HSDS (Highly Scalable Data Service) - REST-based service for HDF5 data

Introduction

HSDS is a web service that implements a REST-based web service for HDF5 data stores. Data can be stored in either a POSIX files system, or using object-based storage such as AWS S3, Azure Blob Storage, or MinIO. HSDS can be run a single machine with or without Docker or on a cluster using Kubernetes (or AKS on Microsoft Azure).

What's New in HSDS v1.0.0

New Features

• Query improvements: added a dedicated /datasets/{id}/query path for read-only queries, restored query-param support on PUT /datasets/{id}/value (query-based conditional update), updated the query syntax and evaluation engine (now backed by the h5json library), and extended query support to multi-dimensional datasets (previously limited to 1-D).
• Region reference support: GET/PUT value requests can now read and write HDF5 region references.
• Consolidated domain metadata: added support for generating and serving a consolidated summary of all objects in a domain, reducing the number of requests needed to inspect a domain's full structure.
• Client-provided object IDs and timestamps: POST requests for datasets, groups, and datatypes can now specify the object's ID and creation timestamp directly (subject to a configurable max_timestamp_drift), rather than always having the server generate them - useful for replication/migration scenarios.
• Richer object-creation payloads: POST requests for datasets and groups can now initialize attributes, links, and (for datasets) initial data values in the same request that creates the object.
• Batch object creation: added multi-object creation support for datasets, groups, and datatypes (create several objects in a single POST), backed by a new async DomainCrawler/PostCrawler-based implementation.
• Improved array (H5T_ARRAY) dtype handling: fixed selection/read/write handling for datasets whose own type is an array (subarray) dtype, not just array-typed fields nested in a compound type. (Note: this covers binary reads/writes; JSON-encoded writes to a top-level H5T_ARRAY dataset are still tracked as a known issue.)
• Formal OpenAPI specification: added openapi.yml, a full OpenAPI 3 description of the HSDS REST API, plus new /about and /info endpoints.

Notable Bug Fixes

• Fixed a race condition in node "ready" state handling that could cause requests to be routed to a node before it was fully initialized.
• Fixed a hang in the DomainCrawler's data-write handler.
• Fixed handling of H5S_UNLIMITED in dataset maxdims.
• Fixed scalar-dataset value access, uninitialized attribute values, and a chunkref-indirect-layout bug.
• Fixed binary field-selection reads/writes (selecting a subset of compound-type fields).
• runall.sh now waits until the service reports a READY state before returning, instead of a fixed sleep.

Other Changes

• Internal data-type, array, object-ID, shape, dataset, filter, link, and time utilities have been migrated to the new standalone h5json library, replacing several local hsds/util/* modules (see Breaking Changes below).
• Removed AWS Lambda support (Dockerfile.lambda, lambda_function.py, and related docs/config).
• Minimum supported Python version is now 3.11 (up from 3.10).

Breaking Changes in v1.0.0

v1.0.0 is a major version bump and includes some deliberate, non-backward-compatible changes. If you're upgrading from a 0.x release, be aware of the following:

• Query response shape changed: previously, using the query parameter on a PUT /datasets/{id}/value request returned the updated values for the matching rows in a "value" field. Now that same request returns the matching row indices in an "indices" field instead (no "value" key). To retrieve matches read-only (without updating), use the new dedicated GET /datasets/{id}/query endpoint, which also returns "indices". GET /datasets/{id}/value?query=... (read, non-update) is unchanged and still returns "value".
• Dataset chunk layout moved in the JSON schema: GET /datasets/{id} no longer returns a top-level "layout" key. Layout information (whether client-specified or server-generated) now always appears nested under "creationProperties"."layout". Clients that read dataset_json["layout"] directly need to switch to dataset_json["creationProperties"]["layout"].
• External link field renamed: external link objects now report the target file/domain under a "file" key instead of "h5domain" in API responses. Creating a link with "h5domain" in the request body is still accepted for backward compatibility, but it will no longer be echoed back that way - expect "file" in the response.
• Status code change for duplicate object IDs: POST requests that specify a client-provided object ID which already exists now return 400 Bad Request (previously 500 Internal Server Error).
• AWS Lambda support removed: HSDS can no longer be deployed as an AWS Lambda function; Dockerfile.lambda, lambda_function.py, hsds/util/awsLambdaClient.py, and the associated setup docs have been removed.
• New required dependency: HSDS now depends on the h5json package for core type/array/object-ID/shape utilities. Code that imported HSDS's own hsds.util.idUtil, hsds.util.timeUtil, hsds.util.hdf5dtype, or hsds.util.arrayUtil modules directly will break, as those modules have been removed in favor of h5json equivalents.
• Minimum Python version raised to 3.11 (from 3.10).

