Fllink实时计算运用（五）Flink Table API & SQL 案例实战-原创手记-慕课网

1. Table API & SQL 实战运用

案例说明
- 功能说明
  
  通过socket读取数据源，进行单词的统计处理。
- 实现流程
  - 初始化Table运行环境
  - 转换操作处理：
    
    1）以空格进行分割
    
    2）给每个单词计数累加1
    
    3）根据单词进行分组处理
    
    4）求和统计
    
    5）输出打印数据
  - 执行任务

FlinkTable API 方式实现

StreamTableApiApplication，代码实现：

//获取流处理的运行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();

//获取Table的运行环境
StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, environmentSettings);

//接入数据源
DataStreamSource<String> lines = env.socketTextStream("10.10.20.15", 9922);

//对字符串进行分词压平
SingleOutputStreamOperator<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
    @Override
    public void flatMap(String line, Collector<String> out) throws Exception {
        Arrays.stream(line.split(" ")).forEach(out::collect);
    }
});

//将DataStream转换成Table对象，字段名默认的是f0，给定字段名是word
Table table = tabEnv.fromDataStream(words, "word");

//按照单词进行分组聚合操作
Table resultTable = table.groupBy("word").select("word, sum(1L) as counts");

//在流处理中，数据会源源不断的产生，需要累加处理，只能采用用toRestractStream
//        DataStream<WordCount> wordCountDataStream = tabEnv.toAppendStream(resultTable, WordCount.class);
//        wordCountDataStream.printToErr("toAppendStream>>>");

DataStream<Tuple2<Boolean, WordCount>> wordCountDataStream = tabEnv.toRetractStream(resultTable, WordCount.class);
wordCountDataStream.printToErr("toRetractStream>>>");

env.execute();

测试验证：

开启socket输入，输入字符串：

[root@flink1 flink-1.11.2]# nc -lk 9922

FlinkTable SQL 方式实现

代码实现：

StreamTableSqlApplication实现类：

//获取流处理的运行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();

//获取Table的运行环境
StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, environmentSettings);

//接入数据源
DataStreamSource<String> lines = env.socketTextStream("10.10.20.15", 9922);

//对字符串进行分词压平
SingleOutputStreamOperator<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
    @Override
    public void flatMap(String line, Collector<String> out) throws Exception {
        Arrays.stream(line.split(" ")).forEach(out::collect);
    }
});

//将DataStream转换成Table对象，字段名默认的是f0，给定字段名是word
tabEnv.registerDataStream("t_wordcount", words, "word");

//按照单词进行分组聚合操作
Table resultTable = tabEnv.sqlQuery("select word,count(1) as counts from t_wordcount group by word");

DataStream<Tuple2<Boolean, WordCount>> wordCountDataStream = tabEnv.toRetractStream(resultTable, WordCount.class);
wordCountDataStream.printToErr("toRetractStream>>>");
env.execute();

2. Flink SQL 滚动窗口实战

Flink SQL 窗口说明

Flink SQL支持的窗口聚合主要是两种：Window聚合和Over聚合。这里主要介绍Window聚合。Window聚合支持两种时间属性定义窗口：Event Time和Processing Time。每种时间属性类型支持三种窗口类型：滚动窗口（TUMBLE）、滑动窗口（HOP）和会话窗口（SESSION）。

案例说明

统计在过去的1分钟内有多少用户点击了某个的网页，可以通过定义一个窗口来收集最近1分钟内的数据，并对这个窗口内的数据进行计算。

测试数据：

用户名	访问地址	访问时间
张三	http://taobao.com/xxx	2021-05-10 10:00:00
张三	http://taobao.com/xxx	2021-05-10 10:00:10
张三	http://taobao.com/xxx	2021-05-10 10:00:49
张三	http://taobao.com/xxx	2021-05-10 10:01:05
张三	http://taobao.com/xxx	2021-05-10 10:01:58
李四	http://taobao.com/xxx	2021-05-10 10:02:10

滚动窗口运用

滚动窗口（Tumbling windows）要用Tumble类来定义，另外还有三个方法：

over：定义窗口长度
on：用来分组（按时间间隔）或者排序（按行数）的时间字段
as：别名，必须出现在后面的groupBy中

实现步骤：

初始化流运行环境
在流模式下使用blink planner
创建用户点击事件数据
将源数据写入临时文件并获取绝对路径
创建表载入用户点击事件数据
对表运行SQL查询，并将结果作为新表检索
Table转换成DataStream
执行任务

TumbleUserClickApplication，实现代码：

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, environmentSettings);

// 将源数据写入临时文件并获取绝对路径
String contents =
        "张三,http://taobao.com/xxx,2021-05-10 10:00:00\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:00:10\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:00:49\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:01:05\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:01:58\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:02:10\n";
String path = FileUtil.createTempFile(contents);

String ddl = "CREATE TABLE user_clicks (\n" +
        "  username varchar,\n" +
        "  click_url varchar,\n" +
        "  ts TIMESTAMP(3),\n" +
        "  WATERMARK FOR ts AS ts - INTERVAL '2' SECOND\n" +
        ") WITH (\n" +
        "  'connector.type' = 'filesystem',\n" +
        "  'connector.path' = '" + path + "',\n" +
        "  'format.type' = 'csv'\n" +
        ")";

tabEnv.sqlUpdate(ddl);

//对表数据进行sql查询，并将结果作为新表进行查询
String query = "SELECT\n" +
        "  TUMBLE_START(ts, INTERVAL '1' MINUTE),\n" +
        "  TUMBLE_END(ts, INTERVAL '1' MINUTE),\n" +
        "  username,\n" +
        "  COUNT(click_url)\n" +
        "FROM user_clicks\n" +
        "GROUP BY TUMBLE(ts, INTERVAL '1' MINUTE), username";

