@ -2,7 +2,7 @@ MCV lists
=========
Multivariate MCV (most-common values) lists are a straightforward extension of
regular MCV list, tracking most frequent combinations of values for a group of
regular MCV lists , tracking most frequent combinations of values for a group of
attributes.
This works particularly well for columns with a small number of distinct values,
@ -18,7 +18,7 @@ Estimates of some clauses (e.g. equality) based on MCV lists are more accurate
than when using histograms.
Also, MCV lists don't necessarily require sorting of the values (the fact that
we use sorting when building them is implementation detail), but even more
we use sorting when building them is an implementation detail), but even more
importantly the ordering is not built into the approximation (while histograms
are built on ordering). So MCV lists work well even for attributes where the
ordering of the data type is disconnected from the meaning of the data. For
@ -53,7 +53,7 @@ Hashed MCV (not yet implemented)
Regular MCV lists have to include actual values for each item, so if those items
are large the list may be quite large. This is especially true for multivariate
MCV lists, although the current implementation partially mitigates this by
performing de-duplicating the values before storing them on disk.
de-duplicating the values before storing them on disk.
It's possible to only store hashes (32-bit values) instead of the actual values,
significantly reducing the space requirements. Obviously, this would only make
@ -77,7 +77,7 @@ to select the columns from pg_stats. The data is encoded as anyarrays, and
all the items have the same data type, so anyarray provides a simple way to
get a text representation.
With multivariate MCV lists the columns may use different data types, making
With multivariate MCV lists, the columns may use different data types, making
it impossible to use anyarrays. It might be possible to produce a similar
array-like representation, but that would complicate further processing and
analysis of the MCV list.