crdb-sql-audit 0.2.5

Analyze PostgreSQL SQL logs for CockroachDB compatibility

crdb-sql-audit

A powerful CLI tool to extract, deduplicate, and analyze SQL logs for CockroachDB compatibility using a flexible, rule-based engine.

🚀 Features

• Works with any SQL dialect (PostgreSQL, MySQL, Oracle, etc.)
• Extracts SQL and function calls using customizable search terms (e.g. execute, pg_)
• Deduplicates repeated SQL statements from logs
• Analyzes SQL using a YAML-based rule engine
• Supports default compatibility rules (PostgreSQL ➜ CockroachDB)
• Allows custom rule sets via --rules
• Logs analysis output to both terminal and crdb_sql_audit.log
• Automatically detects SQL statement types (e.g. SELECT, DELETE)
• Friendly CLI with --help and --version
• Export full reports in multiple formats:
  • .sql: Deduplicated queries
  • .csv: Raw compatibility issue list
  • .md: Developer-friendly Markdown report
  • .html: Interactive browser report with sorting/filtering
  • .png: Visual bar chart of issues

🖼 Sample Output

Report Type	Preview
HTML
Chart
CSV
SQL
Markdown

📦 Installation

Option A: Local Dev Install

git clone https://github.com/your-org/crdb-sql-audit.git
cd crdb-sql-audit
python -m venv venv
source venv/bin/activate
pip install .

Option B: Build via pyproject.toml

python -m build
pip install dist/crdb_sql_audit-0.2.0-py3-none-any.whl

🧪 Usage

crdb-sql-audit \
  --dir /path/to/logs \
  --terms execute,pg_ \
  --out output/report

Additional Options

--rules    Path to YAML rules file (optional, default: built-in PostgreSQL rules)
--help     Show usage help
--version  Show current version

Custom Rules Example

crdb-sql-audit \
  --dir ./logs \
  --terms execute,pg_ \
  --rules ./rules/mysql_to_crdb.yaml \
  --out output/mysql_report

💡 This tool supports auditing any SQL dialect — just provide a rule set for your source database (e.g., PostgreSQL, MySQL, Oracle).

📁 Output

output/
├── report.sql          # Deduplicated SQL
├── report.csv          # Compatibility issues
├── report.md           # Markdown summary
├── report.html         # Interactive dashboard
├── report_chart.png    # Visual chart of issues
├── crdb_sql_audit.log  # Full run log

🧹 Preparing Your Log Files

To analyze SQL logs effectively, we recommend the following preprocessing steps:

1. Extract SQL-related Lines

grep "execute" app.log > sql_only.log
# or to include pg_ built-in function usage:
grep -E "execute|pg_" app.log > sql_only.log

2. Split Into Manageable Chunks (Optional but Recommended)

split -b 50M sql_only.log chunks/sql_chunk_

3. Run the Audit

crdb-sql-audit --dir chunks --terms execute,pg_ --out output/report

📚 Rule Engine Format

Rules are written in YAML and matched against each SQL line. Example:

💡 This is also the default rule if you don't provide --rules param.

- id: malformed_dml_statements
  match: '^(SELECT|INSERT|UPDATE|DELETE FROM)\s*$'
  message: "Possibly malformed or incomplete SQL statement"
  level: warning
  tags: [syntax]

- id: special_char_in_identifier
  match: '"[^"]*#\w*"'
  message: "Table name contains unsupported special character (#)"
  level: error
  tags: [table, identifier]

- id: pg_builtins
  match: '^.*\bpg_\w+\s*\(.*$'
  message: "PostgreSQL pg_* function not supported in CockroachDB"
  level: error
  tags: [function]

#- id: low_token_sql
  #match: '^(\S+\s*){1,2}$'
  #message: "Extremely short SQL likely malformed"
  #level: warning
  #tags: [syntax]

📦 Multiple rule sets can be created to target different SQL dialects (e.g., postgres_to_crdb.yaml, mysql_to_crdb.yaml, etc.)

🧪 Validate Your Regex Rules

🔍 Online (Recommended)

Use regex101.com to test your patterns:

• Set the flavor to Python
• Paste your rule into the regex field
• Paste a sample SQL line into the test area

🐍 In Python

You can also test your rules directly:

import re
pattern = re.compile(r'^.*\bpg_\w+\s*\(.*$', re.IGNORECASE)
sql = "SELECT pg_backend_pid()"
print(bool(pattern.search(sql)))  # ✅ True

🛠 Validate with Shell

You can use basic Unix commands to check for patterns like pg_ functions directly in your log chunks:

Task	Command
Total matches across chunks	grep -oE '\bpg_[a-zA-Z0-9_]+\(' chunks/* | wc -l
Unique function names	grep -oE '\bpg_[a-zA-Z0-9_]+\(' chunks/* | sort | uniq
Count occurrences of each function	grep -oE '\bpg_[a-zA-Z0-9_]+\(' chunks/* | sort | uniq -c | sort -nr
Full SQL lines containing pg_*	grep -E '\bpg_[a-zA-Z0-9_]+\(' chunks/*

---

🧪 Running Tests

This project includes a test suite using sample logs and rules to validate behavior.

🔧 To run locally:

python tests/test_runner.py

🧪 What it does:

• Runs crdb-sql-audit on a small sample of PostgreSQL-style logs
• Uses tests/rules/test_rules.yaml
• Verifies that a CSV report is created with expected issues

✅ This runs automatically in GitHub Actions on every commit to main.