charmatzis/benchmark-rio-s3

Tools for benchmarking multi-threaded performance of Rasterio/GDAL when accessing files on S3

benchmark-rio-s3

Tools for benchmarking multi-threaded performance of Rasterio/GDAL when accessing files on S3.

Benchmark measures how long it takes to

1. Open GeoTiff file
2. Read single block (also referred as tile in GeoTiff spec)
3. How well this process scales with more processing threads

When you see word "block" in this document/command line options, it refers to the same thing as "tile" in GeoTiff spec.

Prerequisites

This has been tested on Ubuntu 16.04 and 18.04 images available in the AWS marketplace, but any Linux with python 3.5 or better should work, so long as you have recent enough versions of GDAL and rasterio installed.

You machine should have properly configured AWS credentials for accessing data. To verify try this command:

rio info s3://mybucket/myfile.tif

or you can use Landsat 8 public archive, for example

rio info s3://landsat-pds/c1/L8/106/070/LC08_L1TP_106070_20180417_20180501_01_T1/LC08_L1TP_106070_20180417_20180501_01_T1_B1.TIF

Installation

On Ubuntu

sudo -H pip3 install 'git+https://github.com/opendatacube/benchmark-rio-s3.git'

Then to check everything went well

bench-rio-s3 --help
bench-rio-s3 run --help

Running Benchmark

First you'll need to generate a list of urls to use for testing. These should point to GeoTiff files on S3. All files have to have the same dtype, they need to be "tiled", and their tile sizes should be the same. For example Landsat 8 images have dtype=uint16 and tile shape is 512x512. You need to pick which block to read, this block should be present in all the images. If you don't specify which block to read, "middle" block will be automatically selected. For example Landsat 8 images are something like 16x16 blocks in size, where each block is 512x512 pixels. If you specify --block 1,2 pixels im[512:1024,1024:1536] will be fetched.

To generate the list you can use a helper command

bench-rio-s3 ls s3://bucket/path/to/images

Example using Ladsat 8

First generate url list

bench-rio-s3 ls --filter '*_B?.TIF' s3://landsat-pds/c1/L8/106/070/ | tee urls.txt

Run a quick test to see things are alright

head -n 8 urls.txt | bench-rio-s3 run --skip-bucket-warmup --no-warmup-more --threads 4 -

Then run a full test

bench-rio-s3 run --threads "$(seq -s, 32)" --times 3 urls.txt

This will repeat test 3 times with number of worker threads from 1 all the way to 32.

Visualising results

To generate graphs from collected statistics you will need matplotlib and nbconvert installed on your system. These will be installed if specify [report] extra when installing benchmark-rio-s3

sudo -H pip3 install 'git+https://github.com/opendatacube/benchmark-rio-s3.git#egg=benchmark-rio-s3[report]'

To visualize collected data

1. Change into a directory containing benchmark *.pickle files
2. Run bench-rio-s3 report

This should produce

• report.html
• Directory named report_images with PNG and SVG versions of graphs