In this tutorial we will cover more advanced/real-world ingestion topics.
Druid can ingest streaming or batch data. Streaming data is ingested via the real-time node, and batch data is ingested via the Hadoop batch indexer. Druid also has a standalone ingestion service called the indexing service.
The data source we'll be using is (surprise!) Wikipedia edits. The data schema is still:
Dimensions (things to filter on):
"page" "language" "user" "unpatrolled" "newPage" "robot" "anonymous" "namespace" "continent" "country" "region" "city"
Metrics (things to aggregate over):
"count" "added" "delta" "deleted"
With real-world data, we recommend having a message bus such as Apache Kafka sit between the data stream and the real-time node. The message bus provides higher availability for production environments. Firehoses are the key abstraction for real-time ingestion.
KafkaFirehoseFactory is how druid communicates with Kafka. Using this Firehose with the right configuration, we can import data into Druid in real-time without writing any code. To load data to a real-time node via Kafka, we'll first need to initialize Zookeeper and Kafka, and then configure and initialize a Realtime node.
The following quick-start instructions for booting a Zookeeper and then Kafka cluster were taken from the Kafka website.
wget http://apache.spinellicreations.com/incubator/kafka/kafka-0.7.2-incubating/kafka-0.7.2-incubating-src.tgz tar -xvzf kafka-0.7.2-incubating-src.tgz cd kafka-0.7.2-incubating-src
./sbt update ./sbt package
cat config/zookeeper.properties bin/zookeeper-server-start.sh config/zookeeper.properties # in a new console bin/kafka-server-start.sh config/server.properties
bin/kafka-console-producer.sh --zookeeper localhost:2181 --topic wikipedia
When things are ready, you should see log messages such as:
[2013-10-09 22:03:07,802] INFO zookeeper state changed (SyncConnected) (org.I0Itec.zkclient.ZkClient)
You should be comfortable starting Druid nodes at this point. If not, it may be worthwhile to revisit the first few tutorials.
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -Ddruid.realtime.specFile=examples/indexing/wikipedia.spec -classpath lib/*:config/realtime io.druid.cli.Main server realtime
examples/indexing/wikipedia.spec
The contents of the file should match:
[ { "schema": { "dataSource": "wikipedia", "aggregators" : [{ "type" : "count", "name" : "count" }, { "type" : "doubleSum", "name" : "added", "fieldName" : "added" }, { "type" : "doubleSum", "name" : "deleted", "fieldName" : "deleted" }, { "type" : "doubleSum", "name" : "delta", "fieldName" : "delta" }], "indexGranularity": "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": "druid-example", "fetch.size": "1048586", "autooffset.reset": "largest", "autocommit.enable": "false" }, "feed": "wikipedia", "parser": { "timestampSpec": { "column": "timestamp" }, "data": { "format": "json", "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } } }, "plumber": { "type": "realtime", "windowPeriod": "PT10m", "segmentGranularity": "hour", "basePersistDirectory": "\/tmp\/realtime\/basePersist", "rejectionPolicy": { "type": "test" } } } ]
Note: This config uses a “test” rejection policy which will accept all events and timely hand off, however, we strongly recommend you do not use this in production. Using this rejection policy, segments for events for the same time range will be overridden.
{"timestamp": "2013-08-31T01:02:33Z", "page": "Gypsy Danger", "language" : "en", "user" : "nuclear", "unpatrolled" : "true", "newPage" : "true", "robot": "false", "anonymous": "false", "namespace":"article", "continent":"North America", "country":"United States", "region":"Bay Area", "city":"San Francisco", "added": 57, "deleted": 200, "delta": -143} {"timestamp": "2013-08-31T03:32:45Z", "page": "Striker Eureka", "language" : "en", "user" : "speed", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Australia", "country":"Australia", "region":"Cantebury", "city":"Syndey", "added": 459, "deleted": 129, "delta": 330} {"timestamp": "2013-08-31T07:11:21Z", "page": "Cherno Alpha", "language" : "ru", "user" : "masterYi", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"article", "continent":"Asia", "country":"Russia", "region":"Oblast", "city":"Moscow", "added": 123, "deleted": 12, "delta": 111} {"timestamp": "2013-08-31T11:58:39Z", "page": "Crimson Typhoon", "language" : "zh", "user" : "triplets", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"China", "region":"Shanxi", "city":"Taiyuan", "added": 905, "deleted": 5, "delta": 900} {"timestamp": "2013-08-31T12:41:27Z", "page": "Coyote Tango", "language" : "ja", "user" : "stringer", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"Japan", "region":"Kanto", "city":"Tokyo", "added": 1, "deleted": 10, "delta": -9}
Disclaimer: We recognize the timestamps of these events aren't actually recent.
... 2013-10-10 05:13:18,976 INFO [chief-wikipedia] io.druid.server.coordination.BatchDataSegmentAnnouncer - Announcing segment[wikipedia_2013-08-31T01:00:00.000Z_2013-08-31T02:00:00.000Z_2013-08-31T01:00:00.000Z] at path[/druid/segments/localhost:8083/2013-10-10T05:13:18.972Z0] 2013-10-10 05:13:18,992 INFO [chief-wikipedia] io.druid.server.coordination.BatchDataSegmentAnnouncer - Announcing segment[wikipedia_2013-08-31T03:00:00.000Z_2013-08-31T04:00:00.000Z_2013-08-31T03:00:00.000Z] at path[/druid/segments/localhost:8083/2013-10-10T05:13:18.972Z0] 2013-10-10 05:13:18,997 INFO [chief-wikipedia] io.druid.server.coordination.BatchDataSegmentAnnouncer - Announcing segment[wikipedia_2013-08-31T07:00:00.000Z_2013-08-31T08:00:00.000Z_2013-08-31T07:00:00.000Z] at path[/druid/segments/localhost:8083/2013-10-10T05:13:18.972Z0] 2013-10-10 05:13:19,003 INFO [chief-wikipedia] io.druid.server.coordination.BatchDataSegmentAnnouncer - Announcing segment[wikipedia_2013-08-31T11:00:00.000Z_2013-08-31T12:00:00.000Z_2013-08-31T11:00:00.000Z] at path[/druid/segments/localhost:8083/2013-10-10T05:13:18.972Z0] 2013-10-10 05:13:19,008 INFO [chief-wikipedia] io.druid.server.coordination.BatchDataSegmentAnnouncer - Announcing segment[wikipedia_2013-08-31T12:00:00.000Z_2013-08-31T13:00:00.000Z_2013-08-31T12:00:00.000Z] at path[/druid/segments/localhost:8083/2013-10-10T05:13:18.972Z0] ...
