45、Flink 的指标体系介绍及验证（1）-指标类型及指标实现示例
Flink 系列文章
1、Flink 部署、概念介绍、source、transformation、sink使用示例、四大基石介绍和示例等系列综合文章链接
13、Flink 的table api与sql的基本概念、通用api介绍及入门示例
14、Flink 的table api与sql之数据类型: 内置数据类型以及它们的属性
15、Flink 的table api与sql之流式概念-详解的介绍了动态表、时间属性配置（如何处理更新结果）、时态表、流上的join、流上的确定性以及查询配置
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及FileSystem示例（1）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Elasticsearch示例（2）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Kafka示例（3）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及JDBC示例（4）
16、Flink 的table api与sql之连接外部系统: 读写外部系统的连接器和格式以及Apache Hive示例（6）
17、Flink 之Table API: Table API 支持的操作（1）
17、Flink 之Table API: Table API 支持的操作（2）
18、Flink的SQL 支持的操作和语法
19、Flink 的Table API 和 SQL 中的内置函数及示例（1）
19、Flink 的Table API 和 SQL 中的自定义函数及示例（2）
19、Flink 的Table API 和 SQL 中的自定义函数及示例（3）
19、Flink 的Table API 和 SQL 中的自定义函数及示例（4）
20、Flink SQL之SQL Client: 不用编写代码就可以尝试 Flink SQL，可以直接提交 SQL 任务到集群上
21、Flink 的table API与DataStream API 集成（1）- 介绍及入门示例、集成说明
21、Flink 的table API与DataStream API 集成（2）- 批处理模式和inser-only流处理
21、Flink 的table API与DataStream API 集成（3）- changelog流处理、管道示例、类型转换和老版本转换示例
21、Flink 的table API与DataStream API 集成（完整版）
22、Flink 的table api与sql之创建表的DDL
24、Flink 的table api与sql之Catalogs（介绍、类型、java api和sql实现ddl、java api和sql操作catalog）-1
24、Flink 的table api与sql之Catalogs（java api操作数据库、表）-2
24、Flink 的table api与sql之Catalogs（java api操作视图）-3
24、Flink 的table api与sql之Catalogs（java api操作分区与函数）-4
25、Flink 的table api与sql之函数(自定义函数示例)
26、Flink 的SQL之概览与入门示例
27、Flink 的SQL之SELECT (select、where、distinct、order by、limit、集合操作和去重)介绍及详细示例（1）
27、Flink 的SQL之SELECT (SQL Hints 和 Joins)介绍及详细示例（2）
27、Flink 的SQL之SELECT (窗口函数)介绍及详细示例（3）
27、Flink 的SQL之SELECT (窗口聚合)介绍及详细示例（4）
27、Flink 的SQL之SELECT (Group Aggregation分组聚合、Over Aggregation Over聚合 和 Window Join 窗口关联)介绍及详细示例（5）
27、Flink 的SQL之SELECT (Top-N、Window Top-N 窗口 Top-N 和 Window Deduplication 窗口去重)介绍及详细示例（6）
27、Flink 的SQL之SELECT (Pattern Recognition 模式检测)介绍及详细示例（7）
28、Flink 的SQL之DROP 、ALTER 、INSERT 、ANALYZE 语句
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（1）
29、Flink SQL之DESCRIBE、EXPLAIN、USE、SHOW、LOAD、UNLOAD、SET、RESET、JAR、JOB Statements、UPDATE、DELETE（2）
30、Flink SQL之SQL 客户端（通过kafka和filesystem的例子介绍了配置文件使用-表、视图等）
31、Flink的SQL Gateway介绍及示例
32、Flink table api和SQL 之用户自定义 Sources & Sinks实现及详细示例
33、Flink 的Table API 和 SQL 中的时区
35、Flink 的 Formats 之CSV 和 JSON Format
36、Flink 的 Formats 之Parquet 和 Orc Format
41、Flink之Hive 方言介绍及详细示例
40、Flink 的Apache Kafka connector（kafka source的介绍及使用示例）-1
40、Flink 的Apache Kafka connector（kafka sink的介绍及使用示例）-2
40、Flink 的Apache Kafka connector（kafka source 和sink 说明及使用示例） 完整版
42、Flink 的table api与sql之Hive Catalog
43、Flink之Hive 读写及详细验证示例
44、Flink之module模块介绍及使用示例和Flink SQL使用hive内置函数及自定义函数详细示例–网上有些说法好像是错误的
45、Flink 的指标体系介绍及验证（1）-指标类型及指标实现示例
45、Flink 的指标体系介绍及验证（2）-指标的scope、报告、系统指标以及追踪、api集成示例和dashboard集成
45、Flink 的指标体系介绍及验证（3）- 完整版
46、Flink 的table api与sql之配项列表及示例
文章目录
- Flink 系列文章
- 一、Flink 指标体系
-
- 1、Registering metrics 注册指标
-
- 1）、指标类型
- 2）、计数器
- 3）、Gauge
- 4）、Histogram
- 5）、Meter
本文简单的介绍了Flink 的指标体系的第一部分，即指标类型以及四种类型的代码实现示例。
本专题分为三部分，即：
45、Flink 的指标体系介绍及验证（1）-指标类型及指标实现示例
45、Flink 的指标体系介绍及验证（2）-指标的scope、报告、系统指标以及追踪、api集成示例和dashboard集成
45、Flink 的指标体系介绍及验证（3）- 完整版
本文依赖nc能正常使用。
本文分为5个部分，即指标分类、计数器、gauge、histogram和meter四个指标的代码实现。
本文的示例是在Flink 1.17版本中运行。
一、Flink 指标体系
Flink暴露了一个度量系统，允许收集度量并将其公开给外部系统。
本文涉及的maven依赖
<properties>
<encoding>UTF-8encoding>
<project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
<maven.compiler.source>1.8maven.compiler.source>
<maven.compiler.target>1.8maven.compiler.target>
<java.version>1.8java.version>
<scala.version>2.12scala.version>
<flink.version>1.17.0flink.version>
properties>
<dependencies>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-clientsartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-javaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-streaming-javaartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-csvartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-jsonartifactId>
