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Tutorial: A First Look at Druid

Greetings! This tutorial will help clarify some core Druid concepts. We will use a realtime dataset and issue some basic Druid queries. If you are ready to explore Druid, and learn a thing or two, read on!

About the data

The data source we'll be working with is Wikipedia edits. Each time an edit is made in Wikipedia, an event gets pushed to an IRC channel associated with the language of the Wikipedia page. We scrape IRC channels for several different languages and load this data into Druid.

Each event has a timestamp indicating the time of the edit (in UTC time), a list of dimensions indicating various metadata about the event (such as information about the user editing the page and where the user resides), and a list of metrics associated with the event (such as the number of characters added and deleted).

Specifically. the data schema looks like so:

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"

These metrics track the number of characters added, deleted, and changed.

Setting Up

There are two ways to setup Druid: download a tarball, or Build From Source. You only need to do one of these.

Download a Tarball

We've built a tarball that contains everything you'll need. You'll find it here. Download this file to a directory of your choosing.

You can extract the awesomeness within by issuing:

tar -zxvf druid-services-*-bin.tar.gz

Not too lost so far right? That's great! If you cd into the directory:

cd druid-services-0.6.160

You should see a bunch of files:

  • run_example_server.sh
  • run_example_client.sh
  • LICENSE, config, examples, lib directories

Setting up Zookeeper

Before we get started, we need to start Apache Zookeeper.

curl http://apache.osuosl.org/zookeeper/zookeeper-3.4.5/zookeeper-3.4.5.tar.gz -o zookeeper-3.4.5.tar.gz
tar xzf zookeeper-3.4.5.tar.gz
cd zookeeper-3.4.5
cp conf/zoo_sample.cfg conf/zoo.cfg
./bin/zkServer.sh start
cd ..

Running Example Scripts

Let's start doing stuff. You can start a Druid Realtime node by issuing:

./run_example_server.sh

Select "wikipedia".

Note that the first time you start the example, it may take some extra time due to its fetching various dependencies. Once the node starts up you will see a bunch of logs about setting up properties and connecting to the data source. If everything was successful, you should see messages of the form shown below.

2013-09-04 19:33:11,922 INFO [main] org.eclipse.jetty.server.AbstractConnector - Started SelectChannelConnector@0.0.0.0:8083
2013-09-04 19:33:11,946 INFO [ApiDaemon] io.druid.segment.realtime.firehose.IrcFirehoseFactory - irc connection to server [irc.wikimedia.org] established
2013-09-04 19:33:11,946 INFO [ApiDaemon] io.druid.segment.realtime.firehose.IrcFirehoseFactory - Joining channel #en.wikipedia
2013-09-04 19:33:11,946 INFO [ApiDaemon] io.druid.segment.realtime.firehose.IrcFirehoseFactory - Joining channel #fr.wikipedia
2013-09-04 19:33:11,946 INFO [ApiDaemon] io.druid.segment.realtime.firehose.IrcFirehoseFactory - Joining channel #de.wikipedia
2013-09-04 19:33:11,946 INFO [ApiDaemon] io.druid.segment.realtime.firehose.IrcFirehoseFactory - Joining channel #ja.wikipedia

The Druid real time-node ingests events in an in-memory buffer. Periodically, these events will be persisted to disk. If you are interested in the details of our real-time architecture and why we persist indexes to disk, I suggest you read our White Paper.

Okay, things are about to get real-time. To query the real-time node you've spun up, you can issue:

./run_example_client.sh

Select "wikipedia" once again. This script issues GroupByQueries to the data we've been ingesting. The query looks like this:

{
   "queryType":"groupBy",
   "dataSource":"wikipedia",
   "granularity":"minute",
   "dimensions":[ "page" ],
   "aggregations":[
      {"type":"count", "name":"rows"},
      {"type":"longSum", "fieldName":"count", "name":"edit_count"}
   ],
   "filter":{ "type":"selector", "dimension":"namespace", "value":"article" },
   "intervals":[ "2013-06-01T00:00/2020-01-01T00" ]
}

This is a groupBy query, which you may be familiar with from SQL. We are grouping, or aggregating, via the dimensions field: ["page"]. We are filtering via the namespace dimension, to only look at edits on articles. Our aggregations are what we are calculating: a count of the number of data rows, and a count of the number of edits that have occurred.

The result looks something like this (when it's prettified):

[
  {
    "version": "v1",
    "timestamp": "2013-09-04T21:44:00.000Z",
    "event": { "count": 0, "page": "2013\u201314_Brentford_F.C._season", "rows": 1 }
  },
  {
    "version": "v1",
    "timestamp": "2013-09-04T21:44:00.000Z",
    "event": { "count": 0, "page": "8e_\u00e9tape_du_Tour_de_France_2013", "rows": 1 }
  },
  {
    "version": "v1",
    "timestamp": "2013-09-04T21:44:00.000Z",
    "event": { "count": 0, "page": "Agenda_of_the_Tea_Party_movement", "rows": 1 }
  },
...

This groupBy query is a bit complicated and we'll return to it later. For the time being, just make sure you are getting some blocks of data back. If you are having problems, make sure you have curl installed. Control+C to break out of the client script.