Quick Start

On your desktop/laptop

Make sure you have Python 3 and Pip installed, then:

1. Run install: $ ./build.sh --no-lint --no-docker from source tree OR install from pypi: $ pip install hsds
2. Create a directory the server will use to store data, example: $ mkdir ~/hsds_data
3. Start server: $ hsds --root_dir ~/hsds_data
4. Run the test suite. In a separate terminal run:
   • Set user_name: $ export USER_NAME=$USER
   • Set user_password: $ export USER_PASSWORD=$USER
   • Set admin name: $ export ADMIN_USERNAME=$USER
   • Set admin password: $ export ADMIN_PASSWORD=$USER
   • Run test suite: $ python testall.py --skip_unit
5. (Optional) Install the h5pyd package for an h5py compatible api and tool suite: https://github.com/HDFGroup/h5pyd
6. (Optional) Post install setup (test data, home folders, cli tools, etc): docs/post_install.md

To shut down the server, and the server is not running in Docker, just control-C.

If using docker, run: $ ./stopall.sh

Note: passwords can (and should for production use) be modified by changing values in hsds/admin/config/password.txt and rebuilding the docker image. Alternatively, an external identity provider such as Azure Active Directory or KeyCloak can be used. See: docs/azure_ad_setup.md for Azure AD setup instructions or docs/keycloak_setup.md for KeyCloak.

Detailed Install Instructions

On AWS

For complete instructions to install on a single Azure VM with Docker:

• See: docs/docker_install_aws.md

For complete instructions to install on AWS Kubernetes Service (EKS):

• See: docs/kubernetes_install_aws.md

On Azure

For complete instructions to install on a single Azure VM with Docker:

• See: docs/docker_install_azure.md

For complete instructions to install on Azure Kubernetes Service (AKS):

• See: docs/kubernetes_install_azure.md

On Prem (POSIX-based storage)

For complete instructions to install on a desktop or local server:

• See: docs/docker_install_posix.md

On DCOS (BETA)

For complete instructions to install on DCOS:

• See: docs/docker_install_dcos.md

General Install Topics

Setting up docker:

• See docs/setup_docker.md

Post install setup and testing:

• See docs/post_install.md

Authorization, ACLs, and Role Based Access Control (RBAC):

• See docs/authorization.md

Monitoring and metrics (Prometheus / Grafana):

• See docs/prometheus_metrics.md

Writing Client Applications

As a REST service, clients be developed using almost any programming language. The test programs under: hsds/test/integ illustrate some of the methods for performing different operations using Python and HSDS REST API (using the requests package).

The related project: https://github.com/HDFGroup/h5pyd provides a (mostly) h5py-compatible interface to the server for Python clients.

For C/C++ clients, the HDF REST VOL is a HDF5 library plugin that enables the HDF5 API to read and write data using HSDS. See: https://github.com/HDFGroup/vol-rest. Note: requires v1.12.0 or greater version of the HDF5 library.

Uninstalling

HSDS only modifies the storage location that it is configured to use, so to uninstall just remove source files, Docker images, and S3 bucket/Azure Container/directory files.

Reporting bugs (and general feedback)

Create new issues at http://github.com/HDFGroup/hsds/issues for any problems you find.

For general questions/feedback, please use the HSDS forum: https://forum.hdfgroup.org/c/hsds.

License

HSDS is licensed under an APACHE 2.0 license. See LICENSE in this directory.

