On 2/12/21 12:46 PM, Karthik Kumar Kondamudi wrote:
Hi,
I'm looking for suggestions on how I can improve the performance of the below merge statement, we have a batch process that batch load the data into the _batch tables using Postgres and the task is to update the main target tables if the record exists else into it, sometime these batch table could go up to 5 billion records. Here is the current scenario
target_table_main has 700,070,247 records and is hash partitioned into 50 chunks, it has an index on logical_ts and the batch table has 2,715,020,546 close to 3 billion records, so I'm dealing with a huge set of data so looking of doing this in the most efficient way.
Many times, I have drastically sped up batch processing by #1 partitioning on the same field as an index, and #2 pre-sorting the input data by that field.
That way, you get excellent "locality of data" (meaning lots of writes to the same hot bits of cache, which later get asynchronously flushed to disk). Unfortunately for your situation, the purpose of hash partitioning is to
reduce locality of data. (Sometimes that's useful, but
not when processing batches.)
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