疾速开始
部署启动
1. 执行以下命令,编译生成 sharding-scaling 二进制包:
git clone https://github.com/apache/shardingsphere.git;cd shardingsphere;
mvn clean install -Prelease;
公布包所在目录为:/sharding-distribution/sharding-scaling-distribution/target/apache-shardingsphere-${latest.release.version}-sharding-scaling-bin.tar.gz
。
2. 解压缩公布包,批改配置文件conf/server.yaml
,这里次要批改启动端口,保障不与本机其余端口抵触,其余值放弃默认即可:
port: 8888
blockQueueSize: 10000
pushTimeout: 1000
workerThread: 30
3. 启动 sharding-scaling:
sh bin/start.sh
留神 :
如果后端连贯 MySQL 数据库,须要下载 MySQL Connector/J,
解压缩后,将 mysql-connector-java-5.1.47.jar 拷贝到 ${sharding-scaling}lib 目录。
4. 查看日志logs/stdout.log
,确保启动胜利。
创立迁徙工作
Sharding-Scaling 提供相应的 HTTP 接口来治理迁徙工作,部署启动胜利后,咱们能够调用相应的接口来启动迁徙工作。
创立迁徙工作:
curl -X POST \
http://localhost:8888/shardingscaling/job/start \
-H 'content-type: application/json' \
-d '{"ruleConfiguration": {"sourceDatasource":"ds_0: !!org.apache.shardingsphere.orchestration.core.configuration.YamlDataSourceConfiguration\n dataSourceClassName: com.zaxxer.hikari.HikariDataSource\n properties:\n jdbcUrl: jdbc:mysql://127.0.0.1:3306/test?serverTimezone=UTC&useSSL=false\n username: root\n password: '\''123456'\''\n connectionTimeout: 30000\n idleTimeout: 60000\n maxLifetime: 1800000\n maxPoolSize: 50\n minPoolSize: 1\n maintenanceIntervalMilliseconds: 30000\n readOnly: false\n","sourceRule":"defaultDatabaseStrategy:\n inline:\n algorithmExpression: ds_${user_id % 2}\n shardingColumn: user_id\ntables:\n t1:\n actualDataNodes: ds_0.t1\n keyGenerator:\n column: order_id\n type: SNOWFLAKE\n logicTable: t1\n tableStrategy:\n inline:\n algorithmExpression: t1\n shardingColumn: order_id\n t2:\n actualDataNodes: ds_0.t2\n keyGenerator:\n column: order_item_id\n type: SNOWFLAKE\n logicTable: t2\n tableStrategy:\n inline:\n algorithmExpression: t2\n shardingColumn: order_id\n","destinationDataSources": {"name":"dt_0","password":"123456","url":"jdbc:mysql://127.0.0.1:3306/test2?serverTimezone=UTC&useSSL=false","username":"root"}
},
"jobConfiguration": {"concurrency": 3}
}'
留神:上述须要批改 ruleConfiguration.sourceDatasource
和ruleConfiguration.sourceRule
,别离为源端 ShardingSphere 数据源和数据表规定相干配置;
以及 ruleConfiguration.destinationDataSources
中指标端 sharding-proxy 的相干信息。
返回如下信息,示意工作创立胜利:
{
"success": true,
"errorCode": 0,
"errorMsg": null,
"model": null
}
须要留神的是,目前 Sharding-Scaling 工作创立胜利后,便会主动运行,进行数据的迁徙。
查问工作进度
执行如下命令获取以后所有迁徙工作:
curl -X GET \
http://localhost:8888/shardingscaling/job/list
返回示例如下:
{
"success": true,
"errorCode": 0,
"model": [
{
"jobId": 1,
"jobName": "Local Sharding Scaling Job",
"status": "RUNNING"
}
]
}
进一步查问工作具体迁徙状态:
curl -X GET \
http://localhost:8888/shardingscaling/job/progress/1
返回工作详细信息如下:
{
"success": true,
"errorCode": 0,
"errorMsg": null,
"model": {
"id": 1,
"jobName": "Local Sharding Scaling Job",
"status": "RUNNING"
"syncTaskProgress": [{
"id": "127.0.0.1-3306-test",
"status": "SYNCHRONIZE_REALTIME_DATA",
"historySyncTaskProgress": [{
"id": "history-test-t1#0",
"estimatedRows": 41147,
"syncedRows": 41147
}, {
"id": "history-test-t1#1",
"estimatedRows": 42917,
"syncedRows": 42917
}, {
"id": "history-test-t1#2",
"estimatedRows": 43543,
"syncedRows": 43543
}, {
"id": "history-test-t2#0",
"estimatedRows": 39679,
"syncedRows": 39679
}, {
"id": "history-test-t2#1",
"estimatedRows": 41483,
"syncedRows": 41483
}, {
"id": "history-test-t2#2",
"estimatedRows": 42107,
"syncedRows": 42107
}],
"realTimeSyncTaskProgress": {
"id": "realtime-test",
"delayMillisecond": 1576563771372,
"logPosition": {
"filename": "ON.000007",
"position": 177532875,
"serverId": 0
}
}
}]
}
}
结束任务
数据迁徙实现后,咱们能够调用接口结束任务:
curl -X POST \
http://localhost:8888/shardingscaling/job/stop \
-H 'content-type: application/json' \
-d '{"jobId":1}'
返回如下信息示意工作胜利完结:
{
"success": true,
"errorCode": 0,
"errorMsg": null,
"model": null
}
完结 Sharding-Scaling
sh bin/stop.sh