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Practical guides to making BigQuery queries faster and cheaper — partitioning, clustering, search indexes, time travel, cost optimization, and query tuning strategies.
So, a couple of months ago, I posted about BigQuery search indexes, interesting to those who work with large volumes of STRING or JSON data. Recently, I came across a blog post introducing, in preview, the indexing of INT64 and TIMESTAMP columns as w...
I recently encountered an interesting case that reminded me of a couple of things and taught me a few lessons. When working with BigQuery tables, partitioning and clustering are often go-to operations. Typically, we would partition by a meaningful da...
Have you ever used ingestion-time partitioning in BigQuery? It's a separate type of partitioning that distributes rows into partitions based on the time they land in BQ. Once such a table is defined, you can query the pseudocolumns PARTITIONDATE and ...
Riding on the back of recent news that BigQuery table partition limit has just increased from 4k to 10k partitions, I wanted to talk a bit about the PARTITIONS view in INFORMATION_SCHEMA. I've previously posted about information schema views, but thi...
Here's something that might be interesting if you analyze large volumes of STRING or JSON data in BigQuery. Let's look at SEARCH indexes and what you need to know to get started. So, what are they used for? You have a big table (>10 GB) with STRING o...
When it comes to performance improvements and cost savings, handling only as much data as we need is very important. And partitioning is a cornerstone here. Now, when working with tables that are partitioned, BigQuery tries to exclude the partitions ...
