FLink学习笔记：12-Flink 的Table API的常用操作

文章目录

Table Api的常用操作
- 创建表环境
- 从datastream创建一张表
- - 指定主键
  - 字段取别名
  - 提取时间字段（用于timewindow）
  - Watermark
- 创建临时视图
- 创建临时表或者表
- 查询操作
- - 查询选取其中某些列
  - distinct去重
  - 查询过滤filter
  - 分组聚合

Table Api的常用操作创建表环境

//构建环境
val env = StreamExecutionEnvironment.getExecutionEnvironment

//构建table环境
val tableEnvironmentSettings = EnvironmentSettings
  .newInstance()
  .inStreamingMode()
  .build()
val tableEnv = StreamTableEnvironment.create(env, tableEnvironmentSettings)

EnvironmentSettings.newInstance().inStreamingMode() 流模式
Flink表环境默认的处理模式就是流模式
EnvironmentSettings.newInstance().inBatchMode() 批模式

从datastream创建一张表

老版本的Flink的实现如下：

val inputTable = tableEnv.fromDataStream("sensor_table",datastream,
  $("id"),
  $("timestamp")
  $("temperature"))

但是在新的版本中这一方式已经被移除掉了，使用下面的方式定义表结构

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING())
    .column("timestamp",DataTypes.BIGINT())
    .column("temperature",DataTypes.DOUBLE())
    .build()
)

指定主键

单值主键

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING().notNull())
    .column("timestamp",DataTypes.BIGINT())
    .column("temperature",DataTypes.DOUBLE())
    .primaryKey("id")
    .build()
)

复合主键

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING().notNull())
    .column("timestamp",DataTypes.BIGINT().notNull())
    .column("temperature",DataTypes.DOUBLE())
    .primaryKey("id","timestamp")
    .build()
)

字段取别名

//整张表按字段顺序重命名
inputTable.as("id","timestamp","ctime")

//重命名指定字段
inputTable.renameColumns($"id","sensor_id")

提取时间字段（用于timewindow）

事件时间EventTime

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING().notNull())
    .column("timestamp",DataTypes.BIGINT().notNull())
    .column("temperature",DataTypes.DOUBLE())
    .primaryKey("id","timestamp")
    .columnByExpression("rowtime","CAST(TO_TIMESTAMP(FROM_UNIXTIME(`timestamp`)) AS TIMESTAMP(3))")
    .build()
)

对应的DDL操作

CREATE TABLE sensor(
    id STRING NOT NULL,
    timestamp BIGINT NOT NULL,
    temperature DOUBLE NOT NULL,
    `proctime` TIMESTAMP_LTZ(3) NOT NULL *PROCTIME* AS proctime(),
    CONSTRAINT `PK_id_timestamp` PRIMARY KEY (`id`, `timestamp`) NOT ENFORCED
)

处理时间ProcessTime

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING().notNull())
    .column("timestamp",DataTypes.BIGINT().notNull())
    .column("temperature",DataTypes.DOUBLE())
    .primaryKey("id","timestamp")
    .columnByExpression("procTime","proctime()")
    .build()
)

对应的DDL操作

CREATE TABLE sensor(
    id STRING NOT NULL,
    timestamp BIGINT NOT NULL,
    temperature DOUBLE NOT NULL,
    `rowtime` TIMESTAMP(3) AS CAST(TO_TIMESTAMP(FROM_UNIXTIME(`timestamp`)) AS TIMESTAMP(3)),
    CONSTRAINT `PK_id_timestamp` PRIMARY KEY (`id`, `timestamp`) NOT ENFORCED
)

Watermark

方式一：在DataStream流上定义好时间戳和watermark

datastream.assignTimestampsAndWatermarks(
WatermarkStrategy
  .forBoundedOutOfOrderness[SensorReading](Duration.ofSeconds(5))
  .withTimestampAssigner(
    new SerializableTimestampAssigner[SensorReading] {
      override def extractTimestamp(element: SensorReading,
                                    recordTimestamp: Long): Long = {
        element.timestamp * 1000L
      }
    })
)
//然后在表中可以指定相应的时间戳字段
val inputTable2 = tableEnv.fromDataStream(datastream,
  $("id"),
  $("timestamp").rowtime(),
  $("temperature"))

