pgreplay-go

See a discussion of building this tool at https://blog.lawrencejones.dev/building-a-postgresql-load-tester/

This tool is a different take on the existing pgreplay project. Where pgreplay will playback Postgres log files while respecting relative chronological order, pgreplay-go plays back statements at approximately the same rate they were originally sent to the database.

When benchmarking database performance, it is better for a replay tool to continue sending traffic than to halt execution until a strangling query has complete. If your new cluster isn't performing, users won't politely wait for your service to catch-up before issuing new commands.

Benchmark strategy

You have an existing cluster and want to trial new hardware/validate configuration changes/move infrastructure providers. Your production services depend on ensuring the new change is safe and doesn't degrade performance.

1. Configure production logging

First capture the logs from your running Postgres instance. You may need to add additional storage to your machine before starting- we often attach a new disk and change our log location to that disk for experiments.

You can turn on logging in a compatible format like so:

ALTER SYSTEM SET log_directory='/postgres-logs';
ALTER SYSTEM SET log_connections='on';
ALTER SYSTEM SET log_disconnections='on';
ALTER SYSTEM SET log_line_prefix='%m|%u|%d|%c|';
ALTER SYSTEM SET log_min_error_statement='log';
ALTER SYSTEM SET log_min_messages='error';
ALTER SYSTEM SET log_statement='all';
ALTER SYSTEM SET log_min_duration_statement=0; 
SELECT pg_reload_conf();

2. Take snapshot

Now we're emitting logs we need to snapshot the database so that we can later restore it to the same moment in time on our benchmark clusters. If you're running in a cloud provider with disk snapshot facilities then this is likely the easiest of options (remember to checkpoint first, to reduce recovery time) but you can also achieve this using pg_basebackup and point-in-time recovery configuration, or by taking a physical copy of a paused replica.

Whatever method used must produce an image that can be restored into new machines later.

3. Extract and process logs

Once you've captured logs for your desired benchmark window, you can optionally pre-process them to create a more realistic sample. Complex database interactions are likely to have transactions that may fail when we play them back out-of-order. Recalling that our primary goal is to create representative load on the database, not a totally faithful replay, we suggest applying the following filters:

$ cat postgresql.log \
| pv --progress --rate --size "$(du postgresql.log | cut -f1)" \
| grep -v "LOG:  statement: BEGIN" \
| grep -v "LOG:  statement: COMMIT" \
| grep -v "LOG:  statement: ROLLBACK TO SAVEPOINT" \
| grep -v "LOG:  statement: SAVEPOINT" \
| grep -v "LOG:  statement: RELEASE SAVEPOINT" \
| grep -v "LOG:  statement: SET LOCAL" \
| sed 's/pg_try_advisory_lock/bool/g' \
| sed 's/pg_advisory_unlock/pg_advisory_unlock_shared/g' \
> postgresql-filtered.log

By removing transactions we avoid skipping work if any of the transaction queries were to fail - the same goes for savepoints. We remove any SET LOCAL statements, as having removed transactions these configuration settings would be present for the duration of the connection. We then modify any advisory lock queries to be shared, preventing us from needlessly blocking while still requiring the database to perform similar levels of work as the exclusive locking.

These transformations mean our replayed queries won't exactly simulate what we saw in production, but that's why we'll compare the performance of these filtered logs against the two clusters rather than the original performance of the production cluster.

4. pgreplay-go against copy of production cluster

Now create a copy of the original production cluster using the snapshot from (2). The aim is to have a cluster that exactly replicates production, providing a reliable control for our experiment.

The goal of this run will be to output Postgres logs that can be parsed by pgBadger to provide an analysis of the benchmark run. See the pgBadger readme for details, or apply the following configuration for defaults that will work:

ALTER SYSTEM SET log_min_duration_statement = 0;
ALTER SYSTEM SET log_line_prefix = '%t [%p]: [%l-1] user=%u,db=%d,app=%a,client=%h '
ALTER SYSTEM SET log_checkpoints = on;
ALTER SYSTEM SET log_connections = on;
ALTER SYSTEM SET log_disconnections = on;
ALTER SYSTEM SET log_lock_waits = on;
ALTER SYSTEM SET log_temp_files = 0;
ALTER SYSTEM SET log_autovacuum_min_duration = 0;
ALTER SYSTEM SET log_error_verbosity = default;
SELECT pg_reload_conf();

Run the benchmark using the pgreplay-go binary from the primary Postgres machine under test. pgreplay-go requires minimal system resource and is unlikely to affect the benchmark when running on the same machine, though you can run it from an alternative location if this is a concern. An example run would look like this:

$ pgreplay \
    --errlog-file /data/postgresql-filtered.log \
    --host 127.0.0.1 \
    --metrics-address 0.0.0.0 \
    --start <benchmark-start-time> \
    --finish <benchmark-finish-time>

If you run Prometheus then pgreplay-go exposes a metrics that can be used to report progress on the benchmark. See Observability for more details.

Once the benchmark is complete, store the logs somewhere for safekeeping. We usually upload the logs to Google cloud storage, though you should use whatever service you are most familiar with.

5. pgreplay-go against new cluster

After provisioning your new candidate cluster, with the hardware/software changes you wish to test, we repeat step (4) for our new cluster. You should use exactly the same pgreplay logs and run the benchmark for the same time-window.

As in step (4), upload your logs in preparation for the next step.

6. User pgBadger to compare performance

We use pgBadger to perform analysis of our performance during the benchmark, along with taking measurements from the node_exporter and postgres_exporter running during the experiment.

pgBadger reports can be used to calculate a query duration histogram for both clusters - these can indicate general speed-up/degradation. Digging into specific queries and the worst performers can provide more insight into which type of queries have degraded, and explaining those query plans on your clusters can help indicate what might have caused the change.

This is the least prescriptive part of our experiment, and answering whether the performance changes are acceptable - and what they may be - will depend on your knowledge of the applications using your database. We've found pgBadger to provide sufficient detail for us to be confident in answering this question, and hope you do too.

Observability

Running benchmarks can be a long process. pgreplay-go provides Prometheus metrics that can help determine how far through the benchmark has progressed, along with estimating how long remains.

Hooking these metrics into a Grafana dashboard can give the following output:

We'd suggest that you integrate these panels into your own dashboard; for example by showing them alongside key PostgreSQL metrics such as transaction commits, active backends and buffer cache hit ratio, as well as node-level metrics to show CPU and IO saturation.

A sample dashboard with the pgreplay-go-specific panels has been provided that may help you get started. Import it into your Grafana dashboard by downloading the dashboard JSON file.

Types of Log

Simple

This is a basic query that is executed directly against Postgres with no other steps.

2010-12-31 10:59:52.243 UTC|postgres|postgres|4d1db7a8.4227|LOG:  statement: set client_encoding to 'LATIN9'

Prepared statement

2010-12-31 10:59:57.870 UTC|postgres|postgres|4d1db7a8.4227|LOG:  execute einf"ug: INSERT INTO runtest (id, c, t, b) VALUES ($1, $2, $3, $4)
2010-12-31 10:59:57.870 UTC|postgres|postgres|4d1db7a8.4227|DETAIL:  parameters: $1 = '6', $2 = 'mit    Tabulator', $3 = '2050-03-31 22:00:00+00', $4 = NULL