CosmosDB connection pool in a FastAPI app

1

What is CosmosDB connection pool in a FastAPI app?

A CosmosDB connection pool in a FastAPI app is a collection of reusable connections to Azure Cosmos DB. Instead of opening a new connection every time the app needs to communicate with the database, the app reuses existing connections from the pool, which improves performance and efficiency. FastAPI's asynchronous nature allows it to handle multiple requests concurrently, and the connection pool helps manage database connections effectively.

Example Code:

from fastapi import FastAPI
from azure.cosmos.aio import CosmosClient

app = FastAPI()

# Initialize CosmosDB client
client = CosmosClient("<your-cosmosdb-endpoint>", "<your-cosmosdb-key>")

@app.on_event("startup")
async def startup_db_client():
    app.state.database = client.get_database_client("my_database")
    app.state.container = app.state.database.get_container_client("my_container")

@app.on_event("shutdown")
async def shutdown_db_client():
    await client.close()

@app.get("/items")
async def get_items():
    query = "SELECT * FROM c"
    items = [item async for item in app.state.container.query_items(query, enable_cross_partition_query=True)]
    return items

Example Output:

GET /items -> Returns a list of items from the CosmosDB container.

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2

How CosmosDB connection pools in FastAPI app is used in IT?

In IT environments, CosmosDB connection pools are used in applications where fast and efficient database access is critical. Connection pooling ensures that applications running on FastAPI can handle many simultaneous user requests by reusing database connections. This setup is common in production systems for web services, e-commerce apps, or real-time data platforms that need high throughput and low latency.

For example, in an e-commerce app with high traffic, connection pooling helps maintain fast access to product data stored in CosmosDB, ensuring users get quick responses without the overhead of opening and closing database connections for every request.

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3

What are the benefits of having a CosmosDB connection pool in a FastAPI app?

Benefits of CosmosDB connection pooling:

1. Faster Requests: Reusing existing connections reduces the time needed to establish new connections.

2. Resource Efficiency: Limits the number of open connections, reducing the load on both the application and CosmosDB.

3. Improved Scalability: The app can handle more users and requests without being slowed down by database connection overhead.

4. Reduced Latency: It minimizes the delay in communicating with the database, which is important for real-time systems.

Example Code:

@app.get("/fast-items")
async def fast_items():
    query = "SELECT * FROM c WHERE c.type='fast'"
    items = [item async for item in app.state.container.query_items(query, enable_cross_partition_query=True)]
    return {"fast_items": items}

Example Output:

GET /fast-items -> Faster responses by reusing pooled connections.

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4

What are the alternatives for CosmosDB connection pool in a FastAPI app?

Some alternatives for using a CosmosDB connection pool include:

1. Direct connection without pooling: Open and close a new connection for each request (less efficient).

2. MongoDB or other databases: Use another NoSQL database like MongoDB, which also supports connection pooling.

3. Relational Databases: Databases like PostgreSQL or MySQL with connection pooling via libraries like asyncpg or aiomysql.

4. Caching with Redis: Use a caching layer (Redis) to reduce the number of database queries.

Example (Direct Connection):

@app.get("/no-pool")
async def no_pool_items():
    client = CosmosClient("<your-cosmosdb-endpoint>", "<your-cosmosdb-key>")
    container = client.get_database_client("my_database").get_container_client("my_container")
    items = [item async for item in container.query_items("SELECT * FROM c", enable_cross_partition_query=True)]
    await client.close()  # No connection pool, open/close each time
    return items

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5

Tell me various topics under CosmosDB connection pool in FastAPI app?

Topics related to CosmosDB connection pooling in FastAPI:

1. Connection Pool Configuration: How to configure pool size, timeouts, etc.

2. Asynchronous Database Access: Using async operations with CosmosDB and FastAPI.

3. Performance Tuning: Techniques to optimize connection pooling and database query performance.

4. Error Handling: Handling connection pool exhaustion and database errors.

5. Scaling FastAPI Apps: Best practices for scaling FastAPI apps with CosmosDB and connection pools.

6. Caching Strategies: Combining connection pooling with caching to reduce database load.

7. Security Considerations: Securely managing credentials in connection pools.

8. Monitoring and Logging: Tracking connection pool usage and diagnosing issues.

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6

What are the pros and cons of CosmosDB connection pool in FastAPI app?

Pros:

1. Faster Performance: Reusing connections speeds up requests by avoiding connection overhead.

2. Scalability: Can handle more users and simultaneous requests efficiently.

3. Reduced Resource Usage: Fewer open connections reduce the load on both the app and the CosmosDB server.

4. Asynchronous Handling: Works well with FastAPI's asynchronous features for non-blocking operations.

Cons:

1. Complex Configuration: Properly configuring the pool (size, timeouts, etc.) can be tricky and application-specific.

2. Potential for Pool Exhaustion: If the number of concurrent requests exceeds the pool size, you may experience delays.

3. Maintenance Overhead: Monitoring and maintaining the health of the connection pool adds complexity to app maintenance.

