Related to the particular details of implementing various aggregation types, we should first put a bit of energy into the abstract API for aggregating data in a multi-threaded setting

Aggregators must support both hash/group (e.g. "group by" in SQL or data frame libraries) modes and non-group modes.

Aggregations ideally should also support filter pushdown. For example:

select $AGG($EXPR)
from $TABLE
where $PREDICATE

Some systems might materialize the post-predicate / filtered version of $EXPR, then aggregate that. pandas does this for example. Vectorized performance can be much improved by filtering inside the aggregation kernel. How the predicate true/false values are handled may depend on the implementation details of the kernel (e.g. SUM or MEAN will be a bit different from PRODUCT)

Reporter: Wes McKinney / @wesm
Assignee: Francois Saint-Jacques / @fsaintjacques

Related issues:

• [C++] Parallelize execution of ScalarAggregateFunction (relates to)
• [C++] Mean kernel aggregate (relates to)
• [C++] Incremental Count, Count Not Null aggregator (relates to)
• [C++] Incremental Variance, Standard Deviation aggregators (relates to)

PRs and other links:

• GitHub Pull Request #3407

_{Note: This issue was originally created as ARROW-4124. Please see the migration documentation for further details.}