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Measure an index’s storage in bytes, then measure its write overhead by comparing the same representative workload with and without that index. The results depend on the database, index definition, data, and workload; there is no reliable universal percentage for how much slower an index makes writes.
What to measure
There are two separate costs to quantify:
- Storage: the bytes occupied by an index now. This is a size measurement, not a forecast of future growth.
- Write maintenance: the change in insert, update, and delete throughput and latency when the index is present. An index can help reads while adding work to writes.
Keep those results alongside the read queries the index is meant to improve. An index is not simply “cheap” or “expensive”: its trade-off depends on which operations matter in the workload.
Define the workload before measuring
Record enough context for someone else to interpret or reproduce the comparison. At minimum, note:
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- Table size and row count, plus the data distribution relevant to the indexed columns.
- Index method and full definition, including indexed columns, expressions, included columns, and storage parameters.
- Hardware and storage, database settings, and concurrency.
- The actual mix of inserts, updates, and deletes.
- Whether the workload is synthetic or sampled from production, and how the data was loaded or restored.
A result from a small test table or a synthetic write pattern may not represent production behavior. Keep the scale and workload representative of the questions the index needs to answer.
Measure index storage in PostgreSQL
PostgreSQL provides size functions that report relation storage in bytes. For a table’s indexes, an individual index, or broader table totals, use the function that matches the question:
| Question | PostgreSQL function | What it measures |
|---|---|---|
| How much space do all indexes attached to this table use? | pg_indexes_size('schema.table') |
Total size of the table’s indexes. |
| How much space does one index relation use? | pg_relation_size('schema.index_name') |
Size of that relation, in bytes. |
| How much table storage is used, excluding indexes? | pg_table_size('schema.table') |
Table storage excluding indexes. |
| What is the table total including indexes and TOAST data? | pg_total_relation_size('schema.table') |
Total including indexes and TOAST data. |
These functions describe current on-disk relation sizes, not expected growth. PostgreSQL documents the size functions in its database object size documentation. The functions and SQL shown here are PostgreSQL-specific; check the relevant documentation for other database engines.
Check whether the index improves reads
Before deciding whether write overhead is worthwhile, check the queries the index is intended to support. PostgreSQL recommends collecting planner statistics and examining query plans rather than judging usefulness from the index definition alone.
- Refresh statistics with
ANALYZEafter loading or materially changing the test data. - Inspect representative queries with
EXPLAINto see the planner’s chosen plan. - Use
EXPLAIN ANALYZEwhere appropriate to execute the query and measure its plan. PostgreSQL’s Examining Index Usage guidance discusses this approach.
EXPLAIN ANALYZE adds measurement overhead and does not include time spent sending results to the client. PostgreSQL warns that this overhead can be significant, particularly on machines with slow operating-system gettimeofday() calls; see Using EXPLAIN. Treat plan timings as diagnostic measurements, not automatically as end-to-end application latency. Results from toy data should not be extrapolated to a different scale.
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Compare writes with and without the index
To estimate write overhead for a candidate index, run a controlled comparison rather than applying a generic multiplier. The following is a measurement design, not a universal benchmark recipe:
- Prepare equivalent starting data. Load or restore identical data for each run. Keep database version, settings, hardware, and other relevant conditions the same.
- Run the representative write mix. Perform the same inserts, updates, and deletes at the intended scale and concurrency. Compare the candidate configuration with the equivalent configuration without that index.
- Record throughput and latency distributions. Capture how many operations complete and how long they take, including latency variation rather than only a single average.
- Track supporting signals. Record CPU and I/O alongside database-level statistics when available.
- Repeat and document conditions. Repeat runs enough to expose variability, and note cache and warm-up conditions so results can be interpreted.
PostgreSQL provides per-index statistics useful for understanding index use. Its monitoring guidance also recommends combining database statistics with operating-system utilities for a fuller I/O picture; see Monitoring Database Activity.
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Compare candidate indexes on the same axes
If you are choosing between real index definitions or settings, compare them under the same data and workload. Report:
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- Index size in bytes.
- Insert, update, and delete throughput and latency under the representative workload.
- Which read queries improved, with their plans and latency changes.
- CPU and I/O implications.
- Index method, definition, and storage settings.
For example, PostgreSQL B-tree fillfactor changes how densely pages are packed and can influence page-split behavior. The effect depends on the workload; changing this setting should be evaluated with the same read and write measurements rather than assumed to reduce cost. See CREATE INDEX.
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When sharing results, state the engine and version, index definition, data scale, workload, environment, and measurement date. A local benchmark describes that setup; it does not establish a general penalty for other systems or workloads.
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