Druid简介

Druid是一个高性能的实时数据分析系统，由MetaMarkets公司在2012开源，专门为OLAP场景而设计。Druid遵从Lambda架构，支持批量和实时两种方式导入数据，并提供高性能的数据查询。

集群准备

Druid模版

登录百度云控制台，选择“产品服务->百度MapReduce BMR”，点击“创建集群”，进入集群创建页。购置集群时选择Druid模版即可，另外Druid的元数据存储支持本地MySQL和云数据库RDS MySQL。如下图所示:

节点类型

BMR的Master节点上部署Druid的Overlord、Coordinator、Broker和Router，供提交任务、查看数据源和数据查询。

为了更容易扩展节点，BMR的Core节点部署Druid的Historical和MiddleManager, Task节点部署Druid的Broker和MiddleManager。可以按照需求变更Core和Task的节点数目。

Druid对内存的要求较高，建议使用4核16GB（或更高配置）的节点配置。在Druid集群内部，建议不要使用MapReduce跑其他开源组件的作业。

使用简介（批量索引）

Druid使用的端口如下：

Druid提供Http访问，每个请求都会返回结果，如果使用curl命令没有任何返回，可以加上-v查看具体的响应信息。

1. 远程登录到创建好的集群中

   ssh root@[master节点公网ip]
   使用创建集群时输入的密码

2. 切换成hdfs用户，在HDFS上创建quickstart目录

   hdfs dfs -mkdir /user/druid/quickstart

3. 将示例数据文件wikiticker-2015-09-12-sampled.json.gz拷贝到HDFS

   hdfs dfs -copyFromLocal /opt/bmr/druid/quickstart/wikiticker-2015-09-12-sampled.json.gz /user/druid/quickstart

4. 向Overlord提交批量索引任务

   curl -v -X POST -H 'Content-Type: application/json' -d @/opt/bmr/druid/quickstart/wikiticker-index.json http://xxx-master-instance-xxx-1（hostname）:8591/druid/indexer/v1/task
   成功返回结果为{"task":"index_hadoop_wikiticker_yyyy-MM-ddThh:mm:ss.xxZ"}

   如果使用的是高可用集群，Overlord只有一个为Active，有效的hostname可能是xxx-master-instance-xxx-2。

5. 使用Web UI查看任务执行情况

   使用ssh -ND [本地未使用端口] root@[公网ip] 并对浏览器做相关配置，例如Chrome浏览器通过Swichysharp配置了VPN之后（通过SSH-Tunnel访问集群），或者使用OpenVPN，可通过Master节点的内网ip加上Overlord端口（8591）查看结果，如下图所示：

   

6. 等到索引任务执行成功，查看数据源

使用Web UI：配置好VPN后，通过Master节点的内网ip加上Coordinator端口（8592）查看结果，如下图所示：

使用Http：

curl -v -X GET http://xxx-master-instance-xxx-1:8592/druid/coordinator/v1/datasources

成功返回结果为["wikiticker"]

如果使用的是高可用集群，Coordinator只有一个为Active，有效的hostname可能是xxx-master-instance-xxx-2。

7. 查询数据

   当Overlord Web UI显示任务成功或者查看数据源显示有wikiticker的时候，可以使用Broker查询数据：
   curl -v -X 'POST' -H 'Content-Type:application/json' -d @/opt/bmr/druid/quickstart/wikiticker-top-pages.json http://[master/task hostname或ip]:8590/druid/v2?pretty

Tips: 如果需要使用自己的Druid Spec JSON，建议在～目录下编辑，并在本地做好备份。Spec的编写请参考Apache Druid官方文档。

使用Kafka Indexing Service

Druid提供了Kafka Indexing Service摄入实时数据，详情参考Druid官方文档。BMR托管的Druid可支持百度消息服务BMS、BMR内部和放在同一VPC下的Kafka集群。本示例使用百度消息服务BMS演示，通过其他方式部署的Kafka集群步骤类似。

