Saturday, February 9, 2008

MapReduce and K-Means Clustering

Google offers slides and presentations on many research topics online including distributed systems. And one of these presentations discusses MapReduce in the context of clustering algorithms.

One of the claims made in this particular presentation is that "it can be necessary to send tons of data to each Mapper Node. Depending on your bandwidth and memory available, this could be impossible." This claim is false, which in turn removes much of the motivation for the alternative algorithm, which called "canopy clustering".

The K-Means Clustering Algorithm

There are many good introductions to k-means clustering available, including our book Data Mining Techniques for Marketing, Sales, and Customer Support. The Google presentation mentioned above provides a very brief introduction.

Let's review the k-means clustering algorithm. Given a data set where all the columns are numeric, the algorithm for k-means clustering is basically the following:

(1) Start with k cluster centers (chosen randomly or according to some specific procedure).
(2) Assign each row in the data to its nearest cluster center.
(3) Re-calculate the cluster centers as the "average" of the rows in (2).
(4) Repeat, until the cluster centers no longer change or some other stopping criterion has been met.

In the end, the k-means algorithm "colors" all the rows in the data set, so similar rows have the same color.

K-Means in a Parallel World

To run this algorithm, it seems, at first, as though all the rows assigned to each cluster in Step (2) need to be brought together to recalculate the cluster centers.

However, this is not true. K-Means clustering is an example of an embarrassingly parallel algorithm, meaning that that it is very well suited to parallel implementations. In fact, it is quite adaptable to both SQL and to MapReduce, with efficient algorithms. By "efficient", I mean that large amounts of data do not need to be sent around processors and that the processors have minimum amounts of communication. It is true that the entire data set does need to be read by the processors for each iteration of the algorithm, but each row only needs to be read by one processor.

A parallel version of the k-means algorithm was incorporated into the Darwin data mining package, developed by Thinking Machines Corporation in the early 1990s. I do not know if this was the first parallel implementation of the algorithm. Darwin was later purchased by Oracle, and became the basis for Oracle Data Mining.

How does the parallel version work? The data can be partitioned among multiple processors (or streams or threads). Each processor can read the previous iteration's cluster centers and assign the rows on the processor to clusters. Each processor then calculates new centers for its of data. Each actual cluster center (for the data across all processors) is then the weighted average of the centers on each processor.

In other words, the rows of data do not need to be combined globally. They can be combined locally, with the reduced set of results combined across all processors. In fact, MapReduce even contains a "combine" method for just this type of algorithm.

All that remains is figuring out how to handle the cluster center information. Let us postulate a shared file that has the centroids as calculated for each processor. This file contains:
  • The iteration number.
  • The cluster id.
  • The cluster coordinates.
  • The number of rows assigned to the cluster.
This is the centroid file. An iteration through the algorithm is going to add another set of rows to this file. This information is the only information that needs to be communicated globally.

There are two ways to do this in the MapReduce framework. The first uses map, combine, and reduce. The second only uses map and reduce.

K-Means Using Map, Combine, Reduce

Before begining, a file is created accessible to all processors that contains initial centers for all clusters. This file contains the cluster centers for each iteration.

The Map function reads this file to get the centers from the last finished iteration. It then reads the input rows (the data) and calculates the distance to each center. For each row, it produces an output pair with:
  • key -- cluster id;
  • value -- coordinates of row.
Now, this is a lot of data, so we use a Combine function to reduce the size before sending it to Reduce. The Combine function calculates the average of the coordinates for each cluster id, along with the number of records. This is simple, and it produces one record of output for each cluster:
  • key is cluster
  • value is number of records and average values of the coordinates.
The amount of data now is the number of clusters times the number of processors times the size of the information needed to define each cluster. This is small relative to the data size.

The Reduce function (and one of these is probably sufficient for this problem regardless of data size and the number of Maps) calcualtes the weighted average of its input. Its output should be written to a file, and contain:
  • the iteration number;
  • the cluster id;
  • the cluster center coordinates;
  • the size of the cluster.
The iteration process can than continue.

K-Means Using Just Map and Reduce

Using just Map and Reduce, it is possible to do the same things. In this case, the Map and Combine functions described above are combined into a single function.

