查看用于分析 Studio 笔记本中的数据的示例查询 - Managed Service for Apache Flink
使用 Amazon msk/Apache Kafka 创建表格使用 Kinesis 创建表格查询翻滚窗口查询滑动窗口使用交互式 SQL使用 BlackHole SQL 连接器使用 Scala 生成样本数据使用交互式 Scala使用交互式 Python结合使用交互式 Python、SQL 和 Scala使用跨账户 Kinesis 数据流
Amazon Web Services 文档中描述的 Amazon Web Services 服务或功能可能因区域而异。要查看适用于中国区域的差异，请参阅 中国的 Amazon Web Services 服务入门 (PDF)。

Amazon Managed Service for Apache Flink 之前称为 Amazon Kinesis Data Analytics for Apache Flink。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

查看用于分析 Studio 笔记本中的数据的示例查询

有关 Apache Flink SQL 查询设置的信息，请参阅用于交互式数据分析的齐柏林飞艇笔记本上的 Flink。

要在 Apache Flink 控制面板中查看您的应用程序，请在应用程序的 Zeppelin Note 页面中选择 FLINK JOB。

有关窗口查询的更多信息，请参阅 Apache Flink 文档中的 Windows。

有关 Apache Flink Streaming SQL 查询的更多示例，请参阅 Apache Flink 文档中的查询。

使用 Amazon msk/Apache Kafka 创建表格

您可以使用带有 Managed Service for Apache Flink Studio 的 Amazon MSK Flink 连接器通过纯文本、SSL 或 IAM 身份验证对您的连接进行身份验证。根据您的要求使用特定属性创建表。

-- Plaintext connection

CREATE TABLE your_table (
  `column1` STRING,
  `column2` BIGINT
) WITH (
  'connector' = 'kafka',
  'topic' = 'your_topic',
  'properties.bootstrap.servers' = '<bootstrap servers>',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'json'
);

-- SSL connection

CREATE TABLE your_table (
  `column1` STRING,
  `column2` BIGINT
) WITH (
  'connector' = 'kafka',
   'topic' = 'your_topic',
  'properties.bootstrap.servers' = '<bootstrap servers>',
  'properties.security.protocol' = 'SSL',
  'properties.ssl.truststore.location' = '/usr/lib/jvm/java-11-amazon-corretto/lib/security/cacerts',
  'properties.ssl.truststore.password' = 'changeit',
  'properties.group.id' = 'myGroup',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'json'
);

-- IAM connection (or for MSK Serverless)

CREATE TABLE your_table (
  `column1` STRING,
  `column2` BIGINT
) WITH (
  'connector' = 'kafka',
  'topic' = 'your_topic',
  'properties.bootstrap.servers' = '<bootstrap servers>',
  'properties.security.protocol' = 'SASL_SSL',
  'properties.sasl.mechanism' = 'AWS_MSK_IAM',
  'properties.sasl.jaas.config' = 'software.amazon.msk.auth.iam.IAMLoginModule required;',
  'properties.sasl.client.callback.handler.class' = 'software.amazon.msk.auth.iam.IAMClientCallbackHandler',
  'properties.group.id' = 'myGroup',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'json'
);

您可以在 Apache Kafka SQL Connector 中将它们与其他属性结合使用。

使用 Kinesis 创建表格

在以下示例中，您将使用 Kinesis 创建表：

CREATE TABLE KinesisTable (
  `column1` BIGINT,
  `column2` BIGINT,
  `column3` BIGINT,
  `column4` STRING,
  `ts` TIMESTAMP(3)
)
PARTITIONED BY (column1, column2)
WITH (
  'connector' = 'kinesis',
  'stream' = 'test_stream',
  'aws.region' = '<region>',
  'scan.stream.initpos' = 'LATEST',
  'format' = 'csv'
);

