{"id":49529,"date":"2023-11-15T13:14:47","date_gmt":"2023-11-15T12:14:47","guid":{"rendered":"https:\/\/www.inovex.de\/?p=49529"},"modified":"2023-11-15T13:14:47","modified_gmt":"2023-11-15T12:14:47","slug":"increasing-query-performance-using-the-snowflake-query-history","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/increasing-query-performance-using-the-snowflake-query-history\/","title":{"rendered":"Increasing Query Performance Using the Snowflake Query History"},"content":{"rendered":"

Picture this: You are responsible for running a big data platform in Snowflake and over the last few months you noticed a steady increase in the consumed credits. There are no new use cases that you know of. You have already talked to the data onboarding team about the amount of data that is ingested every day and they confirmed to you, that the amount of ingested data has been more or less constant over the last half year. So the old use cases are not dealing with an increase in data volume per day. How do you find out what is causing the increase in consumed credits?<\/p>\n

Other than monetary cost, there are many different reasons why you should be concerned about the performance of your data pipelines<\/a>. Faster query execution means users can retrieve the information they need more quickly. This is crucial in applications where real-time or near-real-time responses are required. Faster access to data also means quicker decision-making, which can be crucial in situations where timely information is of utmost importance (e.g., in emergency response systems). Finally, well-optimized query performance enables the system to handle more concurrent users or larger datasets without a significant drop in response times.<\/p>\n

In this article, we take a look at Snowflake’s QUERY_HISTORY view. How can it be used to identify costly queries and what clues does it provide for fixing problematic queries?<\/p>\n

\n

<\/p>\n<\/div>