So, the Map function does the following:
  • Initializes itself with the cluster centers from the previous iteration;
  • Keeps information about each cluster in memory. This information is the total number of records assigned to the cluster in the processor and the total of each coordinate.
  • For each record, it updates the information in memory.
  • It then outputs the key-value pairs for the Combine function described above.
The Reduce function is the same as above.

K-Means Using SQL

Of course, one of my purposes in discussing MapReduce has been to understand whether and how it is more powerful than SQL. For fifteen years, databases have been the only data-parallel application readily available. The parallelism is hidden underneath the SQL language, so many people using SQL do not fully appreciate the power they are using.

An iteration of k-means looks like:

SELECT @iteration+1, cluster_id,
.......AVERAGE( as center
FROM (SELECT, cc.cluster_id,
................................ORDER BY DISTANCE(, as ranking

......FROM data d CROSS JOIN
.....(SELECT *
......FROM cluster_centers cc
......WHERE iteration = @iteration) cc
.....) a
WHERE ranking = 1
GROUP BY cluster_id

This code assumes the existence of functions or code for the AVERAGE() and DISTANCE() functions. These are placeholders for the correct functions. Also, it uses analytic functions. (If you are not familiar with these, I recommend my book Data Analysis Using SQL and Excel.)

The efficiency of the SQL code is determined, to a large extent, by the analytic function that ranks all the cluster centers. We hope that a powerful parallel engine will recognize that the data is all in one place, and hence that this function will be quite efficient.

A Final Note About K-Means Clustering

The K-Means clustering algorithm does require reading through all the data for each iteration through the algorithm. In general, it tends to converge rather quickly (tens of iterations), so this may not be an issue. Also, the I/O for reading the data can all be local I/O, rather than sending large amounts of data through the network.

For most purposes, if you are dealing with a really big dataset, you can sample it down to a fraction of its original size to get reasonable clusters. If you are not satisfied with this method, then sample the data, find the centers of the clusters, and then use these to initialize the centers for the overall data. This will probably reduce the number of iterations through the entire data to less than 10 (one pass for the sample, a handful for the final clustering).

When running the algorithm on very large amounts of data, numeric overflow is a very real issue. This is another reason why clustering locally, taking averages, and then taking the weighted average globally is beneficial -- and why doing sample is a good way to begin.

Also, before clustering, it is a good idea to standardize numeric variables (subtract the average and divide by the standard deviation).

Check out my latest book Data Analysis Using SQL and Excel.

Wednesday, February 6, 2008

Using SQL to Emulate MapReduce Functionality

My previous blog entry explained that there are two ways that MapReduce functionality (here) is more powerful than SQL aggregations:

  • MapReduce implements functions using a full-fledged programming language. This is more powerful than the functions permitted in SQL.
  • MapReduce allows one row to be part of more than one aggregation group.
In fact, SQL can emulate this functionality, bringing it much closer to MapReduce's capabilities. This post discusses how SQL can emulate this functionality and then discusses why this might not be a good idea. (This discussion has been inspired by the rather inflammatory and inaccurate post here.)

First, let me note that the first limitation is not serious, because I assume that SQL can be extended by adding new scalar and aggregation user defined functions. Although more cumbersome than built-in programming constructs, the ability to add user defined functions does make it possible to add in a wide breadth of functionality.

The second strength can be emulated by assuming the existence of a table, which I'll call enumerate, that simply contains one column which contains numbers starting at 1.

How does such a table help us? Consider a table that has a start date and a stop date for each customer. The SQL code to count up the starts and the stops might look like:

SELECT thedate, SUM(isstart) as numstarts, SUM(isstop) as numstops
FROM ((SELECT start_date as thedate, 1 as isstart, 0 as issend
.......FROM customer c) union all
......(SELECT stop_date as thedate, 0 as isstart, 1 as issend
.......FROM customer c)) a
GROUP BY thedate

This is inelegant, particularly as the expressions for the tables get more complicated -- imagine what happens if the customer table is actually a complicated set of joins and aggregations. In addition, we can see how expressing the SQL suggests that two full passes are needed through the table. Yuck!

