前言
前些天可以让批处理的配置变得更优雅
StreamingPro 支持多输入，多输出配置，现在流式计算也支持相同的配置方式了。
另外未来等另外一个项目稳定，会释放出来配合StreamingPro使用，它可以让你很方便的读写HBase,比如可以为HBase 表 添加mapping，类似ES的做法,也可以不用mapping，系统会自动为你创建列(familly:column作为列名)，或者将所有列合并成一个字段让你做处理。
配置
首先需要配置源：
{
"name": "stream.sources.kafka",
"params": [
{
"path": "file:///tmp/sample.csv",
"format": "com.databricks.spark.csv",
"outputTable": "test",
"header": "true"
},
{
"topics":"test",
"zk":"127.0.0.1",
"groupId":"kk3",
"outputTable": "abc"
}
]
}
我们配置了一个Kafka流，一个普通的CSV文件。目前StreamingPro只允许配置一个Kafka流,但是支持多个topic,按逗号分隔即可。你可以配置多个其他非流式源，比如从MySQL,Parquet,CSV同时读取数据并且映射成表。
之后你就可以写SQL进行处理了。
{
"name": "stream.sql",
"params": [
{
"sql": "select abc.content,'abc' as dd from abc left join test on test.content = abc.content",
"outputTableName": "finalOutputTable"
}
]
},
我这里做了简单的join。
{
"name": "stream.outputs",
"params": [
{
"format": "jdbc",
"path": "-",
"driver":"com.mysql.jdbc.Driver",
"url":"jdbc:mysql://127.0.0.1/~?characterEncoding=utf8",
"inputTableName": "finalOutputTable",
"user":"~",
"password":"~",
"dbtable":"aaa",
"mode":"Append"
}
]
}
然后把数据追加到Mysql里去。其实你也可以配置多个输出。
完整配置
{
"example": {
"desc": "测试",
"strategy": "spark",
"algorithm": [],
"ref": [],
"compositor": [
{
"name": "stream.sources.kafka",
"params": [
{
"path": "file:///tmp/sample.csv",
"format": "com.databricks.spark.csv",
"outputTable": "test",
"header": "true"
},
{
"topics":"test",
"zk":"127.0.0.1",
"groupId":"kk3",
"outputTable": "abc"
}
]
},
{
"name": "stream.sql",
"params": [
{
"sql": "select abc.content,'abc' as dd from abc left join test on test.content = abc.content",
"outputTableName": "finalOutputTable"
}
]
},
{
"name": "stream.outputs",
"params": [
{
"format": "jdbc",
"path": "-",
"driver":"com.mysql.jdbc.Driver",
"url":"jdbc:mysql://127.0.0.1/~?characterEncoding=utf8",
"inputTableName": "finalOutputTable",
"user":"~",
"password":"~",
"dbtable":"aaa",
"mode":"Append"
}
]
}
],
"configParams": {
}
}
}
你可以在
StreamingPro-0.4.11 下载到包，然后用命令启动：
SHome=/Users/allwefantasy/streamingpro
./bin/spark-submit --class streaming.core.StreamingApp \
--master local[2] \
--name test \
$SHome/streamingpro-0.4.11-SNAPSHOT-online-1.6.1-jar-with-dependencies.jar \
-streaming.name test \
-streaming.platform spark \
-streaming.job.file.path file://$SHome/batch.json