1. 使用百度消息服务BMS创建主题 kafka_demo，然后下载BMS的证书，用于后续创建Kafka生产者和消费者，参考BMS文档
   

2. 在～目录（/home/hdfs），将下载完毕的BMS证书上传并解压到此处，然后scp到其他节点

   Plain Text 复制

   1unzip -d kafka-key kafka-key.zip
   2scp -rp kafka-key root@[节点ip或hostname]:~/

3. 在～目录（/home/hdfs）下编写Druid Spec文件kafka_demo.json，BMS的主题需要带上创建后生成的前缀

   JSON 复制

   1查看client.properties文件，替换kafka_demo.json里的ssl.keystore.password
   2
   3{
   4  "type": "kafka",
   5  "dataSchema": {
   6    "dataSource": "wiki_kafka_demo",
   7    "parser": {
   8      "type": "string",
   9      "parseSpec": {
   10        "format": "json",
   11        "timestampSpec": {
   12          "column": "time",
   13          "format": "auto"
   14        },
   15        "dimensionsSpec": {
   16          "dimensions": [
   17            "channel",
   18            "cityName",
   19            "comment",
   20            "countryIsoCode",
   21            "countryName",
   22            "isAnonymous",
   23            "isMinor",
   24            "isNew",
   25            "isRobot",
   26            "isUnpatrolled",
   27            "metroCode",
   28            "namespace",
   29            "page",
   30            "regionIsoCode",
   31            "regionName",
   32            "user",
   33            { "name": "added", "type": "long" },
   34            { "name": "deleted", "type": "long" },
   35            { "name": "delta", "type": "long" }
   36          ]
   37        }
   38      }
   39    },
   40    "metricsSpec" : [],
   41    "granularitySpec": {
   42      "type": "uniform",
   43      "segmentGranularity": "DAY",
   44      "queryGranularity": "NONE",
   45      "rollup": false
   46    }
   47  },
   48  "tuningConfig": {
   49    "type": "kafka",
   50    "reportParseExceptions": false
   51  },
   52  "ioConfig": {
   53    "topic": "[accountID]__kafka_demo",
   54    "replicas": 2,
   55    "taskDuration": "PT10M",
   56    "completionTimeout": "PT20M",
   57    "consumerProperties": {
   58      "bootstrap.servers": "[kafka_address]:[kafka_port]",
   59      "security.protocol": "SSL",
   60      "ssl.truststore.password": "kafka",
   61      "ssl.truststore.location": "/home/hdfs/kafka-key/client.truststore.jks",
   62      "ssl.keystore.location": "/home/hdfs/kafka-key/client.keystore.jks",
   63      "ssl.keystore.password": "******"
   64    }
   65  }
   66} 

   若使用的不是开启SSL的Kafka集群，consumerProperties只需要配置bootstrap.servers即可，例如BMR集群中的Kafka。

4. 向Overlord提交Kafka Supervisor任务

   Plain Text 复制

   1 curl -XPOST -H'Content-Type: application/json' -d @kafka_demo.json http://[overlord_ip]:8591/druid/indexer/v1/supervisor

   成功返回：

   {"id":"wiki_kafka_demo"}，Overlord Web UI上也可以看到结果：

   

