Improve the accuracy of floating point statistical aggregates

Enterprise / PostgreSQL - Dean Rasheed [gmail.com] - 6 October 2018 10:20 EDT

When computing statistical aggregates like variance, the common schoolbook algorithm which computes the sum of the squares of the
values and subtracts the square of the mean can lead to a large loss of precision when using floating point arithmetic, because the difference between the two terms is often very small relative to the terms themselves.

To avoid this, re-work these aggregates to use the Youngs-Cramer algorithm, which is a proven, numerically stable algorithm that directly aggregates the sum of the squares of the differences of the
values from the mean in a single pass over the data.

While at it, improve the test coverage to test the aggregate combine functions used during parallel aggregation.

Per report and suggested algorithm from Erich Schubert.

Patch by me, reviewed by Madeleine Thompson.

Discussion: https://postgr.es/m/153313051300.1397.9594490737341194671@wrigleys.postgresql.org

e954a727f0 Improve the accuracy of floating point statistical aggregates.
src/backend/utils/adt/float.c | 732 ++++++++++++++++++++-----------
src/test/regress/expected/aggregates.out | 128 ++++++
src/test/regress/sql/aggregates.sql | 41 ++
3 files changed, 639 insertions(+), 262 deletions(-)

Upstream: git.postgresql.org


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