Published at Aug 20, 2024

Top PostgreSQL Drivers for Python

Written by Junaid Ahmed

In the past few years, PostgreSQL has gained popularity due to its feature sets with data-intensive applications. It has become a choice for developers building complex web and data analytics applications—and data analytics walks hand in hand with Python. 

Source: DB-Engines

At the same time, the landscape of PostgreSQL drivers for Python is changing, with new drivers emerging and existing ones undergoing considerable updates to improve performance and compatibility.

Each driver has features related to its performance and ease of use, which makes the selection process pivotal when optimizing application performance. Understanding the implications of these choices can significantly affect the development process and the general efficiency of your application.

The article explores the top PostgreSQL drivers for Python. We will examine each driver's features, performance statistics, and pros and cons. Then, we’ll do a comparative analysis to determine the optimal driver for PostgreSQL applications. 

Let’s start by exploring the significance of choosing a good driver.

How to Choose the Right PostgreSQL Driver for Python

You probably know where we’re going with this: choosing the proper driver for PostgreSQL is crucial. PostgreSQL drivers are a kind of bridge that facilitates the efficient work of Python applications with PostgreSQL databases. In this section, we’ll review the impact of a good driver choice on performance, usability, and compatibility.

Impact on performance

The performance of your application depends a lot on how well the PostgreSQL driver efficiently manages your application's interaction with the database. Performance-optimized drivers drastically reduce query response times. They increase data retrieval speeds and accommodate high concurrency loads without bottlenecks.

A critical factor is how the driver accomplishes the serialization and deserialization tasks. These processes, by which data is translated into formats that can be stored or transmitted more quickly, can be computationally expensive and may take a long time. 

Hence, drivers optimized for efficient serialization/deserialization can significantly reduce the overhead, leading to quicker data processing and fewer latencies. This speed is particularly significant when handling large datasets or data with intricate structures. 

Some drivers support asynchronous operations. This means applications can handle multiple database operations simultaneously without blocking. It benefits applications requiring high concurrency and low latency, such as those underpinning real-time analytics platforms or web applications with high traffic.

Therefore, adequately selected drivers can significantly increase an application's throughput and responsiveness through efficient maintenance of connection pools and transactions.

Usability

Usability refers to how easy it is to interact with and manage the database using the driver. High-level abstraction and intuitive interfaces can reduce the amount of repetitive code, making the code easier to read and improving overall maintenance.

Drivers that work with the ORMs allow developers to interact with database data by utilizing Python objects and methods, entirely abstracting the complexity of PostgreSQL queries. While there are also setbacks to using ORMs, this abstraction could speed up development time, reduce errors, and make the codebase much more accessible for new developers.

Compatibility

Compatibility with specialized time-series databases is crucial. The right PostgreSQL driver should fully support PostgreSQL's features and optimizations to leverage its capabilities effectively.

TimescaleDB, available as an extension of PostgreSQL optimized for time-series data or as a mature cloud platform, is ideal for large-scale data logging, real-time analytics, and monitoring. It works on time-series queries to manage data partitioning and leverage performance enhancements. 

All these advanced features, including hyperfunctions, continuous aggregates, and real-time data ingestion, require the correct driver on the client side to handle these operations effectively.

Compatibility with these dedicated databases ensures that the driver optimizes time-series queries. It efficiently manages the partitioning of time-series data and can leverage new performance enhancements. This optimization may result in relatively fast time-series applications and better data handling compared to others.

PostgreSQL Drivers Overview

Psycopg2

Psycopg2 is the most popular driver for using the PostgreSQL database in Python. It is known for its reliability, robustness, and performance, hence the go-to choice for developers who need to interact reliably and safely with PostgreSQL databases using Python applications.

Asyncpg

The asyncpg module is designed as an asynchronous PostgreSQL client library for Python. It allows non-blocking database operations to work with low-latency and high-throughput modern asynchronous frameworks.

SQLAlchemy

SQLAlchemy is a complete SQL toolkit and an ORM library for the Python programming language. It deals with very abstract database operations and interactions at a high level, thus making it quite manageable and workable with databases. SQLAlchemy supports many backends of the database and is highly flexible.

Pg8000

Pg8000 is an interface on Python for PostgreSQL. It's written purely using Python, and it’s easy to use and install, especially in environments where installing C extensions is a no-go idea. For compatibility purposes, Pg8000 adheres to the DB-API 2.0 specification to ensure consistency when working with databases.

Features and Performance

Psycopg2

Psycopg2 features

• Native integration with PostgreSQL: Psycopg2 is designed explicitly for PostgreSQL and provides native support for many of its features. It closely adheres to the PostgreSQL API.

• Connection pooling: This enables a persistent connection to the database that can be used and reused, so there is no overhead in frequently establishing the connections.

• Server-side cursors: These allow you to query large amounts of data without overburdening the client's memory.

• Copy commands: This feature enables the use of PostgreSQL's COPY TO and COPY FROM commands, making it possible to perform efficient bulk data operations.

• Notification listening: Allows the listening of PostgreSQL NOTIFY messages, which is very useful for applications that require real-time updates.

