + getRuntimeContext().addAccumulator("num-of-lines", this.numOfLines); + } + + @Override + public void flatMap(String input, Collector collector) throws Exception { + + // 运行过程中调用累加器 + this.numOfLines.add(1); + + if(input.length() > limit) { + for (String word: input.split(" ")) + collector.collect(word); + } + } +} +``` + +在主逻辑中获取作业执行的结果,得到累加器中的值。 + +```java +// 获取作业执行结果 +JobExecutionResult jobExecutionResult = senv.execute("basic flatMap transformation"); +// 执行结束后 获取累加器的结果 +Integer lines = jobExecutionResult.getAccumulatorResult("num-of-lines"); +System.out.println("num of lines: " + lines); +``` + +累加器是RichFunction函数类提供的众多功能之一,RichFunction函数类最具特色的功能是第六章将要介绍的有状态计算。 \ No newline at end of file diff --git a/doc/ch-state-checkpoint/checkpoint.md b/doc/ch-state-checkpoint/checkpoint.md new file mode 100644 index 0000000..8d5be79 --- /dev/null +++ b/doc/ch-state-checkpoint/checkpoint.md @@ -0,0 +1,272 @@ +(checkpoint)= +# Checkpoint + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +在上一节中,我们介绍了Flink的状态都是基于本地的,而Flink又是一个部署在多节点的分布式系统,分布式系统经常出现进程被杀、节点宕机或网络中断等问题,那么本地的状态在遇到故障时如何保证不丢呢?Flink定期保存状态数据到存储上,故障发生后从之前的备份中恢复,这个过程被称为Checkpoint机制。Checkpoint为Flink提供了Exactly-Once的投递保障。本节将介绍Flink的Checkpoint机制的原理,介绍中会使用多个概念:快照(Snapshot)、分布式快照(Distributed Snapshot)、检查点(Checkpoint)等,这些概念均指的是Flink的Checkpoint机制提供的数据备份过程,读者可以将这些概念等同看待。 + +## Flink分布式快照流程 + +首先我们来看一下一个简单的Checkpoint的大致流程: + +1. 暂停处理新流入数据,将新数据缓存起来。 +2. 将算子子任务的本地状态数据拷贝到一个远程的持久化存储上。 +3. 继续处理新流入的数据,包括刚才缓存起来的数据。 + +Flink是在Chandy–Lamport算法[^1]的基础上实现了一种分布式快照算法。在介绍Flink的快照详细流程前,我们先要了解一下检查点分界线(Checkpoint Barrier)的概念。如下图所示,Checkpoint Barrier被插入到数据流中,它将数据流切分成段。Flink的Checkpoint逻辑是,一段新数据流入导致状态发生了变化,Flink的算子接收到Checpoint Barrier后,对状态进行快照。每个Checkpoint Barrier有一个ID,表示该段数据属于哪次Checkpoint。如下图所示,当ID为n的Checkpoint Barrier到达每个算子后,表示要对n-1和n之间状态更新做快照。Checkpoint Barrier有点像Event Time中的Watermark,它被插入到数据流中,但并不影响数据流原有的处理顺序。 + +![Checkpoint Barrier](./img/checkpoint-barrier.png) + +接下来,我们构建一个并行数据流图,用这个并行数据流图来演示Flink的分布式快照机制。这个数据流图的并行度为2,数据流会在这些并行算子上从Source流动到Sink。 + +首先,Flink的检查点协调器(Checkpoint Coordinator)触发一次Checkpoint(Trigger Checkpoint),这个请求会发送给Source的各个子任务。 + +![JobManager触发一次Checkpoint](./img/checkpoint-1.png) + + + +各Source算子子任务接收到这个Checkpoint请求之后,会将自己的状态写入到状态后端,生成一次快照,并且会向下游广播Checkpoint Barrier。 + +![Source将自身状态写入状态后端,向下游发送Checkpoint Barrier](./img/checkpoint-2.png) + + + +Source算子做完快照后,还会给Checkpoint Coodinator发送一个确认,告知自己已经做完了相应的工作。这个确认中包括了一些元数据,其中就包括刚才备份到State Backend的状态句柄,或者说是指向状态的指针。至此,Source完成了一次Checkpoint。跟Watermark的传播一样,一个算子子任务要把Checkpoint Barrier发送给所连接的所有下游子任务。 + +![Snapshot之后发送ACK给JobManager](./img/checkpoint-3.png) + +对于下游算子来说,可能有多个与之相连的上游输入,我们将算子之间的边称为通道。Source要将一个ID为n的Checkpoint Barrier向所有下游算子广播,这也意味着下游算子的多个输入通道里都会收到ID为n的Checkpoint Barrier,而且不同输入通道里Checkpoint Barrier的流入速度不同,ID为n的Checkpoint Barrier到达的时间不同。Checkpoint Barrier传播的过程需要进行对齐(Barrier Alignment),我们从数据流图中截取一小部分,以下图为例,来分析Checkpoint Barrier是如何在算子间传播和对齐的。 + +![Barrier在算子间传播过程](./img/barrier-alignment.png) + + + + + +如上图所示,对齐分为四步: + +1. 算子子任务在某个输入通道中收到第一个ID为n的Checkpoint Barrier,但是其他输入通道中ID为n的Checkpoint Barrier还未到达,该算子子任务开始准备进行对齐。 +2. 算子子任务将第一个输入通道的数据缓存下来,同时继续处理其他输入通道的数据,这个过程被称为对齐。 +3. 第二个输入通道ID为n的Checkpoint Barrier抵达该算子子任务,所有通道ID为n的Checkpoint Barrier都到达该算子子任务,该算子子任务执行快照,将状态写入State Backend,然后将ID为n的Checkpoint Barrier向下游所有输出通道广播。 +4. 对于这个算子子任务,快照执行结束,继续处理各个通道中新流入数据,包括刚才缓存起来的数据。 + +数据流图中的每个算子子任务都要完成一遍上述的对齐、快照、确认的工作,当最后所有Sink算子确认完成快照之后,说明ID为n的Checkpoint执行结束,Checkpoint Coordinator向State Backend写入一些本次Checkpoint的元数据。 + +![Sink算子向JobManager发送ACK,一次Checkpoint完成](./img/checkpoint-4.png) + +之所以要进行对齐,主要是为了保证一个Flink作业所有算子的状态是一致的,也就是说,一个Flink作业前前后后所有算子写入State Backend的状态都是基于同样的数据。 + +## 快照性能优化方案 + +前面和大家介绍了一致性快照的具体流程,这种方式保证了数据的一致性,但有一些潜在的问题: + +1. 每次进行Checkpoint前,都需要暂停处理新流入数据,然后开始执行快照,假如状态比较大,一次快照可能长达几秒甚至几分钟。 +2. Checkpoint Barrier对齐时,必须等待所有上游通道都处理完,假如某个上游通道处理很慢,这可能造成整个数据流堵塞。 + +针对这些问题Flink已经有了一些解决方案,并且还在不断优化。 + +对于第一个问题,Flink提供了异步快照(Asynchronous Snapshot)的机制。当实际执行快照时,Flink可以立即向下广播Checkpoint Barrier,表示自己已经执行完自己部分的快照。同时,Flink启动一个后台线程,它创建本地状态的一份拷贝,这个线程用来将本地状态的拷贝同步到State Backend上,一旦数据同步完成,再给Checkpoint Coordinator发送确认信息。拷贝一份数据肯定占用更多内存,这时可以利用写入时复制(Copy-on-Write)的优化策略。Copy-on-Write指:如果这份内存数据没有任何修改,那没必要生成一份拷贝,只需要有一个指向这份数据的指针,通过指针将本地数据同步到State Backend上;如果这份内存数据有一些更新,那再去申请额外的内存空间并维护两份数据,一份是快照时的数据,一份是更新后的数据。是否开启Asynchronous Snapshot是可以配置的,下一节使用不同的State Backend将介绍如何配置。 + +对于第二个问题,Flink允许跳过对齐这一步,或者说一个算子子任务不需要等待所有上游通道的Checkpoint Barrier,直接将Checkpoint Barrier广播,执行快照并继续处理后续流入数据。为了保证数据一致性,Flink必须将那些上下游正在传输的数据也作为状态保存到快照中,一旦重启,这些元素会被重新处理一遍。这种不需要对齐的Checkpoint机制被称为Unaligned Checkpoint,我们可以通过`env.getCheckpointConfig().enableUnalignedCheckpoints();`开启Unaligned Checkpoint。Unaligned Checkpoint也是支持Exactly-Once的。Unaligned Checkpoint不执行Checkpoint Barrier对齐,因此在负载较重的场景下表现更好,但这并不意味这Unaligned Checkpoint就是最优方案,由于要将正在传输的数据也进行快照,状态数据会很大,磁盘负载会加重,同时更大的状态意味着重启后状态恢复的时间也更长,运维管理的难度更大。 + +## State Backend + +前面已经分享了Flink的快照机制,其中State Backend起到了持久化存储数据的重要功能。Flink将State Backend抽象成了一种插件,并提供了三种State Backend,每种State Backend对数据的保存和恢复方式略有不同。接下来我们开始详细了解一下Flink的State Backend。 + +### MemoryStateBackend + +从名字中可以看出,这种State Backend主要基于内存,它将数据存储在Java的堆区。当进行分布式快照时,所有算子子任务将自己内存上的状态同步到JobManager的堆上。因此,一个作业的所有状态要小于JobManager的内存大小。这种方式显然不能存储过大的状态数据,否则将抛出`OutOfMemoryError`异常。这种方式只适合调试或者实验,不建议在生产环境下使用。下面的代码告知一个Flink作业使用内存作为State Backend,并在参数中指定了状态的最大值,默认情况下,这个最大值是5MB。 + +```java +env.setStateBackend(new MemoryStateBackend(MAX_MEM_STATE_SIZE)); +``` + +如果不做任何配置,默认情况是使用内存作为State Backend。 + +### FsStateBackend + +这种方式下,数据持久化到文件系统上,文件系统包括本地磁盘、HDFS以及包括Amazon、阿里云在内的云存储服务。使用时,我们要提供文件系统的地址,尤其要写明前缀,比如:`file://`、`hdfs://`或`s3://`。此外,这种方式支持Asynchronous Snapshot,默认情况下这个功能是开启的,可加快数据同步速度。 + +```java +// 使用HDFS作为State Backend +env.setStateBackend(new FsStateBackend("hdfs://namenode:port/flink-checkpoints/chk-17/")); + +// 使用阿里云OSS作为State Backend +env.setStateBackend(new FsStateBackend("oss:///")); + +// 使用Amazon作为State Backend +env.setStateBackend(new FsStateBackend("s3:///")); + +// 关闭Asynchronous Snapshot +env.setStateBackend(new FsStateBackend(checkpointPath, false)); +``` + +Flink的本地状态仍然在TaskManager的内存堆区上,直到执行快照时状态数据会写到所配置的文件系统上。因此,这种方式能够享受本地内存的快速读写访问,也能保证大容量状态作业的故障恢复能力。 + +### RocksDBStateBackend + +这种方式下,本地状态存储在本地的RocksDB上。RocksDB是一种嵌入式Key-Value数据库,数据实际保存在本地磁盘上。比起`FsStateBackend`的本地状态存储在内存中,RocksDB利用了磁盘空间,所以可存储的本地状态更大。然而,每次从RocksDB中读写数据都需要进行序列化和反序列化,因此读写本地状态的成本更高。快照执行时,Flink将存储于本地RocksDB的状态同步到远程的存储上,因此使用这种State Backend时,也要配置分布式存储的地址。Asynchronous Snapshot在默认情况也是开启的。 + +此外,这种State Backend允许增量快照(Incremental Checkpoint),Incremental Checkpoint的核心思想是每次快照时只对发生变化的数据增量写到分布式存储上,而不是将所有的本地状态都拷贝过去。Incremental Checkpoint非常适合超大规模的状态,快照的耗时将明显降低,同时,它的代价是重启恢复的时间更长。默认情况下,Incremental Checkpoint没有开启,需要我们手动开启。 + +```java +// 开启Incremental Checkpoint +boolean enableIncrementalCheckpointing = true; +env.setStateBackend(new RocksDBStateBackend(checkpointPath, enableIncrementalCheckpointing)); +``` + +相比`FsStateBackend`,`RocksDBStateBackend`能够支持的本地和远程状态都更大,Flink社区已经有TB级的案例。 + +除了上述三种之外,开发者也可以自行开发State Backend的具体实现。 + +## 故障重启恢复流程 + +### 重启恢复基本流程 + +Flink的重启恢复逻辑相对比较简单: + +1. 重启应用,在集群上重新部署数据流图。 +2. 从持久化存储上读取最近一次的Checkpoint数据,加载到各算子子任务上。 +3. 继续处理新流入的数据。 + +这样的机制可以保证Flink内部状态的Excatly-Once一致性。至于端到端的Exactly-Once一致性,要根据Source和Sink的具体实现而定,我们还会在第7章端到端Exactly-Once详细讨论。当发生故障时,一部分数据有可能已经流入系统,但还未进行Checkpoint,Source的Checkpoint记录了输入的Offset;当重启时,Flink能把最近一次的Checkpoint恢复到内存中,并根据Offset,让Source从该位置重新发送一遍数据,以保证数据不丢不重。像Kafka等消息队列是提供重发功能的,`socketTextStream`就不具有这种功能,也意味着不能保证端到端的Exactly-Once投递保障。 + +当一个作业出现故障,进行重启时,势必会暂停一段时间,这段时间上游数据仍然继续发送过来。作业被重新拉起后,肯定需要将刚才未处理的数据消化掉。这个过程可以被理解为,一次跑步比赛,运动员不慎跌倒,爬起来重新向前追击。为了赶上当前最新进度,作业必须以更快的速度处理囤积的数据。所以,在设定资源时,我们必须留出一定的富余量,以保证重启后这段“赶进度”过程中的资源消耗。 + +### 三种重启策略 + +一般情况下,一个作业遇到一些异常情况会导致运行异常,潜在的异常情况包括:机器故障、部署环境抖动、流量激增、输入数据异常等。以输入数据异常为例,如果一个作业发生了故障重启,如果触发故障的原因没有根除,那么重启之后仍然会出现故障。因此,在解决根本问题之前,一个作业很可能无限次地故障重启,陷入死循环。为了避免重启死循环,Flink提供了三种重启策略: + +* 固定延迟(Fixed Delay)策略:作业每次失败后,按照设定的时间间隔进行重启尝试,重启次数不会超过某个设定值。 +* 失败率(Failure Rate)策略:计算一个时间段内作业失败的次数,如果失败次数小于设定值,继续重启,否则不重启。 +* 不重启(No Restart)策略:不对作业进行重启。 + +重启策略的前提是作业进行了Checkpoint,如果作业未设置Checkpoint,则会使用No Restart的策略。重启策略可以在`conf/flink-conf.yaml`中设置,所有使用这个配置文件执行的作业都将采用这样的重启策略;也可以在单个作业的代码中配置重启策略。 + +#### Fixed Delay + +Fixed Delay策略下,作业最多重启次数不会超过某个设定值,两次重启之间有一个可设定的延迟时间。例如,我们在`conf/flink-conf.yaml`中设置为: + +```yaml +restart-strategy: fixed-delay +restart-strategy.fixed-delay.attempts: 3 +restart-strategy.fixed-delay.delay: 10 s +``` + +这表示作业最多自动重启3次,两次重启之间有10秒的延迟。超过最多重启次数后,该作业被认定为失败。两次重启之间有延迟,是考虑到一些作业与外部系统有连接,连接一般会设置超时,频繁建立连接对数据准确性和作业运行都不利。如果在程序中用代码配置,可以写为: + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +// 开启Checkpoint +env.enableCheckpointing(5000L); +env.setRestartStrategy( + RestartStrategies.fixedDelayRestart( + 3, // 尝试重启次数 + Time.of(10L, TimeUnit.SECONDS) // 两次重启之间的延迟为10秒 + )); +``` + +如果开启了Checkpoint,但没有设置重启策略,Flink会默认使用这个策略,最大重启次数为`Integer.MAX_VALUE`。 + +#### Failure Rate + +Failure Rate策略下,在设定的时间内,重启失败次数小于设定阈值,该作业继续重启,重启失败次数超出设定阈值,该作业被最终认定为失败。两次重启之间会有一个等待的延迟。例如,我们在`conf/flink-conf.yaml`中设置为: + +```yaml +restart-strategy: failure-rate +restart-strategy.failure-rate.max-failures-per-interval: 3 +restart-strategy.failure-rate.failure-rate-interval: 5 min +restart-strategy.failure-rate.delay: 10 s +``` + +这表示在5分钟的时间内,重启次数小于3次时,继续重启,否则认定该作业为失败。两次重启之间的延迟为10秒。在程序中用代码配置,可以写为: + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +// 开启Checkpoint +env.enableCheckpointing(5000L); +env.setRestartStrategy(RestartStrategies.failureRateRestart( + 3, // 5分钟内最多重启3次 + Time.of(5, TimeUnit.MINUTES), + Time.of(10, TimeUnit.SECONDS) // 两次重启之间延迟为10秒 +)); +``` + +#### No Restart + +No Restart策略下,一个作业遇到异常情况后,直接被判定为失败,不进行重启尝试。在`conf/flink-conf.yaml`中设置为: + +```yaml +restart-strategy: none +``` + +使用代码配置,可以写为: + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +env.setRestartStrategy(RestartStrategies.noRestart()); +``` + +## Checkpoint相关配置 + +默认情况下,Checkpoint机制是关闭的,需要调用`env.enableCheckpointing(n)`来开启,每隔n毫秒进行一次Checkpoint。Checkpoint是一种负载较重的任务,如果状态比较大,同时n值又比较小,那可能一次Checkpoint还没完成,下次Checkpoint已经被触发,占用太多本该用于正常数据处理的资源。增大n值意味着一个作业的Checkpoint次数更少,整个作业用于进行Checkpoint的资源更小,可以将更多的资源用于正常的流数据处理。同时,更大的n值意味着重启后,整个作业需要从更长的Offset开始重新处理数据。 + +此外,还有一些其他参数需要配置,这些参数统一封装在了`CheckpointConfig`里: + +```java +CheckpointConfig checkpointCfg = env.getCheckpointConfig(); +``` + +默认的Checkpoint配置使用了Checkpoint Barrier对齐功能,对齐会增加作业的负担,有一定延迟,但是可以支持Exactly-Once投递的。这里的Exactly-Once指的是除去Source和Sink外其他各算子的Exactly-Once,关于Exactly-Once,我们将在第七章进一步详细解释。Checkpoint Barrier对齐能保证在重启恢复时,各算子的状态对任一条数据只处理一次。如果作业对延迟的要求很低,那么应该使用At-Least-Once投递,不进行对齐,但某些数据会被处理多次。 + +```java +// 使用At-Least-Once +checkpointCfg.setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE); +``` + +如果一次Checkpoint超过一定时间仍未完成,直接将其终止,以免其占用太多资源: + +```java +// 超时时间1小时 +checkpointCfg.setCheckpointTimeout(3600*1000); +``` + +如果两次Checkpoint之间的间歇时间太短,那么正常的作业可能获取的资源较少,更多的资源被用在了Checkpoint上。对下面这个参数进行合理配置能保证数据流的正常处理。比如,设置这个参数为60秒,那么前一次Checkpoint结束后60秒内不会启动新的Checkpoint。这种模式只在整个作业最多允许1个Checkpoint时适用。 + +```java +// 两次Checkpoint的间隔为60秒 +checkpointCfg.setMinPauseBetweenCheckpoints(60*1000); +``` + +默认情况下一个作业只允许1个Checkpoint执行,如果某个Checkpoint正在进行,另外一个Checkpoint被启动,新的Checkpoint需要挂起等待。 + +```java +// 最多同时进行3个Checkpoint +checkpointCfg.setMaxConcurrentCheckpoints(3); +``` + +如果这个参数大于1,将与前面提到的最短间隔相冲突。 + +Checkpoint的初衷是用来进行故障恢复,如果作业是因为异常而失败,Flink会保存远程存储上的数据;如果开发者自己取消了作业,远程存储上的数据都会被删除。如果开发者希望通过Checkpoint数据进行调试,自己取消了作业,同时希望将远程数据保存下来,需要设置为: + +```java +// 作业取消后仍然保存Checkpoint +checkpointCfg.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION); +``` + +`RETAIN_ON_CANCELLATION`模式下,用户需要自己手动删除远程存储上的Checkpoint数据。 + +默认情况下,如果Checkpoint过程失败,会导致整个应用重启,我们可以关闭这个功能,这样Checkpoint失败不影响作业的运行。 + +```java +checkpointCfg.setFailOnCheckpointingErrors(false); +``` + +[1]: Leslie Lamport, K. Mani Chandy: Distributed Snapshots: Determining Global States of a Distributed System. In: *ACM Transactions on Computer Systems 3*. Nr. 1, Februar 1985. \ No newline at end of file diff --git a/doc/ch-state-checkpoint/exercise-state.md b/doc/ch-state-checkpoint/exercise-state.md new file mode 100644 index 0000000..6b7c263 --- /dev/null +++ b/doc/ch-state-checkpoint/exercise-state.md @@ -0,0 +1,37 @@ +(exercise-state)= +# 练习:电商用户行为 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +经过本章的学习,相信读者朋友已经了解状态的基本原理,包括如何使用Keyed State或Operator State进行有状态的计算。本节将继续以电商用户行为分析为场景,对状态相关知识进行实践。 + +## 实验目的 + +学习使用Keyed State,设置Checkpoint。 + +## 实验内容 + +在[状态](./state.md)章节的Keyed State部分,我们介绍了电商用户行为场景,并举了一些例子,本次练习仍然基于这个场景。我们知道,一天之内,一个用户第一次产生行为到真正购买,这之间有一个时间差,这个时间是一个非常重要的指标,有助于商家提升产品质量和营销水平。这里我们使用Keyed State来实现一个程序,主要用来计算这个时间差。 + +## 实验要求 + +读者可以根据本书样例程序中提供的数据集和Source作为输入,编程完成下面的要求。 + +* 要求1: + +使用Keyed State,计算每个用户当天第一次产生行为到第一次产生购买行为之间的时间差。在实现时需要注意,这里只考虑第一次产生购买行为,而不是多次产生购买行为中的最后一次。使用`print`将结果打印出来。 + +* 要求2: + +开启Checkpoint,选择一种State Backend,将状态定期保存到存储的某个位置。 + +## 实验报告 + +将思路和程序整理后撰写为实验报告。 \ No newline at end of file diff --git a/doc/ch-state-checkpoint/img/barrier-alignment.png b/doc/ch-state-checkpoint/img/barrier-alignment.png new file mode 100644 index 0000000000000000000000000000000000000000..74d1581dbb7a5fbd9ca0f744db880243e62ecbbd GIT binary patch literal 92352 zcmeFZcRZK<-#`3mC`nRLk&zNg!^$3IR5qzZMnWQGXJj;pL@FdZWF>oVWk)0{J3HBA zW!=xC^Lt&__j}#<@BZih_dXww&Ptzg9PjaZJ=gnqs-kp$2L(L^iA35VdqMgliL}X; zMB121z6C#d!FkqDO|mAJo5aQISM)U?Y(p;KhINqX$1pBpWUg z>DP4!h` ziZWXV$jGl9fJt$*z5@%hry@u(hPOCnj6*$U_w=$sL7q?*wQ!9K9G# zPELO8!Y(rZ=boPvI0_;kJUI1;oc!BnzhmEIFG!k2_`Y!c9oAL5uyw-uxa>g|!;5uJ z+V?-!DGBi!-@g|&%itZ!!F<@2T9TZ_l{%77w&@n1?zNR=gQWHb;wQw9Irw}@wN20e zzJmClFe?kqKY#JKD7Rbkzg`vn|Nod6ivN#ea>!?*r&u}rZr9YpLjCV{iS-q!