Percona Live MySQL User's Conference, San Francisco, April 10-12th, 2012 Book: "Pentaho Kettle Solutions", Matt Casters, Roland Bouman, & Jos van Dongen, Wiley 2010 Book: "Pentaho Solutions", Roland Bouman & Jos van Dongen, Wiley 2009

Monday, May 30, 2011

Cleaning webpages with Pentaho Data Integration and JTidy

Here's an issue I've come across multiple times: I need to scrape HTML websites to extract data. Pentaho Data Integration (kettle) has lots of functionality on-board to make this an easy process, except one: it does not support reading data directly from HTML.

In this short post, I provide a simple tip to clean HTML pages and convert them to XML so you can extract its data using the conventional "Get data from XML" step. The solution hinges on two ingredients:

Standard Kettle tools for Webservices

Kettle is really good at fetching data from the web and extracting data from webservices, be they in a SOAP/XML, REST/JSON or RSS flavor. (There is an extensive chapter on this subject in Pentaho Kettle Solutions). But when you're dealing with plain old HTML, things can get pretty hairy.

If you're lucky, the page may be in XHTML, and in that case it's worth trying the Get Data from XML step. However, quite often a webpage that claims to be XHTML is not well-formed XML, and even if it is, Kettle does not understand things like   entities, which are valid in XHTML, but not in plain XML. And of course, more often than not, you're not lucky, and XHTML represents only a minor fraction of all the web pages out there.

Workaround: JavaScript string manipulation

In the past, I usually worked around these issues. In practice, some quick and dirty string manipulation using the Modified Javascript Value step and some built-in indexOf(), substring and replace() functions go a long way.

In most cases I don't really need the entire web page, but only a <table>, <ul> or <ol> element in the <body>. Excising only the interesting sections out of the page using plain string manipulation will often get rid of most of the cruft that prevents the data from being treated as XML. For example, if we only need to get the rows from a table with a particular id attribute, we can use a JavaScript snippet like this:

//table we're looking for
var startHandle = "<table class=\"lvw\" cellpadding=0 cellspacing=0>";
var startPosition= html.indexOf(startHandle);
//look beyond the start tag to lose the invalid unquoted attributes
startPosition += startHandle.length;

//find where this table ends (lucky us, no nested table elements :)
var endHandle = "</table>";
var endPosition = html.indexOf(endHandle, startPosition);

//make a complete table fragment out of it again
var table = "<table>" + html.substring(startPosition, endPosition + endHandle.length);

//replace nbsp entities, empty unclosed img elements, and value-less nowrap attributes
table = table.replace(/&nbsp;|<img[^>]>|nowrap/ig, "");


There are of course no guarantees that the sections you cut out like that are in fact well-formed XML, but in my experience it's often worth a try.

A better way: using JTidy

While the JavaScript workaround may just work for your particular case, it certainly has disadvantages. Sometimes it may just be not so simple to clean the HTML with plain string manipulation. And of course there is a performance issue too - the JavaScript step can be quite slow.

Fortunately, there is a better way.

Using a user-defined Java Class step we can have JTidy do the dirty work of cleaning the HTML and converting it to XML, which we can then process in a sane way with Kettle's Get Data from XML step.

We need to do two things to make this work: first, you have to download JTidy, unzip it, and place the jtidy-r938.jar in the libext directory, which resides immediately in your kettle installation directory. (note that if you were running spoon, you need to restart it before it will be picked up). Second, you need a little bit of glue code for the User-defined Java class step so Kettle can use the Tidy class inside the jar. With some help from the pentaho wiki and the JTidy JavaDoc documentation, I came up with the following Java snippet to make it work:

import org.w3c.tidy.Tidy;
import java.io.StringReader;
import java.io.StringWriter;

protected Tidy tidy;

public boolean init(StepMetaInterface stepMetaInterface, StepDataInterface stepDataInterface)
{
//create and configure a Tidy instance
tidy = new Tidy();
tidy.setXmlOut(true);
return parent.initImpl(stepMetaInterface, stepDataInterface);
}

public boolean processRow(StepMetaInterface smi, StepDataInterface sdi) throws KettleException {
Object[] r;
//Get row from incoming stream.
//Bail out if its not there.
if ((r = getRow()) == null) {
setOutputDone();
return false;
}

//read the value of the html input field
//the html field happens to be the 5th field in the stream,
//because java arrays start at 0, we use index 4 to reference it
StringReader html = new StringReader((String)r[4]);

//use tidy to parse html to xml
StringWriter xml = new StringWriter();
tidy.parse(html, xml);

//assign the xml to the output row
//note we simply overwrite the original html field from the input row.
r[4] = xml.toString();

//push the output row to the outgoing stream.
putRow(data.outputRowMeta, r);
return true;
}

(Tip: for more examples and background information on the user-defined java class step, check out the excellent blog posts by Slawomir Chodnicki, Matt Casters and the video walk-through by Dein Einspanjer)

The big advantage of using Tidy is that you can be sure that the result is well-formed XML. In addition, you can have JTidy report on any errors or warnings, which makes it much more robust than any ad-hoc string manipulation you can come up with.

