What problem are we solving?• Map/Reduce can be used for aggregation… • Currently being used for totaling, averaging, etc• Map/Reduce is a big hammer • Simpler tasks should be easier • Shouldn’t need to write JavaScript • Avoid the overhead of JavaScript engine• We’re seeing requests for help in handling complex documents • Select only matching subdocuments or arrays
How will we solve the problem?• Our new aggregation framework • Declarative framework • No JavaScript required • Describe a chain of operations to apply • Expression evaluation • Return computed values • Framework: we can add new operations easily • C++ implementation • Higher performance than JavaScript
Aggregation - Pipelines• Aggregation requests specify a pipeline• A pipeline is a series of operations• Conceptually, the members of a collection are passed through a pipeline to produce a result • Similar to a command-line pipe
Pipeline Operations• $match • Uses a query predicate (like .find({…})) as a filter• $project • Uses a sample document to determine the shape of the result (similar to .find()’s optional argument) • This can include computed values• $unwind • Hands out array elements one at a time• $group • Aggregates items into buckets defined by a key
Pipeline Operations (continued)• $sort • Sort documents• $limit • Only allow the specified number of documents to pass• $skip • Skip over the specified number of documents
Computed Expressions• Available in $project operations• Prefix expression language • Add two fields: $add:[“$field1”, “$field2”] • Provide a value for a missing field: $ifNull:[“$field1”, “$field2”] • Nesting: $add:[“$field1”, $ifNull:[“$field2”, “$field3”]] • Other functions…. • And we can easily add more as required
Computed Expressions (continued)• String functions • toUpper, toLower, substr• Date field extraction • Get year, month, day, hour, etc, from ISODate• Date arithmetic• Null value substitution (like MySQL ifnull(), Oracle nvl())• Ternary conditional • Return one of two values based on a predicate
Projections• $project can reshape results • Include or exclude fields • Computed fields • Arithmetic expressions, including built-in functions • Pull fields from nested documents to the top • Push fields from the top down into new virtual documents
Unwinding• $unwind can “stream” arrays • Array values are doled out one at time in the context of their surrounding documents • Makes it possible to filter out elements before returning
Grouping• $group aggregation expressions • Define a grouping key as the _id of the result • Total grouped column values: $sum • Average grouped column values: $avg • Collect grouped column values in an array or set: $push, $addToSet • Other functions • $min, $max, $first, $last
Sorting• $sort can sort documents • Sort specifications are the same as today, e.g., $sort:{ key1: 1, key2: -1, …}
DemoDemo files are at https://gist.github.com/1401585
Usage Tips• Use $match in a pipeline as early as possible • The query optimizer can then be used to choose an index and avoid scanning the entire collection• Use $sort in a pipeline as early as possible • The query optimizer can sometimes be used to choose an index to scan instead of sorting the result
Driver Support• Initial version is a command • For any language, build a JSON database object, and execute the command • { aggregate : <collection>, pipeline : {…} } • Beware of command result size limit • Document size limit is 16MB
When is this being released?• In final development now• Expect to see this in the near future
Sharding support• Initial release will support sharding• Mongos analyzes pipeline, and forwards operations up to $group or $sort to shards; combines shard server results and returns them
Pipeline Operations – Future Plans• $out • Saves the document stream to a collection • Similar to M/R $out, but with sharded output • Functions like a tee, so that intermediate results can be saved
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