AWS Lambda now supports scalable network bandwidth for functions configured with 2,048 MB of memory or more, running outside of a virtual private cloud (VPC). Previously, sustained network throughput was capped at 625 Mbps regardless of your function’s memory configuration. Now, sustained throughput scales proportionally from 625 Mbps at configurations below 2,048 MB up to 3,000 Mbps at 10,240 MB, increasing the rate at which data moves to and from your execution environment.
In this post, you learn how to apply this new capability to latency-sensitive data processing workloads, helping reduce function execution times and per-invocation costs while improving the end-user experience through reduced latency. You also walk through a deployable implementation that demonstrates the performance improvements this capability unlocks. Latency-sensitive data processing applications are data processing workloads that must be completed in a defined period of time.
They often experience bursty, ad hoc traffic patterns while being required to download gigabytes or even terabytes of data from a data store, process it in a compute environment, and return a result to a waiting end user. Latency-sensitive data processing is often highly parallelizable. Data can be divided into smaller pieces with each piece being individually processed before combining them together to obtain a result. These workloads can be found in multiple industries and verticals. Examples include: These workloads are challenging to build on traditional compute clusters.
Their spiky and unpredictable nature forces you to choose between under-provisioning compute to optimize costs (and risk missing your SLA) or over-provisioning and paying for idle capacity. Lambda eliminates this tradeoff. Instead of pre-provisioning a compute cluster, Lambda scales compute capacity in response to incoming requests, matching processing power to unpredictable traffic patterns. Because latency-sensitive data processing is highly parallelizable, the ability of Lambda to rapidly scale out execution environments makes it a natural fit.