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Is your feature request related to a problem? Please describe.
One of the easiest ways to speedup pandas code is to support indexes
Describe the solution you'd like
GFQL has a few interesting scenarios here:
Indexing on node/edge IDs wrt global lookups
Indexing on additional columns, especially text
Indexes being passed in
Indexing being requested
Indexes happening on-the-fly at start & mid-traversal
It's unclear what's most important, I'm guessing:
node/edge ID indexing <-- may give near-parity w/ naive non-DF-based graph traversals
str indexing, esp for initial searches
some sort of triple support
Multicol indexing with some attribs of interest like node/edge type
A tricky aspect here is global vs dynamic indexing. Ex:
ahead of time, or start, generate indexes on the 'global' node + edge DFs, and have those get used mid-traversal when sufficiently small etc, such as during enrichment
dynamic reindexing mid-traversal
A lot of this gets into query planning, so another consideration is identify something very simple now, and defer the rest to a more structured planning system
The text was updated successfully, but these errors were encountered:
Is your feature request related to a problem? Please describe.
One of the easiest ways to speedup pandas code is to support indexes
Describe the solution you'd like
GFQL has a few interesting scenarios here:
It's unclear what's most important, I'm guessing:
A tricky aspect here is global vs dynamic indexing. Ex:
A lot of this gets into query planning, so another consideration is identify something very simple now, and defer the rest to a more structured planning system
The text was updated successfully, but these errors were encountered: