Introduction

Database connection management is critical for application performance. Connection pooling is a technique used to reuse database connections, reducing the overhead of establishing new connections for every query. This article explores the performance impact of connection pooling versus no pooling in PostgreSQL under various workloads.

I benchmarked the following scenarios:

• Row Counts: 100, 1,000, and 10,000 rows.
• Connection Pool Sizes: 5, 10, 20, 50.
• Operations: INSERT, READ, and UPDATE.

Benchmark Script Overview

The Python benchmarking script:

• Simulates concurrency with threads to test database performance.
• Measures execution time for operations (INSERT, READ, UPDATE) with and without connection pooling.

Script Parameters

• Threads: 5 threads simulate concurrent operations.
• Database Configuration: PostgreSQL database credentials.
• Connection Management:
  • With connection pooling: Uses psycopg2.pool.ThreadedConnectionPool.
  • Without pooling: Creates a new connection for each operation.

Results and Analysis

1. For 100 Rows

• Insert Performance:
  

  • Pooling consistently outperforms no pooling due to reduced connection overhead.

• Read Performance:
  

  • Similar trends: Pooling excels at minimizing connection creation time.

• Update Performance:
  

  • Pooling is efficient.

2. For 1,000 Rows

• Insert Performance:
  

  • Pooling shows stable performance, while no pooling introduces higher overhead.

• Read Performance:
  

  • Pooling outperforms no pooling, with similar pronounced different.

• Update Performance:
  

  • No significant advantage of connection pooling because of database-side processing like row locking and disk writes dominate execution time, minimizing the impact of connection pooling benefits.

3. For 10,000 Rows

• Insert Performance:
  

  • Pooling underperforms compared to no pooling due to contention and synchronization overhead.

• Read Performance:
  

  • Pooling exhibits some inconsistency, while no pooling performs more predictably.

• Update Performance:
  

  • Pooling introduces contention at higher workloads, causing performance degradation.

Observations

Why Connection Pooling Performs Poorly at High Row Counts

1. Connection Reuse Overhead: Synchronizing access to the pool adds latency.
2. Resource Contention: Threads compete for pooled connections, especially at higher workloads.
3. Query Complexity: Long-running queries tie up connections, reducing availability.
4. Context Switching: Larger pools increase thread context switching, adding overhead.

Why Connection Pooling Excels at Low Row Counts

1. Reduced Connection Overhead: Pooling eliminates the need to repeatedly establish and close connections.
2. Efficient Resource Utilization: With fewer rows and threads, contention is minimal, and pooled connections are reused efficiently.

Recommendations

1. Optimize Pool Size: Experiment with smaller pool sizes to reduce contention.
2. Partition Workloads: Divide the workload across multiple pools.
3. Adaptive Pooling: Consider libraries that dynamically adjust pool sizes.

Conclusion

• Connection pooling significantly reduces overhead for smaller workloads but may introduce contention at higher workloads.
• Properly tuning the connection pool size and adapting pooling strategies based on workload size is essential for optimizing database performance.

Benchmark Script: github