Impact of Non-Covering Non-Clustered Indexes on Query Performance in PostgreSQL
If a non-clustered index (also known as a secondary index in PostgreSQL) is useful for quickly finding one or more records but does not cover the query (i.e., it does not include all the columns needed for the query), the following sequence of events typically occurs:
1. Index Lookup
- PostgreSQL uses the non-clustered index to perform a quick lookup of the rows that match the query’s conditions. The index contains pointers (typically row IDs or physical addresses) to the actual table rows that satisfy the search criteria.
- This step is efficient because the index is usually smaller and sorted, allowing PostgreSQL to quickly narrow down the list of matching rows.
2. Heap Fetch (Table Access)
- After identifying the relevant rows through the index, PostgreSQL needs to fetch the full row data from the table (also known as the heap). This is necessary because the non-clustered index does not contain all the columns required by the query.
- For each matching row identified in the index, PostgreSQL must perform a random I/O operation to retrieve the complete row from the table. This step can be time-consuming, especially if the table is large or the rows are scattered across different pages on disk.
3. Increased I/O Operations
- Since the index does not “cover” the query (i.e., does not contain all the columns needed to fulfill the query), multiple I/O operations may be required to fetch each row from the table. The cost of these additional reads can become significant if there are many matching rows or if the table’s data pages are not in memory (cached).
- The process of moving back and forth between the index and the table data (also known as “table lookups” or “heap fetches”) can lead to increased disk I/O, particularly if the rows are not stored contiguously on disk.
4. Potential Performance Degradation
- If the query involves a large number of rows or if there is a high degree of fragmentation in the table, the performance can degrade significantly due to the overhead of random I/O operations. The benefit of using the index to find the relevant rows can be outweighed by the cost of fetching those rows from the table.
- This is particularly true for OLTP systems with frequent updates and inserts, which can cause row fragmentation and lead to scattered I/O operations.
Conclusion
While a non-clustered index helps speed up the initial lookup phase of the query, if the index does not cover all the columns required by the query, the subsequent need to fetch additional data from the table can lead to increased I/O operations and potential performance degradation. In such cases, creating a covering index (an index that includes all columns needed by the query) or considering other optimization strategies, such as partitioning or using materialized views, may help improve query performance.
Understanding Clustered and Non-Clustered Indexes in PostgreSQL
How Many Access Paths are Available to the PostgreSQL Optimizer?