5. 本地目录解压BMS证书压缩包，使用client.propertie启动Kafka生产者并发送消息

   Plain Text 复制

   1sh bin/kafka-console-producer.sh --producer.config client.properties --topic <accountID>__kafka_demo --sync --broker-list [kafka_address]:[kafka_port]
   2
   3{"time":"2015-09-12T23:58:31.643Z","channel":"#en.wikipedia","cityName":null,"comment":"Notification: tagging for deletion of [[File:Axintele team.gif]]. ([[WP:TW|TW]])","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"User talk","page":"User talk:AlexGeorge33","regionIsoCode":null,"regionName":null,"user":"Sir Sputnik","delta":1921,"added":1921,"deleted":0}
   4{"time":"2015-09-12T23:58:33.743Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Codiaeum finisterrae","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":18,"added":18,"deleted":0}
   5{"time":"2015-09-12T23:58:35.732Z","channel":"#en.wikipedia","cityName":null,"comment":"Put info into infobox, succession box, split sections a bit","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Jamel Holley","regionIsoCode":null,"regionName":null,"user":"Mr. Matté","delta":3427,"added":3427,"deleted":0}
   6{"time":"2015-09-12T23:58:38.531Z","channel":"#uz.wikipedia","cityName":null,"comment":"clean up, replaced: ozbekcha → oʻzbekcha, olchami → oʻlchami (3) using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":true,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Chambors","regionIsoCode":null,"regionName":null,"user":"Ximik1991Bot","delta":8,"added":8,"deleted":0}
   7{"time":"2015-09-12T23:58:41.619Z","channel":"#it.wikipedia","cityName":null,"comment":"/* Gruppo Discovery Italia */","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":true,"metroCode":null,"namespace":"Main","page":"Deejay TV","regionIsoCode":null,"regionName":null,"user":"Ciosl","delta":4,"added":4,"deleted":0}
   8{"time":"2015-09-12T23:58:43.304Z","channel":"#en.wikipedia","cityName":null,"comment":"/* Plot */","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Paper Moon (film)","regionIsoCode":null,"regionName":null,"user":"Siddharth Mehrotra","delta":0,"added":0,"deleted":0}
   9{"time":"2015-09-12T23:58:46.732Z","channel":"#en.wikipedia","cityName":null,"comment":"/* Jeez */ delete (also fixed Si Trew's syntax error, as I think he meant to make a link there)","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Wikipedia","page":"Wikipedia:Redirects for discussion/Log/2015 September 12","regionIsoCode":null,"regionName":null,"user":"JaykeBird","delta":293,"added":293,"deleted":0}
   10{"time":"2015-09-12T23:58:48.729Z","channel":"#fr.wikipedia","cityName":null,"comment":"MàJ","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Wikipédia","page":"Wikipédia:Bons articles/Nouveau","regionIsoCode":null,"regionName":null,"user":"Gemini1980","delta":76,"added":76,"deleted":0}
   11{"time":"2015-09-12T23:58:51.785Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Amata yezonis","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":10,"added":10,"deleted":0}
   12{"time":"2015-09-12T23:58:53.898Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Codia triverticillata","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":36,"added":36,"deleted":0}

6. 通过Coordinator Web UI查看实时数据源

   

跨集群使用Druid

Druid支持通过跨集群和其他Hadoop组件联合使用，如Hadoop、Hive、Spark等。在集群创建页将多个集群创建在同一个私有网络（VPC）之下，curl命令或者Http客户端就可以指定IP和端口向Druid各组件发送跨集群的请求。

目前只支持通过IP地址访问同一个VPC下的另一个集群的主机。

使用Hive简化Druid操作

在Hive中可以通过指定IP地址和端口访问Druid节点。

本节提供一个能够运行的示例，更多使用详情可参考Apache Hive官方文档https://cwiki.apache.org/confluence/display/Hive/Druid+Integration

示例一：查询

推荐使用Hive查询Druid数据，可以简化Druid常用的查询，不用为了简单的查询而去编写复杂的JSON文件。

1. 创建一个Hive集群，登录Hive集群

   ssh root@[hive集群公网ip]
   使用创建hive集群时输入的密码
   

2. 指定Druid集群Broker的IP地址和端口
   

   hive> SET hive.druid.broker.address.default=x.x.x.x:8590;

3. 创建外表（确保按照之前的示例，创建了wikiticker数据源）

   hive> CREATE EXTERNAL TABLE druid_hive_demo_01 STORED BY 'org.apache.hadoop.hive.druid.DruidStorageHandler' TBLPROPERTIES ("druid.datasource" = "wikiticker");

4. 查看元信息

   hive> DESCRIBE FORMATTED druid_hive_demo_01;
   其中维度列是string类型，指标列是bigint类型，时间戳字段__time是timestamp with local time zone类型

5. 查询数据

   hive> SELECT `__time`, count, page, `user`, added FROM druid_hive_demo_01 LIMIT 10;
   

   注意：使用Hive无法扫描Druid全表，如上面的例子不使用limit会报错

   __time是Druid表（数据源）的时间戳字段，在Hive中需要用反引号转义，返回结果如下：

   Plain Text 复制

   12015-09-12 08:46:58.771 Asia/Shanghai	1	36	Talk:Oswald Tilghman	GELongstreet
   22015-09-12 08:47:00.496 Asia/Shanghai	1	17	Rallicula	PereBot
   32015-09-12 08:47:05.474 Asia/Shanghai	1	0	Peremptory norm	60.225.66.142
   42015-09-12 08:47:08.77 Asia/Shanghai	1	18	Apamea abruzzorum	Cheers!-bot
   52015-09-12 08:47:11.862 Asia/Shanghai	1	18	Atractus flammigerus	ThitxongkhoiAWB
   62015-09-12 08:47:13.987 Asia/Shanghai	1	18	Agama mossambica	ThitxongkhoiAWB
   72015-09-12 08:47:17.009 Asia/Shanghai	1	0	Campanya dels Balcans (1914-1918)	Jaumellecha
   82015-09-12 08:47:19.591 Asia/Shanghai	1	345	Talk:Dani Ploeger	New Media Theorist
   92015-09-12 08:47:21.578 Asia/Shanghai	1	121	User:WP 1.0 bot/Tables/Project/Pubs	WP 1.0 bot
   102015-09-12 08:47:25.821 Asia/Shanghai	1	18	Agama persimilis	ThitxongkhoiAWB