有关您可以使用的其他属性的更多信息，请参阅 Amazon Kinesis Data Streams SQL 连接器。

查询翻滚窗口

以下 Flink Streaming SQL 查询从表中选择每个五秒钟的滚动窗口中的最高价格：ZeppelinTopic

%flink.ssql(type=update)
SELECT TUMBLE_END(event_time, INTERVAL '5' SECOND) as winend, MAX(price) as five_second_high, ticker
FROM ZeppelinTopic
GROUP BY ticker, TUMBLE(event_time, INTERVAL '5' SECOND)

查询滑动窗口

以下 Apache Flink Streaming SQL 查询从表格中选择每个五秒钟滑动窗口中的最高价格：ZeppelinTopic

%flink.ssql(type=update)
SELECT HOP_END(event_time, INTERVAL '3' SECOND, INTERVAL '5' SECOND) AS winend, MAX(price) AS sliding_five_second_max
FROM ZeppelinTopic//or your table name in Amazon Glue
GROUP BY HOP(event_time, INTERVAL '3' SECOND, INTERVAL '5' SECOND)

使用交互式 SQL

此示例打印事件时间和处理时间的最大值以及键值表中的值之和。确保您有使用 Scala 生成样本数据正在运行的示例数据生成脚本。要在 Studio 笔记本中尝试其他 SQL 查询，例如筛选和联接，请参阅 Apache Flink 文档中的 Apache Flink 文档：查询。

%flink.ssql(type=single, parallelism=4, refreshInterval=1000, template=<h1>{2}</h1> records seen until <h1>Processing Time: {1}</h1> and <h1>Event Time: {0}</h1>)

-- An interactive query prints how many records from the `key-value-stream` we have seen so far, along with the current processing and event time.
SELECT
  MAX(`et`) as `et`,
  MAX(`pt`) as `pt`,
  SUM(`value`) as `sum`
FROM
  `key-values`
%flink.ssql(type=update, parallelism=4, refreshInterval=1000)

-- An interactive tumbling window query that displays the number of records observed per (event time) second.
-- Browse through the chart views to see different visualizations of the streaming result.
SELECT
  TUMBLE_START(`et`, INTERVAL '1' SECONDS) as `window`,
  `key`,
  SUM(`value`) as `sum`
FROM
  `key-values`
GROUP BY
  TUMBLE(`et`, INTERVAL '1' SECONDS),
  `key`;

使用 BlackHole SQL 连接器

BlackHole SQL 连接器不需要您创建 Kinesis 数据流或 Amazon MSK 集群来测试您的查询。有关 BlackHole SQL 连接器的信息，请参阅 Apache Flink 文档中的 BlackHole SQL 连接器。在此示例中，默认目录是内存中的目录。

%flink.ssql

CREATE TABLE default_catalog.default_database.blackhole_table (
 `key` BIGINT,
 `value` BIGINT,
 `et` TIMESTAMP(3)
) WITH (
 'connector' = 'blackhole'
)
%flink.ssql(parallelism=1)

INSERT INTO `test-target`
SELECT
  `key`,
  `value`,
  `et`
FROM
  `test-source`
WHERE
  `key` > 3
%flink.ssql(parallelism=2)

INSERT INTO `default_catalog`.`default_database`.`blackhole_table`
SELECT
  `key`,
  `value`,
  `et`
FROM
  `test-target`
WHERE
  `key` > 7

使用 Scala 生成样本数据

此示例使用 Scala 生成示例数据。您可以使用此示例数据来测试各种查询。使用 create table 语句创建键值表。

import org.apache.flink.streaming.api.functions.source.datagen.DataGeneratorSource
import org.apache.flink.streaming.api.functions.source.datagen.RandomGenerator
import org.apache.flink.streaming.api.scala.DataStream

import java.sql.Timestamp

// ad-hoc convenience methods to be defined on Table 
implicit class TableOps[T](table: DataStream[T]) {
    def asView(name: String): DataStream[T] = {
      if (stenv.listTemporaryViews.contains(name)) {
        stenv.dropTemporaryView("`" + name + "`")
      }
      stenv.createTemporaryView("`" + name + "`", table)
      return table;
    }
}
%flink(parallelism=4)
val stream = senv
 .addSource(new DataGeneratorSource(RandomGenerator.intGenerator(1, 10), 1000))
 .map(key => (key, 1, new Timestamp(System.currentTimeMillis)))
 .asView("key-values-data-generator")
%flink.ssql(parallelism=4)
-- no need to define the paragraph type with explicit parallelism (such as "%flink.ssql(parallelism=2)")
-- in this case the INSERT query will inherit the parallelism of the of the above paragraph
INSERT INTO `key-values`
SELECT
 `_1` as `key`,
 `_2` as `value`,
 `_3` as `et`
FROM
 `key-values-data-generator`