$?n2j z+ew{>%1ndm-A{HLs0bCYr5LnJO-(JUtUM4NF76a`_VJdNuV25OtPJA6Lm$k4hesoZ zgTky}ddvNVi4%Uaw11~@+wmrovdzmlqd=5ANs^iZ&w<=TpU zK|w)E%Hle6HvMT6F3W+cjn9k+Kl>NVwOqTEsi_nuTK)3yd2>t4&-YfFnlACt@3?a1 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+# 状态与检查点 + +```{tableofcontents} +``` \ No newline at end of file diff --git a/doc/ch-state-checkpoint/savepoint.md b/doc/ch-state-checkpoint/savepoint.md new file mode 100644 index 0000000..6394271 --- /dev/null +++ b/doc/ch-state-checkpoint/savepoint.md @@ -0,0 +1,497 @@ +(savepoint)= +# Savepoint + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +## Savepoint与Checkpoint的区别 + +目前,Checkpoint和Savepoint在代码层面使用的分布式快照逻辑基本相同,生成的数据也近乎一样,那这两个相似的名字到底有哪些功能性的区别呢?Checkpoint的目的是为了故障重启,使得作业中的状态数据与故障重启之前保持一致,是一种应对意外情况的有力保障。Savepoint的目的是手动备份数据,以便进行调试、迁移、迭代等,是一种协助开发者的支持功能。一方面,一个流处理作业不可能一次性就写好了,我们要在一个初版代码的基础上不断修复问题、增加功能、优化算法、甚至做一些机房迁移,一个程序是在迭代中更新的;另外一方面,流处理作业一般都是长时间运行的,作业内部的状态数据从零开始重新生成的成本很高,状态数据迁移成本高。综合这两方面的因素,Flink提供了Savepoint的机制,允许开发者调试开发有状态的作业。 + +Flink的Checkpoint机制设计初衷为:第一,Checkpoint过程是轻量级的,尽量不影响正常数据处理;第二,故障恢复越快越好。开发者需要进行的操作并不多,少量的操作包括:设置多大的间隔来定期进行Checkpoint,使用何种State Backend。绝大多数工作是由Flink来处理的,比如Flink会定期执行快照,发生故障后,Flink自动从最近一次Checkpoint数据中恢复。随着作业的关停,Checkpoint数据一般会被Flink删除,除非开发者设置了保留Checkpoint数据。原则上,一个作业从Checkpoint数据中恢复,作业的代码和业务逻辑不能发生变化。 + +相比而下,Savepoint机制主要考虑的是:第一,刻意备份,第二,支持修改状态数据或业务逻辑。Savepoint相关操作是有计划的、人为的。开发者要手动触发、管理和删除Savepoint。比如,将当前状态保存下来之后,我们可以更新并行度,修改业务逻辑代码,甚至在某份代码基础上生成一个对照组来验证一些实验猜想。可见,Savepoint的数据备份和恢复都有更高的时间和人力成本,Savepoint数据也必须有一定的可移植性,能够适应数据或逻辑上的改动。具体而言,Savepoint的潜在应用场景有: + +* 我们可以给同一份作业设置不同的并行度,来找到最佳的并行度设置,每次可以从Savepoint中加载原来的状态数据。 + +* 我们想测试一个新功能或修复一个已知的bug,并用新的程序逻辑处理原来的数据。 + +* 进行一些A/B实验,使用相同的数据源测试程序的不同版本。 + +* 因为状态可以被持久化存储到分布式文件系统上,我们甚至可以将同样一份应用程序从一个集群迁移到另一个集群,只需保证不同的集群都可以访问这个文件系统。 + +可见,Checkpoint和Savepoint是Flink提供的两个相似的功能,它们满足了不同的需求,以确保一致性、容错性,满足了作业升级、BUG 修复、迁移、A/B测试等不同场景。 + +## Savepoint的使用方法 + +为了让Savepoint数据能够具有更好的兼容性和可移植性,我们在写一个Flink程序时需要为每个算子分配一个唯一ID。设置算子ID的目的在于将状态与Savepoint中的备份相对应。如下图所示,这个Flink作业共有三个算子:Source、Stateful Map和Stateless Sink。Source和Stateful Map分别有对应Operator State和Keyed State,Stateless Sink没有状态。 + +![将一个流处理作业状态映射到Savepoint](./img/savepoint.png) + +在实现这个数据流图时,我们需要给算子设置ID: + +```java +DataStream stream = env. + // 一个带有Operator State的Source,例如Kafka Source + .addSource(new StatefulSource()).uid("source-id") // 算子ID + .keyBy(...) + // 一个带有Keyed State的Stateful Map + .map(new StatefulMapper()).uid("mapper-id") // 算子ID + // print是一种无状态的Sink + .print(); // Flink为其自动分配一个算子ID +``` + +上面的例子中,我们给算子设置了ID。如果代码中不明确设置算子ID,那么Flink会为其自动分配一个ID。严格来说,我们应该为每个算子都设置ID,因为很多算子内在实现上是有状态的,比如窗口算子。除非我们能够非常确认某个算子无状态,可以不为其设置ID。 + +如果我们想对这个作业进行备份,我们需要使用命令行工具执行下面的命令: + +```bash +$ ./bin/flink savepoint [savepointDirectory] +``` + +这行命令将对一个正在运行的作业触发一次Savepoint的备份,备份数据将写到`savepointDirectory`上。例如,我们可以指定一个HDFS路径作为Savepoint数据存放地址。 + +如果我们想从一个Savepoint数据中恢复一个作业,我们需要执行: + +```bash +$ ./bin/flink run -s [OPTIONS] +``` + +## 读写Savepoint中的数据 + +Flink提供了一个名为State Processor API的功能,允许开发者读写Savepoint中的数据。它主要基于DataSet API,将Savepoint数据从远程存储读到内存中,对Savepoint数据进行处理,然后再保存到远程存储上。有了State Processor API,开发者在状态的修改和更新上有更大的自由度。例如,开发者可以先从其他位置读取数据,生成一份Savepoint,交给一个没有数据积累的流处理程序,用来做数据冷启动。 + +将上节提到的程序中的Savepoint进一步分解,其内在存储形式如下图所示。Savepoint对数据的存储就像数据库存储数据一样,数据是按照一定的模式来组织和存储的。名为`source-id`的算子使用的是一个Operator State,Operator State的名字为`os1`,`os1`中的数据以一个列表的形式存储;名为`mapper-id`的算子使用的是一个Keyed State,Keyed State的名字为`ks1`,`ks1`中的数据是Key-Value对。 + +![Savepoint中的数据存储形式](./img/savepoint-data.png) + +建立好上述的数据模型后,我们就可以像从数据库中读写数据那样,使用State Processor API来读写Savepoint中的数据。 + +State Processor API默认没有放在Flink的核心库中,使用之前需要先在`pom.xml`中引入正确的依赖: + +``` + + org.apache.flink + flink-state-processor-api_${scala.binary.version} + ${flink.version} + provided + +``` + +### 从Savepoint中读数据 + +首先,我们需要从一个存储路径上读取Savepoint。这里要使用批处理的DataSet API,执行环境为`ExecutionEnvironment`。 + +```java +// 使用批处理DataSet API的ExecutionEnvironment +ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment(); +// 存储这个Savepoint所使用的State Backend +StateBackend backend = ... +ExistingSavepoint savepoint = Savepoint.load(bEnv, "hdfs://path/", backend); + +bEnv.execute("read"); +``` + +上面的代码从存储路径上读取一个Savepoint,生成一个`ExistingSavepoint`。`ExistingSavepoint`是一个已经存在的Savepoint,这个类提供了一个从Savepoint中读取数据的入口。 + +#### Operator State + +读取时Operator State,我们需要指定算子ID、Operator State名字、数据类型。下面的代码读取了`source-id`下的`os1`: + +```java +DataSet listState = savepoint.readListState<>( + "source-id", + "os1", + Types.INT); +``` + +`readListState`方法读取ListState,它在源代码中的签名为: + +```java +public DataSet readListState(String uid, String name, TypeInformation typeInfo) { + ... +} + +public DataSet readListState( + String uid, + String name, + TypeInformation typeInfo, + TypeSerializer serializer) { + ... +} +``` + +其中,`uid`为上一节中我们设置的算子ID;`name`为这个状态的名字,我们在使用状态时,会在`ListStateDescriptor`里初始化一个名字;`typeInfo`为该状态的类型,用来进行序列化和反序列化;如果默认的序列化器不支持该类型,也可以传入一个自定义的序列化器。 + +UnionListState的读取方法与之类似: + +```java +DataSet listState = savepoint.readUnionState<>( + "my-uid", + "union-state", + Types.INT); +``` + +#### Keyed State + +流处理中,Keyed State应用在一个`KeyedStream`上,需要在`StateDescriptor`中指定状态的名字和数据类型。例如,在一个流处理作业中,我们继承`KeyedProcessFunction`,实现下面两个状态: + +```java +/** + * StatefulFunctionWithTime继承KeyedProcessFunction + * 接收Integer类型的输入,更新状态 + * 第一个泛型为KeyedStream中Key的类型 + * 第二个泛型为输入数据 + * 第三个泛型为输出数据 + */ +public class StatefulFunctionWithTime extends KeyedProcessFunction { + + ValueState state; + ListState updateTimes; + + @Override + public void open(Configuration parameters) { + ValueStateDescriptor stateDescriptor = new ValueStateDescriptor<>("state", Types.INT); + state = getRuntimeContext().getState(stateDescriptor); + + ListStateDescriptor updateDescriptor = new ListStateDescriptor<>("times", Types.LONG); + updateTimes = getRuntimeContext().getListState(updateDescriptor); + } + + // 接收输入数据value,更新状态 + @Override + public void processElement(Integer value, Context ctx, Collector out) throws Exception { + state.update(value + 1); + updateTimes.add(System.currentTimeMillis()); + } +} +``` + +现在这两个Keyed State已经存储到Savepoint中,从Savepoint中读取它们,我们需要设置算子ID、状态名字和序列化方式。具体到Keyed State的读取时,我们需要继承并实现`KeyedStateReaderFunction`,在这个函数类中使用`readKey`方法读取状态数据。下面的代码继承并实现`KeyedStateReaderFunction`,整个逻辑与上面代码中流处理使用Keyed State的过程有几分相似之处。 + +```java +// 定义一个存储状态的数据结构 +public class KeyedState { + public int key; + public int value; + public List times; +} + +/** + * 从Savepoint中读取Keyed State + * 第一个泛型Integer为Keyed State中Key的类型 + * 第二个泛型KeyedState为输出数据 + */ +public class ReaderFunction extends KeyedStateReaderFunction { + + ValueState state; + ListState updateTimes; + + // 获取状态句柄 + @Override + public void open(Configuration parameters) { + ValueStateDescriptor stateDescriptor = new ValueStateDescriptor<>("state", Types.INT); + state = getRuntimeContext().getState(stateDescriptor); + + ListStateDescriptor updateDescriptor = new ListStateDescriptor<>("times", Types.LONG); + updateTimes = getRuntimeContext().getListState(updateDescriptor); + } + + // 读取状态数据 + @Override + public void readKey( + Integer key, + Context ctx, + Collector out) throws Exception { + + KeyedState data = new KeyedState(); + data.key = key; + data.value = state.value(); + data.times = StreamSupport + .stream(updateTimes.get().spliterator(), false) + .collect(Collectors.toList()); + + // 将数据输出到Collector中 + out.collect(data); + } +} +``` + +读取Savepoint的主逻辑为: + +```java +// 从Savepoint数据中读取Keyed State +// ReaderFunction需要继承并实现KeyedStateReaderFunction +DataSet keyedState = savepoint.readKeyedState("mapper-id", new ReaderFunction()); +``` + +从上面的例子中可以看到,`readKeyedState`需要传入算子的ID和一个`KeyedStateReaderFunction`的具体实现,它在源码中的签名为: + +```java +/* + * 从Savepoint数据中读取Keyed State + * uid:算子ID + * function:一个KeyedStateReaderFunction + * K:Keyed State的Key类型 + * OUT:输出类型 + */ +public DataSet readKeyedState(String uid, KeyedStateReaderFunction function) { + ... +} +``` + +`KeyedStateReaderFunction`允许我们从Savepoint中读取Keyed State数据,我们需要实现`open`方法和`readKey`方法。其中,我们必须在`open`方法中注册`StateDescriptor`,获取状态句柄;在`readKey`方法中逐Key读取数据,输出到`Collector`中。`KeyedStateReaderFunction`和这些方法在源码中的定义为: + +```java +/** + * 从Savepoint中读取Keyed State + * 泛型K:Keyed State中Key的类型 + * 泛型OUT:输出数据 + */ +public abstract class KeyedStateReaderFunction extends AbstractRichFunction { + /** + * 初始化方法,用来注册StateDescriptor,获取状态句柄 + */ + public abstract void open(Configuration parameters) throws Exception; + + /** + * 从Keyed State中逐Key读取数据,输出到Collector + * 参数K为Keyed State中的每个Key + * 参数Context为上下文 + * 参数Collector用来收集输出,可以是零到多个输出 + */ + public abstract void readKey(K key, Context ctx, Collector out) throws Exception; + + /** + * 上下文Context + * Context只在readKey时有效 + */ + public interface Context { + + /** + * 返回当前Key所注册的Event Time Timer + */ + Set registeredEventTimeTimers() throws Exception; + + /** + * 返回当前Key所注册的Processing Time Timer + */ + Set registeredProcessingTimeTimers() throws Exception; + } +} +``` + +### 向Savepoint中写入数据 + +我们也可以从零开始构建状态,向Savepoint中写入数据,这个功能非常适合作业的冷启动。英文常使用Bootstrap这个词描述冷启动的过程,因此Flink设计的类名都会带有Bootstrap字样。具体而言,构建一个新的Savepoint时,需要实现一个名为`BootstrapTransformation`的操作,`BootstrapTransformation`表示一个状态写入的过程。从另一个角度来讲,我们可以将`BootstrapTransformation`理解成流处理时使用的有状态的算子。下面是一个Savepoint构建过程的主逻辑示例: + +```java +ExecutionEnvironment bEnv = ExecutionEnvironment.getExecutionEnvironment(); + +// 最大并行度 +int maxParallelism = 128; +StateBackend backend = ... + +// 准备好需要写入状态的数据 +DataSet accountDataSet = bEnv.fromCollection(accounts); +DataSet currencyDataSet = bEnv.fromCollection(currencyRates); + +// 构建一个BootstrapTransformation,将accountDataSet数据写入 +BootstrapTransformation transformation = OperatorTransformation + .bootstrapWith(accountDataSet) + .keyBy(acc -> acc.id) + .transform(new AccountBootstrapper()); + +// 构建一个BootstrapTransformation,将currencyDataSet数据写入 +BootstrapTransformation broadcastTransformation = OperatorTransformation + .bootstrapWith(currencyDataSet) + .transform(new CurrencyBootstrapFunction()); + +// 创建两个算子,算子ID分别为accounts、currency +Savepoint + .create(backend, maxParallelism) + .withOperator("accounts", transformation) + .withOperator("currency", broadcastTransformation) + .write(savepointPath); + +bEnv.execute("bootstrap"); +``` + +`Savepoint.create(backend, maxParallelism)`创建了一个新的Savepoint。`withOperator`方法向这个Savepoint中添加新的算子,它的两个参数分别为算子ID和一个`BootstrapTransformation`。`transformation`和`broadcastTransformation`就是两个`BootstrapTransformation`对象实例,他们的功能在于去模拟一个流处理中的有状态的算子,并写入状态数据。总体来讲,向Savepoint中写入数据需要三步: + +1. 准备好需要写入状态的数据`DataSet`。 +2. 构建一个`BootstrapTransformation`,将第一步准备好的数据写入这个`BootstrapTransformation`。 +3. 将构建好的`BootstrapTransformation`写入Savepoint。 + +Operator State和Keyed State的原理不同,因此所要实现的不同,下面将分别介绍这两种写入方式。 + +#### Operator State + +对于Operator State,我们要实现`StateBootstrapFunction`来写入状态数据,重点是实现它的`processElement`方法。每个输入进来之后,`processElement`方法都会被调用一次。下面是一个案例: + +```java +/** + * 继承并实现StateBootstrapFunction + * 泛型参数为输入类型 + */ +public class SimpleBootstrapFunction extends StateBootstrapFunction { + + private ListState state; + + // 每个输入都会调用一次processElement,这里将输入加入到状态中 + @Override + public void processElement(Integer value, Context ctx) throws Exception { + state.add(value); + } + + @Override + public void snapshotState(FunctionSnapshotContext context) throws Exception { + } + + // 获取状态句柄 + @Override + public void initializeState(FunctionInitializationContext context) throws Exception { + state = context.getOperatorState().getListState(new ListStateDescriptor<>("state", Types.INT)); + } +} +``` + +对应的主逻辑为: + +```java +ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnviornment(); +DataSet data = env.fromElements(1, 2, 3); + +BootstrapTransformation transformation = OperatorTransformation + // 使用data数据进行初始化 + .bootstrapWith(data) + .transform(new SimpleBootstrapFunction()); +``` + +#### Keyed State + +对于Keyed State,我们要实现`KeyedStateBootstrapFunction`来写入状态数据。同样,每来一个输入,`processElement`都会被调用一次。下面的代码中,Key为`Account`中的`id`。 + +```java +/** + * 表示账户信息的POJO类 + */ +public class Account { + public int id; + public double amount; + public long timestamp; +} + +/** + * AccountBootstrapper继承并实现了KeyedStateBootstrapFunction + * 第一个泛型Integer为Key类型 + * 第二个泛型Account为输入类型 + */ +public class AccountBootstrapper extends KeyedStateBootstrapFunction { + ValueState state; + + // 获取状态句柄 + @Override + public void open(Configuration parameters) { + ValueStateDescriptor descriptor = new ValueStateDescriptor<>("total",Types.DOUBLE); + state = getRuntimeContext().getState(descriptor); + } + + // 每个输入都会调用一次processElement + @Override + public void processElement(Account value, Context ctx) throws Exception { + state.update(value.amount); + } +} +``` + +对应的主逻辑为: + +```java +ExecutionEnvironment bEnv = + ExecutionEnvironment.getExecutionEnvironment(); + +DataSet accountDataSet = bEnv.fromCollection(accounts); + +BootstrapTransformation transformation = OperatorTransformation + // 使用accountDataSet数据进行初始化 + .bootstrapWith(accountDataSet) + .keyBy(acc -> acc.id) + .transform(new AccountBootstrapper()); + +Savepoint + .create(backend, 128) + .withOperator("accounts", transformation) + .write(savepointPath); +``` + +我们可以看看`KeyedStateBootstrapFunction`在源码中的签名: + +```java +/** + * 将Keyed State写入Savepoint + * 第一个泛型K为Key类型 + * 第二个泛型IN为输入类型 + */ +public abstract class KeyedStateBootstrapFunction extends AbstractRichFunction { + + private static final long serialVersionUID = 1L; + + /** + * 处理输入的每行数据,更新Keyed State + * Context可以用来构建时间相关属性 + * 当这个作业在流处理端重启后,时间相关属性可以用来触发计算 + */ + public abstract void processElement(IN value, Context ctx) throws Exception; + + /* 上下文 */ + public abstract class Context { + + // 访问时间,注册Timer + public abstract TimerService timerService(); + + // 返回当前Key + public abstract K getCurrentKey(); + } +} +``` + +可以看到`KeyedStateBootstrapFunction`继承了`AbstractRichFunction`,它拥有RichFunction函数类的方法和属性,比如`open`方法等,因此实现起来也与在流处理中使用状态非常相似。