<version>${flink.version}version>
<scope>providedscope>
dependency>
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-connector-kafkaartifactId>
<version>${flink.version}version>
dependency>
dependencies>
1、Registering metrics 注册指标
通过调用getRuntimeContext().getMetricGroup()，您可以从任何扩展RichFunction的用户函数访问度量系统。此方法返回一个MetricGroup对象，您可以在该对象上创建和注册新度量。
1）、指标类型
Flink支持计数器、仪表盘、柱状图和计量表。Counters, Gauges, Histograms and Meters.
2）、计数器
计数器是用来统计数量的。当前值可以是in-或使用 inc()/inc(long n)或dec()/dec(long n)增减。您可以通过调用MetricGroup上的 counter(String name)来创建和注册计数器。
本示例提供了多种实现方式，供参考。
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.metrics.Counter;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author alanchan
*
*/
public class TestMetricsDemo {
// public class LineMapper extends RichMapFunction {
// private transient Counter counter;
//
// @Override
// public void open(Configuration config) {
// this.counter = getRuntimeContext().getMetricGroup().counter("result2LineCounter");
// }
//
// @Override
// public String map(String value) throws Exception {
// this.counter.inc();
// return value;
// }
// }
public static void test1() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
DataStream<Tuple2<String, Integer>> result = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public void flatMap(String value, Collector<String> out) throws Exception {
String[] arr = value.split(",");
for (String word : arr) {
out.collect(word);
}
}
}).map(new MapFunction<String, Tuple2<String, Integer>>() {
@Override
public Tuple2<String, Integer> map(String value) throws Exception {
return Tuple2.of(value, 1);
}
}).keyBy(t -> t.f0).sum(1);
// SingleOutputStreamOperator> result1 = lines.map(new RichMapFunction>() {
//
// @Override
// public Tuple2 map(String value) throws Exception {
// int subTaskId = getRuntimeContext().getIndexOfThisSubtask();// 子任务id/分区编号
// return new Tuple2(subTaskId, 1);
// }
// // 按照子任务id/分区编号分组,并统计每个子任务/分区中有几个元素
// }).keyBy(t -> t.f0).sum(1);
// RichFlatMapFunction 输入的字符串，行数，统计单词的总数
DataStream<Tuple3<String, Long, Integer>> result2 = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, Long>>() {
// private transient Counter counter;
private long result2LineCounter = 0;
@Override
public void open(Configuration config) {
// this.counter = getRuntimeContext().getMetricGroup().counter("result2LineCounter:");
result2LineCounter = getRuntimeContext().getMetricGroup().counter("result2LineCounter:").getCount();
}
@Override
public void flatMap(String value, Collector<Tuple2<String, Long>> out) throws Exception {
// this.counter.inc();
result2LineCounter++;
System.out.println("计数器行数：" + result2LineCounter);
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, result2LineCounter));
}
}
}).map(new MapFunction<Tuple2<String, Long>, Tuple3<String, Long, Integer>>() {
@Override
public Tuple3<String, Long, Integer> map(Tuple2<String, Long> value) throws Exception {
// Tuple3 t = Tuple3.of(value.f0, value.f1, 1);
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
result2.print("result2:");
env.execute();
}
public static void main(String[] args) throws Exception {
test1();
// StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// env.setParallelism(1);
// DataStream input = env.fromElements("a", "b", "c", "a", "b", "c");
//
// input.keyBy(value -> value).map(new RichMapFunction() {
// private long count = 0;
//
// @Override
// public void open(Configuration parameters) throws Exception {
super.open(parameters);
// count = getRuntimeContext().getMetricGroup().counter("myCounter").getCount();
// }
//
// @Override
// public String map(String value) throws Exception {
// count++;
// return value + ": " + count;
// }
