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Understanding Lambda Local Storage: A Practical Guide for AWS Lambda Developers

Understanding Lambda Local Storage: A Practical Guide for AWS Lambda Developers

In the world of serverless computing, AWS Lambda offers a clean, scalable way to run code without managing servers. Yet when you start building real applications, you quickly encounter a concept that often causes confusion: lambda local storage. This article explains what lambda local storage means, how it behaves in practice, and how to design your functions to use it effectively—whether you are debugging locally, running in production, or developing with infrastructure as code.

What is Lambda Local Storage?

Lambda local storage refers to the temporary filesystem that is available inside the Lambda execution environment. The primary location for this storage is the /tmp directory. The data you write to /tmp persists for the lifetime of the current container instance, which can span multiple invocations as long as the same container remains warm. However, this storage is not guaranteed to persist across cold starts or after the container is terminated. In other words, lambda local storage is fast and convenient for transient data, but it is not a durable data store.

Key characteristics and limits

  • Location: /tmp is the dedicated space for lambda local storage. It is mounted as a temporary filesystem (ephemeral storage).
  • Size and configuration: The default amount of lambda local storage is 512 MB. AWS has introduced configurable ephemeral storage that can be increased, up to 10 GB, to accommodate larger intermediate files and processing needs. This is still considered local storage, but it is now scalable to more demanding workloads.
  • Persistence: Data in lambda local storage is not guaranteed to survive across cold starts or after the container is recycled. While some invocations may reuse the same container, do not rely on local files for long-term persistence.
  • Performance: Access to /tmp is typically fast because it is on the local disk of the Lambda execution environment. This makes it suitable for temporary caches, scratch space, and intermediate results during a function run.

When should you use lambda local storage?

Use lambda local storage for tasks that require fast, temporary access to data during a single invocation or a short sequence of invocations within the same container. Common scenarios include:

  • Staging or transforming data: You can build intermediate results in /tmp before sending them to a durable store or another service.
  • Temporary caches: Caching expensive computations or downloaded assets so subsequent logic in the same invocation or a subsequent warm invocation can reuse them quickly.
  • Binary processing: When you need to hold large in-memory or on-disk artifacts (like images or video frames) for processing steps that happen quickly within the same container.
  • Batch processing pipelines during a single invocation: You can accumulate results in /tmp and clean up at the end of the invocation.

Important: if your workflow requires data to survive across independent invocations or across deployments, lambda local storage is insufficient. In those cases, plan to persist data in a durable store such as S3 or a filesystem-based solution like EFS (Elastic File System) mounted to the Lambda function.

Best practices for using lambda local storage

  • Keep data ephemeral: Treat /tmp as scratch space. Regularly clean up files you no longer need to avoid hitting the storage limit.
  • Be mindful of concurrency: In a highly concurrent setup, multiple invocations might share the same container or run in parallel on different containers. Do not rely on the presence of files across invocations with different execution contexts.
  • Limit disk I/O in production hot paths: Writes to local storage can speed up certain tasks, but excessive I/O can slow down other processes on the same container. Profile and optimize accordingly.
  • Use durable storage for persistence: For results that must survive beyond the life of a single function invocation, use S3, DynamoDB, or EFS. This decouples temporary work from reliable long-term storage.
  • Configure ephemeral storage wisely: If your workload benefits from more local space, consider increasing the ephemeral storage up to the supported limit. Plan for cost and performance trade-offs.
  • Security and leakage prevention: Never store secrets or sensitive data in /tmp. Use AWS Secrets Manager or Parameter Store to retrieve credentials securely at runtime, and store sensitive artifacts securely in durable storage with proper access controls.

Lambda local storage versus persistent storage options

Understanding the difference between lambda local storage and persistent storage is crucial for reliable architectures. Local storage excels for quick, transient work and reduces the need to fetch data multiple times within an invocation. However, it does not provide durability across invocations or across Lambda container recycling. For enduring data needs, you should rely on other services:

  • S3: Ideal for object storage, logs, media, and intermediate data. It is scalable, durable, and accessible from any Lambda function with appropriate permissions.
  • EFS: A managed file system that can be mounted to Lambdas to provide shared, persistent storage across functions and containers. Useful for workloads that require a shared filesystem or large, persistent datasets.
  • DynamoDB or RDS: For structured data, state, and metadata that must survive across invocations and be queried efficiently.

By combining lambda local storage for scratch work with S3 or EFS for persistence, you can design efficient, robust serverless architectures that perform well and scale with demand.

Local development and testing considerations

When developing locally or testing Lambda functions that rely on lambda local storage, you can simulate the Lambda environment using tools such as AWS SAM CLI, Docker-based runtimes, or local emulators. A typical pattern is to write temporary files to /tmp during tests and then assert on their presence or contents. For real-world testing, ensure your tests cover:

  • Behavior under warm and cold starts, especially how /tmp is handled between invocations.
  • Scenarios where the ephemeral storage limit is approached or exceeded, including clean-up strategies.
  • Interaction with durable storage for persistence of results and assets.

Practical examples across languages

Node.js example: using lambda local storage for a transient cache

// Write a small cache in /tmp
const fs = require('fs');
const path = '/tmp/cache.json';
const cache = { timestamp: Date.now(), data: 'sample' };
fs.writeFileSync(path, JSON.stringify(cache));
console.log('Cache stored in /tmp:', path);

Python example: processing a file and cleaning up

import os
def handler(event, context):
    tmp_path = '/tmp/input.txt'
    with open(tmp_path, 'w') as f:
        f.write('data for processing')
    # Do some processing with the file
    # ...
    # Clean up
    os.remove(tmp_path)
    return 'Done'

Design tips for resilient serverless apps

  • Favor stateless design: Keep as much transient state out of /tmp as possible and rely on durable stores for critical data.
  • Implement idempotency: If a function writes to a local scratch file, ensure repeated invocations don’t cause inconsistent results.
  • Plan for scaling: As traffic grows, Lambda may spawn many containers. Ensure your durable storage handles concurrent access safely.
  • Monitor and alert on storage usage: Track how often your functions hit local storage limits and adjust ephemeral storage or offload to durable storage as needed.

Conclusion: a balanced approach to lambda local storage

Lambda local storage (the /tmp space) is a valuable tool for fast, ephemeral computation within a Lambda execution environment. It offers low-latency scratch space for intermediate results, caches, and temporary data, but it should not be treated as a reliable persistent store. By recognizing the limits and combining lambda local storage with durable options such as S3 and EFS, developers can craft robust serverless applications that are both performant and resilient. When you design your Lambda workflows, think about where local storage makes sense and where it is better to offload data to durable storage, ensuring a clean separation between per-invocation scratch work and long-term persistence. In short, lambda local storage is a helper, not a substitute, in modern serverless architectures.