`processElement`对每个输入数据进行处理,我们可以根据业务需要写入到Keyed State中。此外,该方法提供了上下文`Context`,里面包含了第五章[ProcessFunction](../chapter-time-window/process-function.md)中提到的`TimerService`。借助于`TimerService`,我们可以访问时间,注册Timer。这些Timer在当前写入Savepoint的过程并不会触发,仅当Savepoint恢复成一个流处理作业时被触发。 + +### 修改Savepoint + +除了从零开始构建一个新的Savepoint,我们也可以从一个已有的Savepoint基础上做修改,然后再保存起来。比如,下面的代码从一个已存在的Savepoint中获取数据,进行修改,生成新的Savepoint。 + +```java +ExecutionEnvironment bEnv = + ExecutionEnvironment.getExecutionEnvironment(); + +DataSet data = bEnv.fromElements(1, 2, 3); + +BootstrapTransformation transformation = OperatorTransformation + .bootstrapWith(data) + .transform(new ModifyProcessFunction()); + +Savepoint + .load(bEnv, savepointPath, backend) + // 删除名为currency的算子 + .removeOperator("currency") + // 增加名为numbers的算子,使用transformation构建其状态数据 + .withOperator("number", transformation) + // 新的Savepoint会写到modifyPath路径下 + .write(modifyPath); + +bEnv.execute("modify"); +``` + +其中,`removeOperator`方法将一个算子状态数据从Savepoint中删除,`withOperator`方法增加了一个算子。修改完之后,我们可以通过`write`方法,将数据写入一个路径之下。 + +## Queryable State和State Processor API + +Flink提供的另外一个读取状态的API为Queryable State。使用Queryable State可以查询状态中的数据,其原理与State Processor API有相通之处。相比而下,两者侧重点各有不同,Queryable State重在查询状态,主要针对正在运行的线上服务,State Processor API可以修改状态,主要针对写入到Savepoint(或Checkpoint)中的数据。从侧重点上可以看到,两者所要解决的问题略有不同。感兴趣的读者前往官方查询Queryable State的使用方法。 \ No newline at end of file diff --git a/doc/ch-state-checkpoint/state.md b/doc/ch-state-checkpoint/state.md new file mode 100644 index 0000000..0a2f2a4 --- /dev/null +++ b/doc/ch-state-checkpoint/state.md @@ -0,0 +1,544 @@ +(state)= +# 实现有状态的计算 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +## 为什么要管理状态 + +有状态的计算是流处理框架要实现的重要功能,因为复杂的流处理场景都需要记录状态,然后在新流入数据的基础上不断更新状态。下面罗列了几个有状态计算的潜在场景: + +* 数据流中的数据有重复,我们想对重复数据去重,需要记录哪些数据已经流入过应用,当新数据流入时,根据已流入数据来判断去重。 + +* 检查输入流是否符合某个特定的模式,需要将之前流入的元素以状态的形式缓存下来。比如,判断一个温度传感器数据流中的温度是否在持续上升。 +* 对一个时间窗口内的数据进行聚合分析,分析一个小时内某项指标的75分位或99分位的数值。 +* 在线机器学习场景下,需要根据新流入数据不断更新机器学习的模型参数。 + +我们知道,Flink的一个算子有多个子任务,每个子任务分布在不同实例上,我们可以把状态理解为某个算子子任务在其当前实例上的一个变量,变量记录了数据流的历史信息。当新数据流入时,我们可以结合历史信息来进行计算。实际上,Flink的状态是由算子的子任务来创建和管理的。一个状态更新和获取的流程如下图所示,一个算子子任务接收输入流,获取对应的状态,根据新的计算结果更新状态。一个简单的例子是对一个时间窗口内输入流的某个整数字段求和,那么当算子子任务接收到新元素时,会获取已经存储在状态中的数值,然后将新元素加到状态上,并将状态数据更新。 + +![状态获取和更新示意图](./img/state-acquisition-and-update.png) + +获取和更新状态的逻辑其实并不复杂,但流处理框架还需要解决以下几类问题: + +* 数据的产出要保证实时性,延迟不能太高。 +* 需要保证数据不丢不重,恰好计算一次,尤其是当状态数据非常大或者应用出现故障需要恢复时,要保证状态不出任何错误。 +* 一般流处理任务都是7*24小时运行的,程序的可靠性非常高。 + +基于上述要求,我们不能将状态直接交由内存管理,因为内存的容量是有限制的,当状态数据稍微大一些时,就会出现内存不够的问题。假如我们使用一个持久化的备份系统,不断将内存中的状态备份起来,当流处理作业出现故障时,需要考虑如何从备份中恢复。而且,大数据应用一般是横向分布在多个节点上,流处理框架需要保证横向的伸缩扩展性。可见,状态的管理并不那么容易。 + +作为一个计算框架,Flink提供了有状态的计算,封装了一些底层的实现,比如状态的高效存储、Checkpoint和Savepoint持久化备份机制、计算资源扩缩容等问题。因为Flink接管了这些问题,开发者只需调用Flink API,这样可以更加专注于业务逻辑。 + +## Flink的几种状态类型 + +### Managed State和Raw State + +Flink有两种基本类型的状态:托管状态(Managed State)和原生状态(Raw State)。从名称中也能读出两者的区别:Managed State是由Flink管理的,Flink帮忙存储、恢复和优化,Raw State是开发者自己管理的,需要自己序列化。 + +| | Managed State | Raw State | +| :----------: | ------------------------------------------------ | ---------------- | +| 状态管理方式 | Flink Runtime托管,自动存储、自动恢复、自动伸缩 | 用户自己管理 | +| 状态数据结构 | Flink提供的常用数据结构,如ListState、MapState等 | 字节数组:byte[] | +| 使用场景 | 绝大多数Flink算子 | 用户自定义算子 | + +上表展示了两者的区别,主要包括: + +* 从状态管理的方式上来说,Managed State由Flink Runtime托管,状态是自动存储、自动恢复的,Flink在存储管理和持久化上做了一些优化。当我们横向伸缩,或者说我们修改Flink应用的并行度时,状态也能自动重新分布到多个并行实例上。Raw State是用户自定义的状态。 +* 从状态的数据结构上来说,Managed State支持了一系列常见的数据结构,如ValueState、ListState、MapState等。Raw State只支持字节,任何上层数据结构需要序列化为字节数组。使用时,需要用户自己序列化,以非常底层的字节数组形式存储,Flink并不知道存储的是什么样的数据结构。 +* 从具体使用场景来说,绝大多数的算子都可以通过继承RichFunction函数类或其他提供好的接口类,在里面使用Managed State。Raw State是在已有算子和Managed State不够用时,用户自定义算子时使用。 + +下面将重点介绍Managed State。 + +### Keyed State和Operator State + +对Managed State继续细分,它又有两种类型:Keyed State和Operator State。这里先简单对比两种状态,后续还将展示具体的使用方法。 + +Keyed State是`KeyedStream`上的状态。假如输入流按照id为Key进行了`keyBy`分组,形成一个`KeyedStream`,数据流中所有id为1的数据共享一个状态,可以访问和更新这个状态,以此类推,每个Key对应一个自己的状态。下图展示了Keyed State,因为一个算子子任务可以处理一到多个Key,算子子任务1处理了两种Key,两种Key分别对应自己的状态。 + +![Keyed State示意图](./img/keyedstate.png) + +Operator State可以用在所有算子上,每个算子子任务或者说每个算子实例共享一个状态,流入这个算子子任务的所有数据都可以访问和更新这个状态。下图展示了Operator State,算子子任务1上的所有数据可以共享第一个Operator State,以此类推,每个算子子任务上的数据共享自己的状态。 + +![Operator State示意图](./img/operatorstate.png) + +无论是Keyed State还是Operator State,Flink的状态都是基于本地的,即每个算子子任务维护着自身的状态,不能访问其他算子子任务的状态。 + +在之前各算子的介绍中曾提到,为了自定义Flink的算子,我们可以重写RichFunction函数类,比如`RichFlatMapFunction`。使用Keyed State时,我们也可以通过重写RichFunction函数类,在里面创建和访问状态。对于Operator State,我们还需进一步实现`CheckpointedFunction`接口。 + +| | Keyed State | Operator State | +| -------------- | ----------------------------------------------- | -------------------------------- | +| 适用算子类型 | 只适用于`KeyedStream`上的算子 | 可以用于所有算子 | +| 状态分配 | 每个Key对应一个状态 | 一个算子子任务对应一个状态 | +| 创建和访问方式 | 重写Rich Function,通过里面的RuntimeContext访问 | 实现`CheckpointedFunction`等接口 | +| 横向扩展 | 状态随着Key自动在多个算子子任务上迁移 | 有多种状态重新分配的方式 | +| 支持的数据结构 | ValueState、ListState、MapState等 | ListState、BroadcastState等 | + +上表总结了Keyed State和Operator State的区别。 + +## 横向扩展问题 + +状态的横向扩展问题主要是指修改Flink应用的并行度,每个算子的并行实例数或算子子任务数发生了变化,应用需要关停或启动一些算子子任务,某份在原来某个算子子任务上的状态数据需要平滑更新到新的算子子任务上。Flink的Checkpoint可以辅助迁移状态数据。算子的本地状态将数据生成快照(Snapshot),保存到分布式存储(如HDFS)上。横向伸缩后,算子子任务个数变化,子任务重启,相应的状态从分布式存储上重建(Restore)。下图展示了一个算子扩容的状态迁移过程。 + +![Flink算子扩容示意图](./img/rescale.png) + +对于Keyed State和Operator State这两种状态,他们的横向伸缩机制不太相同。由于每个Keyed State总是与某个Key相对应,当横向伸缩时,Key总会被自动分配到某个算子子任务上,因此Keyed State会自动在多个并行子任务之间迁移。对于一个非`KeyedStream`,流入算子子任务的数据可能会随着并行度的改变而改变。如上图所示,假如一个应用的并行度原来为2,那么数据会被分成两份并行地流入两个算子子任务,每个算子子任务有一份自己的状态,当并行度改为3时,数据流被拆成3支,此时状态的存储也相应发生了变化。对于横向伸缩问题,Operator State有两种状态分配方式:一种是均匀分配,另一种是将所有状态合并,再分发给每个实例上。 + +## Keyed State的使用方法 + +### Keyed State简介 + +对于Keyed State,Flink提供了几种现成的数据结构供我们使用,包括`ValueState`、`ListState`等,他们的继承关系如下图所示。首先,`State`主要有三种实现,分别为`ValueState`、`MapState`和`AppendingState`,`AppendingState`又可以细分为`ListState`、`ReducingState`和`AggregatingState`。 + +![Keyed State继承关系](./img/inheritance-relationships-of-keyedState.png) + +这几个状态的具体区别在于: + +* `ValueState`是单一变量的状态,T是某种具体的数据类型,比如`Double`、`String`,或我们自己定义的复杂数据结构。我们可以使用`T value()`方法获取状态,使用`void update(T value)`更新状态。 +* `MapState`存储一个Key-Value Map,其功能与Java的`Map`几乎相同。`UV get(UK key)`可以获取某个Key下的Value值,`void put(UK key, UV value)`可以对某个Key设置Value,`boolean contains(UK key)`判断某个Key是否存在,`void remove(UK key)`删除某个Key以及对应的Value,`Iterable> entries()`返回`MapState`中所有的元素,`Iterator> iterator()`返回状态的迭代器。需要注意的是,`MapState`中的Key和Keyed State的Key不是同一个Key。 +* `ListState`存储了一个由T类型数据组成的列表。我们可以使用`void add(T value)`或`void addAll(List values)`向状态中添加元素,使用`Iterable get()`获取整个列表,使用`void update(List values)`来更新列表,新的列表将替换旧的列表。 +* `ReducingState`和`AggregatingState`与`ListState`同属于`MergingState`。与`ListState`不同的是,`ReducingState`只有一个元素,而不是一个列表。它的原理是:新元素通过`void add(T value)`加入后,与已有的状态元素使用`ReduceFunction`合并为一个元素,并更新到状态里。`AggregatingState`与`ReducingState`类似,也只有一个元素,只不过`AggregatingState`的输入和输出类型可以不一样。`ReducingState`和`AggregatingState`与窗口上进行`ReduceFunction`和`AggregateFunction`很像,都是将新元素与已有元素做聚合。 + +:::info +Flink的核心代码目前使用Java实现的,而Java的很多类型与Scala的类型不太相同,比如`List`和`Map`。这里不再详细解释Java和Scala的数据类型的异同,但是开发者在使用Scala调用这些接口,比如状态的接口,需要注意两种语言间的转换。对于`List`和`Map`的转换,只需要引用`import scala.collection.JavaConversions._`,并在必要的地方添加后缀`asScala`或`asJava`来进行转换。此外,Scala和Java的空对象使用习惯不太相同,Java一般使用`null`表示空,Scala一般使用`None`。 +::: + +### Keyed State的使用方法 + +之前的文章中其实已经多次使用过状态,这里基于电商用户行为分析场景来演示如何使用状态,我们采用了阿里巴巴提供的一个淘宝用户行为数据集,为了精简需要,只节选了部分数据。电商平台会将用户与商品的交互行为收集记录下来,行为数据主要包括几个字段:userId、itemId、categoryId、behavior和timestamp。其中userId和itemId分别代表用户和商品的唯一ID,categoryId为商品类目ID,behavior表示用户的行为类型,包括点击(pv)、购买(buy)、加购物车(cart)、喜欢(fav)等,timestamp记录行为发生时间。我们定义相应的数据结构为: + +```java +public class UserBehavior { + public long userId; + public long itemId; + public int categoryId; + public String behavior; + public long timestamp; + + public UserBehavior() {} + + public UserBehavior(long userId, long itemId, int categoryId, String behavior, long timestamp) { + this.userId = userId; + this.itemId = itemId; + this.categoryId = categoryId; + this.behavior = behavior; + this.timestamp = timestamp; + } + + public static UserBehavior of(long userId, long itemId, int categoryId, String behavior, long timestamp) { + return new UserBehavior(userId, itemId, categoryId, behavior, timestamp); + } + + @Override + public String toString() { + return "(" + userId + "," + itemId + "," + categoryId + "," + + behavior + "," + timestamp + ")"; + } +} +``` + +我们先在主逻辑中读取数据流,生成一个按照用户ID分组的`KeyedStream`,在这之上使用`RichFlatMapFunction`。 + +```java +env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); +DataStream userBehaviorStream = ... + +// 生成一个KeyedStream +KeyedStream keyedStream = + userBehaviorStream.keyBy(user -> user.userId); + +// 在KeyedStream上进行flatMap +DataStream> behaviorCountStream = + keyedStream.flatMap(new MapStateFunction()); +``` + + +下面的代码演示了继承`RichFlatMapFunction`,这里使用`MapState`来记录某个用户某种行为出现的次数。 + +```scala +/** + * MapStateFunction继承并实现RichFlatMapFunction + * 两个泛型分别为输入数据类型和输出数据类型 + */ +public static class MapStateFunction extends RichFlatMapFunction> { + + // 指向MapState的句柄 + private MapState behaviorMapState; + + @Override + public void open(Configuration configuration) { + // 创建StateDescriptor + MapStateDescriptor behaviorMapStateDescriptor = new MapStateDescriptor("behaviorMap", Types.STRING, Types.INT); + // 通过StateDescriptor获取运行时上下文中的状态 + behaviorMapState = getRuntimeContext().getMapState(behaviorMapStateDescriptor); + } + + @Override + public void flatMap(UserBehavior input, Collector> out) throws Exception { + int behaviorCnt = 1; + // behavior有可能为pv、cart、fav、buy等 + // 判断状态中是否有该behavior + if (behaviorMapState.contains(input.behavior)) { + behaviorCnt = behaviorMapState.get(input.behavior) + 1; + } + // 更新状态 + behaviorMapState.put(input.behavior, behaviorCnt); + out.collect(Tuple3.of(input.userId, input.behavior, behaviorCnt)); + } +} +``` + +Keyed State是针对`KeyedStream`的状态,在主逻辑中,必须先对一个`DataStream`进行`keyBy`操作。在本例中,我们对用户ID进行了`keyBy`,那么用户ID为1的数据共享同一状态数据,以此类推,每个用户ID的行为数据共享自己的状态数据。 + +之后,我们需要实现RichFunction函数类,比如`RichFlatMapFunction`,或者`KeyedProcessFunction`等函数类。这些算子函数类都是`RichFunction`的一种实现,他们都有运行时上下文`RuntimeContext`,从`RuntimeContext`中可以获取状态。 +在实现这些算子函数类时,一般是在`open`方法中声明状态。`open`是算子的初始化方法,它在算子实际处理数据之前调用。具体到状态的使用,我们首先要注册一个`StateDescriptor`。从名字中可以看出,`StateDescriptor`是状态的一种描述,它描述了状态的名字和状态的数据结构。状态的名字可以用来区分不同的状态,一个算子内可以有多个不同的状态,每个状态在`StateDescriptor`中设置对应的名字。同时,我们也需要指定状态的具体数据结构,指定具体的数据结构非常重要,因为Flink要对其进行序列化和反序列化,以便进行Checkpoint和必要的恢复,相关内容可以参考[数据类型和序列化机制](../chapter-datastream-api/data-types.md)部分。每种类型的状态都有对应的`StateDescriptor`,比如`MapStateDescriptor`对应`MapState`,`ValueStateDescriptor`对应`ValueState`。 + +在本例中,我们使用下面的代码注册了一个`MapStateStateDescriptor`,Key为某种行为,如pv、buy等,数据类型为`String`,Value为该行为出现的次数,数据类型为`Integer`。 +```java +// 创建StateDescriptor +MapStateDescriptor behaviorMapStateDescriptor = new MapStateDescriptor("behaviorMap", Types.STRING, Types.INT); +``` +接着我们通过`StateDescriptor`向`RuntimeContext`中获取状态句柄。状态句柄并不存储状态,它只是Flink提供的一种访问状态的接口,状态数据实际存储在State Backend中。本例中对应的代码为: +```java +// 通过StateDescriptor获取运行时上下文中的状态 +behaviorMapState = getRuntimeContext().getMapState(behaviorMapStateDescriptor); +``` + +使用和更新状态发生在实际的处理函数上,比如`RichFlatMapFunction`中的`flatMap`方法。在实现自己的业务逻辑时需要访问和修改状态,比如我们可以通过`MapState.get`方法获取状态,通过`MapState.put`方法更新状态中的数据。 + +其他类型的状态使用方法与本例中所展示的大致相同。`ReducingState`和`AggregatingState`在注册`StateDescriptor`时,还需要实现一个`ReduceFunction`或`AggregationFunction`。下面的代码注册`ReducingStateDescriptor`时实现一个`ReduceFunction`。 + +```java +/** + * ReducingStateFlatMap继承并实现了了RichFlatMapFunction + * 第一个泛型Tuple2是输入类型 + * 第二个泛型Integer是输出类型 + */ +private static class ReducingStateFlatMap extends RichFlatMapFunction, Integer> { + + private transient ReducingState state; + + @Override + public void open(Configuration parameters) throws Exception { + // 创建StateDescriptor + // 除了名字和数据类型,还要实现一个ReduceFunction + ReducingStateDescriptor stateDescriptor = + new ReducingStateDescriptor<>( + "reducing-state", + new ReduceSum(), + Types.INT); + + this.state = getRuntimeContext().getReducingState(stateDescriptor); + } + + @Override + public void flatMap(Tuple2 value, Collector out) throws Exception { + state.add(value.f1); + } + + // ReduceSum继承并实现ReduceFunction + private static class ReduceSum implements ReduceFunction { + + @Override + public Integer reduce(Integer value1, Integer value2) throws Exception { + return value1 + value2; + } + } +} +``` + +可以看到,使用`ReducingState`时,除了名字和数据类型,还增加了一个函数,这个函数可以是Lambda表达式,也可以继承并实现函数类`ReduceFunction`。 + +使用`ReducingState`时,我们可以通过`void add(T value)`方法向状态里增加一个元素,新元素和状态中已有数据通过`ReduceFunction`两两聚合。`AggregatingState`的使用方法与之类似。 + +综上,Keyed State的使用方法可以被归纳为: + +1. 创建一个`StateDescriptor`,在里面注册状态的名字和数据类型等。 +2. 从`RuntimeContext`中获取状态句柄。 +3. 使用状态句柄获取和更新状态数据,比如`ValueState.value`、`ValueState.update`、`MapState.get`、`MapState.put`。 + +此外,必要时候,我们还需要调用Keyed State中的`void clear()`方法来清除状态中的数据,以免发生内存问题。 + +## Operator List State的使用方法 + +状态从本质上来说,是Flink算子子任务的一种本地数据,为了保证数据可恢复性,使用Checkpoint机制来将状态数据持久化输出到存储空间上。状态相关的主要逻辑有两项: + +1. 将算子子任务本地内存数据在Checkpoint时写入存储,这步被称为备份(Snapshot); +2. 初始化或重启应用时,以一定的逻辑从存储中读出并变为算子子任务的本地内存数据,这步被称为重建(Restore)。 + +Keyed State对这两项内容做了更完善的封装,开发者可以开箱即用。对于Operator State来说,每个算子子任务管理自己的Operator State,或者说每个算子子任务上的数据流共享同一个状态,可以访问和修改该状态。Flink的算子子任务上的数据在程序重启、横向伸缩等场景下不能保证百分百的一致性。换句话说,重启Flink作业后,某个数据流元素不一定流入重启前的算子子任务上。因此,使用Operator State时,我们需要根据自己的业务场景来设计Snapshot和Restore的逻辑。为了实现这两个步骤,Flink提供了最为基础的`CheckpointedFunction`接口类。 + +```java +public interface CheckpointedFunction { + + // Checkpoint时会调用这个方法,我们要实现具体的snapshot逻辑,比如将哪些本地状态持久化 + void snapshotState(FunctionSnapshotContext context) throws Exception; + + // 初始化时会调用这个方法,向本地状态中填充数据 + void initializeState(FunctionInitializationContext context) throws Exception; + +} +``` + +在Flink的Checkpoint机制下,当一次Snapshot触发后,`snapshotState`会被调用,将本地状态持久化到存储空间上。这里我们可以先不用关心Snapshot是如何被触发的,暂时理解成Snapshot是自动触发的,我们将在[Checkpoint](./checkpoint.md)部分介绍它的触发机制。 + +`initializeState`在算子子任务初始化时被调用,初始化包括两种场景: + +1. 整个Flink作业第一次执行,状态数据被初始化为一个默认值; +2. Flink作业重启,之前的作业已经将状态输出到存储,通过`initializeState`方法将存储上的状态读出并填充到本地状态里。 + +目前Operator State主要有三种,其中ListState和UnionListState在数据结构上都是一种`ListState`,还有一种BroadcastState。这里我们主要介绍`ListState`这种列表形式的状态。 + +`ListState`这种状态以一个列表的形式序列化并存储,以适应横向扩展时状态重分布的问题。每个算子子任务有零到多个状态S,组成一个列表`ListState[S]`。各个算子子任务Snapshot时将自己状态列表的写入存储,整个状态逻辑上可以理解成是将这些列表连接到一起,组成了一个包含所有状态的大列表。当作业重启或横向扩展时,我们需要将这个包含所有状态的列表重新分布到各个算子子任务上。ListState和UnionListState的区别在于:ListState是将整个状态列表按照Round-Robin的模式均匀分布到各个算子子任务上,每个算子子任务得到的是整个列表的子集;UnionListState按照广播的模式,将整个列表发送给每个算子子任务。 + +Operator State的实际应用场景不如Keyed State多,它经常被用在Source或Sink等算子上,用来保存流入数据的偏移量或对输出数据做缓存,以保证Flink作业的端到端的Exactly-Once语义。这里我们来看一个Flink官方提供的Sink案例以了解`CheckpointedFunction`的工作原理。 + +```scala +// BufferingSink需要实现SinkFunction接口类以实现其Sink功能,同时也要实现CheckpointedFunction接口类 +public class BufferingSink + implements SinkFunction>, + CheckpointedFunction { + + private final int threshold; + + // Operator List State句柄 + private transient ListState> checkpointedState; + + // 本地缓存 + private List> bufferedElements; + + public BufferingSink(int threshold) { + this.threshold = threshold; + this.bufferedElements = new ArrayList<>(); + } + + // Sink的核心处理逻辑,将上游数据value输出到外部系统 + @Override + public void invoke(Tuple2 value, Context contex) throws Exception { + // 先将上游数据缓存到本地的缓存 + bufferedElements.add(value); + // 当本地缓存大小到达阈值时,将本地缓存输出到外部系统 + if (bufferedElements.size() == threshold) { + for (Tuple2 element: bufferedElements) { + // 输出到外部系统 + } + // 清空本地缓存 + bufferedElements.clear(); + } + } + + // 重写CheckpointedFunction中的snapshotState + // 将本地缓存Snapshot到存储上 + @Override + public void snapshotState(FunctionSnapshotContext context) throws Exception { + // 将之前的Checkpoint清理 + checkpointedState.clear(); + for (Tuple2 element : bufferedElements) { + // 将最新的数据写到状态中 + checkpointedState.add(element); + } + } + + // 重写CheckpointedFunction中的initializeState + // 初始化状态 + @Override + public void initializeState(FunctionInitializationContext context) throws Exception { + // 注册ListStateDescriptor + ListStateDescriptor> descriptor = + new ListStateDescriptor<>( + "buffered-elements", + TypeInformation.of(new TypeHint>() {})); + + // 从FunctionInitializationContext中获取OperatorStateStore,进而获取ListState + checkpointedState = context.getOperatorStateStore().getListState(descriptor); + + // 如果是作业重启,读取存储中的状态数据并填充到本地缓存中 + if (context.isRestored()) { + for (Tuple2 element : checkpointedState.get()) { + bufferedElements.add(element); + } + } + } +} +``` + +上面的代码中,在输出到Sink之前,程序先将数据放在本地缓存中,并定期进行Snapshot,这实现了批量输出的功能,批量输出能够减少网络等开销。同时,程序能够保证数据一定会输出外部系统,因为即使程序崩溃,状态中存储着还未输出的数据,下次启动后还会将这些未输出数据读取到内存,继续输出到外部系统。 + +注册和使用Operator State的代码和Keyed State相似,也是先注册一个`StateDescriptor`,并指定状态名字和数据类型,然后从`FunctionInitializationContext`中获取`OperatorStateStore`,进而获取ListState。 + +```java +ListStateDescriptor> descriptor = + new ListStateDescriptor<>( + "buffered-elements", + TypeInformation.of(new TypeHint>() {})); + +checkpointedState = context.getOperatorStateStore().getListState(descriptor); +``` + +如果是UnionListState,那么代码改为:`context.getOperatorStateStore().getUnionListState()`。 + +在之前代码的`initializeState`方法里,我们进行了状态的初始化逻辑,我们用`context.isRestored()`来判断是否为重启作业,然后从之前的Checkpoint中恢复并写到本地缓存中。 + +:::info +`CheckpointedFunction`接口类的`initializeState`方法的参数为`FunctionInitializationContext`,基于这个上下文对象我们不仅可以通过`getOperatorStateStore`获取`OperatorStateStore`,也可以通过`getKeyedStateStore`来获取`KeyedStateStore`,进而通过`getState`、`getMapState`等方法获取Keyed State,比如:`context.getKeyedStateStore().getState(stateDescriptor)`。这与在RichFunction函数类中使用Keyed State的方式并不矛盾,因为`CheckpointedFunction`是Flink有状态计算的最底层接口,它提供了最丰富的状态接口。 +::: + +`ListCheckpointed`接口类是`CheckpointedFunction`接口类的一种简写,`ListCheckpointed`提供的功能有限,只支持均匀分布的ListState,不支持全量广播的UnionListState。 + +```java +public interface ListCheckpointed { + + // Checkpoint时会调用这个方法,我们要实现具体的snapshot逻辑,比如将哪些本地状态持久化 + List snapshotState(long checkpointId, long timestamp) throws Exception; + + // 从上次Checkpoint中恢复数据到本地内存 + void restoreState(List state) throws Exception; +} +``` + +跟`CheckpointedFunction`中的`snapshotState`方法一样,这里的`snapshotState`也是在做备份,但这里的参数列表更加精简,其中`checkpointId`是一个单调递增的数字,用来表示某次Checkpoint,`timestamp`是Checkpoint发生的实际时间,这个方法以列表形式返回需要写入存储的状态,Flink会将返回值`List`写入存储。`restoreState`方法用来初始化状态,包括作业第一次启动或者作业失败重启。参数`state`是一个列表形式的状态,是从存储中读取出来的、需要均匀分布给这个算子子任务的状态数据。启动时,Flink会读取存储中的数据,传入参数`state`中,开发者根据业务需求决定如何恢复这些数据。 + +## BroadcastState的使用方法 + +BroadcastState是Flink 1.5引入的功能,本节将跟大家分享BroadcastState的潜在使用场景,并继续使用电商用户行为分析的案例来演示BroadcastState的使用方法。 + +### BroadcastState使用场景 + +无论是分布式批处理还是流处理,将部分数据同步到所有实例上是一个十分常见的需求。例如,我们需要依赖一个不断变化的控制规则来处理主数据流的数据,主数据流数据量比较大,只能分散到多个算子实例上,控制规则数据相对比较小,可以分发到所有的算子实例上。BroadcastState与直接在时间窗口进行两个数据流的Join的不同点在于,控制规则数据量较小,可以直接放到每个算子实例里,这样可以大大提高主数据流的处理速度。下图为BroadcastState工作原理示意图。 + +![BroadcastState](./img/broadcast-state.png) + +我们继续使用电商平台用户行为分析为例,不同类型的用户往往有特定的行为模式,有些用户购买欲望强烈,有些用户反复犹豫才下单,有些用户频繁爬取数据,有盗刷数据的嫌疑,电商平台运营人员为了提升商品的购买转化率,保证平台的使用体验,经常会进行一些用户行为模式分析。基于这个场景,我们可以构建一个Flink作业,实时监控识别不同模式的用户。为了避免每次更新规则模式后重启部署,我们可以将规则模式作为一个数据流与用户行为数据流`connect`在一起,并将规则模式以BroadcastState的形式广播到每个算子实例上。 + +### 电商用户行为识别案例 + +下面开始具体构建一个实例程序。我们定义一些必要的数据结构来描述这个业务场景,包括之前已经定义的用户行为和下面定义的规则模式两个数据结构。 + +```java +/** + * 行为模式 + * 整个模式简化为两个行为 + * */ +public class BehaviorPattern { + + public String firstBehavior; + public String secondBehavior; + + public BehaviorPattern() {} + + public BehaviorPattern(String firstBehavior, String secondBehavior) { + this.firstBehavior = firstBehavior; + this.secondBehavior = secondBehavior; + } + + @Override + public String toString() { + return "first: " + firstBehavior + ", second: " + secondBehavior; + } +} +``` + +然后我们在主逻辑中读取两个数据流: + +```java +// 主数据流 +DataStream userBehaviorStream = ... +// BehaviorPattern数据流 +DataStream patternStream = ... +``` + +目前BroadcastState只支持使用Key-Value形式,需要使用`MapStateDescriptor`来描述。这里我们使用一个比较简单的行为模式,因此Key是一个空类型。当然我们也可以根据业务场景,构造复杂的Key-Value。然后,我们将模式流使用`broadcast`方法广播到所有算子子任务上。 + +```java +// BroadcastState只能使用 Key->Value 结构,基于MapStateDescriptor +MapStateDescriptor broadcastStateDescriptor = + new MapStateDescriptor<>("behaviorPattern", Types.VOID, Types.POJO(BehaviorPattern.class)); + +BroadcastStream broadcastStream = + patternStream.broadcast(broadcastStateDescriptor); +``` + +用户行为模式流先按照用户ID进行`keyBy`,然后与广播流合并: + +```scala +// 生成一个KeyedStream +KeyedStream keyedStream = userBehaviorStream.keyBy(user -> user.userId); + +// 在KeyedStream上进行connect和process +DataStream> matchedStream = keyedStream + .connect(broadcastStream) + .process(new BroadcastPatternFunction()); +``` + +下面的代码展示了BroadcastState完整的使用方法。`BroadcastPatternFunction`是`KeyedBroadcastProcessFunction`的具体实现,它基于BroadcastState处理主数据流,输出`(Long, BehaviorPattern)`,分别表示用户ID和行为模式。 + +```java +/** + * 四个泛型分别为: + * 1. KeyedStream中Key的数据类型 + * 2. 主数据流的数据类型 + * 3. 广播流的数据类型 + * 4. 输出类型 + * */ +public static class BroadcastPatternFunction +extends KeyedBroadcastProcessFunction> { + + // 用户上次行为状态句柄,每个用户存储一个状态 + private ValueState lastBehaviorState; + // BroadcastState Descriptor + private MapStateDescriptor bcPatternDesc; + + @Override + public void open(Configuration configuration) { + lastBehaviorState = getRuntimeContext().getState( + new ValueStateDescriptor("lastBehaviorState", Types.STRING)); + bcPatternDesc = new MapStateDescriptor("behaviorPattern", Types.VOID, Types.POJO(BehaviorPattern.class)); + } + + @Override + public void processBroadcastElement(BehaviorPattern pattern, + Context context, + Collector> collector) throws Exception { + BroadcastState bcPatternState = context.getBroadcastState(bcPatternDesc); + // 将新数据更新至BroadcastState,这里使用一个null作为Key + // 在本场景中所有数据都共享一个Pattern,因此这里伪造了一个Key + bcPatternState.put(null, pattern); + } + + @Override + public void processElement(UserBehavior userBehavior, + ReadOnlyContext context, + Collector> collector) throws Exception { + + // 获取最新的BroadcastState + BehaviorPattern pattern = context.getBroadcastState(bcPatternDesc).get(null); + String lastBehavior = lastBehaviorState.value(); + if (pattern != null && lastBehavior != null) { + // 用户之前有过行为,检查是否符合给定的模式 + if (pattern.firstBehavior.equals(lastBehavior) && + pattern.secondBehavior.equals(userBehavior.behavior)) { + // 当前用户行为符合模式 + collector.collect(Tuple2.of(userBehavior.userId, pattern)); + } + } + lastBehaviorState.update(userBehavior.behavior); + } +} +``` + +对上面的所有流程总结下来,使用BroadcastState需要进行三步: + +1. 接收一个普通数据流,并使用`broadcast`方法将其转换为`BroadcastStream`,因为BroadcastState目前只支持Key-Value结构,需要使用`MapStateDescriptor`描述它的数据结构。 +2. 将`BroadcastStream`与一个`DataStream`或`KeyedStream`使用`connect`方法连接到一起。 +3. 实现一个`ProcessFunction`,如果主流(非Broadcast流)是`DataStream`,则需要实现`BroadcastProcessFunction`;如果主流是`KeyedStream`,则需要实现`KeyedBroadcastProcessFunction`。这两种函数都提供了时间和状态的访问方法。 + +在`KeyedBroadcastProcessFunction`函数类中,有两个方法需要实现: + +* `processElement`:处理主流中的每条元素,输出零到多个数据。`ReadOnlyContext` 可以获取时间和状态,但是只能以只读的形式读取BroadcastState,不能修改,以保证每个算子实例上的BroadcastState都是相同的。 +* `processBroadcastElement`:处理广播流的数据,可以输出零到多个数据,一般用来更新Broadcast State。 + +此外,`KeyedBroadcastProcessFunction`属于[ProcessFunction系列函数](../chapter-time-window/process-function.md),可以注册Timer,并在`onTimer`方法中实现回调逻辑。本例中为了保持代码简洁,没有使用,Timer一般用来清空状态,避免状态无限增长下去。 \ No newline at end of file diff --git a/doc/ch-table-sql/catalog-function.md b/doc/ch-table-sql/catalog-function.md new file mode 100644 index 0000000..d9180ca --- /dev/null +++ b/doc/ch-table-sql/catalog-function.md @@ -0,0 +1,316 @@ +(catalog-function)= +# 用户自定义函数 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +System Function给我们提供了大量内置功能,但对于一些特定领域或特定场景,System Function还远远不够,Flink提供了用户自定义函数功能,开发者可以实现一些特定的需求。用户自定义函数需要注册到Catalog中,因此这类函数又被称为Catalog Function。Catalog Function大大增强了Flink SQL的表达能力。 + +## 注册函数 + +在使用一个函数前,一般需要将这个函数注册到Catalog中。注册时需要调用`TableEnvironment`中的`registerFunction`方法。每个`TableEnvironment`都会有一个成员`FunctionCatalog`,`FunctionCatalog`中存储了函数的定义,当注册函数时,实际上是将这个函数名和对应的实现写入到`FunctionCatalog`中。以注册一个`ScalarFunction`为例,它在源码中如下: + +```java +FunctionCatalog functionCatalog = ... + +/** + * 注册一个ScalaFunction + * name: 函数名 + * function: 一个自定义的ScalaFunction + */ +public void registerFunction(String name, ScalarFunction function) { + functionCatalog.registerTempSystemScalarFunction( + name, + function); +} +``` + +在Flink提供的[System Function](system-function.md)中,我们已经提到,内置的System Function提供了包括数学、比较、字符串、聚合等常用功能,如果这些内置的System Function无法满足我们的需求,我们可以使用Java、Scala和Python语言自定义一个函数。接下来我们将将详细讲解如何自定义函数以及如何使用函数。 + +## 标量函数 + +标量函数(Scalar Function)接收零个、一个或者多个输入,输出一个单值标量。这里以处理经纬度为例来展示如何自定义Scala Function。 + +当前,大量的应用极度依赖地理信息(Geographic Information):打车软件需要用户用户定位起点和终点、外卖平台需要确定用户送餐地点、运动类APP会记录用户的活动轨迹等。我们一般使用精度(Longitude)和纬度(Latitude)来标记一个地点。经纬度作为原始数据很难直接拿来分析,需要做一些转化,而Table API & SQL中没有相应的函数,因此需要我们自己来实现。 + +如果想自定义函数,我们需要继承`org.apache.flink.table.functions.ScalarFunction`类,实现`eval`方法。这与第四章介绍的DataStream API中算子自定义有异曲同工之处。假设我们需要判断一个经纬度数据是否在北京四环以内,可以使用Java实现下面的函数: + +```java +public class IsInFourRing extends ScalarFunction { + + // 北京四环经纬度范围 + private static double LON_EAST = 116.48; + private static double LON_WEST = 116.27; + private static double LAT_NORTH = 39.988; + private static double LAT_SOUTH = 39.83; + + // 判断输入的经纬度是否在四环内 + public boolean eval(double lon, double lat) { + return !(lon > LON_EAST || lon < LON_WEST) && + !(lat > LAT_NORTH || lat < LAT_SOUTH); + } +} +``` + +在这个实现中,`eval`方法接收两个`double`类型的输入,对数据进行处理,生成一个`boolean`类型的输出。整个类中最重要的地方是`eval`方法,它决定了这个自定义函数的内在逻辑。自定义好函数之后,我们还需要用`registerFunction`方法将这个函数注册到Catalog中,并为之起名为`IsInFourRing`,这样就可以在SQL语句中使用`IsInFourRing`的这个名字进行计算了。 + +```java +List> geoList = new ArrayList<>(); +geoList.add(Tuple4.of(1L, 116.2775, 39.91132, Timestamp.valueOf("2020-03-06 00:00:00"))); +geoList.add(Tuple4.of(2L, 116.44095, 39.88319, Timestamp.valueOf("2020-03-06 00:00:01"))); +geoList.add(Tuple4.of(3L, 116.25965, 39.90478, Timestamp.valueOf("2020-03-06 00:00:02"))); +geoList.add(Tuple4.of(4L, 116.27054, 39.87869, Timestamp.valueOf("2020-03-06 00:00:03"))); + +DataStream> geoStream = env + .fromCollection(geoList) + .assignTimestampsAndWatermarks(new AscendingTimestampExtractor>() { + @Override + public long extractAscendingTimestamp(Tuple4 element) { + return element.f3.getTime(); + } + }); + +// 创建表 +Table geoTable = tEnv.fromDataStream(geoStream, "id, long, alt, ts.rowtime, proc.proctime"); +tEnv.createTemporaryView("geo", geoTable); + +// 注册函数到Catalog中,指定名字为IsInFourRing +tEnv.registerFunction("IsInFourRing", new IsInFourRing()); + +// 在SQL语句中使用IsInFourRing函数 +Table inFourRingTab = tEnv.sqlQuery("SELECT id FROM geo WHERE IsInFourRing(long, alt)"); +``` + +我们也可以利用编程语言的重载特性,针对不同类型的输入设计不同的函数。假如经纬度参数以`float`或者`String`形式传入,为了适应这些输入,可以实现多个`eval`方法,让编译器帮忙做重载: + +```java +public boolean eval(double lon, double lat) { + return !(lon > LON_EAST || lon < LON_WEST) && + !(lat > LAT_NORTH || lat < LAT_SOUTH); +} + +public boolean eval(float lon, float lat) { + return !(lon > LON_EAST || lon < LON_WEST) && + !(lat > LAT_NORTH || lat < LAT_SOUTH); +} + +public boolean eval(String lonStr, String latStr) { + double lon = Double.parseDouble(lonStr); + double lat = Double.parseDouble(latStr); + return !(lon > LON_EAST || lon < LON_WEST) && + !(lat > LAT_NORTH || lat < LAT_SOUTH); +} +``` + +`eval`方法的输入和输出类型决定了`ScalarFunction`的输入输出类型。