Benjamin Dwumah

Benjamin Dwumah

Software Engineer - Big Data, AI & Cloud

Delving Deeper into API Performance Optimization

Posted on July 9, 2023

In the backend engineering space, crafting high-performing APIs is crucial. This involves a nuanced understanding of several strategies to enhance API response times and minimize network congestion. This blog presents an in-depth look at three core methods - caching, compression, and payload management, with Python-based code examples.


Caching is a high-impact strategy for performance optimization that involves storing the results of expensive operations to reuse in subsequent requests. Python provides several options to implement caching, such as using built-in data structures like dictionaries or third-party libraries like Redis. One straightforward way to implement server-side caching with Python is to use the Flask-Caching extension:

from flask import Flask
from flask_caching import Cache

app = Flask(__name__)
# Configure your cache
cache = Cache(app, config={'CACHE_TYPE': 'SimpleCache'})

def expensive_operation():
    # Perform your expensive operation here

The @cache.cached decorator caches the result of the expensive_operation function for 60 seconds. This method effectively mitigates unnecessary repeated execution of costly operations, resulting in considerable performance gains. It’s critical to note that data volatility, consistency, and the cost of data generation are key factors to consider when strategizing cache utilization.


Compression is an essential technique to optimize data transfer times between the server and client. It is especially beneficial for APIs transmitting substantial amounts of data. While Flask doesn’t natively support response compression, it can be easily achieved using extensions like Flask-Compress:

from flask import Flask
from flask_compress import Compress

app = Flask(__name__)

def large_data():
    # Your large data generation logic here

In this example, Flask-Compress automatically applies gzip compression to the API responses, which can then be decompressed by the client, reducing the data payload size during network transmission. Keep in mind that while compression reduces data size, it does add some CPU overhead. Consequently, it’s crucial to weigh the trade-offs based on your API’s use-case specifics.

Payload Management

Effectively managing API response payload is another excellent strategy for API optimization. It encompasses techniques such as using sparse fieldsets and pagination to limit the amount of data returned by the API. Here’s an example of how to implement these techniques in Flask with a MongoDB backend using PyMongo:

from flask import Flask, request
from pymongo import MongoClient
from bson.json_util import dumps

app = Flask(__name__)
db = MongoClient("mongodb://localhost:27017").test_database

def get_users():
    page = int(request.args.get('page', '1'))
    limit = int(request.args.get('limit', '10'))
    fields = request.args.get('fields', '')  
    projection = {field: 1 for field in fields.split(',')} if fields else {}

    skip = (page - 1) * limit
    cursor = db.users.find({}, projection).skip(skip).limit(limit)
    users = [doc for doc in cursor]

    return dumps(users)  # convert to JSON

This example allows clients to specify which fields they require (fields), the page number (page), and the number of records per page (limit). By limiting the returned data to only what’s necessary, network utilization is minimized, and API response times are improved.

Creating high-performance APIs requires a deep understanding of various optimization techniques. By leveraging caching, compression, and effective payload management, you can drastically improve API response times and overall performance. However, the application of these strategies necessitates continuous tuning and monitoring based on the unique attributes of your APIs, user requirements, and system constraints. Despite the language or framework in use, the quest for performance optimization is a constant in the landscape of backend engineering.