使用交互式 Scala

这是使用交互式 SQL 的 Scala 翻译。有关更多 Scala 示例，请参阅 Apache Flink 文档中的表 API。

%flink
import org.apache.flink.api.scala._
import org.apache.flink.table.api._
import org.apache.flink.table.api.bridge.scala._

// ad-hoc convenience methods to be defined on Table
implicit class TableOps(table: Table) {
    def asView(name: String): Table = {
      if (stenv.listTemporaryViews.contains(name)) {
        stenv.dropTemporaryView(name)
      }
      stenv.createTemporaryView(name, table)
      return table;
    }
}
%flink(parallelism=4)

// A view that computes many records from the `key-values` we have seen so far, along with the current processing and event time.
val query01 = stenv
  .from("`key-values`")
  .select(
    $"et".max().as("et"),
    $"pt".max().as("pt"),
    $"value".sum().as("sum")
  ).asView("query01")
%flink.ssql(type=single, parallelism=16, refreshInterval=1000, template=<h1>{2}</h1> records seen until <h1>Processing Time: {1}</h1> and <h1>Event Time: {0}</h1>)

-- An interactive query prints the query01 output.
SELECT * FROM query01
%flink(parallelism=4)

// An tumbling window view that displays the number of records observed per (event time) second.
val query02 = stenv
  .from("`key-values`")
  .window(Tumble over 1.seconds on $"et" as $"w")
  .groupBy($"w", $"key")
  .select(
    $"w".start.as("window"),
    $"key",
    $"value".sum().as("sum")
  ).asView("query02")
%flink.ssql(type=update, parallelism=4, refreshInterval=1000)

-- An interactive query prints the query02 output.
-- Browse through the chart views to see different visualizations of the streaming result.
SELECT * FROM `query02`

使用交互式 Python

这是使用交互式 SQL 的 Python 翻译。有关更多 Python 示例，请参阅 Apache Flink 文档中的表 API。

%flink.pyflink
from pyflink.table.table import Table

def as_view(table, name):
  if (name in st_env.list_temporary_views()):
    st_env.drop_temporary_view(name)
  st_env.create_temporary_view(name, table)
  return table

Table.as_view = as_view
%flink.pyflink(parallelism=16)

# A view that computes many records from the `key-values` we have seen so far, along with the current processing and event time
st_env \
  .from_path("`keyvalues`") \
  .select(", ".join([
    "max(et) as et",
    "max(pt) as pt",
    "sum(value) as sum"
  ])) \
  .as_view("query01")
%flink.ssql(type=single, parallelism=16, refreshInterval=1000, template=<h1>{2}</h1> records seen until <h1>Processing Time: {1}</h1> and <h1>Event Time: {0}</h1>)

-- An interactive query prints the query01 output.
SELECT * FROM query01
%flink.pyflink(parallelism=16)

# A view that computes many records from the `key-values` we have seen so far, along with the current processing and event time
st_env \
  .from_path("`key-values`") \
  .window(Tumble.over("1.seconds").on("et").alias("w")) \
  .group_by("w, key") \
  .select(", ".join([
    "w.start as window",
    "key",
    "sum(value) as sum"
  ])) \
  .as_view("query02")
%flink.ssql(type=update, parallelism=16, refreshInterval=1000)

-- An interactive query prints the query02 output.
-- Browse through the chart views to see different visualizations of the streaming result.
SELECT * FROM `query02`