在具体的执行过程中,Flink的类型系统会自动推测输入和输出类型,一些无法被自动推测的类型可以使用`DataTypeHint`来提示Flink使用哪种输入输出类型。下面的代码接收两个`Timestamp`作为输入,返回两个时间戳之间的差,用`DataTypeHint`来提示将返回结果转化为`BIGINT`类型。 + +```java +public class TimeDiff extends ScalarFunction { + + public @DataTypeHint("BIGINT") long eval(Timestamp first, Timestamp second) { + return java.time.Duration.between(first.toInstant(), second.toInstant()).toMillis(); + } +} +``` + +`DataTypeHint`一般可以满足绝大多数的需求,如果类型仍然复杂,开发者可以自己重写`UserDefinedFunction#getTypeInference(DataTypeFactory)`方法,返回合适的类型。 + +## 表函数 + +另一种常见的用户自定义函数为表函数(Table Function)。Table Function能够接收零到多个标量输入,与Scalar Function不同的是,Table Function输出零到多行,每行数据一到多列。从这些特征来看,Table Function更像是一个表,一般出现在`FROM`之后。我们在[Temporal Table Join](sql-join)中提到的Temporal Table就是一种Table Function。 + +为了定义Table Function,我们需要继承`org.apache.flink.table.functions.TableFunction`类,然后实现`eval`方法,这与Scalar Function几乎一致。同样,我们可以利用重载,实现一到多个`eval`方法。与Scala Function中只输出一个标量不同,Table Function可以输出零到多行,`eval`方法里使用`collect`方法将结果输出,输出的数据类型由`TableFunction`中的泛型T决定。 + +下面的代码将字符串输入按照`#`切分,输出零到多行,输出类型为`String`。 + +```java +public class TableFunc extends TableFunction { + + // 按#切分字符串,输出零到多行 + public void eval(String str) { + if (str.contains("#")) { + String[] arr = str.split("#"); + for (String i: arr) { + collect(i); + } + } + } +} +``` + +在主逻辑中,我们需要使用`registerFunction`方法注册函数,并指定一个名字。在SQL语句中,使用`LATERAL TABLE()`来调用这个Table Function。 + +```java +List> list = new ArrayList<>(); +list.add(Tuple4.of(1, 1L, "Jack#22", Timestamp.valueOf("2020-03-06 00:00:00"))); +list.add(Tuple4.of(2, 2L, "John#19", Timestamp.valueOf("2020-03-06 00:00:01"))); +list.add(Tuple4.of(3, 3L, "nosharp", Timestamp.valueOf("2020-03-06 00:00:03"))); + +DataStream> stream = env + .fromCollection(list) + .assignTimestampsAndWatermarks(new AscendingTimestampExtractor>() { + @Override + public long extractAscendingTimestamp(Tuple4 element) { + return element.f3.getTime(); + } + }); +// 获取Table +Table table = tEnv.fromDataStream(stream, "id, long, str, ts.rowtime"); +tEnv.createTemporaryView("input_table", table); + +// 注册函数到Catalog中,指定名字为Func +tEnv.registerFunction("Func", new TableFunc()); + +// input_table与LATERAL TABLE(Func(str))进行JOIN +Table tableFunc = tEnv.sqlQuery("SELECT id, s FROM input_table, LATERAL TABLE(Func(str)) AS T(s)"); +``` + +在这个例子中,`LATERAL TABLE(Func(str))`接受`input_table`中字段`str`作为输入,被命名为一个新表,名为`T`,`T`中有一个字段`s`,`s`是我们刚刚自定义的`TableFunc`的输出。本例中,`input_table`和`LATERAL TABLE(Func(str))`之间使用逗号`,`隔开,实际上这两个表是按照`CROSS JOIN`方式连接起来的,或者说,这两个表在做笛卡尔积,这个SQL语句返回值为: + +``` +1,22 +1,Jack +2,19 +2,John +``` + +我们也可以使用其他类型的`JOIN`,比如`LEFT JOIN`: + +```java +// input_table与LATERAL TABLE(Func(str))进行LEFT JOIN +Table joinTableFunc = tEnv.sqlQuery("SELECT id, s FROM input_table LEFT JOIN LATERAL TABLE(Func(str)) AS T(s) ON TRUE"); +``` + +`ON TRUE`条件表示所有左侧表中的数据都与右侧进行Join,因此结果中多出了一行`3,null`。 + +``` +1,22 +1,Jack +2,19 +2,John +3,null +``` + +## 聚合函数 + +在System Function中我们曾介绍了聚合函数,聚合函数一般将多行数据进行聚合,输出一个标量。常用的聚合函数有`COUNT`、`SUM`等。对于一些特定问题,这些内置函数可能无法满足需求,在Flink SQL中,用户可以对聚合函数进行用户自定义,这种函数被称为用户自定义聚合函数(User-Defined Aggregate Function)。 + +假设我们的表中有下列字段:`id`、数值`v`、权重`w`,我们对`id`进行`GROUP BY`,计算`v`的加权平均值。计算的过程如下表所示。 + +![用户自定义聚合函数:求加权平均](./img/aggregate.png) + +下面的代码实现了一个加权平均函数`WeightedAvg`,这个函数接收两个`Long`类型的输入,返回一个`Double`类型的输出。计算过程基于累加器`WeightedAvgAccum`,它记录了当前加权和`sum`以及权重`weight`。 + +```java +import org.apache.flink.table.functions.AggregateFunction; +import java.util.Iterator; + +/** + * 加权平均函数 + */ +public class WeightedAvg extends AggregateFunction { + + @Override + public WeightedAvgAccum createAccumulator() { + return new WeightedAvgAccum(); + } + + // 需要物化输出时,getValue方法会被调用 + @Override + public Double getValue(WeightedAvgAccum acc) { + if (acc.weight == 0) { + return null; + } else { + return (double) acc.sum / acc.weight; + } + } + + // 新数据到达时,更新ACC + public void accumulate(WeightedAvgAccum acc, long iValue, long iWeight) { + acc.sum += iValue * iWeight; + acc.weight += iWeight; + } + + // 用于BOUNDED OVER WINDOW,将较早的数据剔除 + public void retract(WeightedAvgAccum acc, long iValue, long iWeight) { + acc.sum -= iValue * iWeight; + acc.weight -= iWeight; + } + + // 将多个ACC合并为一个ACC + public void merge(WeightedAvgAccum acc, Iterable it) { + Iterator iter = it.iterator(); + while (iter.hasNext()) { + WeightedAvgAccum a = iter.next(); + acc.weight += a.weight; + acc.sum += a.sum; + } + } + + // 重置ACC + public void resetAccumulator(WeightedAvgAccum acc) { + acc.weight = 0l; + acc.sum = 0l; + } + + /** + * 累加器 Accumulator + * sum: 和 + * weight: 权重 + */ + public static class WeightedAvgAccum { + public long sum = 0; + public long weight = 0; + } +} +``` + +从这个例子我们可以看到,自定义聚合函数时,我们需要继承`org.apache.flink.table.functions.AggregateFunction`类。注意,这个类与[DataStream API的窗口算子](../chapter-time-window/window.md#aggregate-function)中所介绍的`AggregateFunction`命名空间不同,在引用时不要写错。不过这两个`AggregateFunction`的工作原理大同小异。首先,`AggregateFunction`调用`createAccumulator`方法创建一个累加器,这里简称ACC,ACC用来存储中间结果。接着,每当表中有新数据到达,Flink SQL会调用`accumulate`方法,新数据会作用在ACC上,ACC被更新。当一个分组的所有数据都被`accumulate`处理,`getValue`方法可以将ACC中的中间结果输出。 + +综上,定义一个`AggregateFunction`时,这三个方法是必须实现的: + +* `createAccumulator`:创建ACC,可以使用一个自定义的数据结构。 +* `accumulate`:处理新流入数据,更新ACC;第一个参数是ACC,第二个以及以后的参数为流入数据。 +* `getValue`:输出结果,返回值的数据类型T与`AggregateFunction`中定义的泛型T保持一致。 + +`createAccumulator`创建一个ACC。`accumulate`第一个参数为ACC,第二个及以后的参数为整个`AggregateFunction`的输入参数,这个方法的作用就是接受输入,并将输入作用到ACC上,更新ACC。`getValue`返回值的类型`T`为整个`AggregateFunction`的输出类型。 + +除了上面三个方法,下面三个方法需要根据使用情况来决定是否需要定义。例如,在流处理的会话窗口上进行聚合时,必须定义`merge`方法,因为当发现某行数据恰好可以将两个窗口连接为一个窗口时,`merge`方法可以将两个窗口内的ACC合并。 + +* `retract`:有界`OVER WINDOW`场景上,窗口是有界的,需要将早期的数据剔除。 +* `merge`:将多个ACC合并为一个ACC,常用在流处理的会话窗口分组和批处理分组上。 +* `resetAccumulator`:重置ACC,用于批处理分组上。 + +这些方法必须声明为`public`,且不能是`static`的,方法名必须与上述名字保持一致。 + +在主逻辑中,我们注册这个函数,并在SQL语句中使用它: + +```java +List> list = new ArrayList<>(); +list.add(Tuple4.of(1, 100l, 1l, Timestamp.valueOf("2020-03-06 00:00:00"))); +list.add(Tuple4.of(1, 200l, 2l, Timestamp.valueOf("2020-03-06 00:00:01"))); +list.add(Tuple4.of(3, 300l, 3l, Timestamp.valueOf("2020-03-06 00:00:13"))); + +DataStream> stream = env + .fromCollection(list) + .assignTimestampsAndWatermarks(new AscendingTimestampExtractor>() { + @Override + public long extractAscendingTimestamp(Tuple4 element) { + return element.f3.getTime(); + } + }); + +Table table = tEnv.fromDataStream(stream, "id, v, w, ts.rowtime"); + +tEnv.createTemporaryView("input_table", table); + +tEnv.registerFunction("WeightAvg", new WeightedAvg()); + +Table agg = tEnv.sqlQuery("SELECT id, WeightAvg(v, w) FROM input_table GROUP BY id"); +``` \ No newline at end of file diff --git a/doc/ch-table-sql/dynamic-table.md b/doc/ch-table-sql/dynamic-table.md new file mode 100644 index 0000000..026dde2 --- /dev/null +++ b/doc/ch-table-sql/dynamic-table.md @@ -0,0 +1,140 @@ +(dynamic-table)= +# 动态表和持续查询 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +在上一节中,我们已经了解了Table API & SQL的基本使用方法,本节主要探讨一些与流处理上进行关系查询所需注意的一些问题。 + +## 动态表和持续查询 + +首先我们需要了解一下在数据流上进行关系型查询的基本原理。 + +关系型数据库起源于上世纪70年代,距今已经有五十年的历史。关系型数据库主要基于Edgar Codd所提出的关系代数(Relational Algebra),SQL是基于关系代数的查询语言。如今,关系型数据库以及SQL已经成为数据查询领域的标准,特别是SQL因其标准性和易用性,已经不仅仅局限在关系型数据库上做查询,它正在被广泛应用在各类数据分析场景。不过,关系代数模型在其创立和发展过程中并不是为流处理而设计的,更适合进行批处理。 + +### 批处理关系型查询与流处理的区别与联系 + +下面这张表对传统批处理关系型查询和流处理做了比较,传统的关系型查询和流处理在输入数据、执行过程和查询结果等方面存在着一定的区别。 + +| | 批处理关系型查询 / SQL | 流处理 | +| -------- | :----------------------------------------------------------: | :----------------------------------------------------------: | +| 输入数据 | 数据是有界的,在有限的数据上进行查询。 | 数据流是无界的,在源源不断的数据流上进行查询。 | +| 执行过程 | 一次查询是在一个批次的数据上进行查询,所查询的数据是静态确定的。 | 一次查询启动后需要等待数据不断流入,所查询的数据在未来源源不断地到达。 | +| 查询结果 | 一次查询完成后即结束。结果是确定的。 | 一次查询会根据新流入数据不断更新结果。 | + +从表中可以看到,在数据流上应用关系型查询是非常有挑战性的:数据流是无界的,一次查询启动后,需要持续对数据流做处理,查询结果根据新流入数据而不断更新。虽然流处理有其难度,但也并不是无解的,这里就涉及到了物化视图的概念。 + +在传统关系型查询中有物理表和虚拟视图的概念:物理表是真实存在的;虚拟视图是在物理表上基于一个查询生成的虚拟的表,视图有表结构Schema,但并不实现数据存储。我们可以像使用一个物理表一样在一个视图上进行查询,但由于视图没有存储数据,它要先执行定义这个视图的查询。在这两个概念的基础上,一些人提出了物化视图的概念,即将视图里所需要的数据物理化地缓存起来,它提前缓存了数据,因此比起虚拟的视图来说执行速度更快。当物化视图所依赖的物理表发生变化时,在物化视图中的缓存也必须随之变化。流处理上的关系型查询借鉴了物化视图的实现思路,将外部系统中的数据缓存起来,每当数据流入,物化视图随之更新。 + +### 动态表上的持续查询 + +从上面的对比看到,流处理中没有静态的物理表,Flink提出了动态表(Dynamic Table)的概念,旨在解决如何在数据流上进行关系型查询。Dynamic Table用来表示不断流入的数据表,数据流是源源不断流入的,Dynamic Table也是随着新数据的流入而不断更新的。在Dynamic Table上进行查询,被称为持续查询(Continuous Query),因为底层的计算是持续不断的。一个Continuous Query的结果也是一个Dynamic Table,它会根据新流入的数据不断更新结果。在Dynamic Table上进行Continuous Query与物化视图不断更新缓存中的数据非常相似。 + +![Dynamic Table与Continuous Query](./img/dynamic-table.png) + +上面这张图展示了数据流、Dynamic Table和Continuous Query之间的关系: + +1. 数据流被转换为Dynamic Table +2. 在这个Dynamic Table上执行Continuous Query生成一个新的Dynamic Table +3. 将生成的Continuous Query转化为一个数据流 + +我们继续以电商用户行为分析为例来看看数据流和动态表之间的转换。 + +![用户行为数据流转换为动态表](./img/data-to-table.png) + +如图中左侧所示,一个数据流包含用户行为,可以转化成图中右侧的一个动态表,实际上这个动态表是不断更新的。我们在这个动态表上进行下面的查询: + +```sql +SELECT + user_id, + COUNT(behavior) AS behavior_cnt +FROM user_behavior +GROUP BY user_id +``` + +这个SQL语句对`user_id`字段分组,统计每个`user_id`所产生的行为。在批处理时,这样的一个SQL查询会在一个静态的数据集上生成一个确定的结果。但对于流处理来说,每当新数据流入时,查询的结果也要随之更新,下图展示了动态表查询的一个过程。 + +![一个Continuous Query示例:统计用户行为](./img/continuous-query.png) + +当第一条数据`(1, pv, 00:00:00)`插入到源表时,整个源表只有这一条数据,生成的结果为`(1, 1)`。当第二条数据`(2, fav, 00:00:00)`进入源表时,SQL引擎在结果表中插入结果`(2, 1)`。接着,ID为1的用户再次产生了行为,新数据插入会导致原来的统计结果`(1, 1)`发生变化,变成了`(1, 2)`。这里不再是往结果表中插入新数据,而是更改原来的结果数据。 + +这个数据流是源源不断流入的,还会有下一分钟、下一小时的数据继续流入,我们可以在时间维度上对数据分组,这里以一分钟为一组,编写下面的SQL: + +```sql +SELECT + user_id, + COUNT(behavior) AS behavior_cnt, + TUMBLE_END(ts, INTERVAL '1' MINUTE) AS end_ts +FROM user_behavior +GROUP BY user_id, TUMBLE(ts, INTERVAL '1' MINUTE) +``` + +这个查询与第一个查询非常相似,只不过数据是按照滚动时间窗口来分组的。下图展示了这个查询的执行过程,相当于数据按照时间进行了切分,在这个时间窗口内进行用户行为的统计。我们将在本章[窗口](sql-window)部分详细介绍时间窗口的分组方法。 + +![统计用户在一个窗口内的行为](./img/tumble-append.png) + +我们以第一个时间窗口为例,数据在00:00:00和00:00:59之间有5条数据,对这5条数据统计得到一个结果,结果表的最后一列`end_ts`为窗口结束时间戳。下一个一分钟数据到达后,对00:01:00和00:01:59之间的数据进行统计,由于`end_ts`字段发生了变化,新结果并不是直接在老结果上做的更新,而是在结果表中插入新数据。 + +根据前面的两个SQL语句以及执行结果,我们看到流处理上进行关系型查询一般有两种生成结果的方式: + +1. 第二个查询语句只追加结果,或者说只在结果表上进行`INSERT`操作。 +2. 第一个查询语句追加结果的同时,也对结果不断更新,或者说即进行`INSERT`操作又进行`UPDATE`操作或`DELETE`操作。 + +在批处理中,结果生成是确定的,但在流处理中,这两种方式会对计算结果的输出产生影响。 + +### 动态表的两种输出模式 + +前面提到,计算结果的输出会有两种形式。一种是在结果末尾追加,我们称这种模式为追加(Append-only)模式;一种是既在结果末尾追加,又对已有数据更新,这种被称为Update模式。对数据更新又细分为两种方式,可以先将老数据撤回,再添加新数据,这种形式被取名为撤回(Retract)模式;或者直接在老数据上做更新,被称为更新(Upsert)模式。 + +下面的代码将一个`Table`转换为`DataStream`,使用了Append-only模式。这种模式相对比较简单,这里不再仔细分析。 + +```java +StreamTableEnvironment tEnv = ... +Table table = ... + +// 将table转换为DataStream +// Append-only模式 +DataStream dsRow = tEnv.toAppendStream(table, Row.class); +``` + +我们仍然以第一个查询为例,讨论一下Retract和Upsert两种模式。如果使用Retract模式,除了原有的结果,还需要增加一个类型`Boolean`的标志位列,这个标志位列用来确定当前这行数据是新加入的,还是需要撤回的。 + +![Retract模式下对数据的更新](./img/retract.png) + +如上图所示,结果共有三列:`(flag, user_id, behavior_cnt)`,其中第一列为标志位,表示本行数据是加入还是撤回,后两行是查询结果。对于前两行输入,数据经过SQL引擎后,生成的结果追加到结尾,因此,标志位都为`true`。第三行输入进入,我们需要对结果进行更新,这时SQL引擎先将原来的老数据置为`false`:`(false, 1, 1)`,然后将新数据追加进来:`(true, 1, 2)`。 + +```java +// 将table转换为DataStream +// Retract模式,Boolean为标志位 +DataStream> retractStream = tableEnv.toRetractStream(table, Row.class); +``` + +另一种模式为Upsert模式,即对结果中已存在的数据使用SQL中的`UPDATE`操作或`DELETE`操作,更新或删除该行数据。Upsert模式的前提是,输出结果中有一个唯一的ID,可以根据唯一ID更新结果中。在这个例子中,用户ID可以被用来作为唯一ID,因为用户ID一般不会重复。Upsert模式相比Retract模式的成本稍低。Upsert模式也要和特定的`TableSink`紧密结合,比如Key-Value数据库更适合进行Upsert操作。 + +### 流处理的限制 + +至此,我们已经了解了流式关系型型查询的基本原理,但目前Flink的流处理并不能支持所有的SQL语句,因为流处理的本质是使用状态来缓存数据,包括表和中间计算过程都是Flink中的状态。因此,我们写的SQL查询不能占用太多的状态资源。 + +仍然以前面所提到的两个SQL语句为例,目前这两个SQL都是可执行的。第一个SQL语句转化为DataStream API实现的话,整个作业所需保存的状态数据为`(user_id, behavior_cnt)`这样一个二元对。当新数据流入时,如果是状态中已存在的`user_id`,直接更新状态,如果是新`user_id`,则在状态中添加一条数据。除非用户量极大,否则这个状态数据是可维护的。但是如果这个作业源源不断有新用户流入,作业长时间运行,已有状态不进行清理,状态有可能突破内存限制,导致作业崩溃。对于第二个SQL语句,我们建立了滚动时间窗口,在第五章窗口算子的介绍中,我们提到了增量计算的概念,这可以有效避免大量数据缓存在状态中,相对来说内存风险较小。 + +另一个难点是数据的更新。前文已经提到,如果源数据更新,有可能导致整个数据表重新进行一次计算。比如,我们对一个元数据中的某个字段执行`RANK`,或`ORDER BY`,那么每新增加一个数据,都会导致整个表重新进行一次排序,这对流处理引擎的性能要求极高。目前,Flink在这方面的功能正在不断完善。 + +## 状态过期时间 + +通过前面的讨论,我们看到Flink通过状态来保存一些中间数据。但是状态不能无限增加,否则会突破内存限制。Flink提供了一个配置,帮助我们清理一些空闲状态数据: + +```java +tEnv.getConfig.setIdleStateRetentionTime(Time.hours(1), Time.hours(2)); +``` + +空闲状态数据是指该数据长时间没有更新,仍然保留在状态中。上面的方法有两个参数:`minTime`和`maxTime`,空闲状态至少会保留`minTime`的时间,这个时间内数据不会被清理;超过`maxTime`的时间后,空闲状态会被清除。一旦这个数据被清除,那意味着后续数据流入,会被认为是一个新数据重新添加到状态中。基于这样的数据,得到的计算结果是近似准确的。可见,这是一个在结果准确度和计算性能之间的平衡。 + +:::tip +如果将`minTime`和`maxTime`设置为0,表示不作过期时间设置,状态永远不会清除。`maxTime`至少要比`minTime`大5分钟。 +::: diff --git a/doc/ch-table-sql/exercise-iot.md b/doc/ch-table-sql/exercise-iot.md new file mode 100644 index 0000000..78b1f0e --- /dev/null +++ b/doc/ch-table-sql/exercise-iot.md @@ -0,0 +1,59 @@ +(exercise-iot)= +# 练习:SQL on IoT + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +经过本章的学习,结合三到七章的背景知识,读者应该对Table API & SQL有了比较全面的了解,尤其SQL提供了一种更为通用的接口,相比Java/Scala来说更方便易用。本节将结合物联网(Internet of Things,IoT)场景来展示如何使用Flink SQL进行数据处理和分析。 + +## 实验目的 + +熟悉Flink SQL的各类操作:包括使用Flink SQL连接数据源,进行时间窗口操作,进行两个表的Temporal Table Join等。 + +## 实验内容 + +本实验基于德国纽伦堡大学提供的室内气候数据[^1],该团队使用IoT设备来收集温度、湿度、光照等数据,结合室内人数、人物活动以及门窗开闭等情况,进行了研究,我们在原始数据基础上做了调整。 + +假设我们有多个房间(A、B、C等),每个房间内部署有传感器,传感器可以收集温度、相对湿度、光照等数据。下图展示了房间A的布局,其中A1至A4为传感器。 + + +![室内数据收集示意图](./img/iot-room-a.png) + +传感器收集上来的数据集名为`sensor.csv`,包括以下字段:`room,node_id,temp,humidity,light,ts`,各字段含义如下: + +* `room`:房间号,这里为A、B、C等。 +* `node_id`:该房间内的传感器编号,以上图中的房间A为例,共4个传感器,编号为1至4。 +* `temp`:摄氏温度,单位°C。 +* `humidity`:相对湿度,单位%。 +* `light`:光传感器波长,单位nm。 +* `ts`:数据收集时间戳,形如`2016-03-15 19:34:05`。 + +此外,第二个数据流收集房间的环境信息,数据集名为`env.csv`。这个数据记录了房间环境的变动,任何变动都将在数据中增加一条记录。数据包括以下字段:`room,occupant,activity,door,win,ts`,各字段含义如下: + +* `room`:房间号,这里为A、B、C等。 +* `occupant`:房间人数。 +* `activity`:人物行为。