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Setup
Before we start querying druid, we're going to finish setting up a complete cluster on localhost. In Loading Your Data we setup a Realtime, Compute and Master node. If you've already completed that tutorial, you need only follow the directions for 'Booting a Broker Node'.
Booting a Broker Node
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Setup a config file at config/broker/runtime.properties that looks like this:
druid.host=0.0.0.0:8083 druid.port=8083 com.metamx.emitter.logging=true druid.processing.formatString=processing_%s druid.processing.numThreads=1 druid.processing.buffer.sizeBytes=10000000 #emitting, opaque marker druid.service=example druid.request.logging.dir=/tmp/example/log druid.realtime.specFile=realtime.spec com.metamx.emitter.logging=true com.metamx.emitter.logging.level=debug # below are dummy values when operating a realtime only node druid.processing.numThreads=3 com.metamx.aws.accessKey=dummy_access_key com.metamx.aws.secretKey=dummy_secret_key druid.pusher.s3.bucket=dummy_s3_bucket druid.zk.service.host=localhost druid.server.maxSize=300000000000 druid.zk.paths.base=/druid druid.database.segmentTable=prod_segments druid.database.user=druid druid.database.password=diurd druid.database.connectURI=jdbc:mysql://localhost:3306/druid druid.zk.paths.discoveryPath=/druid/discoveryPath druid.database.ruleTable=rules druid.database.configTable=config # Path on local FS for storage of segments; dir will be created if needed druid.paths.indexCache=/tmp/druid/indexCache # Path on local FS for storage of segment metadata; dir will be created if needed druid.paths.segmentInfoCache=/tmp/druid/segmentInfoCache druid.pusher.local.storageDirectory=/tmp/druid/localStorage druid.pusher.local=true # thread pool size for servicing queries druid.client.http.connections=30 -
Run the broker node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 \ -Ddruid.realtime.specFile=realtime.spec \ -classpath services/target/druid-services-0.5.50-SNAPSHOT-selfcontained.jar:config/broker \ com.metamx.druid.http.BrokerMain
Booting a Master Node
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Setup a config file at config/master/runtime.properties that looks like this: https://gist.github.com/rjurney/5818870
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Run the master node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 \ -classpath services/target/druid-services-0.5.50-SNAPSHOT-selfcontained.jar:config/master \ com.metamx.druid.http.MasterMain
Booting a Realtime Node
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Setup a config file at config/realtime/runtime.properties that looks like this: https://gist.github.com/rjurney/5818774
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Setup a realtime.spec file like this: https://gist.github.com/rjurney/5818779
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Run the realtime node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 \ -Ddruid.realtime.specFile=realtime.spec \ -classpath services/target/druid-services-0.5.50-SNAPSHOT-selfcontained.jar:config/realtime \ com.metamx.druid.realtime.RealtimeMain
Booting a Compute Node
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Setup a config file at config/compute/runtime.properties that looks like this: https://gist.github.com/rjurney/5818885
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Run the compute node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 \ -classpath services/target/druid-services-0.5.50-SNAPSHOT-selfcontained.jar:config/compute \ com.metamx.druid.http.ComputeMain
Querying Your Data
Now that we have a complete cluster setup on localhost, we need to load data. To do so, refer to Loading Your Data. Having done that, its time to query our data! For a complete specification of queries, see Querying.
Querying Different Nodes
As a shared-nothing system, there are three ways to query druid, against the Realtime, Compute or Broker node. Querying a Realtime node returns only realtime data, querying a compute node returns only historical segments. Querying the broker will query both realtime and compute segments and compose an overall result for the query. This is the normal mode of operation for queries in druid.
Construct a Query
For constructing this query, see: Querying against the realtime.spec
{
"queryType": "groupBy",
"dataSource": "druidtest",
"granularity": "all",
"dimensions": [],
"aggregations": [
{"type": "count", "name": "rows"},
{"type": "longSum", "name": "imps", "fieldName": "impressions"},
{"type": "doubleSum", "name": "wp", "fieldName": "wp"}
],
"intervals": ["2010-01-01T00:00/2020-01-01T00"]
}
Querying the Realtime Node
Run our query against port 8080:
curl -X POST "http://localhost:8080/druid/v2/?pretty" -H 'content-type: application/json' -d @query.body
See our result:
[ {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 5, "wp" : 15000.0, "rows" : 5 }
} ]
Querying the Compute Node
Run the query against port 8082:
curl -X POST "http://localhost:8082/druid/v2/?pretty" -H 'content-type: application/json' -d @query.body
And get (similar to):
[ {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 27, "wp" : 77000.0, "rows" : 9 }
} ]
Querying both Nodes via the Broker
Run the query against port 8083:
curl -X POST "http://localhost:8083/druid/v2/?pretty" -H 'content-type: application/json' -d @query.body
And get:
[ {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 5, "wp" : 15000.0, "rows" : 5 }
} ]
Now that we know what nodes can be queried (although you should usually use the broker node), lets learn how to know what queries are available.