Issuing a TimeBoundaryQuery to the real-time node should yield valid results:
[ { "timestamp" : "2013-08-31T01:02:33.000Z", "result" : { "minTime" : "2013-08-31T01:02:33.000Z", "maxTime" : "2013-08-31T12:41:27.000Z" } } ]
Druid is designed for large data volumes, and most real-world data sets require batch indexing be done through a Hadoop job.
For this tutorial, we used Hadoop 1.0.3. There are many pages on the Internet showing how to set up a single-node (standalone) Hadoop cluster, which is all that's needed for this example.
For the purposes of this tutorial, we are going to use our very small and simple Wikipedia data set. This data can directly be ingested via other means as shown in the previous tutorial, but we are going to use Hadoop here for demonstration purposes.
Our data is located at:
examples/indexing/wikipedia_data.json
The following events should exist in the file:
{"timestamp": "2013-08-31T01:02:33Z", "page": "Gypsy Danger", "language" : "en", "user" : "nuclear", "unpatrolled" : "true", "newPage" : "true", "robot": "false", "anonymous": "false", "namespace":"article", "continent":"North America", "country":"United States", "region":"Bay Area", "city":"San Francisco", "added": 57, "deleted": 200, "delta": -143} {"timestamp": "2013-08-31T03:32:45Z", "page": "Striker Eureka", "language" : "en", "user" : "speed", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Australia", "country":"Australia", "region":"Cantebury", "city":"Syndey", "added": 459, "deleted": 129, "delta": 330} {"timestamp": "2013-08-31T07:11:21Z", "page": "Cherno Alpha", "language" : "ru", "user" : "masterYi", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"article", "continent":"Asia", "country":"Russia", "region":"Oblast", "city":"Moscow", "added": 123, "deleted": 12, "delta": 111} {"timestamp": "2013-08-31T11:58:39Z", "page": "Crimson Typhoon", "language" : "zh", "user" : "triplets", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"China", "region":"Shanxi", "city":"Taiyuan", "added": 905, "deleted": 5, "delta": 900} {"timestamp": "2013-08-31T12:41:27Z", "page": "Coyote Tango", "language" : "ja", "user" : "stringer", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"Japan", "region":"Kanto", "city":"Tokyo", "added": 1, "deleted": 10, "delta": -9}
To index the data, we are going to need an indexing service, a historical node, and a coordinator node.
Note: If Zookeeper and MySQL aren‘t running, you’ll have to start them again as described in The Druid Cluster.
To start the Indexing Service:
java -Xmx2g -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath lib/*:<hadoop_config_path>:config/overlord io.druid.cli.Main server overlord
To start the Coordinator Node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath lib/*:config/coordinator io.druid.cli.Main server coordinator
To start the Historical Node:
java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath lib/*:config/historical io.druid.cli.Main server historical
Before indexing the data, make sure you have a valid Hadoop cluster running. To build our Druid segment, we are going to submit a Hadoop index task to the indexing service. The grammar for the Hadoop index task is very similar to the index task of the last tutorial. The tutorial Hadoop index task should be located at:
examples/indexing/wikipedia_index_hadoop_task.json
Examining the contents of the file, you should find:
{ "type" : "index_hadoop", "config": { "dataSource" : "wikipedia", "timestampSpec" : { "column" : "timestamp", "format" : "auto" }, "dataSpec" : { "format" : "json", "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] }, "granularitySpec" : { "type" : "uniform", "gran" : "DAY", "intervals" : [ "2013-08-31/2013-09-01" ] }, "pathSpec" : { "type" : "static", "paths" : "examples/indexing/wikipedia_data.json" }, "targetPartitionSize" : 5000000, "rollupSpec" : { "aggs": [{ "type" : "count", "name" : "count" }, { "type" : "doubleSum", "name" : "added", "fieldName" : "added" }, { "type" : "doubleSum", "name" : "deleted", "fieldName" : "deleted" }, { "type" : "doubleSum", "name" : "delta", "fieldName" : "delta" }], "rollupGranularity" : "none" } } }
If you are curious about what all this configuration means, see here.
To submit the task:
curl -X 'POST' -H 'Content-Type:application/json' -d @examples/indexing/wikipedia_index_hadoop_task.json localhost:8087/druid/indexer/v1/task
After the task is completed, the segment should be assigned to your historical node. You should be able to query the segment.
We demonstrated using the indexing service as a way to ingest data into Druid. Previous versions of Druid used the HadoopDruidIndexer to ingest batch data. The HadoopDruidIndexer still remains a valid option for batch ingestion, however, we recommend using the indexing service as the preferred method of getting batch data into Druid.
For more information on querying, check out this tutorial.
Getting data into Druid can definitely be difficult for first time users. Please don't hesitate to ask questions in our IRC channel or on our google groups page.