5 ms·

What do you recommend for binary structured data? (assuming that SQL-like isn't appropriate)

by dguest 3y ago

cjbgkagh 3y ago
Protobuf is my go-to and I have had no issues with it. If I had to do larger memory mapped files I guess flatbuffers would be the way to go. Nympy has a reasonably sensible format, but I haven’t tried to do anything tricky with it.
- dagw 3y ago
  Protobuf has caused me nothing but trouble once I have even moderately large data sets. If what you are storing is large matrices or rasters, honestly nothing beats HDF5.
  - cjbgkagh 3y ago
    I’m old school, from the Managing Gigabytes by Ian H Witton era so I have ways of tailoring the data and protobuf schema to suit my application. Large matrices are stored as contiguous chunks, similar to how tensorflow stores tensors in graphs. I work with TBs of data in GB chunks. I used to work with PBs of data but that was a long time ago. Derialization of protobuf messages is so fast that it hasn’t been an issue and I can use it from any language with protobuf support.
saltcured 3y ago
I think that depends on the use case and full data lifecycle. How is data acquired and added to datasets? How is it consumed? An online, database-like product is most important if you need to coordinate concurrent activity by a number of data producers and consumers while maintaining a coherent view of the growing data. If you can break it into distinct phases with individual actors, passive serialization formats can make more sense. Adoption of bject-storage semantics would help eliminate some of the corruption/concurrency hazards mentioned in Cyrille's post. You write entire files and expose them to read-only consumers once they are valid and complete, side-stepping concurrent writer/reader scenarios. However, object-storage still has coherence problems. If you expect metadata to need rounds of editing or curation, you don't want it embedded in bulk objects, where mutation is expensive, i.e. rewriting an entirely new version of the object. It is easier with a companion file strategy, where you can regenerate smaller metadata files alongside immutable bulk data files. The object store can ensure that concurrent users are only encountering a coherent snapshot of each metadata file, i.e. before or after it was replaced. But it does not provide coherent views of multi-file changes. You avoid low-level codec failures from concurrent writes to shared files, but you may still have semantic inconsistencies when interpreting the evolving multi-object dataset. A hybrid approach would be to have some other data-management system with database-like properties to track the objects. The database stores a coherent catalog of which data objects are available. New objects are added and registered in the catalog for others to read, but you don't mutate them afterwards. The catalog should contain metadata needed to coordinate the workflows of production and consumption of objects. You could benefit from using the catalog as an authoritative store of metadata that is still being curated/refined. But you can also export snapshots of metadata into companion objects at appropriate milestones in the processing workflow. These could even be versioned to record well-defined "data releases" or provenance info related to individual processing tasks. Of course, this still leaves the question of what file format(s) to use for the individual objects in the system... one could do all of the above and still choose to use HDF5! But these individual files would be simpler, i.e. to store a single N-dimensional numerical array in one file. Microscopy projects might choose OME-TIFF for individual image stacks, or something even more mundane like a directory of individual 2D TIFF or PNG files, if that maps well to how their acquisition or processing pipeline works. Sometimes, the right file layout is just as important as choosing chunking parameters within a format like HDF5. Naive pipelines often process whole files in RAM, so you end up choosing appropriate file layouts to align to the kind of sparse or sequential access needed by data producer and consumer programs.
rossant 3y ago
We're using the NPY format [1] at the International Brain Laboratory and we're pretty happy with it. It works well if you have few large arrays that you can store in different files within a folder. Another good option is Apache Parquet. [1] https://numpy.org/devdocs/reference/generated/numpy.lib.format.html https://numpy.org/devdocs/reference/generated/numpy.lib.form...

What do you recommend for binary structured data? (assuming that SQL-like isn't appropriate)

by dguest 3y ago

What do you recommend for binary structured data? (assuming that SQL-like isn't appropriate)

Protobuf is my go-to and I have had no issues with it. If I had to do larger memory mapped files I guess flatbuffers would be the way to go. Nympy has a reasonably sensible format, but I haven’t tried to do anything tricky with it.

dagw 3y ago
Protobuf has caused me nothing but trouble once I have even moderately large data sets. If what you are storing is large matrices or rasters, honestly nothing beats HDF5.
- cjbgkagh 3y ago
  I’m old school, from the Managing Gigabytes by Ian H Witton era so I have ways of tailoring the data and protobuf schema to suit my application. Large matrices are stored as contiguous chunks, similar to how tensorflow stores tensors in graphs. I work with TBs of data in GB chunks. I used to work with PBs of data but that was a long time ago. Derialization of protobuf messages is so fast that it hasn’t been an issue and I can use it from any language with protobuf support.

saltcured 3y ago

I think that depends on the use case and full data lifecycle. How is data acquired and added to datasets? How is it consumed? An online, database-like product is most important if you need to coordinate concurrent activity by a number of data producers and consumers while maintaining a coherent view of the growing data. If you can break it into distinct phases with individual actors, passive serialization formats can make more sense. Adoption of bject-storage semantics would help eliminate some of the corruption/concurrency hazards mentioned in Cyrille's post. You write entire files and expose them to read-only consumers once they are valid and complete, side-stepping concurrent writer/reader scenarios. However, object-storage still has coherence problems. If you expect metadata to need rounds of editing or curation, you don't want it embedded in bulk objects, where mutation is expensive, i.e. rewriting an entirely new version of the object. It is easier with a companion file strategy, where you can regenerate smaller metadata files alongside immutable bulk data files. The object store can ensure that concurrent users are only encountering a coherent snapshot of each metadata file, i.e. before or after it was replaced. But it does not provide coherent views of multi-file changes. You avoid low-level codec failures from concurrent writes to shared files, but you may still have semantic inconsistencies when interpreting the evolving multi-object dataset. A hybrid approach would be to have some other data-management system with database-like properties to track the objects. The database stores a coherent catalog of which data objects are available. New objects are added and registered in the catalog for others to read, but you don't mutate them afterwards. The catalog should contain metadata needed to coordinate the workflows of production and consumption of objects. You could benefit from using the catalog as an authoritative store of metadata that is still being curated/refined. But you can also export snapshots of metadata into companion objects at appropriate milestones in the processing workflow. These could even be versioned to record well-defined "data releases" or provenance info related to individual processing tasks. Of course, this still leaves the question of what file format(s) to use for the individual objects in the system... one could do all of the above and still choose to use HDF5! But these individual files would be simpler, i.e. to store a single N-dimensional numerical array in one file. Microscopy projects might choose OME-TIFF for individual image stacks, or something even more mundane like a directory of individual 2D TIFF or PNG files, if that maps well to how their acquisition or processing pipeline works. Sometimes, the right file layout is just as important as choosing chunking parameters within a format like HDF5. Naive pipelines often process whole files in RAM, so you end up choosing appropriate file layouts to align to the kind of sparse or sequential access needed by data producer and consumer programs.

rossant 3y ago

We're using the NPY format [1] at the International Brain Laboratory and we're pretty happy with it. It works well if you have few large arrays that you can store in different files within a folder. Another good option is Apache Parquet. [1] https://numpy.org/devdocs/reference/generated/numpy.lib.format.html https://numpy.org/devdocs/reference/generated/numpy.lib.form...