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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 the Bit.ly USA Government website statistics stream. You can see the stream here, and read about the stream here . This is a feed of json data that gets updated whenever anyone clicks a bit.ly shortened USA.gov website. A typical event might look something like this:
{
"user_agent": "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0)",
"country": "US",
"known_user": 1,
"timezone": "America/New_York",
"geo_region": "DC",
"global_bitly_hash": "17ctAFs",
"encoding_user_bitly_hash": "17ctAFr",
"encoding_user_login": "senrubiopress",
"aaccept_language": "en-US",
"short_url_cname": "1.usa.gov",
"referring_url": "http://t.co/4Av4NUFAYq",
"long_url": "http://www.rubio.senate.gov/public/index.cfm/fighting-for-florida?ID=c8357d12-9da8-4e9d-b00d-7168e1bf3599",
"timestamp": 1372190407,
"timestamp of time hash was created": 1372190097,
"city": "Washington",
"latitude_longitude": [ 38.893299, -77.014603 ]
}
The "known_user" field is always 1 or 0. It is 1 if the user is known to the server, and 0 otherwise. We will use this field extensively in this demo.
h2. Setting Up
There are two ways to setup Druid: download a tarball, or Build From Source. You only need to do one of these.
h3. 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-0.6.20
You should see a bunch of files:
- run_example_server.sh
- run_example_client.sh
- LICENSE, config, examples, lib directories
h2. Running Example Scripts Let's start doing stuff. You can start a Druid Realtime node by issuing:
./run_example_server.sh
Select "webstream". 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.
Jul 19, 2013 21:54:05 PM com.sun.jersey.guice.spi.container.GuiceComponentProviderFactory getComponentProvider
INFO: Binding io.druid.server.StatusResource to GuiceManagedComponentProvider with the scope "Undefined"
2013-07-19 21:54:05,246 INFO org.mortbay.log - Started SelectChannelConnector@0.0.0.0:8083
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. To query the real-time node you've spun up, you can issue:
./run_example_client.sh
Select "webstream" once again. This script issues GroupByQueries to the data we've been ingesting. The query looks like this:
{
"queryType": "groupBy",
"dataSource": "webstream",
"granularity": "minute",
"dimensions": [ "timezone" ],
"aggregations": [
{ "type": "count", "name": "rows" },
{ "type": "doubleSum", "fieldName": "known_users", "name": "known_users" }
],
"filter": { "type": "selector", "dimension": "country", "value": "US" },
"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: . We are filtering via the "country" dimension, to only look at website hits in the US. Our aggregations are what we are calculating: a row count, and the sum of the number of known users in our data.
The result looks something like this:
[
{
"version": "v1",
"timestamp": "2013-07-18T19:39:00.000Z",
"event": { "timezone": "America/Chicago", "known_users": 10, "rows": 15 }
},
{
"version": "v1",
"timestamp": "2013-07-18T19:39:00.000Z",
"event": { "timezone": "America/Los_Angeles", "known_users": 0, "rows": 3 }
},
...
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.
h2. 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": "webstream"
}
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": "2013-07-18T19:39:00.000Z",
"result": {
"minTime": "2013-07-18T19:39:00.000Z",
"maxTime": "2013-07-18T19:46:00.000Z"
}
}
]
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": "webstream",
"intervals": [ "2010-01-01/2020-01-01" ],
"granularity": "all",
"aggregations": [
{ "type": "count", "name": "rows" },
{ "type": "doubleSum", "fieldName": "known_users", "name": "known_users" }
]
}
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" : "2013-07-18T19:39:00.000Z",
"result" : { "known_users" : 787.0, "rows" : 2004 }
}
]
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": "webstream",
"intervals": [ "2010-01-01/2020-01-01" ],
"granularity": "minute",
"aggregations": [
{ "type": "count", "name": "rows" },
{ "type": "doubleSum", "fieldName": "known_users", "name": "known_users" }
]
}
This gives us something like the following:
[
{
"timestamp": "2013-07-18T19:39:00.000Z",
"result": { "known_users": 33, "rows": 76 }
},
{
"timestamp": "2013-07-18T19:40:00.000Z",
"result": { "known_users": 105, "rows": 221 }
},
{
"timestamp": "2013-07-18T19:41:00.000Z",
"result": { "known_users": 53, "rows": 167 }
},
...
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 states in the US are, ordered by the number of visits by known users over the last few minutes? 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": "webstream",
"granularity": "all",
"dimensions": [ "geo_region" ],
"orderBy": {
"type": "default",
"columns": [
{ "dimension": "known_users", "direction": "DESCENDING" }
],
"limit": 10
},
"aggregations": [
{ "type": "count", "name": "rows" },
{ "type": "doubleSum", "fieldName": "known_users", "name": "known_users" }
],
"filter": { "type": "selector", "dimension": "country", "value": "US" },
"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 OrderBy 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. For my stream, it looks like this:
[
{
"version": "v1",
"timestamp": "2012-10-01T00:00:00.000Z",
"event": { "geo_region": "RI", "known_users": 359, "rows": 143 }
},
{
"version": "v1",
"timestamp": "2012-10-01T00:00:00.000Z",
"event": { "geo_region": "NY", "known_users": 187, "rows": 322 }
},
{
"version": "v1",
"timestamp": "2012-10-01T00:00:00.000Z",
"event": { "geo_region": "CA", "known_users": 145, "rows": 466 }
},
{
"version": "v1",
"timestamp": "2012-10-01T00:00:00.000Z",
"event": { "geo_region": "IL", "known_users": 121, "rows": 185 }
},
...
Feel free to tweak other query parameters to answer other questions you may have about the 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, please read the other links in our wiki.
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.