一、批量加载数据到 Druid 集群
1. 在对应远程 Hadoop 集群上，以 Hadoop 用户执行以下新建目录命令：
hdfs dfs -mkdir /druidhdfs dfs -mkdir /druid/segmentshdfs dfs -mkdir /quickstarthdfs dfs -chmod 777 /druidhdfs dfs -chmod 777 /druid/segmentshdfs dfs -chmod 777 /quickstart
注意：如果 Druid 集群和 Hadoop 集群是两个独立集群，则目录需要建立在对应 Hadoop 集群上（之后的操作类似，注意分辨正确操作对应的集群）；如果在测试环境下 Druid 集群和 Hadoop 集群是同一个集群，则在同集群操作即可。
2. 上传测试包
Druid 集群下自带一个名为 Wikiticker 的数据集示例（默认路径 /usr/local/service/druid/quickstart/tutorial/wikiticker-2015-09-12-sampled.json.gz），将 Druid 集群内的数据集上传到对应远程 Hadoop 集群，是在远程 Hadoop 集群上传。
hdfs dfs -put wikiticker-2015-09-12-sampled.json.gz /quickstart/wikiticker-2015-09-12-sampled.json.gz
3. 编译索引文件
准备一个索引文件，仍然使用 Druid 集群的样例文件 /usr/local/service/druid/quickstart/tutorial/wikipedia-index-hadoop.json，命令如下：
{ "type" : "index_hadoop", "spec" : { "dataSchema" : { "dataSource" : "wikipedia", "parser" : { "type" : "hadoopyString", "parseSpec" : { "format" : "json", "dimensionsSpec" : { "dimensions" : [ "channel", "cityName", "comment", "countryIsoCode", "countryName", "isAnonymous", "isMinor", "isNew", "isRobot", "isUnpatrolled", "metroCode", "namespace", "page", "regionIsoCode", "regionName", "user", { "name": "added", "type": "long" }, { "name": "deleted", "type": "long" }, { "name": "delta", "type": "long" } ] }, "timestampSpec" : { "format" : "auto", "column" : "time" } } }, "metricsSpec" : [], "granularitySpec" : { "type" : "uniform", "segmentGranularity" : "day", "queryGranularity" : "none", "intervals" : ["2015-09-12/2015-09-13"], "rollup" : false } }, "ioConfig" : { "type" : "hadoop", "inputSpec" : { "type" : "static", "paths" : "/quickstart/wikiticker-2015-09-12-sampled.json.gz" } }, "tuningConfig" : { "type" : "hadoop", "partitionsSpec" : { "type" : "hashed", "targetPartitionSize" : 5000000 }, "forceExtendableShardSpecs" : true, "jobProperties" : { "yarn.nodemanager.vmem-check-enabled" : "false", "mapreduce.map.java.opts" : "-Duser.timezone=UTC -Dfile.encoding=UTF-8", "mapreduce.job.user.classpath.first" : "true", "mapreduce.reduce.java.opts" : "-Duser.timezone=UTC -Dfile.encoding=UTF-8", "mapreduce.map.memory.mb" : 1024, "mapreduce.reduce.memory.mb" : 1024 } } }, "hadoopDependencyCoordinates": ["org.apache.hadoop:hadoop-client:2.8.5"]}
说明：
·hadoopDependencyCoordinates 为依赖的 Hadoop 版本。
·spec.ioConfig.inputSpec.paths 为输入文件路径。如果已经在 common.runtime.properties 配置中设置好集群连通性，可以使用相对路径（可参考 Druid 使用）。否则，应该根据情况使用以hdfs://或者cosn://开头的相对路径。
·tuningConfig.jobProperties 参数可以设置 mapreduce job 的相关参数。
4. 提交索引任务
接下来可以在 Druid 集群上提交任务将数据摄入，在 Druid 目录下以 Hadoop 用户执行：
./bin/post-index-task –file quickstart/tutorial/wikipedia-index-hadoop.json –url http://localhost:8090
成功后则会有如下类似的输出：
…Task finished with status: SUCCESSCompleted indexing data for wikipedia. Now loading indexed data onto the cluster…wikipedia loading complete! You may now query your data
二、数据查询
Druid 支持类 SQL 和原生 JSON 查询，下面将分别介绍，更多内容可参考 产品文档。
三、sql 方式查询
Druid 支持多种 SQL 查询方式：
·在 Web UI 的 Query 菜单中查询。
SELECT page, COUNT(*) AS EditsFROM wikipediaWHERE TIMESTAMP '2015-09-12 00:00:00' <= "__time" AND "__time" < TIMESTAMP '2015-09-13 00:00:00'GROUP BY pageORDER BY Edits DESCLIMIT 10
