The tech lead of the Google MapReduce team (which no longer exists) just received their award for turning down mapreduce. IIRC it was officially done 5 years ago. However I believe the code to delete MR was never checked in and I'm not sure if there are still users.
MapReduce was used at Google for highly inappropriate things. For example, the machine learning system I worked on, Sibyl https://www.datanami.com/2014/07/17/inside-sibyl-googles-mas... was implemented using mapreduce but there was no real technical justification for that- it's just that there was no other system that could scale to the volumes required or handle the constant failures endemic to GOogle's internal systems. It ended up requiring all sorts of heroic work to make MR scale, for example map-side combiners (which "reduced" items with common keys in the map output before it gets flushed to the shuffle files). All of this got replaced with TensorFlow and only the good bits of Sibyl were extracted to TFX.
i.e this is a crappy tool but it's best tool we have come up with for this problem thus far.
There are lots of such tools which are used begrudgingly by people that have an intuition for the fact it can be done better but not the concrete idea and/or time to implement it.
it wasn't a crappy tool (mapreduce was amazing) but it definitely was an impedence mismatch for this particular job. Later, we tried to get SIbyl to move to the underlying compute engine that Flume was built on top of it but it turned out to be more profitable to just let it die slowly.
MapReduce was deprecated because flume [0] the successor is better but it does practically the same thing and flume is used massively. I believe dataflow is the public google cloud version.
Yes, which is why it's amusing in hindsight that for a decade everyone* outside Google was forcing all* their distributed data tasks into the MapReduce paradigm, without considering alternative approaches like the one used by Spanner.
I'm not sure how you think a distributed data processing technology would "fake-out" other companies when building/choosing database technology. They are totally different problem sets.
MapReduce does not have a set in stone data source/sink and can use multiple things like bigtable and spanner so they are complementary technologies.
I think the parent commenter might be referring to systems like Hive or HBase built on top of Hadoop and do have a lot of overlap with a large scale database system.
Exactly, thanks. "MapReduce paradigm" wasn't precise, and I can see why that makes everyone want to give a distributed systems 101 lecture in this comment thread :)
Yes it did. Google published a paper about it in 2012, and claimed that at that point it had been in development for 5 years and in production for more than 1.
Mmm, I worked at google in 2012 and F1 was in active development.
I’ve never heard of Spanner internally. Maybe it was in development, but it was not in use.
Edit: went and read more. Looks like Spanner existed but didn’t have sql, so it wasn’t what it is today. And looks like I don’t remember things any more.
at least by 2013 it had some basic SQL (although much more limited subset that one usable today); if you needed more you would be using f1; IIRC I used spanner (without F1) in production around 2013
MapReduce was used at Google for highly inappropriate things. For example, the machine learning system I worked on, Sibyl https://www.datanami.com/2014/07/17/inside-sibyl-googles-mas... was implemented using mapreduce but there was no real technical justification for that- it's just that there was no other system that could scale to the volumes required or handle the constant failures endemic to GOogle's internal systems. It ended up requiring all sorts of heroic work to make MR scale, for example map-side combiners (which "reduced" items with common keys in the map output before it gets flushed to the shuffle files). All of this got replaced with TensorFlow and only the good bits of Sibyl were extracted to TFX.