Queries (particularly) repetitive queries that don't take advantage of results caching can lead to extraordinarily high bills.
StackQL, with it's backend SQL engine, allows you to query Big Query statistics in real time, including identifying queries which are not served from cache and understanding billable charges per query or time slice.
Big Query provides a wealth of metrics and statistics for jobs run against it which could be queries, load jobs or export jobs. This article demonstrates some queries you can run using StackQL to bring back live statistics from load operations into Big Query as well as detail regarding errors encountered during the loading of data into Big Query.
In the previous post, we showed you how to enable usage and storage logging for GCS buckets. Now that we have enabled logging, let's load and analyze the logs using Big Query. We will build up a data file vars.jsonnet as we go and show the queries step by step, at the end we will show how to run this as one batch using StackQL.
In a previous article, Deploying and Querying GCS Buckets using StackQL, we walked through some basic creation and query operations on Google Cloud Storage buckets. In this post we will extend on this by enabling logging on a GCS bucket using StackQL. This post is based upon this article: Usage logs & storage logs.