·在查询节点上使用命令行工具bin/dsql进行交互式查询。
[hadoop@172 druid]$ ./bin/dsqlWelcome to dsql, the command-line client for Druid SQL.Connected to [http://localhost:8082/].Type "\\h" for help.dsql> SELECT page, COUNT(*) AS Edits FROM wikipedia WHERE "__time" BETWEEN TIMESTAMP '2015-09-12 00:00:00' AND TIMESTAMP '2015-09-13 00:00:00' GROUP BY page ORDER BY Edits DESC LIMIT 10;┌──────────────────────────────────────────────────────────┬───────┐│ page │ Edits │├──────────────────────────────────────────────────────────┼───────┤│ Wikipedia:Vandalismusmeldung │ 33 ││ User:Cyde/List of candidates for speedy deletion/Subpage │ 28 ││ Jeremy Corbyn │ 27 ││ Wikipedia:Administrators' noticeboard/Incidents │ 21 ││ Flavia Pennetta │ 20 ││ Total Drama Presents: The Ridonculous Race │ 18 ││ User talk:Dudeperson176123 │ 18 ││ Wikipédia:Le Bistro/12 septembre 2015 │ 18 ││ Wikipedia:In the news/Candidates │ 17 ││ Wikipedia:Requests for page protection │ 17 │└──────────────────────────────────────────────────────────┴───────┘Retrieved 10 rows in 0.06s.
·用 HTTP 服务查询 SQL。
curl -X 'POST' -H 'Content-Type:application/json' -d @quickstart/tutorial/wikipedia-top-pages-sql.json http://localhost:18888/druid/v2/sql
格式化后的输出结果：
[ { "page":"Wikipedia:Vandalismusmeldung", "Edits":33 }, { "page":"User:Cyde/List of candidates for speedy deletion/Subpage", "Edits":28 }, { "page":"Jeremy Corbyn", "Edits":27 }, { "page":"Wikipedia:Administrators' noticeboard/Incidents", "Edits":21 }, { "page":"Flavia Pennetta", "Edits":20 }, { "page":"Total Drama Presents: The Ridonculous Race", "Edits":18 }, { "page":"User talk:Dudeperson176123", "Edits":18 }, { "page":"Wikipédia:Le Bistro/12 septembre 2015", "Edits":18 }, { "page":"Wikipedia:In the news/Candidates", "Edits":17 }, { "page":"Wikipedia:Requests for page protection", "Edits":17 }]
四、原生 JSON 查询
·在 Web UI 上 Query 菜单直接输入 json 查询。
{ "queryType" : "topN", "dataSource" : "wikipedia", "intervals" : ["2015-09-12/2015-09-13"], "granularity" : "all", "dimension" : "page", "metric" : "count", "threshold" : 10, "aggregations" : [ { "type" : "count", "name" : "count" } ]}
·在查询节点上 druid 方式目录下用 HTTP 提交。
curl -X 'POST' -H 'Content-Type:application/json' -d @quickstart/tutorial/wikipedia-top-pages.json http://localhost:18888/druid/v2?pretty
输出结果：
[ {"timestamp" : "2015-09-12T00:46:58.771Z","result" : [ { "count" : 33, "page" : "Wikipedia:Vandalismusmeldung"}, { "count" : 28, "page" : "User:Cyde/List of candidates for speedy deletion/Subpage"}, { "count" : 27, "page" : "Jeremy Corbyn"}, { "count" : 21, "page" : "Wikipedia:Administrators' noticeboard/Incidents"}, { "count" : 20, "page" : "Flavia Pennetta"}, { "count" : 18, "page" : "Total Drama Presents: The Ridonculous Race"}, { "count" : 18, "page" : "User talk:Dudeperson176123"}, { "count" : 18, "page" : "Wikipédia:Le Bistro/12 septembre 2015"}, { "count" : 17, "page" : "Wikipedia:In the news/Candidates"}, { "count" : 17, "page" : "Wikipedia:Requests for page protection"} ]} ]