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对于Flink SQL Client
Flink Table & SQL的API实现了通过SQL语言解决实时技术算业务,但还是要编写局部Java代码(或Scala),并且还要编译构建能力提交到Flink运行环境,这对于不相熟Java或Scala的开发者就略有些不敌对了;
SQL Client的指标就是解决上述问题(官网原话<font color="blue">with a build tool before being submitted to a cluster.</font>)
局限性
遗憾的是,在Flink-1.10.0版本中,SQL Client只是个Beta版本(不适宜用于生产环境),并且只能连贯到本地Flink,不能像mysql、cassandra等客户端工具那样近程连贯server,这些在未来的版本会解决:
环境信息
接下来采纳实战的形式对Flink SQL Client做初步尝试,环境信息如下:
- 电脑:MacBook Pro2018 13寸,macOS Catalina 10.15.3
- Flink:1.10.0
- JDK:1.8.0_211
本地启动flink
- 下载flink包,地址:<font color="blue">http://ftp.kddilabs.jp/infosy...</font>
- 解压:<font color="blue">tar -zxvf flink-1.10.0-bin-scala_2.11.tgz</font>
- 进目录flink-1.10.0/bin/,执行命令<font color="blue">./start-cluster.sh</font>启动本地flink;
- 拜访该机器的8081端口,可见本地flink启动胜利:
启动SQL Client CLI
- 在目录flink-1.10.0/bin/执行./sql-client.sh即可启动SQL Client CLI,如下图所示,红框中的BETA揭示着在生产环境如果要用此工具:
- 第一个要把握的是HELP命令:
- 从hello world开始把,执行命令<font color="blue">select ‘Hello world!’;</font>,控制台输入如下图所示,输出Q可退出:
两种展现模式
- 第一种是table mode,成果像是对一般数据表的查问,设置该模式的命令:
SET execution.result-mode=table;
- 第二种是<font color="blue">changelog mode</font>,成果像是打印每一次数据变更的日志,设置该模式的命令:
SET execution.result-mode=changelog;
- 设置table mode后,执行以下命令作一次简略的分组查问:
SELECT name, COUNT(*) AS cnt FROM (VALUES ('Bob'), ('Alice'), ('Greg'), ('Bob')) AS NameTable(name) GROUP BY name;
- 为了便于比照,下图同时贴上两种模式的查问后果,留神绿框中显示了该行记录是减少还是删除:
- 不论是哪种模式,查问构造都保留在SQL Client CLI过程的堆内存中;
- 在chenglog模式下,为了保障控制台能够失常输入输出,查问后果只展现最近1000条;
- table模式下,能够翻页查问更多后果,后果数量受配置项<font color="blue">max-table-result-rows</font>以及可用堆内存限度;
进一步体验
后面写了几行SQL,对Flink SQL Client有了最根本的感触,接下来做进一步的体验,内容如下:
- 创立CSV文件,这是个最简略的图书信息表,只有三个字段:名字、数量、类目,一共十条记录;
- 创立SQL Client用到的环境配置文件,该文件形容了数据源以及对应的表的信息;
- 启动SQL Client,执行SQL查问上述CSV文件;
- 整个操作步骤如下图所示:
操作
- 首先请确保Flink曾经启动;
- 创立名为<font color="blue">book-store.csv</font>的文件,内容如下:
name001,1,aaaname002,2,aaaname003,3,bbbname004,4,bbbname005,5,bbbname006,6,cccname007,7,cccname008,8,cccname009,9,cccname010,10,ccc
- 在<font color="blue">flink-1.10.0/conf</font>目录下创立名为<font color="blue">book-store.yaml</font>的文件,内容如下:
tables: - name: BookStore type: source-table update-mode: append connector: type: filesystem path: "/Users/zhaoqin/temp/202004/26/book-store.csv" format: type: csv fields: - name: BookName type: VARCHAR - name: BookAmount type: INT - name: BookCatalog type: VARCHAR line-delimiter: "\n" comment-prefix: "," schema: - name: BookName type: VARCHAR - name: BookAmount type: INT - name: BookCatalog type: VARCHAR - name: MyBookView type: view query: "SELECT BookCatalog, SUM(BookAmount) AS Amount FROM BookStore GROUP BY BookCatalog"execution: planner: blink # optional: either 'blink' (default) or 'old' type: streaming # required: execution mode either 'batch' or 'streaming' result-mode: table # required: either 'table' or 'changelog' max-table-result-rows: 1000000 # optional: maximum number of maintained rows in # 'table' mode (1000000 by default, smaller 1 means unlimited) time-characteristic: event-time # optional: 'processing-time' or 'event-time' (default) parallelism: 1 # optional: Flink's parallelism (1 by default) periodic-watermarks-interval: 200 # optional: interval for periodic watermarks (200 ms by default) max-parallelism: 16 # optional: Flink's maximum parallelism (128 by default) min-idle-state-retention: 0 # optional: table program's minimum idle state time max-idle-state-retention: 0 # optional: table program's maximum idle state time # (default database of the current catalog by default) restart-strategy: # optional: restart strategy type: fallback # "fallback" to global restart strategy by default# Configuration options for adjusting and tuning table programs.# A full list of options and their default values can be found# on the dedicated "Configuration" page.configuration: table.optimizer.join-reorder-enabled: true table.exec.spill-compression.enabled: true table.exec.spill-compression.block-size: 128kb# Properties that describe the cluster to which table programs are submitted to.deployment: response-timeout: 5000
- 对于<font color="blue">book-store.yaml</font>文件,有以下几处须要留神:
a. tables.type等于source-table,表明这是数据源的配置信息;
b. tables.connector形容了具体的数据源信息,path是<font color="blue">book-store.csv</font>文件的残缺门路;
c. tables.format形容了文件内容;
d. tables.schema形容了数据源表的表构造;
e. type为view示意MyBookView是个视图(参考数据库的视图概念);
- 在<font color="blue">flink-1.10.0</font>目录执行以下命令,即可启动SQL Client,并指定<font color="blue">book-store.yaml</font>为环境配置:
bin/sql-client.sh embedded -d conf/book-store.yaml
- 查全表:
SELECT * FROM BookStore;
- 依照BookCatalog分组统计记录数:
SELECT BookCatalog, COUNT(*) AS BookCount FROM BookStore GROUP BY BookCatalog;
- 查问视图:
select * from MyBookView;
至此,Flink SQL Client的首次体验就实现了,咱们此工具算是有了根本理解,接下来的文章会进一步应用Flink SQL Client做些简单的操作;
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