Dear Kettle users,
Most of you usually use a data integration engine to process data in a batch-oriented way. Pentaho Data Integration (Kettle) is typically deployed to run monthly, nightly, hourly workloads. Sometimes folks run micro-batches of work every minute or so. However, it’s lesser known that our beloved transformation engine can also be used to stream data indefinitely (never ending) from a source to a target. This sort of data integration is sometimes referred to as being “streaming“, “real-time“, “near real-time“, “continuous” and so on. Typical examples of situations where you have a never-ending supply of data that needs to be processed the instance it becomes available are JMS (Java Message Service), RDBMS log sniffing, on-line fraud analyses, web or application log sniffing or of-course … Twitter! Since Twitter is easily accessed it’s common for examples to pop up regarding it’s usage and in this blog post too we will use this service to demo the Pentaho Data Integration capabilities wrt to processing streaming data.
Here’s what we want to do:
- Continuously read all the tweets that are being sent on Twitter.
- Extract all the hash-tags used
- Count the number of hash-tags used in a one-minute time-window
- Report on all the tags that are being used more than once
- Put the output in a browser window, continuously update every minute.
This is a very generic example but the logic of this can be applied to different fields like JMS, HL7, log sniffing and so on. It differs from the excellent work that Vincent from Open-BI described earlier this week on his blog in the sense that his Talend job finishes where ours will never end and where ours will do time-based aggregation in contrast to aggregation over a finite data set.
Also note that in order for Kettle to fully support multiple streaming data sources we would have to implement support for “windowed” (time-based) joins and other nifty things. We’ve seen very little demand for this sort of requirement in the past, perhaps because people don’t know it’s possible with Kettle. In any case, if you currently are in need of full streaming data support, have a look at SQLStream, they can help you. SQLStream is co-founded by Pentaho’s Julian Hyde of Mondrian fame.
OK, let’s see how we can solve our little problem with Kettle instead…
1. Continuously read all the tweets that are being sent on Twitter.
For this we are going to use one of the public Twitter web services, one that delivers a never-ending stream of JSON messages:
http://stream.twitter.com/1/statuses/sample.json?delimited=length
Since the format of the output is never-ending and specific in nature I wrote a small “User Defined Java Class” script:
public boolean processRow(StepMetaInterface smi, StepDataInterface sdi) throws KettleException
{
HttpClient client = SlaveConnectionManager.getInstance().createHttpClient();
client.setTimeout(10000);
client.setConnectionTimeout(10000);
Credentials creds = new UsernamePasswordCredentials(getParameter("USERNAME"), getParameter("PASSWORD"));
client.getState().setCredentials(AuthScope.ANY, creds);
client.getParams().setAuthenticationPreemptive(true);
HttpMethod method = new PostMethod("http://stream.twitter.com/1/statuses/sample.json?delimited=length");
// Execute request
//
InputStream inputStream=null;
BufferedInputStream bufferedInputStream=null;
try {
int result = client.executeMethod(method);
// the response
//
inputStream = method.getResponseBodyAsStream();
bufferedInputStream = new BufferedInputStream(inputStream, 1000);
StringBuffer bodyBuffer = new StringBuffer();
int opened=0;
int c;
while ( (c=bufferedInputStream.read())!=-1 && !isStopped()) {
char ch = (char)c;
bodyBuffer.append(ch);
if (ch=='{') opened++; else if (ch=='}') opened--;
if (ch=='}' && opened==0) {
// one JSON block, pass it on!
//
Object[] r = createOutputRow(new Object[0], data.outputRowMeta.size());
String jsonString = bodyBuffer.toString();
int startIndex = jsonString.indexOf("{");
if (startIndex<0) startIndex=0;
// System.out.print("index="+startIndex+" json="+jsonString.substring(startIndex));
r[0] = jsonString.substring(startIndex);
putRow(data.outputRowMeta, r);
bodyBuffer.setLength(0);
}
}
} catch(Exception e) {
throw new KettleException("Unable to get tweets", e);
} finally {
bufferedInputStream.reset();
bufferedInputStream.close();
}
setOutputDone();
return false;
}
As the experienced UDJC writers among you will notice: this step never ends as long as the twitter service keeps on sending more data. Depending on the stability and popularity of twitter that can be “a very long time“.
