Datapace

How does Datapace find which code is causing slow queries?

Datapace reads pg_stat_statements to identify expensive queries, then cross-references Git history to attribute each query to the exact file, function, and line that generated it.

Does Datapace automatically fix issues or just detect them?

Both, and the response depends on your policy. Depending on confidence and environment, Datapace opens a pull request with a verified fix, leaves an inline suggestion or PR comment, blocks a merge, or sends an alert for human judgment. Free plans start with detection and alerts; Pro and higher unlock automated fixes.

What types of performance issues can Datapace detect?

Missing indexes, N+1 query patterns, sequential scans on large tables, table bloat, query regressions after deploys, and costly join patterns.

How is Datapace different from traditional monitoring tools?

Monitoring tools show you that something is slow. Datapace traces the problem to the exact line of code and responds with a pull request, inline suggestion, merge block, or comment, following your policy. Dashboards inform; Datapace resolves.

Is my database connection secure?

Yes. The Datapace agent runs inside your VPC with a read-only connection. Only query metadata and performance signals are sent to the cloud, never row-level data. All traffic is encrypted in transit.

Does Datapace ever modify my production database?

No. The agent connects with read-only credentials and never executes writes against your production database. All fixes are delivered as Pull Requests for your team to review and merge.

Will Datapace slow down my application?

No. Datapace reads from pg_stat_statements, which PostgreSQL already maintains. There is no query proxy, no instrumentation layer, and no additional load on your database.

How do I install Datapace?

Install the GitHub App, run the Docker agent in your VPC, and connect your database with a single CLI command. Typical setup takes about 2 minutes.