Example Code (Handling Pool Exhaustion):

from azure.cosmos.exceptions import CosmosHttpResponseError

@app.get("/handle-pool-exhaustion")
async def handle_exhaustion():
    try:
        query = "SELECT * FROM c"
        items = [item async for item in app.state.container.query_items(query, enable_cross_partition_query=True)]
        return items
    except CosmosHttpResponseError as e:
        return {"error": "Connection pool exhausted, please try again later"}

Example Output:

GET /handle-pool-exhaustion -> Returns "Connection pool exhausted, please try again later" if the pool is full.

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7

Max pool size recommendation?

The maximum pool size recommendation for CosmosDB in a FastAPI app depends on several factors like application load, the number of concurrent users, and the complexity of queries. While there is no one-size-fits-all number, here are some key considerations to help determine an appropriate pool size:

Factors Affecting Pool Size:

1. Number of Concurrent Users:

   • If your FastAPI app handles many concurrent users, a larger pool size may be needed to avoid connection exhaustion.

2. Query Complexity and Latency:

   • If your queries are simple and fast, you can get by with a smaller pool. But if queries are complex or take longer, a larger pool may be beneficial.

3. CosmosDB RU (Request Units):

   • If you're using a large number of Request Units (RUs) for throughput, you might want to ensure that your connection pool size can keep up with this level of throughput.

4. Application Architecture:

   • A microservice-based architecture, where different services interact with CosmosDB, may require separate pools or higher pool sizes for each service.

5. Environment:

   • Development environments typically use smaller pools, while production environments should be tuned based on traffic patterns.

General Recommendations for Pool Size:

• For Light Load (Development/Small-scale apps):

  • Start with a pool size of 5-10 connections. This is sufficient for apps with low concurrent traffic or light query load.

• For Medium Load (Moderate Traffic Apps):

  • A pool size of 20-50 connections is typical for apps handling moderate traffic (hundreds of requests per second).

• For High Load (High Traffic/Enterprise Apps):

  • Pool sizes in the range of 100-200 connections may be needed for applications with very high traffic or complex query patterns (e.g., thousands of requests per second).

Example of Setting Pool Size:

In the CosmosDB SDK for Python, the connection pool is managed by the underlying HTTP client. If you're using the aiohttp library (which the azure-cosmos library relies on for async operations), you can set the maximum number of connections (pool size) indirectly via the HTTP transport settings.

Here's an example where you configure the CosmosClient with a custom HTTP session using aiohttp to set the max pool size:

Example Code:

from fastapi import FastAPI
from azure.cosmos.aio import CosmosClient
import aiohttp

app = FastAPI()

cosmos_url = "<your-cosmosdb-endpoint>"
cosmos_key = "<your-cosmosdb-key>"

# Define a custom session with a max connection pool size
connector = aiohttp.TCPConnector(limit=50)  # Set max pool size to 50
session = aiohttp.ClientSession(connector=connector)

client = CosmosClient(cosmos_url, cosmos_key, session=session)

@app.on_event("startup")
async def startup_db_client():
    app.state.container = client.get_database_client("my_database").get_container_client("my_container")

@app.on_event("shutdown")
async def shutdown_db_client():
    await client.close()
    await session.close()

@app.get("/items")
async def get_items():
    query = "SELECT * FROM c"
    items = [item async for item in app.state.container.query_items(query, enable_cross_partition_query=True)]
    return items

Explanation:

• aiohttp.TCPConnector(limit=50): This sets the maximum pool size to 50 connections.

• aiohttp.ClientSession: The session uses the custom connector to control the pool size.

Monitoring and Adjusting Pool Size:

It’s important to monitor your application and database to adjust pool size based on actual performance. You can use:

• Azure Monitor to track metrics like request latency and throttling.

• Application logs to see if you’re hitting connection pool limits.

• Load testing to simulate different traffic patterns and find the optimal pool size.

Key Takeaways:

• Start with a reasonable pool size (e.g., 5-10 for small apps, 20-50 for medium apps, 100+ for high-traffic apps).

• Monitor and tune the pool size based on actual traffic and load patterns.

• Set a higher pool size if you have high concurrent users, long-running queries, or high traffic.

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8

How to monitor pool?

Monitoring the CosmosDB connection pool in a FastAPI app is essential to ensure the pool is appropriately sized and to catch potential issues like connection exhaustion or underutilization. However, CosmosDB's Python SDK doesn't directly expose metrics for connection pooling. Since the connection pool is managed by the underlying HTTP library (e.g., aiohttp for async or requests for sync), monitoring the pool requires a combination of custom logging, monitoring tools, and cloud services like Azure Monitor.

Here are different methods to monitor the connection pool in a FastAPI app connected to CosmosDB:

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1. Custom Logging:

Implement logging to track the usage of the connection pool, including information like the number of connections opened, reused, and closed. This is especially useful when you are using aiohttp (for async operations) or requests.

