If you’ve spent much time working with public repositories of HDF5 data, you’ll often see data organized as a large collection of files where the files are organized by time, geographic location or both. If you are using HSDS, there’s some good news in that you can use these collections as is and also have an aggregated view with HSDS.

News on the H5PYD v0.12.0 release and an install guide for running HSDS on Tencent Cloud.

John Readey, The HDF Group Before there was HSDS, there was h5serv. Released in 2015, h5serv was the first implementation of the HDF Rest API. Designed mainly as a way to demonstrate the RESTful interface for HDF, h5serv had a fairly simple implementation: A single threaded application that on receiving an HTTP request, made the

Highly Scalable Data Service principal architect John Readey covers an update to the Highly Scalable Data Service. The max request size limit per HTTP request no longer applies with the latest HSDS update. In the new version large requests are streamed back to the client as the bytes are fetched from storage. Regardless of the size of the read request, the amount of memory used by the service is limited and clients will start to see bytes coming back while the server is still processing the tail chunks in the selection. The same applies for write operations—the service will fetch some bytes from the connection, update the storage, and fetch more bytes until the entire request is complete. Learn more about this update, plus check out John’s benchmark results using a couple of different MacBook Pros and his new DevOne laptop.