示例二：导入历史数据

可以使用已有的Hive表，通过建外表将HDFS数据导入到Druid中，不必编写复杂的Druid Ingestion Spec文件。注意，必须指定时间戳字段__time，且时间戳类型为timestamp with local time zone。

1. 进入～目录，下载示例数据文件，创建示例Hive表

   SQL 复制

   1wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
   2unzip ml-100k.zip
   3
   4hive>
   5CREATE TABLE u_data (
   6    userid INT,
   7    movieid INT,
   8    rating INT,
   9    unixtime STRING)
   10ROW FORMAT DELIMITED
   11FIELDS TERMINATED BY '\t'
   12STORED AS TEXTFILE;
   13
   14LOAD DATA LOCAL INPATH 'ml-100k/u.data'
   15OVERWRITE INTO TABLE u_data;

2. 设置Hive属性，关联Druid

   SQL 复制

   1// 获取druid集群的元数据库信息：ssh root@public_ip 'cat /etc/druid/conf/_common/common.runtime.properties | grep "druid.metadata.storage.connector"'
   2// 跨集群访问druid集群的mysql，需要使用ip地址，若使用hostname需要更新hive集群的hosts文件
   3// 需要删除外表时开启，可以清空Druid数据: SET external.table.purge=true;
   4SET hive.druid.metadata.uri=jdbc:mysql://[druid_mysql_ip]:3306/druid?characterEncoding=UTF-8;
   5SET hive.druid.metadata.username=[druid_user];
   6SET hive.druid.metadata.password=[druid_passwd];
   7SET hive.druid.broker.address.default=[druid_broker_ip]:8590;
   8SET hive.druid.overlord.address.default=[druid_overlord_ip]:8591;
   9SET hive.druid.coordinator.address.default=[druid_coordinator_ip]:8592;
   10SET hive.druid.storage.storageDirectory=hdfs://[hive_hdfs_ip]:8020/user/druid/warehouses;

3. 方法一：先建表再导入数据

   SQL 复制

   1CREATE EXTERNAL TABLE druid_hive_demo_02
   2(`__time` TIMESTAMP WITH LOCAL TIME ZONE, userid STRING, moveid STRING, rating INT)
   3STORED BY 'org.apache.hadoop.hive.druid.DruidStorageHandler'
   4TBLPROPERTIES (
   5    "druid.segment.granularity" = "MONTH",
   6    "druid.query.granularity" = "DAY"
   7);
   8
   9INSERT INTO druid_hive_demo_02
   10SELECT
   11    cast (from_unixtime(cast(unixtime as int)) as timestamp with local time zone)  as `__time`,
   12    cast(userid as STRING) userid,
   13    cast(movieid as STRING) moveid,
   14    cast(rating as INT) rating
   15FROM u_data;

   方法二，使用CTAS语句创建Druid表并导入数据

   SQL 复制

   1// 首先打开Hive外表的CTAS开关
   2SET hive.ctas.external.tables=true;
   3
   4CREATE EXTERNAL TABLE druid_hive_demo_02
   5STORED BY 'org.apache.hadoop.hive.druid.DruidStorageHandler'
   6TBLPROPERTIES (
   7    "druid.segment.granularity" = "MONTH",
   8    "druid.query.granularity" = "DAY")
   9AS
   10SELECT
   11    cast (from_unixtime(cast(unixtime as int)) as timestamp with local time zone)  as `__time`,
   12    cast(userid as STRING) userid,
   13    cast(movieid as STRING) moveid,
   14    cast(rating as INT) rating
   15FROM u_data;

