The document discusses MongoDB's new aggregation framework, which provides a declarative pipeline for performing data aggregation operations on complex documents. The framework allows users to describe a chain of operations without writing JavaScript. It will offer high-performance operators like $match, $project, $unwind, $group, $sort, and computed expressions to reshape and analyze document data without the overhead of JavaScript. The aggregation framework is nearing release and will support sharding by forwarding pipeline operations to shards and combining results.

1. Chris Westin
Software Engineer, 10gen
© Copyright 2010 10gen Inc.

2. 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

3. 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

4. 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

5. 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

6. Pipeline Operations (continued)
• $sort
• Sort documents
• $limit
• Only allow the specified number of documents to
pass
• $skip
• Skip over the specified number of documents

7. 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

8. 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

9. 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

10. 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

11. 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

12. Sorting
• $sort can sort documents
• Sort specifications are the same as today, e.g.,
$sort:{ key1: 1, key2: -1, …}

13. Demo
Demo files are at https://gist.github.com/1401585

14. 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

15. 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

16. When is this being released?
• In final development now
• Expect to see this in the near future

17. 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

18. 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