vds-slice

VDS slice

VDS slice is a web API for accessing seismic slices and fences from VDS files. It also serves basic metadata from the Survey.

Motivation

This API exist in order to reduce the amount of compute- and network-resources needed by a caller in order to fetch a single slice or fences from VDS data. Requesting this data directly through the OpenVDS SDK involves downloading and extracting much more data than asked for. In many scenarios that is completely fine, but there are cases where that extra resource use is unacceptable. Suppose you run a service that caters requests from hundreds of users and as part of each request you have to fetch a slice. Then the resource overhead of OpenVDS will quickly saturate your servers CPU and network throughput, leaving few resource for the rest of you request pipeline.

With this API we move the resource use of OpenVDS away from your server, by serving you exactly the data you ask for and nothing more, keeping each request as lightweight as possible.

Should I use VDS Slice or the OpenVDS SDK?

This API does not serve you any data that cannot be access directly through the OpenVDS SDK. As mentioned, the motivation for VDS Slice is to reduce resource use when fetching small amounts of data, such as slices or fences. However, if your use-case involve reading larger amounts of data, such as in a ML pipeline or other seismic processing you are better of using OpenVDS directly.

Development

Running the server

Server can be run from the project root directory

go run cmd/query/main.go --storage-accounts "https://<account>.blob.core.windows.net"

Or it can be installed first and then run

export VDSSLICE_INSTALL_DIR=<where to install the server>
GOBIN=$VDSSLICE_INSTALL_DIR  go install ./...
VDSSLICE_STORAGE_ACCOUNTS="https://<account>.blob.core.windows.net" $VDSSLICE_INSTALL_DIR/query

Run $VDSSLICE_INSTALL_DIR/query --help to print available server options.

Note that for server to build and run properly openvds library should be reachable. For example:

PATH_TO_OPENVDS_LIBRARY=<location where openvds is installed>
export CGO_CPPFLAGS="-isystem $PATH_TO_OPENVDS_LIBRARY/include"
export CGO_LDFLAGS="-L$PATH_TO_OPENVDS_LIBRARY/lib"
export LD_LIBRARY_PATH=$PATH_TO_OPENVDS_LIBRARY/lib:$LD_LIBRARY_PATH

Testing

Project has following test suits:

1. Developer test suites

Go

The go test suite is the main test suite of the project. In addition to testing low level internal functionality, it spins up a test server and runs on it high level setup tests. Note that all the tests run on vds files stored on disk.

To run the tests switch to the root directory and call

go test -failfast -race ./...

C++

The gtest test suite is a test suite for C++ code. When choosing where to implement new tests keep in mind that C++ is deeper internal code and thus its interface is more prone to change than go one.

To run the tests one has to build C++ project outside of go build. The following builds the project in directory build and runs the tests:

cmake -S . -B build -DCMAKE_PREFIX_PATH=/path/to/openvds
cmake --build build
ctest --test-dir build --output-on-failure

2. E2E test suite

Python E2E test suite is a supplementary test suite. Its main goals are to assure that server works correctly with Azure cloud infrastructure, server setup which could not be directly tested through go tests is correct, and that real-world user experience for each category of requests is without surprises. Suite is not expected to go into details more than necessary.

To run the tests:

1. Define the following environment variables:

   export STORAGE_ACCOUNT_NAME=<name of Azure storage account/datalake gen2 resource>
   export STORAGE_ACCOUNT_KEY="<storage account key>"
   export ENDPOINT=<endpoint on which server will be running. Defaults to http://localhost:8080>
   

   Suite assumes that default vds well_known/well_known_default file is uploaded under container testdata in STORAGE_ACCOUNT_NAME. Files can be uploaded using the corresponding bash scripts under testdata directory. See variables declared in the files in e2e directory for better overview over what is expected.

2. Start the server if tests are supposed to run against local server

   go run cmd/query/main.go --storage-accounts "https://$STORAGE_ACCOUNT_NAME.blob.core.windows.net" --cache-size 50
   

   Cache is expected to be turned on.

3. Create Python test environment (if needed) These commands will create, activate and install the required packages for the E2E tests.

   cd tests
   python -m venv venv
   source venv/bin/activate
   pip install -r e2e/requirements-dev.txt
   

4. Run E2E tests from the tests directory

   cd tests
   python -m pytest -s -rsx e2e
   

3. Performance test suite

Javascript performance test suite which is run through python is intended to be run on cloud CI, not locally. As getting data as fast as possible is the main point behind the project, making sure response time on target server never declines significantly regardless on the inner changes is sensible. However in reality it is difficult to compare performance due to both environment setup and network/local speed fluctuations. At the moment we are still assessing how much practical use this suite has.

To locally run a file <chosen_script>.js from the suite:

1. Install k6 and make it available on PATH
2. pip-install requirements under tests/performance/requirements-dev.txt
3. Add test directory to python path

   export PYTHONPATH=<path-to-the-repo>/vds-slice/tests:$PYTHONPATH
   

4. Set all environment variables

   # set the 3 following variables needed to create sas
   
   export STORAGE_ACCOUNT_NAME=<storage account name>
   export STORAGE_ACCOUNT_KEY="<storage account key>"
   export EXPECTED_RUN_TIME=<validity of the sas token (seconds)>
   
   # or instead provide SAS itself
   
   export SAS="<token>"
   

   export ENDPOINT=<endpoint on which server is running>
   export VDS=<https://account.blob.core.windows.net/path-to-the-vds>
   export LOGPATH=<directory to store output files>
   export MEDTIME=<maximum acceptable median response time (ms) against provided server>
   export MAXTIME=<time (ms) in which 95% of all requests are expected to return against provided server>
   

5. Run the script

   python tests/performance/performance.py tests/performance/<chosen_script>.js
   

Errors and output reports can be found under the LOGPATH directory.

CI

E2E tests use secrets to access Azure environment and due to security reasons secrets are not accessible in pull requests. Thus e2e tests can't run on PRs, but are set up to run on merge to master.

If one wants to run e2e tests oneself, one must set up own fork with required secrets to dedicated test storage account. Then e2e tests run can be triggered either manually or by pushing code to a dedicated e2e_tests branch.