AWS Lambda Hack: Boost Memory to Slash Costs and Turbocharge Heavy Workloads

When working with AWS Lambda, a common challenge is balancing execution performance with cost efficiency. While it might seem counterintuitive, increasing the memory allocated to a Lambda function can, in some cases, reduce overall costs by significantly improving execution speed. In this article, we explore a scenario where this strategy proves beneficial, particularly for data processing tasks involving complex computations.

The Context: Memory and CPU Relationship in AWS Lambda

AWS Lambda ties CPU power to the amount of memory allocated to a function. Therefore, by increasing memory, you’re also increasing the available CPU power, which can reduce the time required to execute the function. This is especially valuable in scenarios where the function performs CPU-intensive operations, such as aggregating and transforming large datasets.

Use Case: Data Aggregation and Transformation

Imagine a Lambda function designed to process large datasets, performing operations like aggregations, filtering, and transformations. These operations are computationally intensive, and more CPU power can drastically improve execution times. Let’s examine how increasing memory affects both execution time and costs in this scenario.

Scenario:

• Initial Configuration: 512 MB of memory, processing time of 6 seconds.
• Optimized Configuration: 1024 MB of memory, processing time reduced to 2.5 seconds.

Cost Analysis

Let’s break down the costs for both configurations based on the updated pricing structure.

512 MB of Memory:

• Execution Time: 6 seconds = 6000 milliseconds
• Price per 1 ms: $0.0000000067
• Total Cost per Execution: 6000 ms * $0.0000000067 = $0.0000402
• Total Cost per Million Executions: $0.0000402 * 1,000,000 = $40.20

1024 MB of Memory:

• Execution Time: 2.5 seconds = 2500 milliseconds
• Price per 1 ms: $0.0000000133
• Total Cost per Execution: 2500 ms * $0.0000000133 = $0.00003325
• Total Cost per Million Executions: $0.00003325 * 1,000,000 = $33.25

The graph below illustrates the impact of increasing memory from 512 MB to 1024 MB on both execution time and costs.

Conclusion

In this data processing use case, increasing the memory allocation from 512 MB to 1024 MB not only reduced the execution time from 6 seconds to 2.5 seconds but also lowered the overall cost from $40.20 to $33.25 per million executions. This case demonstrates that for tasks involving complex data operations, increasing the memory allocation can lead to significant cost savings and performance improvements.

However, it’s important to note that this strategy may not apply universally to all Lambda functions. It’s essential to evaluate each function individually, testing different memory configurations to find the optimal balance between performance and cost.

For developers and engineers working with AWS Lambda, understanding the relationship between memory allocation and CPU performance is key to optimizing both performance and costs. In scenarios like data processing, where CPU-intensive operations are common, increasing memory can be a highly effective strategy.