In my use case I have a 2billion / 1To table. I have daily data to upsert around 2milion, with say 50% inserts, based on the primary key in a fresh analyzed table.
I have tested multiple strategies to merge the data, all based on first stage to copy the 2m dataset in an staging unlogged / indexed table: 1. Join insert then join update 2.1. Usage of the new merge statement 2.2 Usage of merge on two hash partitioned tables wit partition wide join enabled 3. Usage of merge by batch of 1000 rows First remark is the merge statement is almost 30% faster than two statements in my benchmarks. Thanks to the pg community for this. While the strategies 1 and 2.x are incredibly slow (canceled after 10 hours), the third one finishes within 30 minutes. My interpretation reading the query plan is: well sized small batches of upserts leverage the indexes while the regular join choose the sequential scan, including sorting and hashing which takes forever time and resources including disk. Sadly my incoming dataset is too small to benefit from a seq scan and too large to benefit from an index scan join. However when splited manuallyin N portions, the problem can be tackled with N * small cost, which is cheap anyway. Questions: 1. Is there another strategy ? 2. Could postgres support a "batched indexed join itself", leveraging indexes itself by dynamic sized batches ? It is error prone write code to split and iterate I suspect postgres has everything internally (indexes catalog, planner) to split itself the job, making David vs Goliath something trivial.