Querying Druid

In your favorite editor, create the file:

time_boundary_query.body

Druid queries are JSON blobs which are relatively painless to create programmatically, but an absolute pain to write by hand. So anyway, we are going to create a Druid query by hand. Add the following to the file you just created:

{
    "queryType": "timeBoundary", 
    "dataSource": "wikipedia"
}

The TimeBoundaryQuery is one of the simplest Druid queries. To run the query, you can issue:

curl -X POST 'http://localhost:8083/druid/v2/?pretty' -H 'content-type: application/json' -d @time_boundary_query.body

We get something like this JSON back:

[ {
  "timestamp" : "2013-09-04T21:44:00.000Z",
  "result" : {
    "minTime" : "2013-09-04T21:44:00.000Z",
    "maxTime" : "2013-09-04T21:47:00.000Z"
  }
} ]

As you can probably tell, the result is indicating the maximum and minimum timestamps we've seen thus far (summarized to a minutely granularity). Let's explore a bit further.

Return to your favorite editor and create the file:

timeseries_query.body

We are going to make a slightly more complicated query, the TimeseriesQuery. Copy and paste the following into the file:

{
    "queryType": "timeseries", 
    "dataSource": "wikipedia", 
    "intervals": [ "2010-01-01/2020-01-01" ], 
    "granularity": "all", 
    "aggregations": [
        {"type": "longSum", "fieldName": "count", "name": "edit_count"}, 
        {"type": "doubleSum", "fieldName": "added", "name": "chars_added"}
    ]
}

You are probably wondering, what are these Granularities and Aggregations things? What the query is doing is aggregating some metrics over some span of time. To issue the query and get some results, run the following in your command line:

curl -X POST 'http://localhost:8083/druid/v2/?pretty' -H 'content-type: application/json'  -d  @timeseries_query.body

Once again, you should get a JSON blob of text back with your results, that looks something like this:

[ {
 "timestamp" : "2013-09-04T21:44:00.000Z",
 "result" : { "chars_added" : 312670.0, "edit_count" : 733 }
} ]

If you issue the query again, you should notice your results updating.

Right now all the results you are getting back are being aggregated into a single timestamp bucket. What if we wanted to see our aggregations on a per minute basis? What field can we change in the query to accomplish this?

If you loudly exclaimed "we can change granularity to minute", you are absolutely correct! We can specify different granularities to bucket our results, like so:

{
  "queryType": "timeseries", 
  "dataSource": "wikipedia", 
  "intervals": [ "2010-01-01/2020-01-01" ], 
  "granularity": "minute", 
  "aggregations": [
     {"type": "longSum", "fieldName": "count", "name": "edit_count"}, 
     {"type": "doubleSum", "fieldName": "added", "name": "chars_added"}
  ]
}

This gives us something like the following:

[
 {
   "timestamp" : "2013-09-04T21:44:00.000Z",
   "result" : { "chars_added" : 30665.0, "edit_count" : 128 }
 }, 
 {
   "timestamp" : "2013-09-04T21:45:00.000Z",
   "result" : { "chars_added" : 122637.0, "edit_count" : 167 }
 }, 
 {
   "timestamp" : "2013-09-04T21:46:00.000Z",
   "result" : { "chars_added" : 78938.0, "edit_count" : 159 }
 },
...
]

Solving a Problem

One of Druid's main powers is to provide answers to problems, so let's pose a problem. What if we wanted to know what the top pages in the US are, ordered by the number of edits over the last few minutes you've been going through this tutorial? To solve this problem, we have to return to the query we introduced at the very beginning of this tutorial, the GroupByQuery. It would be nice if we could group by results by dimension value and somehow sort those results... and it turns out we can!

Let's create the file:

group_by_query.body

and put the following in there:

{
  "queryType": "groupBy", 
  "dataSource": "wikipedia", 
  "granularity": "all", 
  "dimensions": [ "page" ], 
  "limitSpec": {
     "type": "default", 
     "columns": [ { "dimension": "edit_count", "direction": "DESCENDING" } ], 
     "limit": 10
  }, 
  "aggregations": [
    {"type": "longSum", "fieldName": "count", "name": "edit_count"}
  ], 
  "filter": { "type": "selector", "dimension": "country", "value": "United States" }, 
  "intervals": ["2012-10-01T00:00/2020-01-01T00"]
}

Woah! Our query just got a way more complicated. Now we have these Filters things and this LimitSpec thing. Fear not, it turns out the new objects we've introduced to our query can help define the format of our results and provide an answer to our question.

If you issue the query:

curl -X POST 'http://localhost:8083/druid/v2/?pretty' -H 'content-type: application/json'  -d @group_by_query.body

You should see an answer to our question. As an example, some results are shown below:

[
 {
   "version" : "v1",
   "timestamp" : "2012-10-01T00:00:00.000Z",
   "event" : { "page" : "RTC_Transit", "edit_count" : 6 }
 }, 
 {
   "version" : "v1",
   "timestamp" : "2012-10-01T00:00:00.000Z",
   "event" : { "page" : "List_of_Deadly_Women_episodes", "edit_count" : 4 }
 }, 
 {
   "version" : "v1",
   "timestamp" : "2012-10-01T00:00:00.000Z",
   "event" : { "page" : "User_talk:David_Biddulph", "edit_count" : 4 }
 },
...

Feel free to tweak other query parameters to answer other questions you may have about the data.

Next Steps

Want to know even more information about the Druid Cluster? Check out The Druid Cluster.

Druid is even more fun if you load your own data into it! To learn how to load your data, see Loading Your Data.

Additional Information

This tutorial is merely showcasing a small fraction of what Druid can do. If you are interested in more information about Druid, including setting up a more sophisticated Druid cluster, read more of the Druid documentation and the blogs found on druid.io.

And thus concludes our journey! Hopefully you learned a thing or two about Druid real-time ingestion, querying Druid, and how Druid can be used to solve problems. If you have additional questions, feel free to post in our google groups page.