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Why would you shorten an embedding from 1536 dimensions to 256, and what do you give up by doing it?
Fewer dimensions means less storage per vector and faster similarity computation at query time, which matters directly at scale, a vector database holding millions of embeddings pays that storage and compute cost on every one of them. Modern embedding models are specifically trained to support this trade-off gracefully: a newer model shortened to 256 dimensions can still outperform an older, unshortened model at 1536 dimensions, so shortening isn't simply "less accurate," it's a deliberate trade against a specific model's own accuracy ceiling. What you give up is some of that ceiling, the shortened embedding is measurably less precise than the same model's full-length embedding, which is why the right dimension count depends on how much accuracy a specific use case can actually trade away.
A query filters WHERE logdate >= 2008-01-01 against a table range-partitioned by logdate across dozens of monthly partitions. What does partition pruning actually do, and what does it depend on?
Partition pruning lets the planner prove, from the query's WHERE clause and each partition's declared bounds, that some partitions cannot possibly contain a matching row, and it excludes them from the plan entirely rather than scanning and filtering every partition. In this example, `logdate >= 2008-01-01` has no upper bound, so it prunes only the partitions entirely before 2008-01, decades of older monthly partitions are eliminated before execution, while every partition from 2008-01 onward, including all of them up to the present, is still considered and scanned. Pruning down to a single partition would need a bounded predicate on both ends, for example `logdate >= 2008-01-01 AND logdate < 2008-02-01`. This depends entirely on the partition bounds themselves, not on any index, a partitioned table with no indexes at all still benefits from pruning, and pruning specifically requires the WHERE clause to reference the partition key directly with values (or parameters) the planner can actually compare against those bounds.