   如果CTAS语句执行失败，创建的表仍然存在，可以继续使用INSERT语句插入数据，或者删除该表并重新执行。

4. 查询结果

   SQL 复制

   1hive> SELECT * FROM druid_hive_demo_02 LIMIT 10;
   2OK
   31997-09-19 08:00:00.0 Asia/Shanghai	259	1074 3
   41997-09-19 08:00:00.0 Asia/Shanghai	259	108	 4
   51997-09-19 08:00:00.0 Asia/Shanghai	259	117	 4
   61997-09-19 08:00:00.0 Asia/Shanghai	259	173	 4
   71997-09-19 08:00:00.0 Asia/Shanghai	259	176	 4
   81997-09-19 08:00:00.0 Asia/Shanghai	259	185	 4
   91997-09-19 08:00:00.0 Asia/Shanghai	259	200	 4
   101997-09-19 08:00:00.0 Asia/Shanghai	259	210	 4
   111997-09-19 08:00:00.0 Asia/Shanghai	259	255	 4
   121997-09-19 08:00:00.0 Asia/Shanghai	259	286	 4

示例三：导入Kafka实时数据

1. 使用百度消息服务BMS创建主题 kafka_demo，并下载证书scp到druid集群各节点，参考BMS文档。
   

scp -rp kafka-key root@[druid集群的节点ip或hostname]:~/

2. 创建Hive表，通过属性关联BMS Kafka

   SQL 复制

   1// 获取druid集群的元数据库信息：ssh hdfs@public_ip 'cat /etc/druid/conf/_common/common.runtime.properties | grep "druid.metadata.storage.connector"'
   2// 跨集群访问druid集群的mysql，需要使用ip地址，若使用hostname需要更新hive集群的hosts文件
   3SET hive.druid.metadata.uri=jdbc:mysql://[druid_mysql_ip]:3306/druid?characterEncoding=UTF-8;
   4SET hive.druid.metadata.username=[druid_user];
   5SET hive.druid.metadata.password=[druid_passwd];
   6SET hive.druid.broker.address.default=[druid_broker_ip]:8590;
   7SET hive.druid.overlord.address.default=[druid_overlord_ip]:8591;
   8SET hive.druid.coordinator.address.default=[druid_coordinator_ip]:8592;
   9SET hive.druid.storage.storageDirectory=hdfs://[hive_hdfs_ip]:8020/user/druid/warehouses;
   10// Kafka Supervisor配置前缀是druid.kafka.ingestion
   11CREATE EXTERNAL TABLE druid_kafka_demo
   12(
   13    `__time`          TIMESTAMP WITH LOCAL TIME ZONE, 
   14    `channel`         STRING,
   15    `cityName`        STRING,
   16    `comment`         STRING,
   17    `countryIsoCode`  STRING,
   18    `isAnonymous`     STRING,
   19    `isMinor`         STRING,
   20    `isNew`           STRING,
   21    `isRobot`         STRING,
   22    `isUnpatrolled`   STRING,
   23    `metroCode`       STRING,
   24    `namespace`       STRING,
   25    `page`            STRING,
   26    `regionIsoCode`   STRING,
   27    `regionName`      STRING,
   28    `added`           INT,
   29    `user`            STRING,
   30    `deleted`         INT,
   31    `delta`           INT
   32)
   33STORED BY 'org.apache.hadoop.hive.druid.DruidStorageHandler'
   34TBLPROPERTIES (
   35    "kafka.bootstrap.servers" = "[kafka_address]:[kafka_port]",
   36    "kafka.security.protocol" = "SSL",
   37    "kafka.ssl.truststore.password" = "kafka",
   38    "kafka.ssl.truststore.location" =  "/home/hdfs/kafka-key/client.truststore.jks",
   39    "kafka.ssl.keystore.location" =  "/home/hdfs/kafka-key/client.keystore.jks",
   40    "kafka.ssl.keystore.password" =  "******",
   41    "kafka.topic" = "[account_id]__kafka_demo",
   42    "druid.kafka.ingestion.useEarliestOffset" = "true",
   43    "druid.kafka.ingestion.maxRowsInMemory" = "5",
   44    "druid.kafka.ingestion.startDelay" = "PT1S",
   45    "druid.kafka.ingestion.period" = "PT1S",
   46    "druid.kafka.ingestion.taskDuration" = "PT10M",
   47    "druid.kafka.ingestion.completionTimeout" = "PT20M",
   48    "druid.kafka.ingestion.consumer.retries" = "2"
   49);