Querying Against the realtime.spec
How are we to know what queries we can run? Although Querying is a helpful index, to get a handle on querying our data we need to look at our Realtime node's realtime.spec file:
[{
"schema" : { "dataSource":"druidtest",
"aggregators":[ {"type":"count", "name":"impressions"},
{"type":"doubleSum","name":"wp","fieldName":"wp"}],
"indexGranularity":"minute",
"shardSpec" : { "type": "none" } },
"config" : { "maxRowsInMemory" : 500000,
"intermediatePersistPeriod" : "PT10m" },
"firehose" : { "type" : "kafka-0.7.2",
"consumerProps" : { "zk.connect" : "localhost:2181",
"zk.connectiontimeout.ms" : "15000",
"zk.sessiontimeout.ms" : "15000",
"zk.synctime.ms" : "5000",
"groupid" : "topic-pixel-local",
"fetch.size" : "1048586",
"autooffset.reset" : "largest",
"autocommit.enable" : "false" },
"feed" : "druidtest",
"parser" : { "timestampSpec" : { "column" : "utcdt", "format" : "iso" },
"data" : { "format" : "json" },
"dimensionExclusions" : ["wp"] } },
"plumber" : { "type" : "realtime",
"windowPeriod" : "PT10m",
"segmentGranularity":"hour",
"basePersistDirectory" : "/tmp/realtime/basePersist",
"rejectionPolicy": {"type": "messageTime"} }
}]
dataSource
"dataSource":"druidtest"
Our dataSource tells us the name of the relation/table, or 'source of data', to query in both our realtime.spec and query.body!
aggregations
Note the Aggregations in our query:
"aggregations": [
{"type": "count", "name": "rows"},
{"type": "longSum", "name": "imps", "fieldName": "impressions"},
{"type": "doubleSum", "name": "wp", "fieldName": "wp"}
],
this matches up to the aggregators in the schema of our realtime.spec!
"aggregators":[ {"type":"count", "name":"impressions"},
{"type":"doubleSum","name":"wp","fieldName":"wp"}],
dimensions
Lets look back at our actual records (from [Loading Your Data](Loading Your Data.html)):
{"utcdt": "2010-01-01T01:01:01", "wp": 1000, "gender": "male", "age": 100}
{"utcdt": "2010-01-01T01:01:02", "wp": 2000, "gender": "female", "age": 50}
{"utcdt": "2010-01-01T01:01:03", "wp": 3000, "gender": "male", "age": 20}
{"utcdt": "2010-01-01T01:01:04", "wp": 4000, "gender": "female", "age": 30}
{"utcdt": "2010-01-01T01:01:05", "wp": 5000, "gender": "male", "age": 40}
Note that we have two dimensions to our data, other than our primary metric, wp. They are 'gender' and 'age'. We can specify these in our query! Note that we have added a dimension: age, below.
{
"queryType": "groupBy",
"dataSource": "druidtest",
"granularity": "all",
"dimensions": ["age"],
"aggregations": [
{"type": "count", "name": "rows"},
{"type": "longSum", "name": "imps", "fieldName": "impressions"},
{"type": "doubleSum", "name": "wp", "fieldName": "wp"}
],
"intervals": ["2010-01-01T00:00/2020-01-01T00"]
}
Which gets us grouped data in return!
[ {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 1, "age" : "100", "wp" : 1000.0, "rows" : 1 }
}, {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 1, "age" : "20", "wp" : 3000.0, "rows" : 1 }
}, {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 1, "age" : "30", "wp" : 4000.0, "rows" : 1 }
}, {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 1, "age" : "40", "wp" : 5000.0, "rows" : 1 }
}, {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 1, "age" : "50", "wp" : 2000.0, "rows" : 1 }
} ]
filtering
Now that we've observed our dimensions, we can also filter:
{
"queryType": "groupBy",
"dataSource": "druidtest",
"granularity": "all",
"filter": { "type": "selector", "dimension": "gender", "value": "male" },
"aggregations": [
{"type": "count", "name": "rows"},
{"type": "longSum", "name": "imps", "fieldName": "impressions"},
{"type": "doubleSum", "name": "wp", "fieldName": "wp"}
],
"intervals": ["2010-01-01T00:00/2020-01-01T00"]
}
Which gets us just people aged 40:
[ {
"version" : "v1",
"timestamp" : "2010-01-01T00:00:00.000Z",
"event" : { "imps" : 3, "wp" : 9000.0, "rows" : 3 }
} ]
Check out Filters for more.
Learn More
You can learn more about querying at Querying! Now check out Booting a production cluster!