// }).print();
//
// env.execute("Flink Count Counter Example");
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
计数器行数：1
result:> (hello,1)
result2:> (hello,1,1)
result:> (123,1)
result2:> (123,1,1)
计数器行数：2
result2:> (alan,2,1)
result:> (alan,1)
result2:> (flink,2,1)
result:> (flink,1)
result2:> (good,2,1)
result:> (good,1)
计数器行数：3
result:> (alan_chan,1)
result2:> (alan_chan,3,1)
result:> (hi,1)
result2:> (hi,3,1)
result:> (flink,2)
result2:> (flink,2,2)
或者，您也可以使用自己的Counter实现：
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.metrics.Counter;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author alanchan
*
*/
public class TestMetricsDemo {
public static void test2() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// Tuple3 输入的字符串，行数，统计单词的总数
DataStream<Tuple3<String, Long, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, Long>>() {
private transient Counter counter;
@Override
public void open(Configuration config) {
this.counter = getRuntimeContext().getMetricGroup().counter("result2LineCounter", new AlanCustomCounter());
}
@Override
public void flatMap(String value, Collector<Tuple2<String, Long>> out) throws Exception {
this.counter.inc();
// result2LineCounter++;
System.out.println("计数器行数：" + this.counter.getCount());
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, this.counter.getCount()));
}
}
}).map(new MapFunction<Tuple2<String, Long>, Tuple3<String, Long, Integer>>() {
@Override
public Tuple3<String, Long, Integer> map(Tuple2<String, Long> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static class AlanCustomCounter implements Counter {
private long count;
@Override
public void inc() {
count += 2;
}
@Override
public void inc(long n) {
count += n;
}
@Override
public void dec() {
count -= 2;
}
@Override
public void dec(long n) {
count -= n;
}
@Override
public long getCount() {
return count;
}
}
public static void main(String[] args) throws Exception {
test2();
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
计数器行数：2
result:> (hello,2,1)
result:> (123,2,1)
计数器行数：4
result:> (alan,4,1)
result:> (flink,4,1)
result:> (good,4,1)
计数器行数：6
result:> (alan_chan,6,1)
result:> (hi,6,1)
result:> (flink,4,2)
3）、Gauge
仪表可根据需要提供任何类型的值。为了使用Gauge，您必须首先创建一个实现org.apache.flink.metrics.Guge接口的类。返回值的类型没有限制。您可以通过调用MetricGroup上的gauge(String name, Gauge gauge) 来注册gauge。
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.metrics.Counter;
import org.apache.flink.metrics.Gauge;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author alanchan
*
*/
public class TestMetricsGaugeDemo {
// public class MyMapper extends RichMapFunction {
// private transient int valueToExpose = 0;
//
// @Override
// public void open(Configuration config) {
// getRuntimeContext().getMetricGroup().gauge("MyGauge", new Gauge() {
// @Override
// public Integer getValue() {
// return valueToExpose;
// }
// });
// }
//
// @Override
// public String map(String value) throws Exception {
// valueToExpose++;
// return value;
// }
// }
public static void test1() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// RichFlatMapFunction 输入的字符串，alan lines[行数]，统计单词的总数
DataStream<Tuple3<String, String, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, String>>() {
private long result2LineCounter = 0;
private Gauge<String> gauge = null;
@Override
public void open(Configuration config) {
result2LineCounter = getRuntimeContext().getMetricGroup().counter("resultLineCounter:").getCount();
gauge = getRuntimeContext().getMetricGroup().gauge("alanGauge", new Gauge<String>() {
@Override
public String getValue() {
return "alan lines[" + result2LineCounter + "]";
}
});
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out) throws Exception {
result2LineCounter++;
System.out.println("计数器行数：" + result2LineCounter);
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, gauge.getValue()));
