MR is actually growing at Google still and heavily used by legacy systems and experienced engineers.
Compared to newer frameworks like those described in the FlumeJava and MillWheel papers, MR's growth is flat.
This is also happening in the Hadoop ecosystem too: if you're writing JavaMR by hand, you're probably spending more time and writing less efficient jobs than what you might get by an optimized pig/hive job with tez under the hood. Or through something in the Cascading or Crunch family, which provides useful abstractions on top of MR or other execution engines.
Then there are also a lot of tools popping up that take some if the use cases that were shoehorned into MR which are more natural outside, like ML/iterative computation through Spark.
MR isn't dying inside or outside Google, it's just being abstracted away.
Compared to newer frameworks like those described in the FlumeJava and MillWheel papers, MR's growth is flat.
This is also happening in the Hadoop ecosystem too: if you're writing JavaMR by hand, you're probably spending more time and writing less efficient jobs than what you might get by an optimized pig/hive job with tez under the hood. Or through something in the Cascading or Crunch family, which provides useful abstractions on top of MR or other execution engines.
Then there are also a lot of tools popping up that take some if the use cases that were shoehorned into MR which are more natural outside, like ML/iterative computation through Spark.
MR isn't dying inside or outside Google, it's just being abstracted away.