You could improve the code even further to re-connect to the service every time it drops away. Personally I would not do this. I would rather have the transformation terminate with an error (as it is implemented now), send an alert (e-mail, database, SNMP) and re-start the transformation in a loop in a job. That way you have a trace in case twitter dies for a few hours.
2. Extract all the hash-tags used
First we’ll parse the JSON returned by the twitter service, extract the first 5 hash-tags from the message, split this up into 5 rows and count the tags…
3. Count the number of hash-tags used in a one-minute time-window
The counting is easy as you can simply use a “Group by” step. However, how can we aggregate in a time-based fashion without too much tinkering? Well, we now have the “Single Threader” step which has the option to aggregate in a time-based manner so we might as well use this option:
This step simply accumulates all records in memory until 60 seconds have passed and then performs one iteration of the single threaded execution of the specified transformation. This is a special execution method that doesn’t use the typical parallel engine. Another cool thing about this engine is that the records that go into the engine in the time-window can be grouped and sorted without the transformation being restarted every minute.
4. Report on all the tags that are being used more than once
The filtering is done with a simple “Filter Rows” step. However, thanks to the magic of the “Single Threader” step we can sort the rows descending by the tag occurrence count in that one-minute time-window. It’s also interesting to note that if you have huge amounts of data, that you can easily parallelize your work by starting multiple copies of the single threader step and/or with some clever data partitioning. In our case we could partition by hash-tag or re-aggregate the aggregated data.
5. Put the output in a browser window, continuously update every minute.
As shown in an earlier blog post, we can do this quite easily with a “Text File Output” step. However, we also want to put a small header and a separator between the data from every minute so we end up with a transformation that looks like this:
The script to print the header looks like this:
var out;
if (out==null) {
out = _step_.getTrans().getServletPrintWriter();
out.println("'Real-time' twitter hashtag report, minute based");
out.flush();
}
The separator between each minute is simple too:
if (nr==1) {
var out = _step_.getTrans().getServletPrintWriter();
out.println("============================================");
out.println();
out.flush();
}
You can execute this transformation on a Carte instance (4.2.0) and see the following output:
'Real-time' twitter hashtag report, minute based
=================================================
nr;hashtag;count;from;to
1;tatilmayonezi;5;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
2;AUGUST6THBUZZNIGHTCLUB;3;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
3;teamfollowback;3;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
4;ayamzaman;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
5;dnd;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
6;follow;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
7;malhação;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
8;rappernames;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
9;thingswelearnedontwitter;2;2011/07/27 22:52:43.000;2011/07/27 22:53:32.000
=================================================
1;ska;5;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
2;followplanetjedward;4;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
3;chistede3pesos;3;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
4;NP;3;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
5;rappernames;3;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
6;tatilmayonezi;3;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
7;teamfollowback;3;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
8;AvrilBeatsVolcano;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
9;CM6;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
10;followme;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
11;Leão;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
12;NewArtists;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
13;OOMF;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
14;RETWEET;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
15;sougofollow;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
16;swag;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
17;thingswelearnedontwitter;2;2011/07/27 22:53:35.000;2011/07/27 22:54:47.000
...
For reference, I used the following URL to start the streaming report:
http://cluster:cluster@127.0.0.1:8282/kettle/executeTrans/?trans=%2Fhome%2Fmatt%2Ftest-stuff%2FTwitter Stream%2FRead a twitter stream.ktr&USERNAME=MyTwitterAccount&PASSWORD=MyPassword
I placed the complete example over here in case you want to try this yourself on PDI/Kettle version 4.2.0-RC1 or later. Things you can add to make it even cooler is to have this transformation send an e-mail every time a certain hash-tag gets used more than 15 times in a given minute. That sort of alerting support for example gives you easy access to emerging new trends, events and memes.
For reference, take a look at this earlier blog post of mine where I describe the internal cleanup mechanisms inside of Kettle that prevent our transformation from ever running out of memory or resources.
Until next time,
Matt
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