}
}
}).map(new MapFunction<Tuple2<String, String>, Tuple3<String, String, Integer>>() {
@Override
public Tuple3<String, String, Integer> map(Tuple2<String, String> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static void main(String[] args) throws Exception {
test1();
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
计数器行数：1
result:> (hello,alan lines[1],1)
result:> (123,alan lines[1],1)
计数器行数：2
result:> (alan,alan lines[2],1)
result:> (flink,alan lines[2],1)
result:> (good,alan lines[2],1)
计数器行数：3
result:> (alan_chan,alan lines[3],1)
result:> (hi,alan lines[3],1)
result:> (flink,alan lines[2],2)
报告器会将暴露的对象转换为String，这意味着需要一个有意义的toString()实现。
4）、Histogram
直方图测量长值的分布。您可以通过调用MetricGroup上的histogram(String name, Histogram histogram) 来注册一个对象。
下面的示例是自己实现的Histogram接口，仅仅用于演示实现过程。
import java.io.Serializable;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.metrics.Gauge;
//import com.codahale.metrics.Histogram;
import org.apache.flink.metrics.Histogram;
import org.apache.flink.metrics.HistogramStatistics;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* @author alanchan
*
*/
public class TestMetricsHistogramDemo {
// public class MyMapper extends RichMapFunction {
// private transient Histogram histogram;
//
// @Override
// public void open(Configuration config) {
// this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new AlanHistogram());
// }
//
// @Override
// public Long map(Long value) throws Exception {
// this.histogram.update(value);
// return value;
// }
// }
public static class AlanHistogram implements Histogram {
private CircularDoubleArray descriptiveStatistics = new CircularDoubleArray(10);;
public AlanHistogram() {
}
public AlanHistogram(int windowSize) {
this.descriptiveStatistics = new CircularDoubleArray(windowSize);
}
@Override
public void update(long value) {
this.descriptiveStatistics.addValue(value);
}
@Override
public long getCount() {
return this.descriptiveStatistics.getElementsSeen();
}
@Override
public HistogramStatistics getStatistics() {
// return new DescriptiveStatisticsHistogramStatistics(this.descriptiveStatistics);
return null;
}
class CircularDoubleArray implements Serializable {
private static final long serialVersionUID = 1L;
private final double[] backingArray;
private int nextPos = 0;
private boolean fullSize = false;
private long elementsSeen = 0;
CircularDoubleArray(int windowSize) {
this.backingArray = new double[windowSize];
}
synchronized void addValue(double value) {
backingArray[nextPos] = value;
++elementsSeen;
++nextPos;
if (nextPos == backingArray.length) {
nextPos = 0;
fullSize = true;
}
}
synchronized double[] toUnsortedArray() {
final int size = getSize();
double[] result = new double[size];
System.arraycopy(backingArray, 0, result, 0, result.length);
return result;
}
private synchronized int getSize() {
return fullSize ? backingArray.length : nextPos;
}
private synchronized long getElementsSeen() {
return elementsSeen;
}
}
}
public static void test1() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// RichFlatMapFunction 输入的字符串，alan lines[行数]，统计单词的总数
DataStream<Tuple3<String, String, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, String>>() {
private long result2LineCounter = 0;
private Gauge<String> gauge = null;
private Histogram histogram = null;;
@Override
public void open(Configuration config) {
result2LineCounter = getRuntimeContext().getMetricGroup().counter("resultLineCounter:").getCount();
gauge = getRuntimeContext().getMetricGroup().gauge("alanGauge", new Gauge<String>() {
@Override
public String getValue() {
return "alan lines[" + result2LineCounter + "]";
}
});
this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new AlanHistogram());
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out) throws Exception {
result2LineCounter++;