Example for Async (using aiohttp):

You can monitor the number of connections in the pool by tracking the behavior of aiohttp's TCPConnector.

import logging
import aiohttp
from fastapi import FastAPI

app = FastAPI()

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configure the pool size
connector = aiohttp.TCPConnector(limit=50)  # Pool size of 50 connections
session = aiohttp.ClientSession(connector=connector)

@app.on_event("startup")
async def startup_db_client():
    logger.info(f"Starting the app with max pool size: {connector.limit}")
    logger.info(f"Current connection count: {connector._conns}")

@app.get("/pool-status")
async def pool_status():
    # Monitor pool usage by logging the number of active connections
    total_connections = len(connector._conns)  # Active connections
    logger.info(f"Current total active connections: {total_connections}")
    return {"total_active_connections": total_connections}

@app.on_event("shutdown")
async def shutdown_db_client():
    await session.close()
    logger.info("Shutting down the session")

Example Output:

INFO: Starting the app with max pool size: 50
INFO: Current connection count: {}
INFO: Current total active connections: 5

In this example, the connector._conns dictionary is being used to track active connections.

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2. Azure Monitor & Application Insights:

Azure provides monitoring tools such as Azure Monitor and Application Insights that can help you track the performance of your CosmosDB connections at a broader level, such as:

• Request Latency: Track the time taken for requests to complete.

• Request Rate: How often your app is querying CosmosDB.

• Throttling: Monitor if CosmosDB throttles any requests due to exceeding Request Unit (RU) limits.

Steps:

1. Enable Azure Monitor for CosmosDB:

   • Go to your CosmosDB account in Azure.

   • Under Monitoring, enable Azure Monitor.

   • Track metrics like Request Units (RUs), request latency, and throttling.

2. Integrate Application Insights with FastAPI: You can use Application Insights to monitor requests in your FastAPI app and how they interact with CosmosDB.

Example:

Install the opencensus-ext-azure package for integrating Application Insights:

pip install opencensus-ext-azure

Then configure it in your FastAPI app:

from fastapi import FastAPI
from opencensus.ext.azure.log_exporter import AzureLogHandler

app = FastAPI()

# Azure Application Insights instrumentation key
app_insights_key = "<your-instrumentation-key>"
logger = logging.getLogger(__name__)
logger.addHandler(AzureLogHandler(connection_string=f'InstrumentationKey={app_insights_key}'))

@app.get("/items")
async def get_items():
    try:
        # Add some custom logging
        logger.info("Fetching items from CosmosDB")
        # Perform database query here
        return {"message": "Items fetched"}
    except Exception as e:
        logger.error(f"Error fetching items: {e}")
        raise

Azure Monitor and Application Insights will capture the following:

• HTTP request details (e.g., time to connect, response time).

• CosmosDB performance metrics (e.g., RUs consumed, errors).

• Connection failures, timeouts, and latency issues.

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3. Health Check Endpoints:

You can create a health check endpoint in FastAPI that provides details on the connection pool’s status, such as the number of active connections or if the pool is saturated.

Example:

@app.get("/health")
async def health_check():
    total_connections = len(connector._conns)
    return {"status": "healthy", "active_connections": total_connections}

This endpoint could be queried periodically by a monitoring tool to ensure that the connection pool is operating correctly.

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4. Prometheus/Grafana for Pool Metrics:

If you're using Prometheus for monitoring and Grafana for visualization, you can set up custom metrics in your FastAPI app to track connection pool usage.

Steps:

1. Install the Prometheus FastAPI integration:

   pip install prometheus-fastapi-instrumentator

2. Create a custom metric for tracking active connections:

   from prometheus_fastapi_instrumentator import Instrumentator
   from prometheus_client import Gauge
   
   active_connections_gauge = Gauge('active_connections', 'Number of active connections in pool')
   
   @app.on_event("startup")
   async def startup_event():
       Instrumentator().instrument(app).expose(app)
   
   @app.get("/monitor-connections")
   async def monitor_connections():
       total_connections = len(connector._conns)
       active_connections_gauge.set(total_connections)
       return {"active_connections": total_connections}

3. Set up Prometheus to scrape metrics and visualize them in Grafana for real-time monitoring of connection pool status.

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5. Load Testing Tools (e.g., Locust):

Using tools like Locust or Apache JMeter, you can simulate high loads to monitor how your connection pool behaves under stress. This helps in identifying if the pool size is sufficient or if there are bottlenecks.

Steps:

1. Install Locust:

   pip install locust

2. Write a simple Locust test to simulate traffic to your FastAPI app:

   from locust import HttpUser, task
   
   class FastAPIUser(HttpUser):
       @task
       def test_pool_status(self):
           self.client.get("/pool-status")

3. Run Locust to simulate multiple users hitting your API and monitor how the connection pool behaves under different loads.

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Summary of Monitoring Methods:

• Custom Logging: Track pool size and connections in your app logs.

• Azure Monitor & Application Insights: Use Azure-native tools for in-depth performance and request monitoring.

• Health Check Endpoints: Expose an API route to monitor pool status.

• Prometheus/Grafana: Collect custom metrics and visualize them.

• Load Testing (Locust/JMeter): Simulate load to see how the pool handles high traffic.

By combining these tools and techniques, you can effectively monitor the connection pool in your FastAPI app and ensure it is functioning optimally.