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Map-Reduce and Sharded Collections

Map-Reduce and Sharded Collections¶

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Map-reduce supports operations on sharded collections, both as an input and as an output. This section describes the behaviors of mapReduce specific to sharded collections.

Sharded Collection as Input¶

When using sharded collection as the input for a map-reduce operation, mongos will automatically dispatch the map-reduce job to each shard in parallel. There is no special option required. mongos will wait for jobs on all shards to finish.

Sharded Collection as Output¶

If the out field for mapReduce has the sharded value, MongoDB shards the output collection using the _id field as the shard key.

To output to a sharded collection:

If the output collection does not exist, MongoDB creates and shards the collection on the _id field.
For a new or an empty sharded collection, MongoDB uses the results of the first stage of the map-reduce operation to create the initial chunks distributed among the shards.
mongos dispatches, in parallel, a map-reduce post-processing job to every shard that owns a chunk. During the post-processing, each shard will pull the results for its own chunks from the other shards, run the final reduce/finalize, and write locally to the output collection.

Note

During later map-reduce jobs, MongoDB splits chunks as needed.
Balancing of chunks for the output collection is automatically prevented during post-processing to avoid concurrency issues.

In MongoDB 2.0:

mongos retrieves the results from each shard, performs a merge sort to order the results, and proceeds to the reduce/finalize phase as needed. mongos then writes the result to the output collection in sharded mode.
This model requires only a small amount of memory, even for large data sets.
Shard chunks are not automatically split during insertion. This requires manual intervention until the chunks are granular and balanced.

Important

For best results, only use the sharded output options for mapReduce in version 2.2 or later.

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