Command Line Interface (CLI) for Complex Operations

Source: Command-line Tools can be 235x Faster than your Hadoop Cluster

Problem

How to process text data and aggregate results from it using only GNU/Linux commands

Solution

Use CLI for:

• Text stream processing
• Parallelism

Information copied from source article, Quoting and Credit to Adam Drake

Learn about the data

The first step in the pipeline is to get the data out of the PGN files. Since I had no idea what kind of format this was, I checked it out on Wikipedia.

[Event "F/S Return Match"]
[Site "Belgrade, Serbia Yugoslavia|JUG"]
[Date "1992.11.04"]
[Round "29"]
[White "Fischer, Robert J."]
[Black "Spassky, Boris V."]
[Result "1/2-1/2"]
(moves from the game follow...)

We are only interested in the results of the game, which only have 3 real outcomes. The 1-0 case means that white won, the 0-1 case means that black won, and the 1/2-1/2 case means the game was a draw. There is also a - case meaning the game is ongoing or cannot be scored, but we ignore that for our purposes.

Acquire sample data

The first thing to do is get a lot of game data. This proved more difficult than I thought it would be, but after some looking around online I found a git repository on GitHub from rozim that had plenty of games. I used this to compile a set of 3.46GB of data, which is about twice what Tom used in his test. The next step is to get all that data into our pipeline.

Build a processing pipeline

If you are following along and timing your processing, don’t forget to clear your OS page cache as otherwise you won’t get valid processing times.

Shell commands are great for data processing pipelines because you get parallelism for free. For proof, try a simple example in your terminal.

sleep 3 | echo "Hello world."

Intuitively it may seem that the above will sleep for 3 seconds and then print Hello world but in fact both steps are done at the same time. This basic fact is what can offer such great speedups for simple non-IO-bound processing systems capable of running on a single machine.

Before starting the analysis pipeline, it is good to get a reference for how fast it could be and for this we can simply dump the data to /dev/null.

cat *.pgn > /dev/null

In this case, it takes about 13 seconds to go through the 3.46GB, which is about 272MB/sec. This would be a kind of upper-bound on how quickly data could be processed on this system due to IO constraints.

Now we can start on the analysis pipeline, the first step of which is using cat to generate the stream of data.

cat *.pgn

Since only the result lines in the files are interesting, we can simply scan through all the data files, and pick out the lines containing ‘Results’ with grep.

cat *.pgn | grep "Result"

This will give us only the Result lines from the files. Now if we want, we can simply use the sort and uniq commands in order to get a list of all the unique items in the file along with their counts.

cat *.pgn | grep "Result" | sort | uniq -c

This is a very straightforward analysis pipeline, and gives us the results in about 70 seconds. While we can certainly do better, assuming linear scaling this would have taken the Hadoop cluster approximately 52 minutes to process.

In order to reduce the speed further, we can take out the sort | uniq steps from the pipeline, and replace them with AWK, which is a wonderful tool/language for event-based data processing.

cat *.pgn | grep "Result" | awk '{ split($0, a, "-"); res = substr(a[1], length(a[1]), 1); if (res == 1) white++; if (res == 0) black++; if (res == 2) draw++;} END { print white+black+draw, white, black, draw }'

This will take each result record, split it on the hyphen, and take the character immediately to the left, which will be a 0 in the case of a win for black, a 1 in the case of a win for white, or a 2 in the case of a draw. Note that $0 is a built-in variable that represents the entire record.

This reduces the running time to approximately 65 seconds, and since we’re processing twice as much data this is a speedup of around 47 times.

So even at this point we already have a speedup of around 47 with a naive local solution. Additionally, the memory usage is effectively zero since the only data stored is the actual counts, and incrementing 3 integers is almost free in memory space terms. However, looking at htop while this is running shows that grep is currently the bottleneck with full usage of a single CPU core.

Parallelize the bottlenecks

This problem of unused cores can be fixed with the wonderful xargs command, which will allow us to parallelize the grep. Since xargs expects input in a certain way, it is safer and easier to use find with the -print0 argument in order to make sure that each file name being passed to xargs is null-terminated. The corresponding -0 tells xargs to expected null-terminated input. Additionally, the -n how many inputs to give each process and the -P indicates the number of processes to run in parallel. Also important to be aware of is that such a parallel pipeline doesn’t guarantee delivery order, but this isn’t a problem if you are used to dealing with distributed processing systems. The -F for grep indicates that we are only matching on fixed strings and not doing any fancy regex, and can offer a small speedup, which I did not notice in my testing.

find . -type f -name '*.pgn' -print0 | xargs -0 -n1 -P4 grep -F "Result" | gawk '{ split($0, a, "-"); res = substr(a[1], length(a[1]), 1); if (res == 1) white++; if (res == 0) black++; if (res == 2) draw++;} END { print NR, white, black, draw }'

This results in a run time of about 38 seconds, which is an additional 40% or so reduction in processing time from parallelizing the grep step in our pipeline. This gets us up to approximately 77 times faster than the Hadoop implementation.

Although we have improved the performance dramatically by parallelizing the grep step in our pipeline, we can actually remove this entirely by having awk filter the input records (lines in this case) and only operate on those containing the string “Result”.

find . -type f -name '*.pgn' -print0 | xargs -0 -n1 -P4 awk '/Result/ { split($0, a, "-"); res = substr(a[1], length(a[1]), 1); if (res == 1) white++; if (res == 0) black++; if (res == 2) draw++;} END { print white+black+draw, white, black, draw }'

You may think that would be the correct solution, but this will output the results of each file individually, when we want to aggregate them all together. The resulting correct implementation is conceptually very similar to what the MapReduce implementation would be.

find . -type f -name '*.pgn' -print0 | xargs -0 -n4 -P4 awk '/Result/ { split($0, a, "-"); res = substr(a[1], length(a[1]), 1); if (res == 1) white++; if (res == 0) black++; if (res == 2) draw++ } END { print white+black+draw, white, black, draw }' | awk '{games += $1; white += $2; black += $3; draw += $4; } END { print games, white, black, draw }'

By adding the second awk step at the end, we obtain the aggregated game information as desired.

This further improves the speed dramatically, achieving a running time of about 18 seconds, or about 174 times faster than the Hadoop implementation.

However, we can make it a bit faster still by using mawk, which is often a drop-in replacement for gawk and can offer better performance.

find . -type f -name '*.pgn' -print0 | xargs -0 -n4 -P4 mawk '/Result/ { split($0, a, "-"); res = substr(a[1], length(a[1]), 1); if (res == 1) white++; if (res == 0) black++; if (res == 2) draw++ } END { print white+black+draw, white, black, draw }' | mawk '{games += $1; white += $2; black += $3; draw += $4; } END { print games, white, black, draw }'

This find | xargs mawk | mawk pipeline gets us down to a runtime of about 12 seconds, or about 270MB/sec, which is around 235 times faster than the Hadoop implementation.

Conclusion

Using and abusing tools like Hadoop for data processing tasks that can better be accomplished on a single machine with simple shell commands and tools. If you have a huge amount of data or really need distributed processing, then tools like Hadoop may be required, but more often than not these days I see Hadoop used where a traditional relational database or other solutions would be far better in terms of performance, cost of implementation, and ongoing maintenance.