结合使用交互式 Python、SQL 和 Scala

您可以在笔记本中使用 SQL、Python 和 Scala 的任意组合进行交互式分析。在计划部署为具有持久状态的应用程序的 Studio 笔记本中，可以组合使用 SQL 和 Scala。此示例向您展示了被忽略的部分以及那些在应用程序中部署的具有持久状态的部分。

%flink.ssql
CREATE TABLE `default_catalog`.`default_database`.`my-test-source` (
  `key` BIGINT NOT NULL,
  `value` BIGINT NOT NULL,
  `et` TIMESTAMP(3) NOT NULL,
  `pt` AS PROCTIME(),
  WATERMARK FOR `et` AS `et` - INTERVAL '5' SECOND
)
WITH (
  'connector' = 'kinesis',
  'stream' = 'kda-notebook-example-test-source-stream',
  'aws.region' = 'eu-west-1',
  'scan.stream.initpos' = 'LATEST',
  'format' = 'json',
  'json.timestamp-format.standard' = 'ISO-8601'
)
%flink.ssql
CREATE TABLE `default_catalog`.`default_database`.`my-test-target` (
  `key` BIGINT NOT NULL,
  `value` BIGINT NOT NULL,
  `et` TIMESTAMP(3) NOT NULL,
  `pt` AS PROCTIME(),
  WATERMARK FOR `et` AS `et` - INTERVAL '5' SECOND
)
WITH (
  'connector' = 'kinesis',
  'stream' = 'kda-notebook-example-test-target-stream',
  'aws.region' = 'eu-west-1',
  'scan.stream.initpos' = 'LATEST',
  'format' = 'json',
  'json.timestamp-format.standard' = 'ISO-8601'
)
%flink()

// ad-hoc convenience methods to be defined on Table
implicit class TableOps(table: Table) {
  def asView(name: String): Table = {
    if (stenv.listTemporaryViews.contains(name)) {
      stenv.dropTemporaryView(name)
    }
    stenv.createTemporaryView(name, table)
    return table;
  }
}
%flink(parallelism=1)
val table = stenv
  .from("`default_catalog`.`default_database`.`my-test-source`")
  .select($"key", $"value", $"et")
  .filter($"key" > 10)
  .asView("query01")
%flink.ssql(parallelism=1)

-- forward data
INSERT INTO `default_catalog`.`default_database`.`my-test-target`
SELECT * FROM `query01`
%flink.ssql(type=update, parallelism=1, refreshInterval=1000)

-- forward data to local stream (ignored when deployed as application)
SELECT * FROM `query01`
%flink

// tell me the meaning of life (ignored when deployed as application!)
print("42!")

使用跨账户 Kinesis 数据流

要使用除拥有 Studio 笔记本的账户之外的账户中的 Kinesis 数据流，请在运行 Studio 笔记本的账户中创建服务执行角色，在拥有数据流的账户中创建角色信任策略。在创建表 DDL 语句的 Kinesis 连接器中使用aws.credentials.provider、aws.credentials.role.arn和aws.credentials.role.sessionName，根据数据流创建表。

为 Studio 笔记本帐户使用以下服务执行角色。

{
 "Sid": "AllowNotebookToAssumeRole",
 "Effect": "Allow",
 "Action": "sts:AssumeRole"
 "Resource": "*"
}

对数据流帐户使用AmazonKinesisFullAccess策略和以下角色信任策略。

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Principal": {
 "AWS": "arn:aws:iam::<accountID>:root"
 },
 "Action": "sts:AssumeRole",
 "Condition": {}
 }
 ]
}

使用以下段落作为创建表语句。

%flink.ssql
CREATE TABLE test1 (
name VARCHAR,
age BIGINT
) WITH (
'connector' = 'kinesis',
'stream' = 'stream-assume-role-test',
'aws.region' = 'us-east-1',
'aws.credentials.provider' = 'ASSUME_ROLE',
'aws.credentials.role.arn' = 'arn:aws:iam::<accountID>:role/stream-assume-role-test-role',
'aws.credentials.role.sessionName' = 'stream-assume-role-test-session',
'scan.stream.initpos' = 'TRIM_HORIZON',
'format' = 'json'
)