this.histogram.update(result2LineCounter * 3);
// 此处仅仅示例this.histogram.getCount()的值，没有实际的意义
System.out.println("计数器行数：" + result2LineCounter + " histogram:" + this.histogram.getCount());
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, gauge.getValue()));
}
}
}).map(new MapFunction<Tuple2<String, String>, Tuple3<String, String, Integer>>() {
@Override
public Tuple3<String, String, Integer> map(Tuple2<String, String> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static void main(String[] args) throws Exception {
test1();
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
计数器行数：1 histogram:1
result:> (hello,alan lines[1],1)
result:> (123,alan lines[1],1)
计数器行数：2 histogram:2
result:> (alan,alan lines[2],1)
result:> (flink,alan lines[2],1)
result:> (good,alan lines[2],1)
计数器行数：3 histogram:3
result:> (alan_chan,alan lines[3],1)
result:> (hi,alan lines[3],1)
result:> (flink,alan lines[2],2)
Flink没有提供直方图的默认实现，但提供了一个允许使用Codahale/DropWizard直方图的包装器。要使用此包装器，
在pom.xml中添加以下依赖项：
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-metrics-dropwizardartifactId>
<version>1.17.1version>
dependency>
下面的示例是使用 Codahale/DropWizard直方图，如下所示：
import java.io.Serializable;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.dropwizard.metrics.DropwizardHistogramWrapper;
import org.apache.flink.metrics.Gauge;
//import com.codahale.metrics.Histogram;
import org.apache.flink.metrics.Histogram;
import org.apache.flink.metrics.HistogramStatistics;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
import com.codahale.metrics.SlidingWindowReservoir;
/**
* @author alanchan
*
*/
public class TestMetricsHistogramDemo {
public static void test2() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// RichFlatMapFunction 输入的字符串，alan lines[行数]，统计单词的总数
DataStream<Tuple3<String, String, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, String>>() {
private long result2LineCounter = 0;
private Gauge<String> gauge = null;
private Histogram histogram = null;;
@Override
public void open(Configuration config) {
result2LineCounter = getRuntimeContext().getMetricGroup().counter("resultLineCounter:").getCount();
gauge = getRuntimeContext().getMetricGroup().gauge("alanGauge", new Gauge<String>() {
@Override
public String getValue() {
return "alan lines[" + result2LineCounter + "]";
}
});
com.codahale.metrics.Histogram dropwizardHistogram = new com.codahale.metrics.Histogram(new SlidingWindowReservoir(500));
// this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new AlanHistogram());
this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new DropwizardHistogramWrapper(dropwizardHistogram));
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out) throws Exception {
result2LineCounter++;
this.histogram.update(result2LineCounter * 3);
// 此处仅仅示例this.histogram.getCount()的值，没有实际的意义
System.out.println("计数器行数：" + result2LineCounter + " histogram:" + this.histogram.getCount());
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, gauge.getValue()));
}
}
}).map(new MapFunction<Tuple2<String, String>, Tuple3<String, String, Integer>>() {
@Override
public Tuple3<String, String, Integer> map(Tuple2<String, String> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static void main(String[] args) throws Exception {
test2();
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
//控制台输出：
计数器行数：1 histogram:1
result:> (hello,alan lines[1],1)
result:> (123,alan lines[1],1)
计数器行数：2 histogram:2
result:> (alan,alan lines[2],1)
result:> (flink,alan lines[2],1)
result:> (good,alan lines[2],1)
计数器行数：3 histogram:3
result:> (alan_chan,alan lines[3],1)
result:> (hi,alan lines[3],1)
result:> (flink,alan lines[2],2)
5）、Meter
仪表测量平均吞吐量。可以使用markEvent()方法注册事件的发生。可以使用markEvent(long n)方法注册同时发生多个事件。您可以通过在MetricGroup上调用meter(String name, Meter meter)来注册meter。
下面的示例展示了自定义的Meter实现，可能很不严谨，实际上应用更多的是本部分的第二个示例。