Table result = tabEnv.sqlQuery(query);

tabEnv.toAppendStream(result, Row.class).print();

env.execute();

以1分钟作为时间滚动窗口，水印延迟2秒。

输出结果：

4> 2021-10-10T10:00,2021-10-10T10:01,张三,3
4> 2021-10-10T10:01,2021-10-10T10:02,张三,2
4> 2021-10-10T10:02,2021-10-10T10:03,张三,1

3. Flink SQL 滑动窗口实战

实现步骤
- 初始化流运行环境
- 在流模式下使用blink planner
- 创建用户点击事件数据
- 将源数据写入临时文件并获取绝对路径
- 创建表载入用户点击事件数据
- 对表运行SQL查询，并将结果作为新表检索
- Table转换成DataStream
- 执行任务

实现代码

代码HopUserClickApplication：

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, environmentSettings);

// 将源数据写入临时文件并获取绝对路径
String contents =
        "张三,http://taobao.com/xxx,2020-10-10 10:00:00\n" +
                "张三,http://taobao.com/xxx,2020-10-10 10:00:10\n" +
                "张三,http://taobao.com/xxx,2020-10-10 10:00:49\n" +
                "张三,http://taobao.com/xxx,2020-10-10 10:01:05\n" +
                "张三,http://taobao.com/xxx,2020-10-10 10:01:58\n" +
                "张三,http://taobao.com/xxx,2020-10-10 10:02:10\n";
String path = FileUtil.createTempFile(contents);

String ddl = "CREATE TABLE user_clicks (\n" +
        "  username varchar,\n" +
        "  click_url varchar,\n" +
        "  ts TIMESTAMP(3),\n" +
        "  WATERMARK FOR ts AS ts - INTERVAL '2' SECOND\n" +
        ") WITH (\n" +
        "  'connector.type' = 'filesystem',\n" +
        "  'connector.path' = '" + path + "',\n" +
        "  'format.type' = 'csv'\n" +
        ")";

tabEnv.sqlUpdate(ddl);

//对表数据进行sql查询，并将结果作为新表进行查询，每隔30秒，统计一次过去1分钟的数据
String query = "SELECT\n" +
        "  HOP_START(ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE),\n" +
        "  HOP_END(ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE),\n" +
        "  username,\n" +
        "  COUNT(click_url)\n" +
        "FROM user_clicks\n" +
        "GROUP BY HOP (ts, INTERVAL '30' SECOND, INTERVAL '1' MINUTE), username";

Table result = tabEnv.sqlQuery(query);

tabEnv.toAppendStream(result, Row.class).print();

env.execute();

每隔30秒，统计一次过去1分钟的用户点击数量。

输出结果：

4> 2021-05-10T09:59:30,2021-05-10T10:00:30,张三,2
4> 2021-05-10T10:00,2021-05-10T10:01,张三,3
4> 2021-05-10T10:00:30,2021-05-10T10:01:30,张三,2
4> 2021-05-10T10:01,2021-05-10T10:02,张三,2
4> 2021-05-10T10:01:30,2021-05-10T10:02:30,张三,2
4> 2021-05-10T10:02,2021-05-10T10:03,张三,1

4. Flink SQL 会话窗口实战

实现步骤
- 初始化流运行环境
- 在流模式下使用blink planner
- 创建用户点击事件数据
- 将源数据写入临时文件并获取绝对路径
- 创建表载入用户点击事件数据
- 对表运行SQL查询，并将结果作为新表检索
- Table转换成DataStream
- 执行任务

代码实现：

代码：SessionUserClickApplication

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

EnvironmentSettings environmentSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env, environmentSettings);

// 将源数据写入临时文件并获取绝对路径
String contents =
        "张三,http://taobao.com/xxx,2021-05-10 10:00:00\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:00:10\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:00:49\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:01:05\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:01:58\n" +
                "张三,http://taobao.com/xxx,2021-05-10 10:02:10\n";
String path = FileUtil.createTempFile(contents);

String ddl = "CREATE TABLE user_clicks (\n" +
        "  username varchar,\n" +
        "  click_url varchar,\n" +
        "  ts TIMESTAMP(3),\n" +
        "  WATERMARK FOR ts AS ts - INTERVAL '2' SECOND\n" +
        ") WITH (\n" +
        "  'connector.type' = 'filesystem',\n" +
        "  'connector.path' = '" + path + "',\n" +
        "  'format.type' = 'csv'\n" +
        ")";

tabEnv.sqlUpdate(ddl);

//对表数据进行sql查询，并将结果作为新表进行查询，每隔30秒统计一次数据
String query = "SELECT\n" +
        "  SESSION_START(ts, INTERVAL '30' SECOND),\n" +
        "  SESSION_END(ts, INTERVAL '30' SECOND),\n" +
        "  username,\n" +
        "  COUNT(click_url)\n" +
        "FROM user_clicks\n" +
        "GROUP BY SESSION (ts, INTERVAL '30' SECOND), username";

Table result = tabEnv.sqlQuery(query);

tabEnv.toAppendStream(result, Row.class).print();

env.execute();

每隔30秒统计一次用户点击数据.

输出结果：

4> 2021-05-10T10:00,2021-05-10T10:00:40,张三,2
4> 2021-05-10T10:00:49,2021-05-10T10:01:35,张三,2
4> 2021-05-10T10:01:58,2021-05-10T10:02:40,张三,2