Let's assume that we have the enumerate table. In this case, the same query could be expressed as:

SELECT (CASE WHEN e.i = 1 THEN start_date ELSE end_date END) as thedate,
.......SUM(CASE WHEN e.i = 1 THEN 1 ELSE 0 END) as numstarts,
.......SUM(CASE WHEN e.i = 2 THEN 1 ELSE 0 END) as numstops
FROM customer c CROSS JOIN
.....(SELECT * FROM enumerate WHERE i <= 2) e
GROUP BY (CASE WHEN e.i = 1 THEN start_date ELSE end_date END)

This query is setting up a counter that lets us, conceptually, loop through each row in the table. The counter is set to take on two values. On the first pass through the loop, the query uses the start date; on the second, it uses the stop date. The result is the same as for the previous query. The SQL, though, is different because it does not express two passes through the data.

This example is simple. It is obvious how to extend it further, for instance, if there were more dates stored in each row. It should also be obvious how this can be expressed as map/reduce functions.

One of the most common places where MapReduce is used is for parsing text strings. Say we have a list of product descriptions that are like:

  • "green,big,square"
  • "red,small,square"
  • "grey"
  • "medium,smelly,cube,cotton"
The idea here is that the description strings consist of any number of comma separate values. Now, let's say that our goal is to count the number of times that each keyword appears in a set of products. The first thought is that something like this really cannot be done in SQL. So, to give a help, let's assume that there are two helper functions:

  • NumWords(string, sepchar): This function takes a string and a separate character and returns the number of words in the string.
  • GetWord(string, sepchar, i): This function takes a string, a separator character, and a word number and returns the word in the string.
For instance, for the examples above, NumWords() and GetWord() return the following using comma as a separator and when called with 1, 2, 3, and so on:

  • 3 and "green", "big", and "square"
  • 3 and "red", "small", "square"
  • 1 and "grey"
  • 4 and "medium", "smelly", "cube", "cotton"

These functions are not difficult to write in a procedural programming language.

With such functions, the SQL to count up the attributes in our products looks like:

SELECT GetWord(p.desc, ',', e.i) as attribute, COUNT(*)
FROM product p JOIN
.....enumerate e
.....ON e.i <= NumWords(p.desc, ',')
GROUP BY GetWord(p.desc, ',', e.i)

The structure of this query is very similar to the previous query. The one difference is that each row has a different loop counter, because there are a different number of words in any given product description. Hence, the two tables are joined using a standard inner join operator, rather than a cross join.

An enumerate table, in conjunction with user defined functions, can give SQL much of the functionality of MapReduce.

One objection to the previous example is that such a database structure violates many rules of good database design. Such a product description string is not normalized, for instance. And packing values in strings is not a good design practice. My first reaction is that the real world is filled with similar examples, so regardless of what constitutes good design, we still need to deal with it.

A more honest answer is that the world is filled with strings that contain useful information -- description strings, URLs, and so on. SQL cannot just ignore such data, or dismiss it as not being normalized.

There is a problem: performance. One way to do the join is to create an intermediate table that is the cross product (or a large subset of the cross product) of the two tables. Of course, such an intermediate table is equivalent to reading the first table twice, so we have not gained anything.

This is likely to happen in the first case. Without more information, SQL is likely to do nested loop joins. If the customer table is the outer loop, then each customer row is read and duplicated, once with i=1 and the second time with i=2. This is not actually so bad. The original row is actually read once and then processed multiple times in memory for each value of i.

Of course, there is no guarantee that the SQL engine won't put the enumerate table as the outer loop, which requires two full reads of the customer table.

The situation becomes worse if the data is partitioned in a parallel environment. This is important, because MapReduce's advantage is that it always runs in parallel.

The SQL engine is likely to run a nested loop join on a single processor, even if the customer table is partitioned (or if the database is configured to be "multithreaded" or "multiprocessor"). Only a very smart optimizer would figure out that the enumerate table could be duplicated and distributed so the nested loop join could run in parallel.

The optimization problem is even worse in the second case, because the number of rows needed from enumerate varies for different products. Of course, database directives were invested to tell SQL optimizers how to do joins. I would prefer that the database have the enumerate table built-in, so the optimize can take full advantage of it.

Much of MapReduce's functionality comes from the ability to give a singel row multiple aggregation keys, while running in parallel. Even on large datasets, we can set up SQL to solve many problems that MapReduce does by combining user-defined functions, the enumerate table, and appropriate compiler directives so the large joins are done in parallel.