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.dropwizard.metrics.DropwizardHistogramWrapper;
import org.apache.flink.metrics.Counter;
import org.apache.flink.metrics.Gauge;
import org.apache.flink.metrics.Histogram;
import org.apache.flink.metrics.Meter;
import org.apache.flink.metrics.SimpleCounter;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
//import com.codahale.metrics.Meter;
import com.codahale.metrics.SlidingWindowReservoir;
/**
* @author alanchan
*
*/
public class TestMetricsMeterDemo {
public class MyMapper extends RichMapFunction<Long, Long> {
private transient Meter meter;
@Override
public void open(Configuration config) {
this.meter = getRuntimeContext().getMetricGroup().meter("myMeter", new AlanMeter());
}
@Override
public Long map(Long value) throws Exception {
this.meter.markEvent();
return value;
}
}
public static class AlanMeter implements Meter {
/** The underlying counter maintaining the count. */
private final Counter counter = new SimpleCounter();;
/** The time-span over which the average is calculated. */
private final int timeSpanInSeconds = 0;
/** Circular array containing the history of values. */
private final long[] values = null;;
/** The index in the array for the current time. */
private int time = 0;
/** The last rate we computed. */
private double currentRate = 0;
@Override
public void markEvent() {
this.counter.inc();
}
@Override
public void markEvent(long n) {
this.counter.inc(n);
}
@Override
public long getCount() {
return counter.getCount();
}
@Override
public double getRate() {
return currentRate;
}
public void update() {
time = (time + 1) % values.length;
values[time] = counter.getCount();
currentRate = ((double) (values[time] - values[(time + 1) % values.length]) / timeSpanInSeconds);
}
}
public static void test1() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// RichFlatMapFunction 输入的字符串，alan lines[行数]，统计单词的总数
DataStream<Tuple3<String, String, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, String>>() {
private long result2LineCounter = 0;
private Gauge<String> gauge = null;
private Histogram histogram = null;
private Meter meter;
@Override
public void open(Configuration config) {
result2LineCounter = getRuntimeContext().getMetricGroup().counter("resultLineCounter:").getCount();
gauge = getRuntimeContext().getMetricGroup().gauge("alanGauge", new Gauge<String>() {
@Override
public String getValue() {
return "alan lines[" + result2LineCounter + "]";
}
});
com.codahale.metrics.Histogram dropwizardHistogram = new com.codahale.metrics.Histogram(new SlidingWindowReservoir(500));
this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new DropwizardHistogramWrapper(dropwizardHistogram));
this.meter = getRuntimeContext().getMetricGroup().meter("alanMeter", new AlanMeter());
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out) throws Exception {
result2LineCounter++;
this.histogram.update(result2LineCounter * 3);
this.meter.markEvent();
// 此处仅仅示例this.histogram.getCount()、this.meter.getRate()的值，没有实际的意义，具体使用以实际使用场景为准
System.out.println("计数器行数：" + result2LineCounter + ", histogram:" + this.histogram.getCount() + ", meter.getRate:" + this.meter.getRate());
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, gauge.getValue()));
}
}
}).map(new MapFunction<Tuple2<String, String>, Tuple3<String, String, Integer>>() {
@Override
public Tuple3<String, String, Integer> map(Tuple2<String, String> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static void main(String[] args) throws Exception {
test1();
}
}
///验证数据///
// 输入数据
[alanchan@server2 bin]$ nc -lk 9999
hello,123
alan,flink,good
alan_chan,hi,flink
//控制台输出：
计数器行数：1, histogram:1, meter.getRate:0.0
result:> (hello,alan lines[1],1)
result:> (123,alan lines[1],1)
计数器行数：2, histogram:2, meter.getRate:0.0
result:> (alan,alan lines[2],1)
result:> (flink,alan lines[2],1)