0:无活动、1:读、2:站、3:行走、4:工作。 +* `door`:门的状态。0:关、1:开。 +* `win`:窗的状态,0:关、1:开。 +* `ts`:以上环境信息发生改动的时间戳,形如`2016-03-15 19:34:05`。 + +两份数据集在工程目录的`src/main/resources/iot`下。 + +## 实验内容 + +结合本书内容和Flink官方文档,完成下面程序: + +* 程序1:使用流处理模式,读取`sensor.csv`和`env.csv`数据源,注意`ts`字段作为时间属性,并定义Watermark策略。 +* 程序2:按房间号分组,计算该房间内每分钟的平均温度和湿度,输出到文件系统上。 +* 程序3:基于Temporal Table Join,将`sensor.csv`和`env.csv`按照房间号连接,生成一个宽表(宽表有助于哦后续数据分析流程),将结果输出到文件系统上。宽表包括以下字段:`room,node_id,temp,humidity,light,occupant,activity,door,win,ts`。 + +## 实验报告 +将思路和程序撰写成实验报告。 + +[^1]: P. Morgner, C. Müller, M. Ring, B. Eskofier, C. Riess, F. Armknecht, Z. Benenson: Privacy Implications of Room Climate Data. 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zy-Mb_=kH&BtlDZUqwl$T|NRATqsv$O?_YQ9%EK>TCYN*-omS(I)4vE@W?1(*Hr3`o zaM>a72nXl2mzSs4nR)tem0kk8#yfQTF5rHSZy~_8E^tbFvdY?+-3M+|yqNj#uE}d> zec-~sfbV_ISC+?zA3bvC!lU)|0X{y!qh2zX$?dfQkGB?SgJ!j_33eS={Ndr6PSCQE zu=RHmM1-rqy!S-WNL`{S>vc zY~QNu1y?RkFsKDK@}8ZITmsBepml)V;b#IHnf|;7snCqUVSz)yaHPoMi2j%|l`?vr5^Ld9~+g#8#_mJyLzcD`sPpp`O zwjV77?#)_rOb$59*C?|pD79T=wD4^X5Tp^jZHwI;Q^T8S5)2dMj~qJ&oY%SrQve() z6v@f;ezA07^BF&82DzOxW=Mc%`wlo8Je35_<;l5$S4i^%cP|p)%>$m}5xOqsCc6_4HS?GpzLnb_0;$8Eoy(q2CUJj*@d@lD_n zH{YgjIrYXxZV7M%cUSTAK9E-?fF^=g0tfGNLO_Gq2Udmm$u9)%rO*eh09X~iejaEs zJ~wb5kP7J7jXTRvXaZLNECn4R0bE}W)Xx0$hhP`*6fSV*{L@xoRb>o5g`pwzoczKF z;5OCG!60=&P~gwFSs21(m{1Lwf(3F>7F$5Ji2y|*sQ{$fVHP+zLCj&GH0;H_{j<;d zfrqz2UV?xpivRidIBYR!?c5AHX;`(yHKHUXu_V ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +通过前面的一些介绍,我们已经对Flink SQL有了一些了解。Flink SQL底层使用Dynamic Table来进行流处理,它支持了时间窗口和Join操作。本节将围绕SQL DDL,主要介绍创建、获取、修改和删除元数据时所涉及的一些注意事项。 + +## 注册和获取表 + +### Catalog简介 + +Catalog记录并管理了各类元数据信息,比如我们有哪些数据库(Database)、数据库是以文件还是消息队列的形式存储、某个数据库中有哪些表和视图、当前有哪些可用的函数等。Catalog提供了一个注册、管理和访问这些元数据的API。一般情况下,一个Catalog中包含一到多个Database,一个Database包含一到多个Table。 + +常见的Catalog包括:`GenericInMemoryCatalog`、`HiveCatalog`和用户自定义的Catalog。 + +`GenericInMemoryCatalog`将元数据存储在内存中,只在一个Session会话内生效。默认情况下,都是使用这种Catalog。 + +Hive的元数据管理功能是SQL-on-Hadoop领域事实上的标准,很多企业的生产环境使用Hive管理元数据,Hive可以管理包括纯Hive表和非Hive表。为了与Hive生态兼容,Flink推出了`HiveCatalog`。对于同时部署了Hive和Flink的环境,`HiveCatalog`允许用户在Flink SQL上读取原来Hive中的各个表。对于只部署了Flink的环境,`HiveCatalog`是目前将元数据持久化的唯一方式。持久化意味着某些元数据管理团队可以先将Kafka或HDFS中的数据注册到Catalog中,其他数据分析团队无需再次注册表,只需每次从Catalog中获取表,不用关心数据管理相关问题。如果没有进行元数据持久化,用户每次都需要注册表。 + +用户也可以自定义Catalog,需要实现`Catalog`接口类。 + +### 获取表 + +我们编写好SQL查询语句(`SELECT ...`语句)后,需要使用`TableEnvironment.sqlQuery("SELECT ...")`来执行这个SQL语句,这个方法返回的结果是一个`Table`。`Table`可以用于后续的Table API & SQL查询,也可以将`Table`转换为`DataStream`或者`DataSet`。总之,Table API和SQL可以完美融合。 + +我们使用`FROM table_name`从某个表中查询数据,这个`table_name`表必须被注册到`TableEnvironment`中。注册有以下几种方式: + +* 使用SQL DDL中的`CREATE TABLE ...`创建表 +* `TableEnvironment.connect()`连接一个外部系统 +* 从Catalog中获取已注册的表 + +第一种SQL DDL的方式,我们会用`CREATE TABLE table_name ...`来明确指定一个表的名字为`table_name`。而第二种和第三种方式,我们是在Java/Scala代码中获取一个`Table`对象,获取对象后,明确使用`createTemporaryView()`方法声明一个`Table`并指定一个名字。例如,下面的代码中,我们获取了一个`Table`,并通过`createTemporaryView()`注册该`Table`名字为`user_behavior`。 + +```java +Table userBehaviorTable = tEnv.fromDataStream(userBehaviorStream, "user_id, item_id, behavior, ts.rowtime"); + +// createTemporaryView创建名为user_behavior的表 +tEnv.createTemporaryView("user_behavior", userBehaviorTable); +``` + +对于`TableEnvironment.connect()`所连接的外部系统,也可以使用`createTemporaryTable`方法,以链式调用的方式注册名字: + +```java +tEnv + // 使用connect函数连接外部系统 + .connect(...) + // 序列化方式 可以是JSON、Avro等 + .withFormat(...) + // 数据的Schema + .withSchema(...) + // 临时表的表名,后续可以在SQL语句中使用这个表名 + .createTemporaryTable("user_behavior"); +``` + +注册好表名后,就可以在SQL语句中使用`FROM user_behavior`来查询该表。 + +即使没有明确指定表名,也没有问题。`Table.toString()`可以返回表的名字,如果没有给这个`Table`指定名字,Flink会为其自动分配一个唯一的表名,不会与其他表名冲突。如下所示: + +```java +// 获取一个Table,并没有明确为其分配表名 +Table table = ...; +String tableName = table.toString(); +tEnv.sqlQuery("SELECT * FROM " + tableName); +``` + +甚至可以省略`toString`方法,直接将`Table`对象与SQL语句用加号`+`连接,因为`+`会自动调用`Table.toString()`方法。 + +```java +// 获取一个Table,并没有明确为其分配表名 +Table table = ...; +// 加号+操作符会在编译期间自动调用Table.toString()方法 +tEnv.sqlQuery("SELECT * FROM " + table); +``` + +### HiveCatalog + +如果想将元数据持久化到`HiveCatalog`中: + +```java +TableEnvironment tEnv = ... + +// 创建一个HiveCatalog +// 四个参数分别为:catalogName、databaseName、hiveConfDir、hiveVersion +Catalog catalog = new HiveCatalog("mycatalog", null, "", ""); + +// 注册catalog,取名为mycatalog +tEnv.registerCatalog("mycatalog", catalog); + +// 创建一个Database,取名为mydb +tEnv.sqlUpdate("CREATE DATABASE mydb WITH (...)"); + +// 创建一个Table,取名为mytable +tEnv.sqlUpdate("CREATE TABLE mytable (name STRING, age INT) WITH (...)"); + +// 返回所有Table +tEnv.listTables(); +``` + +## USE和SHOW语句 + +创建完Catalog、Database后,可以像其他SQL引擎一样,使用`SHOW`和`USE`语句: + +```sql +-- 展示所有Catalog +SHOW CATALOGS; + +-- 使用mycatalog +USE CATALOG mycatalog; + +-- 展示当前Catalog里所有Database +SHOW DATABASES; + +-- 使用mydatabase +USE mydb; + +-- 展示当前Catalog里当前Database里所有Table +SHOW TABLES; +``` + +这些语句需要粘贴到`sqlUpdate`方法中: + +```java +Catalog catalog = ... + +tEnv.registerCatalog("mycatalog", catalog); +tEnv.sqlUpdate("USE catalog mycatalog"); +tEnv.sqlUpdate("CREATE DATABASE mydb"); +tEnv.sqlUpdate("USE mydb"); +``` + +## CREATE、DROP、ALTER + +`CREATE`、`ALTER`、`DROP`是SQL中最常见的三种DDL语句,可以创建、修改和删除数据库(Database)、表(Table)和函数(Function)。 + +`CREATE`语句可以创建一个Database、Table和Function: + +* `CREATE TABLE` +* `CREATE DATABASE` +* `CREATE FNCTION` + +`ALTER`语句可以修改已有的Database、Table和Function: + +* `ALTER TABLE` +* `ALTER DATABASE` +* `ALTER FNCTION` + +`DROP`语句可以删除之前创建的Database、Table和Function: + +* `DROP TABLE` +* `DROP DATABASE` +* `DROP FUNCTION` + +这些语句可以放到`TableEnvironment.sqlUpdate()`的参数里,也可以在SQL Client里执行。`TableEnvironment.sqlUpdate()`执行成功后没有返回结果,执行失败则会抛出异常。下面例子展示了如何在`TableEnvironment`中使用`sqlUpdate`: + +```java +// tEnv:TableEnvironment +// CREATE TABLE +tEnv.sqlUpdate("CREATE TABLE user_behavior (" + + " user_id BIGINT," + + " item_id BIGINT," + + " category_id BIGINT," + + " behavior STRING," + + " ts TIMESTAMP(3)," + + " WATERMARK FOR ts as ts - INTERVAL '5' SECOND -- 在ts上定义watermark,ts成为事件时间列" + + ") ..."); + +// ALTER DATABASE +tEnv.sqlUpdate("ALTER DATABASE db1 set ('k1' = 'a', 'k2' = 'b')") + +// DROP TABLE +tEnv.sqlUpdate("DROP TABLE user_behavior"); +``` + +### CREATE/ALTER/DROP TABLE + +#### CREATE TABLE + +`CREATE TABLE`需要按照下面的模板编写: + +```sql +CREATE TABLE [catalog_name.][db_name.]table_name + ( + { | }[ , ...n] + [ ] + ) + [COMMENT table_comment] + [PARTITIONED BY (partition_column_name1, partition_column_name2, ...)] + WITH (key1=val1, key2=val2, ...) +``` + +一个名为`table_name`的`Table`隶属于一个名为`db_name`的Database,`db_name`又隶属于名为`catalog_name`的Catalog。如果不明确指定`db_name`和`catalog_name`,该表被注册到默认的Catalog和默认的Database中。如果`table_name`与已有的表名重复,会抛出异常。 + +``、``和``需要符合下面的模板: + +```sql +: + column_name column_type [COMMENT column_comment] + +: + column_name AS computed_column_expression [COMMENT column_comment] + +: + WATERMARK FOR rowtime_column_name AS watermark_strategy_expression +``` + +`COMMENT`用来对字段做注释,使用`DESCRIBE table_name`命令时,可以查看到字段的一些注释和描述信息。 + +``在传统的SQL DDL中经常见到,Watermark策略在[窗口](sql-window)部分已经介绍,不再赘述,这里主要介绍计算列(Computed Column)。 + +#### 计算列 + +计算列是虚拟字段,不是一个实际存储在表中的字段。计算列可以通过表达式、内置函数、或是自定义函数等方式,使用其它列的数据,计算出其该列的数值。比如,一个订单表中有单价`price`和数量`quantity`,总价可以被定义为`total AS price * quantity`。计算列表达式可以是已有的物理列、常量、函数的组合,但不能是`SELECT ...`式的子查询。计算列虽然是个虚拟字段,但在Flink SQL中可以像普通字段一样被使用。 + +计算列常常被用于定义时间属性,在[窗口](sql-window)部分我们曾介绍了如何定义Processing Time:使用`proc AS PROCTIME()`来定义一个名为`proc`的计算列,该列可以在后续计算中被用做时间属性。`PROCTIME()`是Flink提供的内置函数,用来生成Processing Time。 + +```sql +CREATE TABLE mytable ( + id BIGINT, + -- 在原有Schema基础上添加一列proc + proc as PROCTIME() +) WITH ( + ... +); +``` + +关于计算列,相关知识点可以总结为: + +* 计算列是一个虚拟列,计算的过程可以由函数、已有物理列、常量等组成,可以用在`SELECT`语句中。 +* 计算列不能作为`INSERT`语句的输出目的地,或者说我们不能使用`INSERT`语句将数据插入到目标表的计算列上。 + +#### WITH + +在[Table API & SQL简介](table-overview.md)中我们曾介绍连接外部系统时必须配置相应参数,这些参数以`key=value`等形式被放在`WITH`语句中。 + +```sql +CREATE TABLE my_table ( + -- Schema + ... +) WITH ( + -- 声明参数 + 'connector.type' = 'kafka', + 'connector.version' = 'universal', + ... +) +``` + +在这个例子中使用的外部系统是Kafka,我们需要配置一些Kafka连接相关的参数。Flink的官方文档中有不同Connector的详细参数配置示例,这里不再详细介绍每种Connector需要配置哪些参数, + +使用`CREATE TABLE`创建的表可以作为Source也可以作为Sink。 + +#### PARTITIONED BY + +根据某个字段进行分区。如果这个表中的数据实际存储在一个文件系统上,Flink会为每个分区创建一个文件夹。例如,以日期为分区,那么每天的数据被放在一个文件夹中。`PARTITIONED BY`经常被用在批处理中。 + +:::note +这里是`PARTITIONED BY`,与`OVER WINDOW`中的`PARTITON BY`语法和含义均不同。 +::: + +#### ALTER TABLE + +`ALTER TABLE`目前支持修改表名和一些参数。 + +```sql +-- 修改表名 +ALTER TABLE [catalog_name.][db_name.]table_name RENAME TO new_table_name + +-- 修改参数,如果某个Key之前被WITH语句设置过,再次设置会将老数据覆盖 +ALTER TABLE [catalog_name.][db_name.]table_name SET (key1=val1, key2=val2, ...) +``` + +#### DROP TABLE + +`DROP TABLE`用来删除一个表。`IF EXISTS`表示只对已经存在的表进行删除。 + +```sql +DROP TABLE [IF EXISTS] [catalog_name.][db_name.]table_name +``` + +### CREATE/ALTER/DROP DATABASE + +#### CREATE DATABASE + +`CREATE DATABASE`一般使用下面的模板: + +```sql +CREATE DATABASE [IF NOT EXISTS] [catalog_name.]db_name + [COMMENT database_comment] + WITH (key1=val1, key2=val2, ...) +``` + +这里的语法与其他SQL引擎比较相似。其中`IF NOT EXISTS`表示,如果这个Database不存在才创建,否则不会创建。 + +#### ALTER DATABASE + +```sql +ALTER DATABASE [catalog_name.]db_name SET (key1=val1, key2=val2, ...) +``` + +`ALTER DATABASE`支持修改参数,新数据会覆盖老数据。 + +#### DROP DATABASE + +```sql +DROP DATABASE [IF EXISTS] [catalog_name.]db_name [ (RESTRICT | CASCADE) ] +``` + +`RESTRICT`选项表示如果Database非空,那么会抛出异常,默认开启本选项;`CASCADE`选项表示会将Database下所属的Table和Function等都删除。 + +### CREATE/ALTER/DROP FUNCTION + +除了传统SQL引擎都会提供的上述`CREATE`功能,Table API & SQL还提供了函数(Function)功能。我们也可以用Java或Scala语言自定义一个函数,然后注册进来,在SQL语句中使用。`CREATE FUNCTION`的模板如下所示,我们将在[用户自定义函数](catalog-function.md)部分的详细介绍如何使用Java/Scala自定义函数。 + +```sql +CREATE [TEMPORARY|TEMPORARY SYSTEM] FUNCTION + [IF NOT EXISTS] [catalog_name.][db_name.]function_name + AS identifier [LANGUAGE JAVA|SCALA] +``` + +## INSERT + +`INSERT`语句可以向表中插入数据,一般用于向外部系统输出数据。它的语法模板如下: + +```sql +INSERT { INTO | OVERWRITE } [catalog_name.][db_name.]table_name [PARTITION part_spec] +SELECT ... + +part_spec: + (part_col_name1=val1 [, part_col_name2=val2, ...]) +``` + +上面的SQL语句将`SELECT`查询结果写入到目标表中。`OVERWRITE`选项表示将原来的数据覆盖,否则新数据只是追加进去。`PARTITION`表示数据将写入哪个分区。如果`CREATE TABLE`是按照日期进行`PARTITIONED BY`分区,那么每个日期会有一个文件夹,`PARTITION`后要填入一个日期,数据将写入日期对应目录中。分区一般常用于批处理场景。 + +```sql +-- 创建一个表,用来做输出 +CREATE TABLE behavior_cnt ( + user_id BIGINT, + cnt BIGINT +) WITH ( + 'connector.type' = 'filesystem', -- 使用 filesystem connector + 'connector.path' = 'file:///tmp/behavior_cnt', -- 输出地址为一个本地路径 + 'format.type' = 'csv' -- 数据源格式为 json +) + +-- 向一个Append-only表中输出数据 +INSERT INTO behavior_cnt +SELECT + user_id, + COUNT(behavior) AS cnt +FROM user_behavior +GROUP BY user_id, TUMBLE(ts, INTERVAL '10' SECOND) +``` + +或者是将特定的值写入目标表: + +```sql +INSERT { INTO | OVERWRITE } [catalog_name.][db_name.]table_name VALUES values_row [, values_row ...] +``` + +例如: + +```sql +CREATE TABLE students (name STRING, age INT, score DECIMAL(3, 2)) WITH (...); + +INSERT INTO students + VALUES ('Li Lei', 35, 1.28), ('Han Meimei', 32, 2.32); +``` \ No newline at end of file diff --git a/doc/ch-table-sql/system-function.md b/doc/ch-table-sql/system-function.md new file mode 100644 index 0000000..a4ca9b9 --- /dev/null +++ b/doc/ch-table-sql/system-function.md @@ -0,0 +1,323 @@ +(system-function)= +# 系统函数 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + + +## 函数简介 + +Table API & SQL提供给用户强大的操作数据的方法:函数(Function)。对于Function,可以有两种维度来对其分类。 + +第一种维度根据是否为系统内置(System)来分类。System Function是Flink提供的内置函数,在任何地方都可以直接拿来使用。非系统内置函数一般注册到一个Catalog下的Database里,该函数有自己的命名空间(Namespace),表示该函数归属于哪个Catalog和Database。例如,使用名为`func`的函数时需要加上Namespace前缀:`mycatalog.mydb.func`。由于函数被注册到Catalog中,这种函数被称为表目录函数(Catalog Function)。 + +第二种维度根据是否为临时函数来分类。临时函数(Temporary Function)只存在于一个Flink Session中,Session结束后就被销毁,其他Session无法使用。非临时函数,又被成为持久化函数(Persistent Function),可以存在于多个Flink Session中,它可以是一个System Function,也可以是一个Catalog Function。 + +根据这两个维度,Function可以被划分四类: + +* Temporary System Function +* System Function +* Temporary Catalog Function +* Catalog Function + +这些函数可以在Table API中使用Java、Scala或Python语言调用,也可以在Flink SQL中以SQL语句的形式调用。这里以SQL为例来介绍如何使用这些函数。绝大多数System Function已经内置在Table API & SQL中,它们也是Persistent Function,本节将主要介绍这部分内容,下节将介绍Catalog Function。由于System Function较多,这里只介绍一些常用的函数并提供一些例子,其他函数的具体使用方法可以参考Flink的官方文档。 + +## 标量函数 + +标量函数(Scalar Function)接收零个、一个或者多个输入,生成一个单值输出。 + +### 比较函数 + +* `value1 = value2` + +如果`value1`和`value2`相等,返回`TRUE`;如果`value1`或`value2`任何一个值为`NULL`,返回`UNKNOWN`。 + +* `value1 <> value2` + +如果`value1`和`value2`不相等,返回`TRUE`;如果`value1`或`value2`任何一个值为`NULL`,返回`UNKNOWN`。 +* `value1 >= value2` + +如果`value1`大于等于`value2`,返回`TRUE`;如果`value1`或`value2`任何一个值为`NULL`,返回`UNKNOWN`。其他`>`、`<`、<`=`比较函数与此相似。 +* `value IS NULL`和`value IS NOT NULL` + +判断`value`是否为`NULL`。 +* `value1 BETWEEN [ ASYMMETRIC | SYMMETRIC ] value2 AND value3` + +判断`value1`是否在一个区间。支持`DOUBLE`、`BIGINT`、`INT`、`VARCHAR`、`DATE`、`TIMESTAMP`、`TIME`这些类型。 + +例如,`12 BETWEEN 15 AND 12`返回`FALSE`,`12 BETWEEN SYMMETRIC 15 AND 12` returns `TRUE`。`SYMMETRIC`表示包含区间边界。`value1 NOT BETWEEN [ ASYMMETRIC | SYMMETRIC ] value2 AND value3`与之相似。 + +* `string1 LIKE string2` + +如果`string1`符合`string2`的模板,返回`TRUE`。`LIKE`主要用于字符串匹配,`string2`中可以使用 `%`来定义通配符。例如,`'TEST' LIKE '%EST'`返回`TRUE`。`string1 NOT LIKE string2`与之类似。 + +* `string1 SIMILAR TO string2` + +如果`string1`符合SQL正则表达式`string2`,返回`TRUE`。例如,`'TEST' SIMILAR TO '.EST'`返回`TRUE`。`string1 NOT SIMILAR TO string2`与之类似。 + +* `value1 IN (value2 [, value3]* )`:如果`value1`在列表中,列表包括`value2`、`value3`等元素,返回`TRUE`。例如,`'TEST' IN ('west', 'TEST', 'rest')`返回`TRUE`;`'TEST' IN ('west', 'rest')`返回`FALSE`。`value1 NOT IN (value2 [, value3]* )`与之类似。 + +* `EXISTS (sub-query)`:如果子查询有至少一行结果,返回`TRUE`。例如,下面的SQL语句使用了`EXISTS`,实际上起到了Join的作用: + +```sql +SELECT * +FROM l +WHERE EXISTS (select * from r where l.a = r.c) +``` + +* `value IN (sub-query)`:如果`value`等于子查询中的一行结果,返回`TRUE`。`value NOT IN (sub-query)`与之类似。例如: + +```sql +SELECT * +FROM tab +WHERE a IN (SELECT c FROM r) +``` + +:::note +在流处理模式下,`EXISTS(sub-query)`和`value IN (sub-query)`都需要使用状态进行计算,我们必须确保配置了状态过期时间,否则状态可能会无限增大。 +::: + +### 逻辑函数 + +* `boolean1 OR boolean2` + +如果`boolean1`或`boolean2`任何一个为`TRUE`,返回`TRUE`。 + +* `boolean1 AND boolean2` + +如果`boolean1`和`boolean2`都为`TRUE`,返回`TRUE`。 + +* `NOT boolean` + +如果`boolean`为`TRUE`,返回`FALSE`;`boolean`为`FALSE`,返回`TRUE`。 + +* `boolean IS FALSE`、`boolean IS TRUE`和`boolean IS UNKNOWN` + +根据`boolean`结果,判断是否为`FALSE`、`TRUE`或者`UNKNOWN`。`boolean IS NOT FALSE`等与之类似。 + +### 数学函数 + +* 加减乘除 + +加(`+`)减(`-`)乘(`*`)除(`/`)对数字字段做运算。下面的例子以加法为例,其他运算与之类似。 + +```sql +SELECT int1+int2 AS add +FROM tab +``` + +* `ABS(numeric)` + +返回`numeric`的绝对值。 + +* `MOD(numeric1, numeric2)` + +余数函数,`numeric1`除以`numeric2`,返回余数。 + +* `SQRT(numeric)` + +平方根函数,返回`numeric`的平方根。 + +* `LN(numeric)`、`LOG10(numeric)`和`LOG2(numeric)` + + +对数函数,返回`numeric`的对数,分别以e为底、以10为底和以2为底。 + +* `EXP(numeric)` + +指数函数,返回以e为底`numeric`的指数。 + +* `SIN(numeric)`、`COS(numeric)`等 + +三角函数,包括`SIN`、`COS`、`TAN`等。 + +* `RAND()` + +返回0到1之间的一个伪随机数。 + +* `CEIL(numeric)`和`FLOOR(numeric)` + +向上和向下取整。 + +### 字符串函数 + +* `string1 || string2` + +连接两个字符串。 + +* `CONCAT(string1, string2,...)` + +连接多个字符串。 + +* `CHAR_LENGTH(string)`和`CHARACTER_LENGTH(string)` + +返回字符串`string`的长度。 + +* `SUBSTRING(string FROM start [ FOR length ])` + +对字符串做截断,返回`string`的一部分,从`start`位置开始,默认到字符串结尾结束,填写`length`参数后,字符串截断到`length`长度。 + +* `POSITION(string1 IN string2)` + +返回`string1`在`string2`中第一次出现的位置,如果未曾出现则返回0。 + +* `TRIM([ BOTH | LEADING | TRAILING ] string1 FROM string2)` + +将`string2`中出现的`string1`移除。`BOTH`选项表示移除左右两侧的字符串。一般情况下,如果不指定`string1`,默认移除空格。例如,`TRIM(LEADING 'x' FROM 'xxxxSTRINGxxxx')`返回`STRINGxxxx`。 + +* `REGEXP_REPLACE(string1, string2, string3)` + +替换函数,将`string1`中符合正则表达式`string2`的字符全替换为`string3`。例如,`REGEXP_REPLACE('foobar', 'oo|ar', '')`移除了正则表达式`oo|ar`,返回`fb`。 + +### 时间函数 + +* `DATE string`、`TIME string`和`TIMESTAMP string` + +将字符串`string`转换为`java.sql.Date`、`java.sql.Time`或`java.sql.Timestamp`。我们可以在`WHERE`语句中做过滤: + +```sql +SELECT * FROM tab +WHERE b = DATE '1984-07-12' +AND c = TIME '14:34:24' +AND d = TIMESTAMP '1984-07-12 14:34:24 +``` + +或者应用在`SELECT`语句中: + +```sql +SELECT a, b, c, + DATE '1984-07-12', + TIME '14:34:24', + TIMESTAMP '1984-07-12 14:34:24' +FROM tab +``` + +* `LOCALTIME`、`LOCALTIMESTAMP` + +返回当前本地时间,格式为`java.sql.Time`和`java.sql.Timestamp`。 + +* `YEAR(date)`、`MONTH(date)`和`DAYOFWEEK(date)`等 + +将`java.sql.Date`转化为年月日。例如,`YEAR(DATE '1994-09-27')`返回为1994,`MONTH(DATE '1994-09-27')`返回为9,`DAYOFYEAR(DATE '1994-09-27')`返回为270。 + +* `HOUR(timestamp)`、`MINUTE(timestamp)`和`SECOND(timestamp)` + +将`java.sql.Timestamp`转化为时分秒。例如,`HOUR(TIMESTAMP '1994-09-27 13:14:15')`返回13,`MINUTE(TIMESTAMP '1994-09-27 13:14:15')`返回14。 + +* `FLOOR(timepoint TO timeintervalunit)`和`CEIL(timepoint TO timeintervalunit)` + +向下和向上取整。例如,`FLOOR(TIME '12:44:31' TO MINUTE)`返回12:44:00,`CEIL(TIME '12:44:31' TO MINUTE)`返回12:45:00。 + +### 判断函数 + +* `CASE ... WHEN ... END` + +类似很多编程语言提供的`switch ... case ...`判断逻辑。在Flink SQL中可以对某个字段进行判断,其模板为: +```sql +CASE value + WHEN value1_1 [, value1_2 ]* THEN result1 + [ WHEN value2_1 [, value2_2 ]* THEN result2 ]* + [ ELSE resultZ ] +END +``` +例如,对表中字段`a`进行判断,生成一个新字段`correct`,SQL语句可以写为: + +```sql +SELECT + CASE a + WHEN 1 THEN 1 + ELSE 99 + END AS correct +FROM tab +``` + +也可以对一个表达式进行判断,其模板为: + +```sql +CASE + WHEN condition1 THEN result1 + [ WHEN condition2 THEN result2 ]* + [ ELSE resultZ ] +END +``` + +例如,对表中字段`c`进行`c > 0`的判断,为`TRUE`时生成`b`,SQL语句可以写为: + +```sql +SELECT + CASE + WHEN c > 0 THEN b + ELSE NULL + END +FROM tab +``` + +### 类型转化 + +* `CAST(value AS type)` + +将字段`value`转化为类型`type`。例如,`int1`字段原本为`INT`,现将其转化为`DOUBLE`: + +```sql +SELECT CAST(int1 AS DOUBLE) as aa +FROM tab +``` + +### 集合函数 + +* `ARRAY ‘[’ value1 [, value2 ]* ‘]’` + +将多个字段连接成一个列表。例如,某表中两个字段`a`和`b`均为`INT`类型,将其连接到一起,后面再添加一个数字99: + +```sql +SELECT + ARRAY[a, b, 99] +FROM tab +``` + +* `CARDINALITY(array)` + +返回列表中元素的个数。例如前面这个例子中: + +```sql +SELECT CARDINALITY(arr) +FROM ( + SELECT ARRAY[a, b, 99] AS arr FROM tab +) +``` + +`ARRAY[a, b, 99]`创建一个3个字段组成的列表,`CARDINALITY(arr) `返回值为3。 + +## 聚合函数 + +在[窗口](sql-window)部分我们重点讲解了`GROUP BY`和`OVER WINDOW`的窗口划分方式,聚合函数一般应用在窗口上,对窗口内的多行数据进行处理,并生成一个聚合后的结果。 + +* `COUNT([ ALL ] expression | DISTINCT expression1 [, expression2]*)` + +返回行数,默认情况下是开启了`ALL`选项,即返回所有行。使用`DISTINCT`选项后,对数据做去重处理。 + +* `AVG([ ALL | DISTINCT ] expression)` + +返回平均值,默认情况下开启了`ALL`选项。使用`DISTINCT`选项后,对数据做去重处理。 + +* `SUM([ ALL | DISTINCT ] expression)` + +对数据求和,默认情况下开启了`ALL`选项。使用`DISTINCT`选项后,对数据做去重处理。 + +* `MAX([ ALL | DISTINCT ] expression)`和`MIN([ ALL | DISTINCT ] expression)` + +求数据中的最大值/最小值,默认情况下开启了`ALL`选项。使用`DISTINCT`选项后,对数据做去重处理。 + +* `STDDEV_POP([ ALL | DISTINCT ] expression)` + +求数据总体的标准差,默认情况下开启了`ALL`选项。使用`DISTINCT`选项后,对数据做去重处理。 + +## 时间单位 + +一些时间相关计算需要使用时间单位,常见的有`YEAR`、`MONTH`、`WEEK`、`DAY`、`HOUR`、`MINUTE`和`SECOND`等。 \ No newline at end of file diff --git a/doc/ch-table-sql/table-overview.md b/doc/ch-table-sql/table-overview.md new file mode 100644 index 0000000..668e28e --- /dev/null +++ b/doc/ch-table-sql/table-overview.md @@ -0,0 +1,384 @@ +(table-overview)= +# Table API & SQL综述 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +Table API和SQL两者结合非常紧密,它们的API与关系型数据库中查询非常相似,本质上它们都依赖于一个像数据表的结构:`Table`。 + +在具体执行层面,Flink将Table API或SQL语句使用一个名为执行计划器(Planner)的组件将关系型查询转换为可执行的Flink作业,并对作业进行一些优化。在本书写作期间出现了阿里巴巴的Blink版本的Planner(或者称为Blink Planner)和Flink社区版本的老Planner(或者称为Flink Planner、Old Planner)并存的现象,Flink社区正在进行这方面的迭代和融合。从名称中可以看出,Blink Planner未来将逐步取代Flink Planner,读者可以根据需求来确定使用哪种Planner。同时,Table API & SQL的迭代速度较快,读者可以根据Flink官方文档查询最新的使用方法。 + +本节主要介绍Table API & SQL程序的骨架结构以及如何连接外部系统。 + +## Table API & SQL程序骨架结构 + +下面的代码展示了Table API & SQL的骨架结构: + +```java +// 基于StreamExecutionEnvironment创建TableEnvironment +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); + +// 读取数据源,创建数据表Table +tableEnv.connect(...).createTemporaryTable("user_behavior"); +// 注册输出数据表Table +tableEnv.connect(...).createTemporaryTable("output_table"); + +// 使用Table API查询user_behavior +Table tabApiResult = tableEnv.from("user_behavior").select(...); +// 使用SQL查询table1 +Table sqlResult = tableEnv.sqlQuery("SELECT ... FROM user_behavior ... "); + +// 将查询结果输出到outputTable +tabApiResult.insertInto("output_table"); +sqlResult.insertInto("output_table"); + +// execute +tableEnv.execute("table"); +``` + +从程序骨架结构上来看,目前的Table API & SQL要与DataStream/DataSet API相结合来使用,主要需要以下步骤: + +1. 创建执行环境(ExecutionEnvironment)和表环境(TableEnvironment) +2. 获取数据表`Table` +3. 使用Table API或SQL在`Table`上做查询等操作 +4. 将结果输出到外部系统 +5. 调用`execute()`,执行作业 + +在真正编写一个作业之前,我们还需要在Maven中添加相应的依赖。根据用户选择Java还是Scala,需要引用`flink-table-api-*-bridge`项目,这个项目是Table API与DataStream/DataSet API之间的桥梁。 + +``` + + + org.apache.flink + flink-table-api-java-bridge_${scala.binary.version} + ${flink.version} + provided + + + + org.apache.flink + flink-table-api-scala-bridge_${scala.binary.version} + ${flink.version} + provided + +``` + +此外,还需要添加Planner相关依赖: + +``` + + + org.apache.flink + flink-table-planner_${scala.binary.version} + ${flink.version} + provided + + + + org.apache.flink + flink-table-planner-blink_${scala.binary.version} + ${flink.version} + provided + +``` + +其中,`${scala.binary.version}`是你所在环境中Scala的版本号,可以是2.11或2.12,`{flink.version}`是所采用的Flink版本号。Maven的配置和参数可以参考本书提供的样例程序中的`pom.xml`文件。 + +## 创建TableEnvironment + +`TableEnvironment`是Table API & SQL编程中最基础的类,也是整个程序的入口,它包含了程序的核心上下文信息。`TableEnvironment`的核心功能包括: + +* 连接外部系统 +* 向目录(Catalog)中注册`Table`或者从中获取`Table` +* 执行Table API或SQL操作 +* 注册用户自定义函数 +* 提供一些其他配置功能 + +在Flink社区对未来的规划中,`TableEnvironment`将统一流批处理,兼容Java和Scala两种语言。我们在第四章Flink的骨架结构中曾提到,在Flink 1.10中,针对流处理和批处理分别使用了`StreamExecutionEnvironment`和`ExecutionEnvironment`两套执行环境,底层有些逻辑还没完全统一,加上Java和Scala两种语言的区别,仅执行环境就四种之多。在Table API & SQL中,`TableEnvironment`也没有完全将上述问题统一,再加上Blink Planner与原有老Planner的区别,读者在编程时一定要注意如何初始化的`TableEnvironment`。 + +![Flink 1.10中保留了5个TableEnvironment](./img/environment.png) + +从上图中可以看到,Flink 1.10保留了5个`TableEnvironment`。其中,`TableEnvironment`是最顶级的接口,`StreamTableEnvironment`和`BatchTableEnvironment`都提供了Java和Scala两个实现: + +* `org.apache.flink.table.api.TableEnvironment`:兼容Java和Scala,统一流批处理,适用于整个作业都使用 Table API & SQL 编写程序的场景。 +* `org.apache.flink.table.api.java.StreamTableEnvironment`和`org.apache.flink.table.api.scala.StreamTableEnvironment`:分别用于Java和Scala的流处理场景,提供了`DataStream`和`Table`之间相互转换的接口。如果作业除了基于Table API & SQL外,还有和`DataStream`之间的转化,则需要使用`StreamTableEnvironment`。 +* `org.apache.flink.table.api.java.BatchTableEnvironment`和`org.apache.flink.table.api.scala.BatchTableEnvironment`:分别用于Java和Scala的批处理场景,提供了`DataSet`和`Table`之间相互转换的接口。如果作业除了基于Table API & SQL外,还有和`DataSet`之间的转化,则使用`BatchTableEnvironment`。 + +下面的代码使用Java语言进行流处理,它基于老Planner创建`TableEnvironment`。 + +```java +// 使用Java和老Planner进行流处理 +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.table.api.EnvironmentSettings; +import org.apache.flink.table.api.java.StreamTableEnvironment; + +// 使用老Planner注意相应的Planner包要加入到Maven中 +EnvironmentSettings fsSettings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build(); +// 基于StreamExecutionEnvironment创建StreamTableEnvironment +StreamExecutionEnvironment fsEnv = StreamExecutionEnvironment.getExecutionEnvironment(); +StreamTableEnvironment fsTableEnv = StreamTableEnvironment.create(fsEnv, fsSettings); +// 或者基于TableEnvironment +TableEnvironment fsTableEnv = TableEnvironment.create(fsSettings); +``` + +如果想基于Blink Planner进行流处理,那么需要改为: + +```java +// 使用Java和Blink Planner进行流处理 +import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.table.api.EnvironmentSettings; +import org.apache.flink.table.api.java.StreamTableEnvironment; + +// 使用Blink Planner注意相应的Planner包要加入到Maven中 +EnvironmentSettings bsSettings = +EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build(); +// 基于StreamExecutionEnvironment创建StreamTableEnvironment +StreamExecutionEnvironment bsEnv = StreamExecutionEnvironment.getExecutionEnvironment(); +StreamTableEnvironment bsTableEnv = StreamTableEnvironment.create(bsEnv, bsSettings); +// 或者基于TableEnvironment +TableEnvironment bsTableEnv = TableEnvironment.create(bsSettings); +``` + +如果想基于老Planner进行批处理: + +```java +// 使用Java和老Planner进行批处理 +import org.apache.flink.api.java.ExecutionEnvironment; +import org.apache.flink.table.api.java.BatchTableEnvironment; + +ExecutionEnvironment fbEnv = ExecutionEnvironment.getExecutionEnvironment(); +BatchTableEnvironment fbTableEnv = BatchTableEnvironment.create(fbEnv); +``` + +基于Blink Planner进行批处理: + +```java +// 使用Java和Blink Planner进行批处理 +import org.apache.flink.table.api.EnvironmentSettings; +import org.apache.flink.table.api.TableEnvironment; + +EnvironmentSettings bbSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inBatchMode().build(); +TableEnvironment bbTableEnv = TableEnvironment.create(bbSettings); +``` + +总结下来,使用Table API & SQL之前,要确定使用何种编程语言(Java/Scala),进行批处理还是流处理以及使用哪种Planner。 + +## 获取Table + +在关系型数据库中,表是描述数据的基本单元。数据库表一般由行和列组成,如果以电商用户行为数据为例,我们可以将这张表理解为一个Excel表格,每一列代表一种属性,比如`user_id`、`behavior`等,每一行表示一个用户的一次行为,比如某个用户在哪个时间对哪些商品产生了哪些行为。我们一般用表模式(Schema)来描述一个表中有哪些列,这些列的数据类型。例如,我们定义电商用户行为的Schema为: + +```java +Schema schema = new Schema() + .field("user_id", DataTypes.BIGINT()) + .field("item_id", DataTypes.BIGINT()) + .field("category", DataTypes.BIGINT()) + .field("behavior", DataTypes.STRING()) + .field("ts", DataTypes.TIMESTAMP(3)); +``` + +在传统的关系型数据库中,数据库表一般由开发者定义好,在后续对外地提供服务过程中,表是常驻数据库的,开发者不断在表上进行增删查改。在数据分析领域,表的概念被拓展,表不仅包括了关系型数据库中传统意义上的表,也包括了存储数据的文件,可以传输数据的消息队列等。Flink是一个计算引擎,它不提供数据存储的功能,但是可以通过Connector去连接不同的外部系统。为了基于外部数据进行Table API & SQL计算,Flink使用`Table`的概念表示广义上的表。它包括物理上确实存在的表,也包括基于物理表经过一些计算而生成的虚拟表,虚拟表又被称为视图(View)。 + +可见,如果想在Flink中使用`Table`来查询数据,最重要的一步是将数据(数据库、文件或消息队列)读取并转化成一个`Table`。我们可以在一个Flink作业运行时注册一个新的`Table`,也可以获取已创建好的常驻集群的`Table`。在每个Flink作业启动后临时创建的表是临时表(Temporary Table),随着这个Flink作业的结束,这种表也被销毁,它只能在一个Flink Session中使用。在骨架程序中`tableEnv.connect(...).createTemporaryTable("user_behavior");`就是创建了一个Temporary Table。但是更多的情况下,我们想跟传统的数据库那样提前创建好表,这些表后续可以为整个集群上的所有用户和所有作业提供服务,这种表被称为常驻表(Permanent Table)。Permanent Table可以在多个Flink Session中使用。 + +为了管理多个Permanent Table,Flink使用Catalog来维护多个Permanent Table的名字、类型(文件、消息队列或数据库)、数据存储位置等元数据(Metadata)信息。一个Flink作业可以连接某个Catalog,这样就可以直接读取其中的各个表,生成`Table`。有了Catalog功能,数据管理团队对数据源更了解,他们可以提前在Catalog中创建Permanent Table,注册好该表的Schema、注明该表使用何种底层技术、写明数据存储位置等;数据分析团队可以完全不用关心这些元数据信息,无需了解这个表到底是存储在Kafka还是HDFS,直接在这个表上进行查询。 + +本节后续部分将介绍注册表的几种常见方式。 + +## 在Table上执行语句 + +### Table API + +基于`Table`,我们可以调用Table API或者SQL来查询其中的数据。Table API和编程语言结合更紧密,我们可以在`Table`类上使用链式调用,调用`Table`类中的各种方法,执行各类关系型操作。下面的代码在`user_behavior`表上进行`groupBy`和`select`操作。 + +```java +StreamTableEnvironment tEnv = ... +// 创建一个TemporaryTable:user_behavior +tEnv.connect(new FileSystem().path("...")) + .withFormat(new Csv()) + .withSchema(schema) + .createTemporaryTable("user_behavior"); +Table userBehaviorTable = tEnv.from("user_behavior"); + +// 在Table上使用Table API执行关系型操作 +Table groupByUserId = userBehaviorTable.groupBy("user_id").select("user_id, COUNT(behavior) as cnt"); +``` + +### SQL + +我们也可以直接在`Table`执行SQL语句。SQL标准中定义了一系列语法和关键字,开发者可以基于SQL标准来编写SQL语句。与Table API中函数调用的方式不同,SQL语句是纯文本形式的。Flink SQL基于Apache Calcite(以下简称Calcite),将SQL语句转换为Flink可执行程序。Calcite支持SQL标准,因此Flink SQL也支持SQL标准。 + +下面的代码展示了如何使用Flink SQL对一个`Table`做查询: + +```java +StreamTableEnvironment tEnv = ... +// 创建一个TemporaryTable:user_behavior +tEnv.connect(new FileSystem().path("...")) + .withFormat(new Csv()) + .withSchema(schema) + .createTemporaryTable("user_behavior"); + +// 在Table上使用SQL执行关系型操作 +Table groupByUserId = tEnv.sqlQuery("SELECT user_id, COUNT(behavior) FROM user_behavior GROUP BY user_id"); +``` + +由于Table API和SQL都基于`Table`类,我们可以使用Table API,生成一个`Table`,再在此之上进行SQL查询,也可以先进行SQL查询得到一个`Table`,再在此之上调用Table API。由此可见,Table API和SQL的结合非常紧密。本书后半部分将主要介绍Flink SQL。 + +## 将Table结果输出 + +我们可以将查询结果通过`TableSink`输出到外部系统。`TableSink`和之前提到的Sink很像,它是一个数据输出的统一接口,可以将数据以CSV、Parquet、Avro等格式序列化,并将数据发送到关系型数据库、KV数据库、消息队列或文件系统上。`TableSink`与Catalog、Schema等概念紧密相关。下面的代码展示了如何将查询结果输出到文件系统。 + +```java +StreamTableEnvironment tEnv = ... +tEnv.connect(new FileSystem().path("...")) + .withFormat(new Csv().fieldDelimiter('|')) + .withSchema(schema) + .createTemporaryTable("CsvSinkTable"); + +// 执行查询操作,得到一个名为result的Table +Table result = ... +// 将result发送到名为CsvSinkTable的TableSink +result.insertInto("CsvSinkTable"); +``` + +## 执行作业 + +以上部分是一个Table API & SQL作业的核心代码编写阶段,但千万不要忘记调用`execute`方法来执行这个作业,否则作业无法被真正执行。 + +![Table API & SQL从调用到执行的大致流程](./img/table-internal.png) + +上图为Table API & SQL从调用到执行的大致流程。一个Table API或者SQL调用经过Planner最终转化为一个`JobGraph`,Planner在中间起到一个转换和优化的作用。对于流作业和批作业,Blink Planner分别有相应的优化规则。 + +我们可以使用`TableEnvironment.explain(table)`来将查询转化为物理执行计划。 + +```java +// 声明一个SQL查询 +Table groupByUserId = tEnv.sqlQuery(...) + +String explanation = tEnv.explain(groupByUserId); +System.out.println(explanation); +``` + +我们可以得到相应的语法树(未优化的逻辑执行计划)、优化后的逻辑执行计划以及最终的物理执行计划,如下所示: + +``` +== Abstract Syntax Tree == +LogicalAggregate(group=[{0}], behavior_cnt=[COUNT($1)]) ++- LogicalProject(user_id=[$0], behavior=[$3]) + ... + +== Optimized Logical Plan == +GroupAggregate(groupBy=[user_id], select=[user_id, COUNT(behavior) AS behavior_cnt]) ++- Exchange(distribution=[hash[user_id]]) + +- Calc(select=[user_id, behavior]) + ... + +== Physical Execution Plan == +Stage 1 : Data Source + content : Source: KafkaTableSource(user_id, item_id, category_id, behavior, ts) + + Stage 2 : Operator + content : SourceConversion(table=[default_catalog.default_database.user_behavior, source: [KafkaTableSource(user_id, item_id, category_id, behavior, ts)]], fields=[user_id, item_id, category_id, behavior, ts]) + ship_strategy : FORWARD + ... +``` + +综上,Table API & SQL使用Planner将作业转化为具体可执行的程序。 + +Flink社区试图保证流批处理从使用体验到内部执行上的一致性。我们曾提到,Flink 1.10版本存在着DataStream API和DataSet API并存的现象,即DataStream API处理无界数据流,DataSet API处理有界数据集;也存在着Flink Planner和Blink Planner并存的现象。开源社区版的Flink Planner需要适配DataStream API和DataSet API,而Blink Planner的核心思想是将流批统一,它认为批处理是流处理的一个子集,是对有界数据流进行处理。因此,这两种Planner在具体实现上有一些区别。从名字中也看出,Flink社区决定逐渐将老的Flink Planner废弃,并不断推动流批一体化。因此,读者在使用时最好根据最新文档来选择合适的Planner。 + +## 获取表的具体方式 + +在Flink 1.10中,Table API & SQL与外部系统交互主要有两大类方式: + +1. 在程序中使用代码编程配置 +2. 使用声明式的语言,如SQL的数据库定义语言(Data Definition Language,DDL)或YAML文件。 + +无论哪类方式,都需要配置外部系统的必要参数、序列化方式和Schema。 + +### 代码配置方式 + +在程序中使用代码配置的方式又具体分为: + +1. 使用`connect`方法连接外部系统 +2. 将`DataStream`或`DataSet`转换为`Table` + +其中,第一种支持的外部系统有限,目前可以支持文件系统、Kafka、Elasticsearch和HBase。第二种方式和第七章所提到的Flink Connector使用方法相似,以流处理为例,我们需要首先获取一个`DataStream`,再进一步将其转化为`Table`。 + +我们先看`connect`方法,下面的代码展示了一个比较详细的例子,它从一个Kafka数据流中获取`Table`,数据使用JSON序列化,最终创建一个名为`user_behavior`的表。 + +```java +tEnv + // 使用connect函数连接外部系统 + .connect( + new Kafka() + .version("universal") // 必填,Kafka版本,合法的参数有"0.8", "0.9", "0.10", "0.11"或"universal" + .topic("user_behavior") // 必填,Topic名 + .startFromLatest() // 首次消费时数据读取的位置 + .property("zookeeper.connect", "localhost:2181") // Kafka连接参数 + .property("bootstrap.servers", "localhost:9092") + ) + // 序列化方式 可以是JSON、Avro等 + .withFormat(new Json()) + // 数据的Schema + .withSchema( + new Schema() + .field("user_id", DataTypes.BIGINT()) + .field("item_id", DataTypes.BIGINT()) + .field("category_id", DataTypes.BIGINT()) + .field("behavior", DataTypes.STRING()) + .field("ts", DataTypes.TIMESTAMP(3)) + ) + // 临时表的表名,后续可以在SQL语句中使用这个表名 + .createTemporaryTable("user_behavior"); +``` + +关于`connect`以及各个外部系统的具体连接方法,本书将不逐一覆盖,读者可以根据Flink官网的最新文档来学习使用。 + +`connect`方法可以直接连接外部数据并创建表,此外,我们也可以依托DataStream API,将一个`DataStream`转换为`Table`: + +```java +DataStream userBehaviorDataStream = ... +// 将数据流转换为一个视图,使用UserBehavior这个POJO类各字段名作为user_behavior表的字段名 +tEnv.createTemporaryView("user_behavior", userBehaviorDataStream); +``` + +:::note +Flink 1.10的`TableEnvironment`不支持将`DataStream`或`DataSet`转换为`Table`,使用此功能需要使用`StreamTableEnvironment`或者`BatchTableEnvironment`。 +::: + +### 声明式方式 + +另一种获取表的方式是使用SQL DDL或YAML等声明式方式来配置外部系统。 + +很多系统用YAML文件来配置参数,不过目前YAML只能和SQL Client配合,而1.10版本的SQL Client暂时是一个测试功能,还不能用于生产环境,这里暂不介绍,感兴趣的读者可以通过官网文档了解。SQL DDL是很多熟悉SQL的朋友经常使用的功能,比如`CREATE TABLE`、`DROP TABLE`等,是很多SQL用户经常使用的语句。同样的一个Kafka数据流,使用SQL DDL可以这样定义: + +```sql +CREATE TABLE user_behavior ( + -- 表的Schema + user_id BIGINT, + item_id BIGINT, + category_id BIGINT, + behavior STRING, + ts TIMESTAMP(3), + WATERMARK FOR ts as ts - INTERVAL '5' SECOND -- 定义Watermark ts为EventTime +) WITH ( + -- 外部系统连接参数 + 'connector.type' = 'kafka', + 'connector.version' = 'universal', -- Kafka版本 + 'connector.topic' = 'user_behavior', -- Kafka Topic + 'connector.startup-mode' = 'latest-offset', -- 从最近的offset开始读取数据 + 'connector.properties.zookeeper.connect' = 'localhost:2181', -- Kafka连接参数 + 'connector.properties.bootstrap.servers' = 'localhost:9092', + -- 序列化方式 + 'format.type' = 'json' -- 数据源格式为 json +); +``` + +将上面的SQL语句粘贴到`tEnv.sqlUpdate("CREATE TABLE ...")`中,放在主逻辑中执行即可。我们将在[SQL DDL](sql-ddl.md)部分详细介绍如何使用这些语句创建表。 \ No newline at end of file diff --git a/doc/ch-time-window/exercise-stock.md b/doc/ch-time-window/exercise-stock.md new file mode 100644 index 0000000..159b1b7 --- /dev/null +++ b/doc/ch-time-window/exercise-stock.md @@ -0,0 +1,39 @@ +(exercise-stock)= +# 练习:股票价格 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +经过本章的学习,读者应该可以对时间处理有了比较全面的了解,本节继续以股票价格这个场景来实践时间处理相关内容。 + +## 实验目的 + +针对具体的业务场景,学习如何设置窗口,如何在窗口上进行计算。 + +## 实验内容 + +股票交易场景,我们经常见到名为“K线”的概念。K线形如蜡烛,它反应了价格的走势,在一个K线内同时记录了开盘价(Open)、最高价(High)、最低价(Low)、收盘价(Close)。这里我们以5分钟为一个周期,计算该周期内的K线数据,即5分钟内的开盘价、最高价、最低价和收盘价(OHLC)。 + +关于价格,常见的是计算一个时间段内的平均值,考虑到交易量的权重,另外一个经常使用的一个计算价格的方式为交易量加权平均值(Volume Weighted Average Price,VWAP)。它的公式为: + +$$ +VWAP = \frac{\sum price \times volume}{\sum volume} +$$ + +## 实验要求 + +使用你认为合适的算子和函数,完成下面两个程序,使用`print`将结果打印输出。你可以根据需要自定义中间数据结构。 + +* 程序1:以5分钟为一个时间单位,计算其OHLC各值。 + +* 程序2:以5分钟为一个时间单位,计算VWAP。 + +## 实验报告 + +将思路和程序整理后撰写为实验报告。 \ No newline at end of file diff --git a/doc/ch-time-window/img/aggregate.png b/doc/ch-time-window/img/aggregate.png new file mode 100644 index 0000000000000000000000000000000000000000..931998e7a97ed53431b62e8bcba7decce5a05ad6 GIT binary patch literal 43013 zcmeFZcQo7IA3q*q&l)vj#a7fNb{ny3>!9|g_NqNnBZ%6yOQTAuqM`PPQDQ4vReRIg zqbTJU-k1IH2|ReCAS3b7pa$~sV@K^%6jXT(kFnrbiMpT)p^s)Loc=KkA0S8VKRQ<^rlVR^#`k|kMCOA%L%^zq#JCA zUVo$c1^gIkaGxnU`X=P{V_T$Tv#{&e{}9CNTt5O1d2v7B`jJ$nd#L7{kqX|7THMT_ z|Nn;hzu`@hf)W77>Swx{8(T-5_H@N^E#uHzsci3X?6jZvISsjdb=T)yk6KMeH{*O? zprFU@c`zp!&3(N!TMww(2MRw8Dkd*9g81sjfoZx1%O00mDy#ZIfV-;@1tk#8)bs1T z`-4h8crep%e8wE^T#5kaRdzQA(T%Kf1afNx1dD;uAEk63;id}$>i6u8T%_Zlgb9uy zPg$zgyA-2Gc8{#p?9BvmLNVoZ8E3V`9I9n=u6*(#6fU5GWxOHh32!qM$@E#rNbOAT-PB2Tl;Nb%r>>LnpVS8MnZ( zdeqRmA?`DaxdFuUU?wnH_vTI3-cw&oY!~hd`eQf}c{cuf?phkpdlcYV*Q}fV 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z5Q26^YWC?@US3ORQ|ptiVTe&cELu!MiAD?I-pdW|i)mjCAaEq$@vq@rYsA#{B=Dvj^AzrO{D znvqddTwK?*2#n9*U>_XQLJmujwBHEi*efg5Ax9zloX+teF+5@lFG_Gi$@qEUY!5Qe zm36uj-5NSPOvlE?>K!&D!3k_wac4yaqsnkT zes~PBblTcTVz1k5tgOzW?UegY@(LegR-7<86%i3Zf^c;r&lX9G3dcIPM)mX|?r!qYxXp8~8bHwaQITPlp8r1dM(;a&3dMiHQkL{}Mn{ zq{0|IpS82L^(grF7ujuWJmS*tOmE-|^c_ zODn$(8!6?bHugR|b{X7h?@HU~|K|VArl)S8;PF1mP-d__Z}Vpd`9-4?`(F z*PUmT4bNIxoVAud?QV^KQTB`M7ZDPX5E7Bn6*(X+dQe(Ia<7Ppw1|knUSiT~PS`~}KC${I?!isqO958nE7k^lez literal 0 HcmV?d00001 diff --git a/doc/ch-time-window/index.md b/doc/ch-time-window/index.md new file mode 100644 index 0000000..743a187 --- /dev/null +++ b/doc/ch-time-window/index.md @@ -0,0 +1,4 @@ +# 时间和窗口 + +```{tableofcontents} +``` diff --git a/doc/ch-time-window/join.md b/doc/ch-time-window/join.md new file mode 100644 index 0000000..350e514 --- /dev/null +++ b/doc/ch-time-window/join.md @@ -0,0 +1,143 @@ +(join)= +# 双流关联 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +批处理经常要解决的问题是将两个数据源做关联,或者称为Join。例如,很多手机APP都有一个用户数据源User,同时APP会记录用户的行为,我们称之为Behavior,两个表按照userId来进行Join,如下图所示。Flink支持流处理上的Join,只不过Flink是在一个时间窗口上来进行两个流的Join。 + +![Join示例图](./img/join.png) + +目前,Flink支持了两种Join:Window Join(窗口连接)和Interval Join(时间间隔连接)。 + +## Window Join + +从名字中能猜到,Window Join主要在Flink的窗口上进行操作,它将两个流中落在相同窗口的元素按照某个Key进行Join。一个Window Join的大致骨架结构为: + +```scala +input1.join(input2) + .where() <- input1使用哪个字段作为Key + .equalTo() <- input2使用哪个字段作为Key + .window() <- 指定WindowAssigner + [.trigger()] <- 指定Trigger(可选) + [.evictor()] <- 指定Evictor(可选) + .apply() <- 指定JoinFunction +``` + +下图展示了Join的大致过程。两个输入数据流先分别按Key进行分组,然后将元素划分到窗口中。窗口的划分需要使用`WindowAssigner`来定义,这里可以使用Flink提供的滚动窗口、滑动窗口或会话窗口等默认的`WindowAssigner`。随后两个数据流中的元素会被分配到各个窗口上,也就是说一个窗口会包含来自两个数据流的元素。相同窗口内的数据会以内连接(Inner Join)的语义来相互关联,形成一个数据对。当窗口的时间结束,Flink会调用`JoinFunction`来对窗口内的数据对进行处理。当然,我们也可以使用`Trigger`或`Evictor`做一些自定义优化,他们的使用方法和普通窗口的使用方法一样。 + +![Join的大致流程](./img/join-lifecycle.png) + +接下来我们重点分析一下两个数据流是如何进行Inner Join的。 + +一般滴,Inner Join只对两个数据源同时出现的元素做连接,形成一个数据对,即数据源input1中的某个元素与数据源input2中的所有元素逐个配对。当数据源某个窗口内没数据时,比如图中的第三个窗口,Join的结果也是空的。 + +![窗口内的数据INNER JOIN](./img/tumbling-window-join.png) + +下面的代码自定义了`JoinFunction`,并将Join结果打印出来。无论代码中演示的滚动窗口,还是滑动窗口或会话窗口,其原理都是一样的。 + +```java +public static class MyJoinFunction + implements JoinFunction, Tuple2, String> { + + @Override + public String join(Tuple2 input1, Tuple2 input2) { + return "input 1 :" + input1.f1 + ", input 2 :" + input2.f1; + } +} + +DataStream> input1 = ... +DataStream> input2 = ... + +DataStream joinResult = input1.join(input2) + .where(i1 -> i1.f0) + .equalTo(i2 -> i2.f0) + .window(TumblingProcessingTimeWindows.of(Time.seconds(60))) + .apply(new MyJoinFunction()); +``` + +除了`JoinFunction`,Flink还提供了`FlatJoinFunction`,其功能是输出零到多个结果。 + +如果Inner Join不能满足我们的需求,`CoGroupFunction`提供了更多可自定义的功能,我们可以获得两个数据流中的所有元素,元素以`Iterable`的形式供开发者使用。如果第一个数据流中的某些Key是空的,`CoGroupFunction`被触发时,这个Key上的元素为空,开发者自己决定如何处理两个流里的数据。 + +```java +public static class MyCoGroupFunction + implements CoGroupFunction, Tuple2, String> { + @Override + public void coGroup(Iterable> input1, Iterable> input2, Collector out) { + input1.forEach(element -> System.out.println("input1 :" + element.f1)); + input2.forEach(element -> System.out.println("input2 :" + element.f1)); + } +} +``` + +在主逻辑调用时,要写成`input1.coGroup(input2).where().equalTo()`。 + +```java +DataStream> input1 = ... +DataStream> input2 = ... + +DataStream coGroupResult = input1.coGroup(input2) + .where(i1 -> i1.f0) + .equalTo(i2 -> i2.f0) + .window(TumblingProcessingTimeWindows.of(Time.seconds(10))) + .apply(new MyCoGroupFunction()); +``` + +## Interval Join + +与Window Join不同,Interval Join不依赖Flink的`WindowAssigner`,而是根据一个时间间隔(Interval)界定时间。Interval需要一个时间下界(Lower Bound)和上界(Upper Bound),如果我们将input1和input2进行Interval Join,input1中的某个元素为input1.element1,时间戳为input1.element1.ts,那么一个Interval就是[input1.element1.ts + lowerBound, input1.element1.ts + upperBound],input2中落在这个时间段内的元素将会和input1.element1组成一个数据对。用数学公式表达为,凡是符合下面公式的元素,会两两组合在一起。 +$$ +input1.element1.ts + lowerBound \le input2.elementX.ts \le input1.element1.ts + upperBound +$$ +上下界可以是正数也可以是负数。 + +:::info +Flink(1.10)的Interval Join只支持Event Time语义。 +::: + +![Interval Join](./img/interval-join.png) + +下面的代码展示了如何对两个数据流进行Interval Join: + +```java +public static class MyProcessFunction extends ProcessJoinFunction, Tuple3, String> { + @Override + public void processElement(Tuple3 input1, + Tuple3 input2, + Context context, + Collector out) { + out.collect("input 1: " + input1.toString() + ", input 2: " + input2.toString()); + } +} + +// 使用EventTime时间语义 +env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); +// 数据流有三个字段:(key, 时间戳, 数值) +DataStream> input1 = ... +DataStream> input2 = ... + +DataStream intervalJoinResult = input1.keyBy(i -> i.f0) + .intervalJoin(input2.keyBy(i -> i.f0)) + .between(Time.milliseconds(-5), Time.milliseconds(10)) + .process(new MyProcessFunction()); +``` + +默认的时间间隔是包含上下界的,我们可以使用`.lowerBoundExclusive()` 和`.upperBoundExclusive`来确定是否需要包含上下界。 + +```java +DataStream intervalJoinResult = input1.keyBy(i -> i.f0) + .intervalJoin(input2.keyBy(i -> i.f0)) + .between(Time.milliseconds(-5), Time.milliseconds(10)) + .upperBoundExclusive() + .lowerBoundExclusive() + .process(new MyProcessFunction()); +``` + +Interval Join内部是用缓存来存储所有数据的,因此需要注意缓存数据不能太大,以免对内存造成太大压力。 \ No newline at end of file diff --git a/doc/ch-time-window/late-elements.md b/doc/ch-time-window/late-elements.md new file mode 100644 index 0000000..bac727f --- /dev/null +++ b/doc/ch-time-window/late-elements.md @@ -0,0 +1,103 @@ +(late-elements)= +# 迟到数据 + +:::{note} + +本教程已出版为《Flink原理与实践》,感兴趣的读者请在各大电商平台购买! + + ![](https://img.shields.io/badge/JD-%E8%B4%AD%E4%B9%B0%E9%93%BE%E6%8E%A5-red) + + +::: + +Event Time语义下我们使用Watermark来判断数据是否迟到。一个迟到元素是指元素到达窗口算子时,该元素本该被分配到某个窗口,但由于延迟,窗口已经触发计算。目前Flink有三种处理迟到数据的方式: + +* 直接将迟到数据丢弃 +* 将迟到数据发送到另一个流 +* 重新执行一次计算,将迟到数据考虑进来,更新计算结果 + +## 将迟到数据丢弃 + +如果不做其他操作,默认情况下迟到数据会被直接丢弃。 + +## 将迟到数据发送到另外一个流 + +如果想对这些迟到数据处理,我们可以使用[ProcessFunction](./process-function.md)系列函数的侧输出功能,将迟到数据发到某个特定的流上。后续我们可以根据业务逻辑的要求,对迟到的数据流进行处理。 + +```scala +final OutputTag lateOutputTag = new OutputTag("late-data"){}; + +DataStream input = ... + +SingleOutputStreamOperator result = input + .keyBy() + .window() + .allowedLateness(

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