   如果使用的不是开启了SSL的Kafka，可移除ssl相关配置，例如BMR集群里的Kafka

3. 启动Kafka生产者发送数据

   Plain Text 复制

   1sh bin/kafka-console-producer.sh --producer.config client.properties --topic druid_kafka_demo --sync --broker-list [kafka_address]:[kafka_port]
   2
   3{"__time":"2015-09-12T23:58:31.643Z","channel":"#en.wikipedia","cityName":null,"comment":"Notification: tagging for deletion of [[File:Axintele team.gif]]. ([[WP:TW|TW]])","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"User talk","page":"User talk:AlexGeorge33","regionIsoCode":null,"regionName":null,"user":"Sir Sputnik","delta":1921,"added":1921,"deleted":0}
   4{"__time":"2015-09-12T23:58:33.743Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Codiaeum finisterrae","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":18,"added":18,"deleted":0}
   5{"__time":"2015-09-12T23:58:35.732Z","channel":"#en.wikipedia","cityName":null,"comment":"Put info into infobox, succession box, split sections a bit","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Jamel Holley","regionIsoCode":null,"regionName":null,"user":"Mr. Matté","delta":3427,"added":3427,"deleted":0}
   6{"__time":"2015-09-12T23:58:38.531Z","channel":"#uz.wikipedia","cityName":null,"comment":"clean up, replaced: ozbekcha → oʻzbekcha, olchami → oʻlchami (3) using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":true,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Chambors","regionIsoCode":null,"regionName":null,"user":"Ximik1991Bot","delta":8,"added":8,"deleted":0}
   7{"__time":"2015-09-12T23:58:41.619Z","channel":"#it.wikipedia","cityName":null,"comment":"/* Gruppo Discovery Italia */","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":true,"metroCode":null,"namespace":"Main","page":"Deejay TV","regionIsoCode":null,"regionName":null,"user":"Ciosl","delta":4,"added":4,"deleted":0}
   8{"__time":"2015-09-12T23:58:43.304Z","channel":"#en.wikipedia","cityName":null,"comment":"/* Plot */","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Paper Moon (film)","regionIsoCode":null,"regionName":null,"user":"Siddharth Mehrotra","delta":0,"added":0,"deleted":0}
   9{"__time":"2015-09-12T23:58:46.732Z","channel":"#en.wikipedia","cityName":null,"comment":"/* Jeez */ delete (also fixed Si Trew's syntax error, as I think he meant to make a link there)","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Wikipedia","page":"Wikipedia:Redirects for discussion/Log/2015 September 12","regionIsoCode":null,"regionName":null,"user":"JaykeBird","delta":293,"added":293,"deleted":0}
   10{"__time":"2015-09-12T23:58:48.729Z","channel":"#fr.wikipedia","cityName":null,"comment":"MàJ","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":false,"isUnpatrolled":false,"metroCode":null,"namespace":"Wikipédia","page":"Wikipédia:Bons articles/Nouveau","regionIsoCode":null,"regionName":null,"user":"Gemini1980","delta":76,"added":76,"deleted":0}
   11{"__time":"2015-09-12T23:58:51.785Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Amata yezonis","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":10,"added":10,"deleted":0}
   12{"__time":"2015-09-12T23:58:53.898Z","channel":"#vi.wikipedia","cityName":null,"comment":"clean up using [[Project:AWB|AWB]]","countryIsoCode":null,"countryName":null,"isAnonymous":false,"isMinor":false,"isNew":false,"isRobot":true,"isUnpatrolled":false,"metroCode":null,"namespace":"Main","page":"Codia triverticillata","regionIsoCode":null,"regionName":null,"user":"ThitxongkhoiAWB","delta":36,"added":36,"deleted":0}

4. 可以通过DDL控制Kafka导入数据的启停

   SQL 复制

   1// 只有druid.kafka.ingestion为START，Hive才能够提交实时任务到Druid
   2ALTER TABLE druid_kafka_demo SET TBLPROPERTIES('druid.kafka.ingestion' = 'START');
   3ALTER TABLE druid_kafka_demo SET TBLPROPERTIES('druid.kafka.ingestion' = 'STOP');
   4ALTER TABLE druid_kafka_demo SET TBLPROPERTIES('druid.kafka.ingestion' = 'RESET');

5. 当第一个Segment上传成功，Coordinator显示出数据源后即可使用Hive查询数据，见示例一