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What the benchmark should answer
Start by defining the production decision you need to make. Are you choosing a primary key for a write-heavy table, optimizing point lookups, supporting time-range queries, or evaluating a mix of operations? A benchmark that measures only inserts cannot settle a decision driven by joins, lookups, or range scans.
Compare the same workload with UUIDv4, UUIDv7, and ULID where each is relevant. Record rows or transactions per second, median and tail latency (at least p95, preferably p99), table and index size, point-lookup behavior, and range-query behavior when the application uses time ordering. Repeat the runs and report variation rather than selecting the best result.
What differs between UUIDv4, UUIDv7, and ULID
| Identifier | Ordering and format | Benchmark implications |
|---|---|---|
| UUIDv4 | Randomly generated UUID. | Its random insertion pattern is the useful baseline for testing locality effects. |
| UUIDv7 | Time-ordered UUID. PostgreSQL 18 documents the native uuidv7() generator. |
Test its locality and range-query effects on your workload; do not assume they determine whole-workload performance. |
| ULID | 128-bit identifier encoded in a canonical 26-character Crockford Base32 string, with a 48-bit Unix-millisecond timestamp and 80 random bits. Same-millisecond ordering is not guaranteed by default; some generators offer monotonic behavior. | Identify the specific generator and whether it is monotonic. If stored as text, representation and collation are part of the test. |
PostgreSQL’s uuid type accepts UUID values regardless of their source or version. PostgreSQL 17 documentation lists UUIDv4 generation but not native UUIDv7 generation; PostgreSQL 18 documents uuidv7() as generating a version 7, time-ordered UUID. Disclose the exact major and minor release and, for UUIDv7 on versions without the documented native generator, whether an external library or custom function creates the values. ULID is a separate format, not a built-in PostgreSQL UUID generator. See the PostgreSQL 18 UUID type documentation and UUID functions documentation.
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Does UUIDv7 make PostgreSQL indexes faster?
It can improve insert locality, but that is a reason to test, not a guarantee of faster overall queries or transactions. RFC 9562 says: “UUID versions that are not time ordered, such as UUIDv4 (described in Section 5.4), have poor database-index locality.” Time-ordered identifiers can place successive inserts closer together in a B-tree. The actual outcome depends on table growth, concurrency, PostgreSQL version, identifier implementation, and the read and write mix.
PostgreSQL Conference Europe 2025 slides discuss improved B-tree locality and range-query behavior as potential benefits, while noting that join-heavy workloads may differ. Treat those observations as context, not a prediction for your system. Test the operations your application actually performs.
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Build an equivalent test
- Choose the target environment. Record the PostgreSQL major and minor version, operating system, CPU, memory, storage, database settings, client location, and concurrency. Use the target host when possible; remote clients can add network latency to measurements.
- Keep the schemas matched. Use equivalent tables with the same non-key columns, constraints, transaction shape, fill settings, and secondary indexes. Change the identifier format or generation method under test, not row width or index count.
- Make representation explicit. If comparing ULID stored as text with UUID stored as
uuid, state that storage representation and collation are part of the comparison. For a narrower question about ordering or generation, use representations that make that question as comparable as practical. - Test the relevant lifecycle. Run an empty-table test and a grown-table test if both reflect production. Use the same row counts and workload phases for each identifier option; record whether the cache is warm or cold and the checkpoint and vacuum state.
- Control ID generation. Record the exact UUID or ULID generator and whether it runs in the application or database. If application-generated ULIDs are compared with database-generated UUIDs, measure generation separately from insertion so the database-index result is not confused with client-side generation cost.
- Run and repeat representative workloads. Include the real write/read mix and concurrency levels. Warm up, repeat runs, avoid competing activity where possible, and report the spread along with medians and tail latencies.
- Check that the intended indexes are used. Inspect query plans for point lookups and range scans. A fast query that does not use the index under test does not establish its performance.
Run the workload with pgbench or an application harness
pgbench supports multiple clients and threads, transaction logging, and latency reporting. It is a practical driver for repeatable database workloads; an application-specific harness may be more representative if it can reproduce your production transaction and query mix.
For pgbench, prepare a separate equivalent table and workload for each identifier option, then run the same script and client settings against each one. Save the SQL script, exact command, output, and environment details with the results. Configure the script to exercise the operation being measured—such as inserts, point lookups, or time-range queries—instead of timing identifier function calls alone. Report the client count, thread count, transaction count or run duration, and whether each run starts from an empty or grown table. The exact command and SQL depend on the chosen schema and operation, so do not treat a generic pgbench invocation as a substitute for documenting those choices.
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Measure inserts, reads, and storage separately
- Writes: Record throughput and median, p95, and preferably p99 latency at representative concurrency. Keep transaction boundaries and batch sizes identical.
- Point lookups: Measure lookups by primary key using the same query shape and comparable key selection. Confirm the plan uses the intended index.
- Range scans: If the application queries by time or ID order, measure those scans with equivalent bounds and result sizes. Time ordering may matter more here than in a workload dominated by unrelated lookups.
- Size: Record table and index relation sizes at the same row count and after the same workload. A size comparison is meaningful only if the schemas and secondary indexes are matched.
- Generation: If generator overhead matters, benchmark it separately from database writes and identify the implementation and runtime. A function-only timing is not an index benchmark.
Interpret results as workload-specific
Compare each identifier across the axes that matter to the application: insert throughput and tail latency, table and B-tree size, point lookups, relevant range queries, generation cost and deployment complexity, and whether timestamp visibility or ordering is acceptable. A result can favor one format for writes and another for a read-heavy or join-heavy workload.
ULID’s canonical text representation can affect storage and index behavior compared with PostgreSQL’s uuid representation. Report the collation used and the generator’s same-millisecond behavior; monotonic ULID generation depends on implementation. UUIDv7’s time ordering likewise does not by itself establish a whole-system win.
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Public repositories demonstrate possible benchmark designs, including warmups, repeated cycles, concurrency, and queries for table and index size. Their generator timings and database outcomes are specific to their versions, implementations, and environments, so they are not a general performance forecast. No broadly applicable UUID-versus-ULID PostgreSQL index percentage should be inferred from them.
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