Skip to content

README.md

Latest commit

 

History

History
76 lines (50 loc) · 2.29 KB

README.md

File metadata and controls

76 lines (50 loc) · 2.29 KB

CloudQuery Benchmark

This is a benchmark for CloudQuery. It is used to measure the performance of CloudQuery.

Prerequisites

  • Python version equal or higher than 3.11
  • CloudQuery CLI installed (see CloudQuery CLI)

Setup

The benchmark will use only the configured sources, and optionally the S3 destination.

AWS Source

Set an AWS_LOCAL_PROFILE environment variable to the AWS local profile you want to use for the benchmark.

For example, if your AWS credentials files looks like this:

[default]
aws_access_key_id = YOUR_ACCESS_KEY_ID
aws_secret_access_key = YOUR_SECRET_ACCESS_KEY

[benchmark]
aws_access_key_id = YOUR_ACCESS_KEY_ID
aws_secret_access_key = YOUR_SECRET_ACCESS_KEY

Then you can set the AWS_LOCAL_PROFILE environment variable to benchmark to use the benchmark profile, via:

export AWS_LOCAL_PROFILE=benchmark

Alternatively, you can set the AWS_LOCAL_PROFILE environment variable in the bench_configs/source_aws.yml file.

Azure Source

  1. Follow the instructions in Azure Source to authenticate with Azure using environment variables.
  2. Set an AZURE_SUBSCRIPTION_ID environment variable to the Azure subscription ID you want to use for the benchmark, for example:
export AZURE_SUBSCRIPTION_ID=YOUR_AZURE_SUBSCRIPTION_ID

GCP Source

  1. Ensure you have the Google Cloud CLI installed and run gcloud auth application-default login to set up Application Default Credentials.
  2. Set an GCP_PROJECT_ID environment variable to the GCP project ID you want to use for the benchmark, for example:
export GCP_PROJECT_ID=YOUR_GCP_PROJECT_ID

S3 Destination

To enable sending data to S3, set the following environment variables:

export S3_BUCKET_NAME=YOUR_S3_BUCKET_NAME
export S3_REGION=YOUR_S3_REGION
export S3_LOCAL_PROFILE=YOUR_S3_LOCAL_PROFILE

Running the benchmark

pip install -r requirements.txt
python benchmark.py

Advanced configuration

To generate enough resources to sync, we use the CQ_DEBUG_SYNC_MULTIPLIER to simulate more API calls to AWS, GCP and Azure. You can update the code to generate more or less resources if you'd like.