方式二：TableAPI实现

val inputTable = tableEnv.fromDataStream(
  datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING().notNull())
    .column("timestamp",DataTypes.BIGINT().notNull())
    .column("temperature",DataTypes.DOUBLE())
    .primaryKey("id","timestamp")
    .columnByExpression("rowtime","CAST(TO_TIMESTAMP(FROM_UNIXTIME(`timestamp`)) AS TIMESTAMP(3))")
    .watermark("rowtime","rowtime - interval '5' SECOND ")
    .build()
)

方式三：SQL实现

语法：WATERMARK FOR order_time AS order_time - INTERVAL ‘5’ SECOND

其中的5，表示的最大延迟时间

CREATE TABLE sensor(
  `id` STRING NOT NULL,
  `timestamp` BIGINT NOT NULL,
  `temperature` DOUBLE,
  `rowtime` TIMESTAMP(3) AS CAST(TO_TIMESTAMP(FROM_UNIXTIME(`timestamp`)) AS TIMESTAMP(3)),
  WATERMARK FOR `rowtime`: TIMESTAMP(3) AS rowtime - interval '5' SECOND ,
  CONSTRAINT `PK_id_timestamp` PRIMARY KEY (`id`, `timestamp`) NOT ENFORCED
)

创建临时视图

方式一：tableAPI

tableEnv.createTemporaryView("sensor_view",datastream,
  Schema
    .newBuilder()
    .column("id",DataTypes.STRING())
    .build()
)
val result = tableEnv.sqlQuery("SELECt * FROM sensor_view")
tableEnv.toDataStream(result).print()

方式二：SQL语句实现

|CREATE TEMPORARY VIEW sensor_view
|AS
|SELECt id,temperature,rowtime FROM sensor

创建临时表或者表

方式一:TABLE API

具体设置见TableAPI的Connector操作一文

val targetTable = tableEnv.createTemporaryTable("sensor_temp_table",
  TableDescriptor
    .forConnector("filesystem")
    .schema(Schema.newBuilder()
      .column("id",DataTypes.STRING())
      .column("temperature",DataTypes.DOUBLE())
      .build())
    .format("csv")
    .option("path","D:\LearnWorkSpace\FlinkDemo\src\main\resources\out")
    .build()
)

方式二:SQL

CREATE TEMPORARY TABLE sensor_temp_table (
    id STRING NOT NULL,
    temperature DOUBLE
) WITH (
'connector'='filesystem',
'format'='csv',
'path'='file:///filepath/'
...
)

查询操作查询选取其中某些列

方式一：Table Api操作

var result = inputTable.select($"id",$"timestamp",'temperature,'rowtime)
tableEnv.toDataStream(result).print()

方式二：SQL实现

SELECT id,timestamp,temperature,rowtime FROM inputtable

distinct去重

方式一：Table Api操作

var result = inputTable.select($"id",$"timestamp",'temperature,'rowtime).dictinct()
tableEnv.toDataStream(result).print()

方式二：SQL实现

SELECT distinct id,timestamp,temperature,rowtime FROM inputtable

查询过滤filter

.filter 可以级联操作，多个filter之间是and的关系

var result = inputTable.select($"id",$"timestamp",'temperature,'rowtime)
  .filter($"id" === "sensor_1")
  .filter($"temperature" >= 40.8 )
    .distinct()
tableEnv.toDataStream(result).print()

.filter 里面也可以写多个条件，多个条件之间可以使用“or”或者“and”连接。

    var result = inputTable.select($"id",$"timestamp",'temperature,'rowtime)
      .filter($"id" === "sensor_1" or $"id" === "sensor_2")
        .distinct()
    tableEnv.toDataStream(result).print()

SQL实现

SELECT distinct id,timestamp,temperature,rowtime FROM inputtable
WHERe id = 'sensor_1' or id = 'sensor_2'

然后使用tableEnv.sqlQuery(sqlStatement)执行查询

分组聚合

方式一：Table Api操作

var result = inputTable
    .groupBy($"id")
    .select($"id",$"id".count() as "cnt")
tableEnv.toDataStream(result).print()

方式二：SQL实现

SELECT id,count(id) as cnt FROM inputtable
group by id

FLink学习笔记：12-Flink 的Table API的常用操作

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