result:> (good,alan lines[2],1)
计数器行数：3, histogram:3, meter.getRate:0.0
result:> (alan_chan,alan lines[3],1)
result:> (hi,alan lines[3],1)
result:> (flink,alan lines[2],2)
Flink提供了一个允许使用Codahale/DropWizard仪表的包装器。要使用此包装器，
在pom.xml中添加以下依赖项：
<dependency>
<groupId>org.apache.flinkgroupId>
<artifactId>flink-metrics-dropwizardartifactId>
<version>1.17.1version>
dependency>
下面使用Codahale/DropWizard注册的示例，如下所示：
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.dropwizard.metrics.DropwizardHistogramWrapper;
import org.apache.flink.dropwizard.metrics.DropwizardMeterWrapper;
import org.apache.flink.metrics.Counter;
import org.apache.flink.metrics.Gauge;
import org.apache.flink.metrics.Histogram;
import org.apache.flink.metrics.Meter;
import org.apache.flink.metrics.SimpleCounter;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
//import com.codahale.metrics.Meter;
import com.codahale.metrics.SlidingWindowReservoir;
/**
* @author alanchan
*
*/
public class TestMetricsMeterDemo {
public static void test2() throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
// source
DataStream<String> lines = env.socketTextStream("192.168.10.42", 9999);
// transformation
// RichFlatMapFunction 输入的字符串，alan lines[行数]，统计单词的总数
DataStream<Tuple3<String, String, Integer>> result = lines.flatMap(new RichFlatMapFunction<String, Tuple2<String, String>>() {
private long result2LineCounter = 0;
private Gauge<String> gauge = null;
private Histogram histogram = null;
private Meter meter;
@Override
public void open(Configuration config) {
result2LineCounter = getRuntimeContext().getMetricGroup().counter("resultLineCounter:").getCount();
gauge = getRuntimeContext().getMetricGroup().gauge("alanGauge", new Gauge<String>() {
@Override
public String getValue() {
return "alan lines[" + result2LineCounter + "]";
}
});
com.codahale.metrics.Histogram dropwizardHistogram = new com.codahale.metrics.Histogram(new SlidingWindowReservoir(500));
this.histogram = getRuntimeContext().getMetricGroup().histogram("alanHistogram", new DropwizardHistogramWrapper(dropwizardHistogram));
// this.meter = getRuntimeContext().getMetricGroup().meter("alanMeter", new AlanMeter());
com.codahale.metrics.Meter dropwizardMeter = new com.codahale.metrics.Meter();
this.meter = getRuntimeContext().getMetricGroup().meter("alanMeter", new DropwizardMeterWrapper(dropwizardMeter));
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out) throws Exception {
result2LineCounter++;
this.histogram.update(result2LineCounter * 3);
this.meter.markEvent();
// 此处仅仅示例this.histogram.getCount()、this.meter.getRate()的值，没有实际的意义，具体使用以实际使用场景为准
System.out.println("计数器行数：" + result2LineCounter + ", histogram:" + this.histogram.getCount() + ", meter.getRate:" + this.meter.getRate());
String[] arr = value.split(",");
for (String word : arr) {
out.collect(Tuple2.of(word, gauge.getValue()));
}
}
}).map(new MapFunction<Tuple2<String, String>, Tuple3<String, String, Integer>>() {
@Override
public Tuple3<String, String, Integer> map(Tuple2<String, String> value) throws Exception {
return Tuple3.of(value.f0, value.f1, 1);
}
}).keyBy(t -> t.f0).sum(2);
// sink
result.print("result:");
env.execute();
}
public static void main(String[] args) throws Exception {
test2();
}
}
//控制台输出：
计数器行数：1, histogram:1, meter.getRate:0.0
result:> (hello,alan lines[1],1)
result:> (123,alan lines[1],1)
计数器行数：2, histogram:2, meter.getRate:0.0
result:> (alan,alan lines[2],1)
result:> (flink,alan lines[2],1)
result:> (good,alan lines[2],1)
计数器行数：3, histogram:3, meter.getRate:0.0
result:> (alan_chan,alan lines[3],1)
result:> (hi,alan lines[3],1)
result:> (flink,alan lines[2],2)
以上，本文简单的介绍了Flink 的指标体系的第一部分，即指标类型以及四种类型的代码实现示例。
本专题分为三部分，即：
45、Flink 的指标体系介绍及验证（1）-指标类型及指标实现示例
45、Flink 的指标体系介绍及验证（2）-指标的scope、报告、系统指标以及追踪、api集成示例和dashboard集成
45、Flink 的指标体系介绍及验证（3）- 完整版