sqltap-profiler 1.2.0
Enhanced SQL profiling and introspection for applications using sqlalchemy
License: Apache-2.0
Requires Python: >=3.7
Keywords: sqlalchemy, sql, profiling, debugging, performance

# sqltap - a library for profiling and introspecting SQL queries made through SQLAlchemy

sqltap helps you quickly understand:

   * how many times a sql query is executed
   * how much time your sql queries take
   * where your application is issuing sql queries from

![](https://inconshreveable.github.io/sqltap/_images/sqltap-report-example.png)

## Full Documentation

[http://sqltap.inconshreveable.com](http://sqltap.inconshreveable.com)

## Motivation

When you work at a high level of abstraction, it’s more common for your code to be inefficient and cause performance problems. SQLAlchemy’s ORM is excellent and gives you the flexibility to fix these inefficiencies if you know where to look! sqltap is a library that hooks into SQLAlchemy to collect metrics on all queries you send to your databases. sqltap can help you find where in your application you are generating slow or redundant queries so that you can fix them with minimal effort.

## Quickstart Example

    import sqltap

    profiler = sqltap.start()
    session.query(Knights).filter_by(who_say = 'Ni').all()
    statistics = profiler.collect()
    sqltap.report(statistics, "report.html")

## WSGI integration

You can easily integrate SQLTap into any WSGI application. This will create an up-to-date report page at /\_\_sqltap\_\_ where
you can dynamically enable/disable the profiling so you can easily run it selectively in production. Integrating is super-easy:

    import sqltap.wsgi

    wsgi_app = sqltap.wsgi.SQLTapMiddleware(wsgi_app)

For example, to integrate with a Flask application:

    import sqltap.wsgi

    app.wsgi_app = sqltap.wsgi.SQLTapMiddleware(app.wsgi_app)

## Text report

Sometimes we want to profile sqlalchemy on remote servers. It's very
inconvenient to view HTML format SQLTap report on these servers. Alternatively,
SQLTap provides text profiling report in a human-readable way.

    import sqltap

    profiler = sqltap.start()
    session.query(Knights).filter_by(who_say = 'Ni').all()
    statistics = profiler.collect()
    sqltap.report(statistics, "report.txt", report_format="text")

## Profiling Utilities (NEW in v1.1.0)

SQLTap now includes convenient profiling utilities that work anywhere in your application - tests, development, staging, or production. The `sqltap.profiling` module provides a simple context manager with comprehensive statistics and automatic HTML report generation.

### Basic Usage

    from sqltap.profiling import sqltap_profiler

    def test_my_endpoint():
        with sqltap_profiler("my-test") as stats:
            response = my_function()

        # Assert on query count
        assert stats.query_count <= 5
        assert stats.total_time <= 1.0

### Detailed Analysis

Check for N+1 queries and analyze query patterns:

    def test_check_n_plus_one():
        with sqltap_profiler("n-plus-one-test") as stats:
            # Your code that might have N+1 queries
            posts = session.query(Post).all()
            for post in posts:
                author = post.author  # Potential N+1!

        # Check for N+1 queries
        selects = stats.get_queries_by_type('SELECT')
        for qg in selects:
            assert qg.query_count <= 10, f"Potential N+1: {qg.sql_text[:100]}"

        # Print detailed summary
        print(stats.summary())

### Available Statistics

The `stats` object provides comprehensive query metrics:

    # Summary statistics
    stats.query_count      # Total number of queries
    stats.unique_queries   # Number of unique SQL queries
    stats.total_time       # Total DB time in seconds
    stats.mean_time        # Mean query time
    stats.median_time      # Median query time
    stats.min_time         # Fastest query
    stats.max_time         # Slowest query
    
    # Query analysis
    stats.query_groups              # All query groups (sorted by total time)
    stats.get_queries_by_type('SELECT')  # Filter by query type
    stats.get_slowest_query()       # Get the slowest query group
    stats.summary()                 # Get formatted text summary

### Custom Report Location

Save reports to a custom directory:

    with sqltap_profiler("my-test", report_dir="/tmp/reports") as stats:
        response = my_function()

### Disable Report Generation

For CI environments or when you only need assertions:

    with sqltap_profiler("my-test", save_report=False) as stats:
        response = my_function()

    assert stats.query_count <= 5

### Integration with pytest fixtures

SQLTap profiler works seamlessly with pytest fixtures, allowing you to profile queries across your entire test without wrapping test code in `with` statements:

    import pytest
    from sqltap.profiling import sqltap_profiler

    @pytest.fixture
    def db_profiler(request):
        """Fixture that profiles all DB queries in a test"""
        with sqltap_profiler(request.node.name, save_report=False) as stats:
            yield stats

    def test_with_profiler(db_profiler):
        # Your test code here
        response = my_function()

        # Assert on the profiler stats
        assert db_profiler.query_count <= 10

        # Log detailed summary for debugging
        print(profile_db.summary())

### Using with Report Generation

Enable reports in your local environment:

    @pytest.fixture
    def db_profiler(request):
        """Fixture with automatic report generation"""
        save_report = os.getenv("SAVE_SQLTAP_REPORTS", "false") == "true"
        with sqltap_profiler(request.node.name, save_report=save_report) as stats:
            yield stats

    # Run tests with reports:
    # SAVE_SQLTAP_REPORTS=true pytest tests/

    def test_list_users(db_profiler):
        users = db.query(User).all()
        
        # Check for N+1 queries
        selects = db_profiler.get_queries_by_type('SELECT')
        for qg in selects:
            assert qg.query_count <= 2, f"N+1 detected: {qg.sql_text[:80]}"

## Advanced Example

    import sqltap

    def context_fn(*args):
        """ Associate the request path, unique id with each query statistic """
        return (framework.current_request().path,
                framework.current_request().id)

    # start the profiler immediately
    profiler = sqltap.start(user_context_fn=context_fn)

    def generate_reports():
        """ call this at any time to generate query reports reports """
        all_stats = []
        per_request_stats = collections.defaultdict(list)
        per_page_stats = collections.defaultdict(list)

        qstats = profiler.collect()
        for qs in qstats:
            all_stats.append(qs)

            page = qstats.user_context[0]
            per_page_stats[page].append(qs)

            request_id = qstats.user_context[1]
            per_request_stats[request_id].append(qs)

        # report with all queries
        sqltap.report(all_stats, "report_all.html")

        # a report per page
        for page, stats in per_page_stats.items():
            sqltap.report(stats, "report_page_%s.html" % page)

        # a report per request
        for request_id, stats in per_request_stats.items():
            sqltap.report(stats, "report_request_%s.html" % request_id)

## Testing
Run the sqltap tests:

    python setup.py test

## License
Apache