Wednesday, May 18, 2011

Check out this excellent article by Shlomi Noach!!

Check out this excellent article by Shlomi Noach!!

Really - my life is much happier now, and as a bonus I got a free set of steak knives and even lost 20 pounds. (and MySQL!)

Tuesday, May 10, 2011

Managing kettle job configuration

Over time I've grown a habit of making a configuration file for my kettle jobs. This is especially useful if you have a reusable job, where the same work has to be done but against different conditions. A simple example where I found this useful is when you have separate development, testing and production environments: when you're done developing your job, you transfer the .kjb file (and its dependencies) to the testing environment. This is the easy part. But the job still has to run within the new environment, against different database connections, webservice urls and file system paths.

Variables


In the past, much has been written about using kettle variables, parameters and arguments. Variables are the basic features that provide the mechanism to configure the transformation steps and job entries: instead of using literal configuration values, you use a variable reference. This way, you can initialize all variables to whatever values are appropriate at that time, and for that environment. Today, I don't want to discuss variables and variable references - instead I'm just focussing on how to manage the configuration once you already used variable references inside your your jobs and transformations.

Managing configuration


To manage the configuration, I typically start the main job with a set-variables.ktr transformation. This transformation reads configuration data from a config.properties file and assigns it to the variables so any subsequent jobs and transformations can access the configration data through variable references. The main job has one parameter called ${CONFIG_DIR} which has to be set by the caller so the set-variables.ktr transformation knows where to look for its config.properties file:


Reading configuration properties


The config.properties file is just a list of key/value pairs separated by an equals sign. Each key represents a variable name, and the value the appropriate value. The following snippet should give you an idea:
#staging database connection
STAGING_DATABASE=staging
STAGING_HOST=localhost
STAGING_PORT=3351
STAGING_USER=staging
STAGING_PASSWORD=$74g!n9
The set-variables.ktr transformation reads it using a "Property Input" step, and this yields a stream of key/value pairs:


Pivoting key/value pairs to use the "set variables" step


In the past, I used to set the variables using the "Set variables" step. This step works by creating a variable from selected fields in the incoming stream and assigning the field value to it. This means that you can't just feed the stream of key/value pairs from the property input step into the set variables step: the stream coming out of the property input step contains multiple rows with just two fields called "Key" and "value". Feeding it directly into the "Set variables" step would just lead to creating two variables called Key and Value, and they would be assigned values multiple times for all key/value pairs in the stream. So in order to meaningfully assign variable, I used to pivot the stream of key/value pairs into a single row having one field for each key in the stream using the "Row Denormaliser" step:

As you can see in the screenshot, "Key" is the key field: the value of this field is scanned to determine in which output fields to put the corresponding value. There are no fields that make up a grouping: rather, we want all key/value pairs to end up in one big row. Or put another way, there is just one group comprising all key/value pairs. Finally, the grid below specifies for each distinct value of the "Key" field to which output field name it should be mapped, and in all cases, we want the value of the "Value" field to be stored in those fields.

Drawbacks


There are two important drawbacks to this approach:

  • The "Row Normaliser" uses the value of the keys to map the value to a new field. This means that we have to type the name of each and every variable appearing in the config.properties file. So you manually need to keep he config.propeties and the "Denormaliser" synchronized, and in practice it's very easy to make mistakes here.
  • Due to the fact that the "Row Denormaliser" step literally needs to know all variables, the set-variables.ktr transformation becomes specific for just one particular project.

Given these drawbacks, I seriously started to question the usefulness of a separate configuration file: because the set-variables.ktr transformation has to know all variables names anyway, I was tempted to store the configration values themselves also inside the transformation (using a "generate rows" or "data grid" step or something like that), and "simply" make a new set-variables.ktr transformation for every environment. Of course, that didn't feel right either.

Solution: Javascript


As it turns out, there is in fact a very simple solution that solves all of these problems: don't use the "set variables" step for this kind of problem! We still need to set the variables of course, but we can conveniently do this using a JavaScript step. The new set-variables.ktr transformation now looks like this:



The actual variable assignemnt is done with Kettle's built-in setVariable(key, value, scope). The key and value from the incoming stream are passed as arguments to the key and value arguments of the setVariable() function. The third argument of the setVariable() function is a string that identifies the scope of the variable, and must have one of the following values:

  • "s" - system-wide

  • "r" - up to the root

  • "p" - up to the parent job of this transormation

  • "g" - up to the grandparent job of this transormation

For my purpose, I settle for "r".

The bonus is that this set-variables.ktr is less complex than the previous one and is now even completely independent of the content of the configuration. It has become a reusable transformation that you can use over and over.