This topic describes use cases for columnstore indexes.
Use case | Description |
In a wide table scenario, use an In-Memory Column Index (IMCI) to improve the performance of queries that analyze only a subset of columns. | |
IMCI supports partitioned tables to handle statistical and analytical workloads on large and growing datasets. | |
Using columnstore indexes to accelerate materialized view refreshes | When you process large volumes of data, such as billions of records, refreshing PostgreSQL materialized views can be extremely slow. This leads to low data freshness and negatively impacts the efficiency of BI analysis and report generation. Using IMCI significantly reduces the time required to refresh materialized views. This improves data freshness and speeds up BI analysis and report generation. |
Using columnstore indexes to accelerate time series data analytics | Business scenarios such as finance, logistics, and the Internet of Things (IoT) generate massive amounts of time series data, such as transaction records, trajectory data, and monitoring logs. Performing real-time analysis on terabytes of this data often presents performance challenges. IMCI enables real-time, high-performance analysis of massive time series data without complex data pre-processing, which helps you effectively extract value from the data. |