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May 29, 2018
AWS Fargate is an interesting service in that it allows you to run containers without having to think (much) about cluster management, task scheduling, instance provisioning etc. It transforms the application deployment process into one where the application is packaged into a container and resources (CPU, RAM, Networking, access policies etc) are defined as part of a Fargate task. As such, it’s an attractive option that we’re evaluating as a deployment foundation for Cribl labs.
A Fargate task/container can be configured with a vCPU range of 0.25-4 and a RAM(GB) range of 0.5-30. As it’s typical of us, we went ahead and analyzed its physics to get a better understanding of what we’re dealing with. We have also done a similar analysis on Lambda.
We were specifically interested in the following measurements:
Note that at this point we are not interested in the absolute performance of Fargate instances. We want to primarily get an understanding of their performance relative to each other; feel free to ignore the y-axis units.
fio (direct IO, non-buffered) with block sizes of 1k, 4k, and 8k, across instances with these vCPUxRAM(GB) configurations: 1×2, 2×4, 4×8, and measure
clat perc95 .
Test: download a file from an S3 bucket using
curl to the local filesystem across instances with these vCPU x RAM(GB) configurations: 1×2, 4×8, 4×16, 4×30 and analyze throughput (wall clock
time was used)
gunzip a local file across instances with these vCPU x RAM(GB) configurations: 1×2, 4×8, 4×16, 4×30 and analyze throughput (compressed file size / wall clock
time was used)
Test: pull images of various sizes from ECR, track pull time, convert it to throughput (imageSize/Pulltime) and see how it varies against different vCPUs and RAM(GB) allocations (1×2, 4×16, 4×30).
How will these findings affect our architectural design?
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