Azure Marketplace

VM Offer for Azure Marketplace. HSDS for Azure Marketplace provides an easy way to setup a Azure instance with HSDS. See: https://azuremarketplace.microsoft.com/en-us/marketplace/apps/thehdfgroup1616725197741.hsdsazurevm?tab=Overview for more information.

Websites

• Main website: https://www.hdfgroup.org/solutions/highly-scalable-data-service-hsds/
• Source code: https://github.com/HDFGroup/hsds
• Forum: https://forum.hdfgroup.org/c/hsds
• Documentation: https://support.hdfgroup.org/documentation/index.html
• REST API: https://github.com/HDFGroup/hdf-rest-api

Other useful resources

HDF Group Blog Posts

• Web Caching: https://www.hdfgroup.org/2022/10/improve-hdf5-performance-using-caching/
• HSDS Streaming: https://www.hdfgroup.org/2022/08/hsds-streaming/
• Cloud Storage Options for HDF5: https://www.hdfgroup.org/2022/08/cloud-storage-options-for-hdf5/
• HSDS Docker Images: https://www.hdfgroup.org/2022/07/hsds-docker-images/
• HSDS Container Types: https://www.hdfgroup.org/2022/07/deep-dive-hsds-container-types/
• Using Multiprocessing in Python: https://www.hdfgroup.org/2022/06/speed-up-cloud-access-using-multiprocessing/
• Biosimulations - case study with HSDS and Vega: https://www.hdfgroup.org/2022/02/biosimulations-a-platform-for-sharing-and-reusing-biological-simulations/
• HSDS for Microsoft Azure: https://www.hdfgroup.org/2021/08/hsds-for-azure/
• New Features in HSDS v0.6: https://www.hdfgroup.org/2020/10/new-features-in-hsds-version-0-6/
• HSDS Security: https://hdfgroup.org/wp/2015/12/serve-protect-web-security-hdf5
• HDF for the Web: HDF Server: https://www.hdfgroup.org/2015/04/hdf5-for-the-web-hdf-server/

External Blogs and Articles

• A RESTful Meeting Between MATLAB and HDF Server: https://www.mathworks.com/matlabcentral/fileexchange/59072-a-restful-meeting-between-matlab-and-hdf-server-web-based-hdf5-access-using-matlab
• AWS Big Data Blog: https://aws.amazon.com/blogs/big-data/power-from-wind-open-data-on-aws/

Slide Decks

• HSDS v0.7 New Features, EUHUG 2022: https://www.hdfgroup.org/wp-content/uploads/2022/05/HSDS_New_Feautres_7.0.pdf
• HSDS Serverless, EUHUG 2021: https://www.hdfgroup.org/wp-content/uploads/2021/07/ServerlessHSDS.pdf
• HSDS REST, HUG 2020: https://www.hdfgroup.org/wp-content/uploads/2020/10/HSDS_Rest_Service_HDF5_Readey.pdf
• HSDS with Jupyter, ESIP 2018: https://www.slideshare.net/HDFEOS/hdf-kita-lab-jupyterlab-hdf-service
• HDF Data Services, SciPy17: http://s3.amazonaws.com/hdfgroup/docs/hdf_data_services_scipy2017.pdf

Videos

• HSDS Webinar: https://www.youtube.com/watch?v=9b5TO7drqqE
• HSDS Overview, Allotrope Connect Day: https://www.youtube.com/watch?v=nRHXEkhlfZ0
• The Use of HSDS on SlideRule, HUG 2020: https://www.youtube.com/watch?v=i-KIoGqdEMg
• HDF Data Services, SciPy 2017: https://www.youtube.com/watch?v=EmnCz1Hg-VM
• RESTful HDF, SciPy 2015: https://www.youtube.com/watch?v=JSFZ3i3WcjQ

Papers

• restfulSE: A semantically rich interface for cloud-scale genomics with Bioconductor: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6392152
• RESTful HDF5 White Paper: https://www.hdfgroup.org/pubs/papers/RESTful_HDF5.pdf