Manage databases

Schemas with many measurements that contain focused sets of tags and fields can make it easier for the query engine to identify what partitions contain the queried data, resulting in better query performance.

By default, InfluxDB Clustered partitions data by measurement and time range and stores each partition as a Parquet file in your cluster’s object store. By increasing the number of measurements (tables) you can store in your database, you also increase the potential for more PUT requests into your object store as InfluxDB creates more partitions. Each PUT request incurs a monetary cost and will increase the operating cost of your cluster.

To optimize storage over time, your InfluxDB cluster contains a compactor that routinely compacts Parquet files in object storage. With more tables and partitions to compact, the compactor may need to be scaled (either vertically or horizontally) to keep up with demand, adding to the operating cost of your cluster.

At query time, the InfluxDB query engine identifies what table contains the queried data and then evaluates each row in the table to match the conditions of the query. The more columns that are in each row, the longer it takes to evaluate each row.

Through performance testing, InfluxData has identified 250 columns as the threshold beyond which query performance may be affected (depending on the shape of and data types in your schema).