How can you optimize database performance for Apache Kudu?

1 Choose the right schema

The schema of your Kudu tables has a major influence on how data is stored, partitioned, and accessed. To optimize your schema, you should choose the primary key carefully, as it determines the order and distribution of the data across the Kudu tablets. High cardinality and low skew are ideal characteristics for a primary key, as well as frequent use in queries. Additionally, you should avoid using sequential or monotonically increasing keys, since they can cause hotspots and imbalance in the cluster. Furthermore, Kudu supports different data types like integers, strings, decimals, and timestamps; therefore, it is important to use the most suitable type for your data to optimize storage size, compression, and encoding. Additionally, you can define column defaults and nullability constraints for each column to avoid inserting null values and simplify data loading and ingestion.

3 Tune your queries and settings

The performance of your Kudu queries can be improved by how you write and execute them, as well as adjusting the cluster and client settings. To achieve this, you should use predicates and projections that match the primary key and partitioning columns. This will leverage the index and reduce the amount of data that Kudu has to scan and transfer. Additionally, you should optimize your joins and aggregations by using techniques like co-location and pre-aggregation. Finally, you should adjust parameters such as scan cache size, scan batch size, number of scan tokens, and scan timeout according to your workload and environment. By following these best practices, you can enjoy the benefits of faster analytics and real-time updates on large data sets.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?