I’ve been working with Hadoop a lot lately in order to do some exploratory data analysis on traffic logs. Hadoop is great; it makes things that were taking 30 minutes run 10x faster, which means that I can iterate a lot faster and experiment with more ways to slice the data.
I wanted an easy way of running Clojure programs under Hadoop, and ended up writing a silly, simple little shim that would simply take a Clojure file with a mapper and reducer function. It means that there is no .JAR building and no AOT compiling – just write your mapper and reducer function, bundle up the file, and go. Note that the JAR is built for Hadoop 0.20 and later.
The quickest way of downloading the .JAR is to just download it.
Build the shim:
javac -cp /usr/lib/hadoop/hadoop-core.jar:/usr/lib/hadoop/lib/*:lib/* -d classes Shim.java
Create a lib directory and add the clojure classes:mkdir lib cp /from/wherever/clojure-1.2.0.jar lib cp /from/wherever/clojure-contrib-1.2.0.jar lib
Bundle it together into one .JAR file:jar -cvf shim.jar -C classes/ . lib/*
The directory of your .JAR should look something like this when you’ve finished:[~/github/hadoop-shim] $ jar tf shim.jar META-INF/ META-INF/MANIFEST.MF com/ com/brool/ com/brool/Shim.class com/brool/Shim$Reduce.class com/brool/Shim$Map.class lib/clojure-1.2.0.jar lib/clojure-contrib-1.2.0.jar
To run the example wordcount:hadoop jar shim.jar com.brool.Shim -files wordcount.clj input-file output-file
The output from the run will be in output-file/part-r-00000
The Clojure file that you provide should be in the user namespace, and must provide two functions named mapper and reducer.
The mapper function takes a string representing one line in the input file and returns a list of [ key value ] pairs. A given input line can generate any number of map lines.
The reducer is given a key and all values that were associated with that key. The reducer’s function is to consolidate all of that into one output line.
Since word count is the canonical example, let’s do that. The mapper for word count is:(defn mapper [v] (let [words (enumeration-seq (StringTokenizer. v))] (map #(do [ % "1" ] ) words) ))
As an example, for an input line of “This is a test, is it not?”, the following map will be generated:(["This" "1"] ["is" "1"] ["a" "1"] ["test," "1"] ["is" "1"] ["it" "1"] ["not?" "1"])
Before being given the reducer, the Hadoop system will group all like keys together, resulting in:"This" => [ "1" ] "is" => [ "1" "1" ]
So on and so forth. The reducer is given the key and the list of all values that had that key, and then emits the final result – for a word count, the correct code would be:(defn reducer [k v] [ k (reduce + (map #(Integer/parseInt %) v)) ])
This simply adds up the counts.
In wordcount.clj there are two handy functions that allow for debugging of the mapper and reducer before submitting it to Hadoop.
Given a local file, the test-mapper will load the file and run it through the mapper; if your file is large you may just want to (take 20 (test-mapper “/my/filename”)).
The test-reducer function will load the file and run it through both the mapper and the reducer. Taking the example sentence above:user> (test-reducer "/tmp/one-sentence") (["This" 1] ["a" 1] ["is" 2] ["it" 1] ["not?" 1] ["test," 1])
As another example: let’s say that you have a collection of log entries, and would like to record the first and last log entry for every user. Assume that the files are in a CSV format, with the fields being in the order of timehit, userid. Example:2010-10-04 13:04:22,112334 2010-10-04 10:04:22,182994 2010-10-04 10:05:18,182994 2010-10-04 10:07:19,182994 2010-10-04 13:28:41,112334 2010-10-04 10:09:22,182994 2010-10-04 13:56:22,112334 2010-10-04 11:30:01,182994
The mapper for this:(defn mapper [v] (let [[timehit userid] (.split v ",")] [ [ userid timehit ] ] ))
The reducer:(defn reducer [k v] (let [s (sort v)] [k (str (first s) "," (last s))]))
The source is all on Github.
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