Yesterday I have been immersed in a MapReduce assignment for my data centre class, so this week I want to give you guys a taste of MapReduce, one of the most used data processing frameworks, and also base of many other great frameworks.
Intro to MapReduce
First of all, MapReduce falls into the umbrella of “Big Data”, which has been hot for the past few years with the rise of AI, so for the very least it is worth taking a look over it. Usually MapReduce deals with a large amount of data, of magnitude of GB and above, that usually follows a “logical record” format. Normally this just means we can treat data as lines of strings, one at a time. Later in this post, I will have an example to explain everything, but first let’s take a look at MapReduce in a high-level of abstraction.
From the highest level, MapReduce exposes a pipeline-like* structure, and usually is used for batch processing of large data. Note that there is an asterisk there, we will address it later. Going a little bit deeper, and we see MapReduce consists of these following stages:
- Map: for each line of String, extract the Key and the Value.
- Shuffle: sort all the key value pairs so that all values with the same key stick together, usually in ascending order. (This step is usually hidden from us developers)
- Reduce: for each key, reduce all the values associated with it down to one.
- Generate output: this is not really part of MapReduce, but you still need it to get meaningful result.
You might wonder what is this “Key Value” thing. Unfortunately it is not a definitive thing. These are developer-defined types that caters to their goals. For example, if we are to count the number of occurrences for each word in a novel, the key would be each word, and the value would be the number of times they appear in the book. Again these will be a lot clearer after you see the example later.
Before I go into the example, let me talk about a few things. First the asterisk about pipeline. Also it does seems like each of the steps above cannot proceed until the previous one is done, but behind the scenes the transition is streamed. The Shuffler will start working before getting all the mapper output, and so forth. So it isn’t that bad in performance.
Speaking of performance, MapReduce is very efficient, especially when used in a cluster in a data centre. The reason for this is that the steps needn’t to be done on just one process, or one computer in general. Take the example above, if we partition the novel into many little “shards” and distribute them to many computers, and run mapper and reducers on each of them, we can get it done a lot quicker, in a highly parallelised paradigm.
from the original Google paper
Now let’s look at a real world example. As a disclaimer, this example is put together by my professor, Prof. Don Smith, I cannot take any credit.
First let’s take a look at the data. This is a typical line:
ADU: 1208557512.904791 126.96.36.199.443 < 188.8.131.52.2872 641 SEQ 0.000757
This records a package sent from
184.108.40.206 at port
220.127.116.11 at port
641 bytes sent over. Our goal is to count the number of sends for each IP present.
Note the type of the Mapper. The first
LongWritable is the index of line in the file, which usually doesn’t get used; the second
Text is the line itself, like the one above. The following two types are the key and value for the mapper output.
Text for the sending IP,
IntWritable for the send occurrence, in our case would be 1.
The rest of the Mapper is pretty straightforward. After some string processing, we got the sending IP, the last thing we need to do is write it into
context, which gets shuffled and sorted automatically by the framework.
Next to the Reducer
Again the type of Reducer. The first two match the output from the mapper(the
Iterable<> is because we have got many values for one key, natrually). The following two are the final output type,
Text for the sending IP again,
LongWritable for the total number of 1s we got from Mapper.
The body is simple again, just adding the 1s together, and finally writing it back into
context again, which later will be merged into a final output.
There isn’t much to explain really. Master control sets the config of the job, with the most important setting: number of reducers, which impacts the performance of the job.
The example above, as you probably think, is pretty simple, but it should give you a clear idea of what MapReduce is under the hood. There are many other frameworks built on top of MapReduce, such as Spark, which adds more features here and there.