• Type casting: Allows data types to be converted automatically between Python and PostgreSQL, supporting custom conversions.

Psycopg2 is highly robust and performs efficiently. Here are its main performance metrics:

• Throughput: Psycopg2 supports thousands of transactions per second and is efficient in high-load scenarios.

• Memory usage: It has very low overhead compared to other PostgreSQL Python drivers.

• Concurrency: It works well in a concurrent environment, making it suitable for web and enterprise applications.

Since most recent benchmarks depend on the configuration of your system and its use case, it is advisable to make tailored benchmarks in your environment. For a comparison between psycopg2 and psycopg3 and a tutorial on how to create scalable applications in Python, check out this blog post.

Psycopg2 pros and cons 

Asyncpg

Asyncpg features

• High performance: it uses the binary protocol and is built to perform at its peak with PostgreSQL.

• Built-in caching: it supports caching for improved query performance.

• Asynchronous design: the library fully embraces Python's async/await syntax for use in modern web applications and services that require nonblocking operations.

• Connection pooling: it has built-in support for connection pooling, which is essential to make database operations efficient in a high-concurrency environment.

• Transaction management: it supports asynchronous transactions for consistent data operations.

Asyncpg performance statistics

• High throughput: it processes thousands of queries every second.

• Low latency: enables low-latency responses due to non-blocking features.

• Efficient resource utilization: it uses fewer CPU resources than synchronous drivers because it does not block I/O (input/output) operations.

Performance benchmarks have consistently indicated that asyncpg is more performant than other PostgreSQL drivers in Python, especially in high-concurrency scenarios. 

Asyncpg pros and cons

Pg8000

Pg8000 features

•  DB-API 2.0 compliant: it follows the Python Database API Specification v2.0, which allows for a standard interface with databases in Python.

•  Supports major PostgreSQL features and types: Though minimalistic, pg8000 supports major PostgreSQL features and types such as JSON, arrays, and hstore.

•  SSL support: supports making an SSL connection to secure communications between a client and a database.

•  Simple interface: it contains a simple API, thus is easy to understand and quick to implement in a project.

Pg8000 performance statistics

• Throughput and latency: it usually holds up well for small-to-medium workload applications, but it might lag in high-performance situations.

•  Memory use: effective for most everyday purposes, but slightly less than drivers that use compiled binaries.

Pg8000 pros and cons

SQLAlchemy

SQLAlchemy is a full-scale Python library that allows the communication of Python programs with databases using a structured query language. One of its unique features is its ORM, which allows developers to easily query databases with Python objects for better intuitive access. 

Let’s take a look at features and performance metrics.

SQLAlchemy features

• ORM and Core: ships with both a high-level ORM and a low-level SQL expression language.

•  Database support: provides full support for various databases, including PostgreSQL, MySQL, SQLite, Oracle, and more.

• Dynamic query generation: allows programmatically building of SQL statements and permits some flexibility in query formation.

• Automatic schema translation: maps Python classes to database tables without defining the schema repetitively.

• Data integrity: supports transactions and provides guaranteed data integrity with the help of the unit-of-work and identity map patterns.

SQLAlchemy performance stats

SQLAlchemy performs differently in some usage patterns (ORM vs. Core), where the ORM does introduce more abstraction and ease but also some extra overhead compared to what you'd have using the Core directly. Here are some considerations:

• Overhead: ORM operations generally have more overhead than raw SQL or using the Core.

• Batch operations: these can be optimized with the help of batching, a technique where session flushes are tuned when using the ORM.

• Query optimization: SQLAlchemy effectively writes queries, but the complex usage of ORM can lead to suboptimal SQL if not managed well.

SQLAlchemy pros and cons

Performance Comparison With PostgreSQL

While integrating the driver, it’s important to know each driver's performance statistics. For that, we have used the specialized time-series database TimescaleDB to compare each driver's performance. We will retrieve data from TimescaleDB to find the driver performing best in terms of data extraction.

Psycopg2

Here is the psycopg2 code for connecting to TimescaleDB and retrieving data from the finance database:

Code

import time import psycopg2 # Connect to TimescaleDB conn = psycopg2.connect("user:pass@localhost:5432/db") cur = conn.cursor() Copy

# Query for data retrieval query = """     SELECT bucket, open, high, low, close     FROM one_min_candle     WHERE symbol = 'BTC/USD'     AND bucket >= NOW() - INTERVAL '1 months'     ORDER BY bucket DESC; """ # Benchmark data retrieval performance start_time = time.time() cur.execute(query) rows = cur.fetchall() end_time = time.time() Copy

print("Psycopg2 retrieval performance: {} rows/s".format(len(rows) / (end_time - start_time))) cur.close() conn.close() Copy

Upon running the code, we got the following result:

This reveals that pyscogp2 retrieves ~1,764 rows per second.

