In real-world applications, functions can be computationally expensive or time-consuming. To improve efficiency, we can cache and reuse results for identical inputs, a pattern known as idempotency. AWS Lambda Powertools provides an idempotency utility that returns the previously successful result when a function is called repeatedly with the same input. In this post, we will explore how to use it.

AWS X-Ray helps developers analyze and debug distributed applications by providing a holistic view of requests as they travel through the system.¹ Tracer is a wrapper around the AWS X-Ray SDK for Python that offers a simplified interface for instrumenting Lambda functions.² In this post, I will show you how to use Tracer to gain insights into your function’s execution.

Metrics are a key component of monitoring and observability, allowing you to understand the behavior of your Lambda functions and identify potential issues. In this post, we will explore how to use AWS Lambda Powertools to simplify metric collection.

Powertools for AWS Lambda is a library designed to implement best practices and boost productivity when building AWS Lambda functions. In this post, we will explore its core features, focusing on logging and validation.

Building on my previous post about deploying a Lambda function using AWS SAM, this guide demonstrates how to deploy a containerized Lambda function using Terraform. The example application queries an external API (https://api.github.com) and returns the response.

In a past project, I built a pipeline to ingest crawled job postings from Elasticsearch and standardize them into a unified schema. Since the postings couldn’t all fit in memory, I needed to process them in batches. The Elasticsearch client yields postings one by one, so I wrapped it with a batching generator that groups them into chunks.

To illustrate, suppose you have a generator that yields numbers 0 to 9 (in reality, it might yield thousands or millions of items). To read them in batches (for example, batch size of 4), you can loop and use itertools.islice to take the next 4 items. Each call to itertools.islice consumes up to 4 elements from the generator, and the loop continues until the generator is exhausted.

A hallmark of clean code is consistent formatting, which becomes important when collaborating in a team. While manually using formatters or linters to check code is possible, it’s easy to forget. A more effective approach is to use tools like pre-commit hooks for automated code inspection before committing.