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Former[1] primary mrjob maintainer here, thanks for the shout-out! I'd like to make a couple of notes and corrections. In particular, I thank you for recommendi

Former[1] primary mrjob maintainer here, thanks for the shout-out! I'd like to make a couple of notes and corrections. In particular, I thank you for recommending mrjob for EMR usage, as it's something we've made a point of trying to be the best at. First of all, all of these frameworks use Hadoop Streaming. As mentioned in the mrjob 0.4-dev docs [2] (pardon the run-on sentence): "Although Hadoop is primarly designed to work with JVM code, it supports other languages via Hadoop Streaming, a special jar which calls an arbitrary program as a subprocess, passing input via stdin and gathering results via stdout." mrjob's role is to give you a structured way to write Hadoop Streaming jobs in Python (or, recently, any language). When your task runs, it's taking input and output the same way as your raw Python example does, except mrjob is passing it through to the methods you've defined after running each line through a deserialization function. It picks the code to run based on command line arguments such as --mapper, --reducer, etc. The output of the code is again serialized. The methods of [de]serialization are defined declaritively in your class, as you showed. So why did you find mrjob to be slower than bare Hadoop Streaming? I don't know! In theory, you're running approximately the same code between the mrjob and the bare Python versions of your script. If anyone has time to dig into this and find out where that time is being spent, I would be grateful. Results should be sent to the issue tracker on the Github page [3]. Feel free to ask clarifying questions. I realize I may not be explaining this effectively to people unfamiliar with the ins and outs of Python MapReduce frameworks. I'm thinking of organizing the 2nd "mrjob hackathon" in the near future, so please ping me if you're interested in contributing to an easy-to-handle lots-of-low-hanging-fruit OSS project. (Particularly if you're a Rubyist, because we have an experimental way to use Ruby with it.) [1] mrjob is maintained by Yelp, where I worked until recently. It's still under active development, though it's slowed somewhat since Dave and I moved on. [2] http://mrjob.readthedocs.org/en/latest/guides/concepts.html#hadoop-streaming-and-mrjob http://mrjob.readthedocs.org/en/latest/guides/concepts.html#... [3] http://www.github.com/yelp/mrjob/issues/new http://www.github.com/yelp/mrjob/issues/new

stevejohnson 14y ago
One last thing I forgot to mention: there was a typedbytes support pull request for mrjob at one point, but it was behind the dev branch enough that it wasn't practical to merge. It is possible (probable?) that typedbytes support will make it into a future release if an interested party could put in the time. I would have done it myself if I had been able to make head or tail of the typedbytes documentation. Also, if you're just starting out or want to look over the docs, I'd recommend using the dev version hosted on readthedocs instead of the PyPI version, as the author did: http://mrjob.readthedocs.org/en/latest/index.html http://mrjob.readthedocs.org/en/latest/index.html
- laserson 14y ago
  I did only a bit of profiling on mrjob, but it seemed that the ser/de was significantly slowing down the overall computation.
  - stevejohnson 14y ago
    Here's the code that's doing the actual line parsing: https://github.com/Yelp/mrjob/blob/master/mrjob/protocol.py#L144 https://github.com/Yelp/mrjob/blob/master/mrjob/protocol.py#... It's just splitting on tab for input and re-joining on tab for output. Some extra logic for lines without tabs. EDIT: The example in the post is using JSON for communication between intermediate steps, while the Hadoop Streaming example is using a custom delimiter format. So this isn't really a fair comparison; the mrjob example could just as easily use the same efficient intermediate format.
    - laserson 14y ago
      Here is the profiling output: https://gist.github.com/4478737 https://gist.github.com/4478737 The input is RawProtocol, which simply splits on tab. But after that, mrjob defaults to using JSON internally, and this is causing a lot of slowdown.
      - stevejohnson 14y ago
        So mrjob isn't slower at all! You just chose to use JSON for your intermediate steps in the mrjob example, instead of the delimiter format you used in the raw Python example. I believe the mrjob docs do say what the defaults are. RawProtocol can be used for intermediate steps just fine. Please either mention this difference in your post or update the code and conclusions. If there's a place in the documentation where we should mention optimizations or details like this, I'd be interested to know. I should have thought of this before. Oh well.
        laserson 14y ago
        My goal was to use mrjob's features, not strip them down for performance. I find mrjob's natural use of JSON very appealing in terms of user-experience. It means that keys can be more complex types without the user having to manually figure out the best way to encode them. I make it clear that this is an appealing property of mrjob, and I make it clear that this is the reason for the slowdown. As is, the code will not work with RawProtocol internally because the key is a tuple of words.

stevejohnson 14y ago

One last thing I forgot to mention: there was a typedbytes support pull request for mrjob at one point, but it was behind the dev branch enough that it wasn't practical to merge. It is possible (probable?) that typedbytes support will make it into a future release if an interested party could put in the time. I would have done it myself if I had been able to make head or tail of the typedbytes documentation. Also, if you're just starting out or want to look over the docs, I'd recommend using the dev version hosted on readthedocs instead of the PyPI version, as the author did: http://mrjob.readthedocs.org/en/latest/index.html http://mrjob.readthedocs.org/en/latest/index.html

laserson 14y ago
I did only a bit of profiling on mrjob, but it seemed that the ser/de was significantly slowing down the overall computation.
- stevejohnson 14y ago
  Here's the code that's doing the actual line parsing: https://github.com/Yelp/mrjob/blob/master/mrjob/protocol.py#L144 https://github.com/Yelp/mrjob/blob/master/mrjob/protocol.py#... It's just splitting on tab for input and re-joining on tab for output. Some extra logic for lines without tabs. EDIT: The example in the post is using JSON for communication between intermediate steps, while the Hadoop Streaming example is using a custom delimiter format. So this isn't really a fair comparison; the mrjob example could just as easily use the same efficient intermediate format.
  - laserson 14y ago
    Here is the profiling output: https://gist.github.com/4478737 https://gist.github.com/4478737 The input is RawProtocol, which simply splits on tab. But after that, mrjob defaults to using JSON internally, and this is causing a lot of slowdown.
    - stevejohnson 14y ago
      So mrjob isn't slower at all! You just chose to use JSON for your intermediate steps in the mrjob example, instead of the delimiter format you used in the raw Python example. I believe the mrjob docs do say what the defaults are. RawProtocol can be used for intermediate steps just fine. Please either mention this difference in your post or update the code and conclusions. If there's a place in the documentation where we should mention optimizations or details like this, I'd be interested to know. I should have thought of this before. Oh well.
      - laserson 14y ago
        My goal was to use mrjob's features, not strip them down for performance. I find mrjob's natural use of JSON very appealing in terms of user-experience. It means that keys can be more complex types without the user having to manually figure out the best way to encode them. I make it clear that this is an appealing property of mrjob, and I make it clear that this is the reason for the slowdown. As is, the code will not work with RawProtocol internally because the key is a tuple of words.