Asyncpg

The code for asyncpg for connecting with TimescaleDB and data retrieval is:

Code import time import asyncio import asyncpg Copy

async def benchmark_asyncpg():     conn = await asyncpg.connect("user:pass@localhost:5432/db")     # Query for data retrieval     query = """         SELECT bucket, open, high, low, close         FROM one_min_candle         WHERE symbol = 'BTC/USD'         AND bucket >= NOW() - INTERVAL '1 months'         ORDER BY bucket DESC;     """Copy

    # Benchmark data retrieval performance    start_time = time.time()     rows = await conn.fetch(query)     end_time = time.time()  Copy

   print("Asyncpg retrieval performance: {} rows/s".format(len(rows) / (end_time - start_time))) Copy

    await conn.close() Copy

# Run the coroutine await benchmark_asyncpg() Copy

Output shows the following result:

Pg8000

The code for connecting with TimescaleDB is:

Code

import time import pg8000 import ssl Copy

# Create SSL context ssl_context = ssl.create_default_context() Copy

# Connect to TimescaleDB #paste your own credentials conn = pg8000.connect(     user="",     password="",     host="",     port= ,     database="",     ssl_context=ssl_context ) cur = conn.cursor() Copy

# Query for data retrieval query = """     SELECT bucket, open, high, low, close     FROM one_min_candle     WHERE symbol = 'BTC/USD'     AND bucket >= NOW() - INTERVAL '1 months'     ORDER BY bucket DESC; """ # Benchmark data retrieval performance start_time = time.time() cur.execute(query) rows = cur.fetchall() end_time = time.time() Copy

print("Pg8000 retrieval performance: {} rows/s".format(len(rows) / (end_time - start_time))) cur.close() conn.close() Copy

Output

Output shows the better performance than the previous drivers:

SQLAlchemy

The code for SQLAlchemy is:

Code

import time from sqlalchemy import create_engine, Table, MetaData, text Copy

# Connect to TimescaleDB engine = create_engine('postgresql+psycopg2:user:pass@localhost:5432/db') connection = engine.connect() metadata = MetaData() # Query for data retrieval query = text("""     SELECT bucket, open, high, low, close     FROM one_min_candle     WHERE symbol = 'BTC/USD'     AND bucket >= NOW() - INTERVAL '1 months'     ORDER BY bucket DESC; """) Copy

# Benchmark data retrieval performance start_time = time.time() result = connection.execute(query) rows = result.fetchall() end_time = time.time() print("SQLAlchemy retrieval performance: {} rows/s".format(len(rows) / (end_time - start_time))) Copy

connection.close() Copy

Output

Output for the code is:

PostgreSQL Drivers Performance Comparison Table

For this benchmark, we ran each test several times for reproducible results and included a cache warm-up process to assess the retrieval performance only. All the drivers were run one after the other, and the number of rows done per second was determined from the repeated runs to ensure fairness in the test results. 

The performance results of PostgreSQL drivers in Python reveal that SQLAlchemy is the fastest with 2867.52 rows/s, followed by pg8000 at 2604.70 rows/s, asyncpg at 2533.42 rows/s, and psycopg2 at 1764.89. SQLAlchemy's strong performance demonstrates its effectiveness when combined with efficient drivers, making it ideal for ORM applications.

Pg8000 and asyncpg are appropriate for high-concurrency jobs, with asyncpg excelling at asynchronous operations. Psycopg2, while slower, is nonetheless dependable and commonly used because of its reliability and accessibility in synchronous situations.

Practices for Using PostgreSQL Drivers

Effectively using PostgreSQL drivers in Python is crucial to managing connections, handling errors, and optimization:

Connection management

• Using connection pools: Rather than opening and closing connections for each request, it will be helpful to use some form of connection pool to manage them more efficiently. Libraries SQLalchemy and psycopg2.pool have built-in pooling features. Here at Timescale, we use pgBouncer for connection pooling.

• Transaction management: Specify how transaction management is done to avoid accidental data integrity problems. In other words, explicitly commit or roll back transactions to maintain the database state.

Contextual error handling

• Catch specific exceptions: Psycopg2 raises a specific exception for different errors, such as OperationalError and DatabaseError. Catch them specifically to handle anticipated problems gracefully.

•  Use logging: Log errors and important transactional information to help debug and monitor the application's behavior with the database.

• Retry logic: Implement means of retrying transient failures, keeping in mind that a database connection might not be as stable in a cloud application system.

PostgreSQL Drivers Optimization Tips

Bulk operations

Whenever working with lots of data, reduce round-trips to the database by resorting to bulk operations with COPY FROM/COPY TO or batch inserts.

Indexes and query optimization 

Always have optimized queries. Use EXPLAIN plans to know and optimize the plan selected for optimum performance. Proper indexing is very important for appropriate query performance.

Avoid connection overhead

Use the least possible overhead by reusing connections and reducing the number of connect/disconnect cycles.

Follow these best practices and troubleshooting tips to improve the performance, reliability, and security of your Python applications when using the PostgreSQL drivers.

Summary Table of Top PostgreSQL Drivers

Next Steps

PostgreSQL drivers focus on enhanced asynchronous support, performance, and compatibility with new PostgreSQL extensions. Additionally, cross-platform compatibility makes these drivers more universal and easier to deploy. 

For a detailed performance benchmark of psycopg2 and psycopg3, check out our post. If you are building a Python application and don’t have the time to worry about your database, check out Timescale. You can create a free account and take it out for a spin today.