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How Bytebase Handles Audit Logging

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Database Access Control (DAC) Best Practices

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Database Design Patterns Every Developer Should Know

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pgvector: How to Add Vector Search to PostgreSQL

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Upsert in SQL: INSERT ON DUPLICATE KEY vs ON CONFLICT Explained

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How to Fix Slow MySQL Queries: A Practical Guide

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Database Blue-Green Deployment: A Practical Guide

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How to Govern AI Agent Database Access

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Postgres Query Plan: How to Read and Fix Performance Issues

A walk through every layer of the Postgres permission system that can raise ERROR 42501, why each one fires, and how the behavior differs from MySQL.

The Anatomy of Postgres Permission ERROR 42501

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Postgres Logs Explained: A Practical Guide for Production

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Database Multi-Environment Deployments - The Challenges and Patterns

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Database Audit Logging: Two Layers, One Trail

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Database Compliance for HITRUST: Controls, Risks, and Best Practices

What the "Require Primary Key" SQL review rule checks, the production problems a missing primary key tends to cause, and the SQL to fix tables that don't have one.

Require Primary Key: A SQL Review Rule Explained

What the "Enforce NOT VALID in CHECK" SQL Review rule does, why adding a CHECK constraint can lock your table, and how the two-step NOT VALID approach avoids it.

Enforce NOT VALID in CHECK: A SQL Review Rule Explained

The practical ways to audit a PostgreSQL database, from native logging to triggers, logical replication, pgAudit, and Bytebase, with the trade-offs of each.

Postgres Audit Logging Guide

Learn why CASCADE operations in SQL can lead to catastrophic data loss and how the "Prohibit CASCADE" review rule protects your production database.

Prohibit CASCADE: A SQL Review Rule Explained

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How to Migrate from Supabase to AWS

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What is Enterprise Database Management (EDM)?

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Database Compliance for LGPD: Implications and Best Practices

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SQL Table Naming Dilemma: Singular vs. Plural

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Postgres Case Sensitivity Explained

What pg_database_owner does in Postgres, why it was added, and how to use it without hitting the "cannot have explicit members" error.

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Postgres Row-Level Security Footguns

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Postgres Schema Migration without Downtime

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## Overview

Reviewing SQL change scripts used to be the DBA's job, and it happened too late. Developers
handed migrations to the DBA just before go-live, leaving little time to review the scripts or
request changes. Problems surfaced in production instead of in review.

The fix is to move SQL review left — into the pull request, where every other change is already
reviewed. Bytebase does exactly that: it runs automatic SQL review on each pull request, so unsafe
migrations are caught at PR time rather than at deployment.

![SQL review shifts left into the pull request, between develop and release](/content/blog/integrate-sql-review-into-github/shift-left.svg)

## Where SQL review fits

Bytebase models database changes as a three-stage GitOps pipeline:

```
Develop → Review (Pull Request) → Release (Bytebase)
```

Developers write migration files in a feature branch and open a pull request. SQL review runs as a
CI step on that pull request — this is the **Review** stage. Once the review passes and the PR
merges, Bytebase creates a release and rolls the change out across environments in the **Release**
stage. SQL review is the gate between writing a change and shipping it.

## How it works

A single GitHub Action — [`bytebase/sql-review-action`](https://github.com/marketplace/actions/sql-review) —
runs on pull requests that touch your migration files. It connects to your Bytebase server, which
holds two things the review needs:

- The **SQL Review Policy** — [200+ configurable rules](https://docs.bytebase.com/sql-review/review-policy)
  covering naming, anti-patterns, indexing, and dangerous statements.
- The **database connection** — so the review is schema-aware. Because Bytebase can read the live
  schema, it catches issues that text-only linting can't: missing indexes, backward-incompatible
  changes, and other problems that only make sense against the actual table.

```yaml
# .github/workflows/sql-review.yml
name: SQL Review
on:
  pull_request:
    paths:
      - 'migrations/**'

jobs:
  sql-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: SQL Review
        uses: bytebase/sql-review-action@v1
        with:
          bytebase-url: ${{ secrets.BYTEBASE_URL }}
          bytebase-token: ${{ secrets.BYTEBASE_SERVICE_ACCOUNT_TOKEN }}
          file-pattern: 'migrations/**/*.sql'
```

## Setup

1. **Configure the SQL Review Policy.** Define your standards in Bytebase and set each rule's
   severity — `ERROR` to block the merge, `WARNING` to flag it, or off. See the
   [SQL Review Policy docs](https://docs.bytebase.com/sql-review/review-policy).

1. **Connect Bytebase to your repository.** Follow the
   [GitOps installation guide](https://docs.bytebase.com/gitops/installation) to wire Bytebase to
   your VCS.

1. **Create a service account.** Use a dedicated account with the `GitOps Service Agent` role rather
   than a personal token, and store its secret in your repository's Actions secrets.

1. **Add the workflow.** Drop the `sql-review.yml` workflow above into `.github/workflows/` and point
   `file-pattern` at your migration directory.

The full walkthrough lives in the
[SQL Review CI documentation](https://docs.bytebase.com/gitops/migration-based-workflow/sql-review-ci)
and the [GitHub Actions tutorial](https://docs.bytebase.com/tutorials/gitops-github-workflow).

## What gets reviewed

On every pull request, the action validates:

- **SQL syntax** — malformed or dialect-incompatible statements.
- **Policy rules** — the 200+ configurable rules from your SQL Review Policy.
- **Naming conventions** — table, column, and index naming compliance.
- **Risk assessment** — dangerous operations such as `DROP TABLE` or an `UPDATE` with no `WHERE`.
- **Schema compatibility** — backward-incompatible changes, checked against the live schema.

## Review feedback in the pull request

The review bot posts its results directly on the pull request, both as a summary and inline on the
changed files:

- ✅ **Passed** — the migration meets every policy requirement.
- ⚠️ **Warning** — a best-practice violation, non-blocking by default.
- ❌ **Error** — a policy violation that blocks the merge by default.
- 📊 **Risk assessment** — the evaluated impact of the change.
- 📝 **Explanations** — why a rule failed, with a suggested fix.

![file-comment](/content/blog/integrate-sql-review-into-github/file-comment.webp)

Developers fix the flagged issues and push again; the action re-runs until the review passes. Only
then can the change merge.

## Beyond GitHub

The same `bytebase-action` runs anywhere your CI does. The mechanism is identical — connect to the
Bytebase server, check the migration files, comment on the request — across GitLab CI, Azure DevOps,
and Bitbucket Pipelines, so a team on any of them gets the same review standard. See the
[SQL Review CI docs](https://docs.bytebase.com/gitops/migration-based-workflow/sql-review-ci) for a
ready-to-use config per platform.

## From review to deployment

Catching bad SQL in the pull request is half the value; the other half is that the same pipeline
deploys the change. Once review passes and the PR merges, Bytebase creates a release and rolls it out
across environments with approval gates where you want them — no second tool, and the same review
standard applied by developers and DBAs alike. SQL review is one stage of the full
[database GitOps workflow](https://docs.bytebase.com/gitops/overview).


Run automatic SQL review on every GitHub pull request. A single GitHub Action connects to your Bytebase server to enforce 200+ schema-aware rules and post results as PR comments — before any change is deployed.

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