Replies: 8 comments 7 replies
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Thank you for taking the lead on this initiative! Over at the openPMD metadata standard, we need a notion of sparseness for blockstructured mesh-refinement: Our applications are beam, plasma and particle accelerator modeling codes that are implemented against the AMReX library for block-structured AMR. |
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FWIW, I captured some thoughts here |
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I think markcmiller86 raises some valid criticisms in the linked comment, including the point that that API-compatibility with readers made for dense data is not completely necessary when thinking about datasets too large for RAM so that no existing analysis code anyway uses the dense representation (rather application-level schemas/combinations of datasets). My own application is on the small side, with images of "only" a million pixels which is not too bad to have dense in memory, but where writing a sparse representation of only the very few (e.g. 1%) bright pixels offers a faster compression to achieve sufficiently high frame rate. My gut feeling is that my data is more suited for a regular filter (function/plugin) with run-length encoding of zeros, so that I don't have to do calls to a HDF5-sparse-API for each individual pixel. (They are not clustered into dense regions of interest.) I'm working towards such a filter and may post links once there's something to share. The proposal to define a HDF5-API targeted at use-cases where rectangular (hyperrectangular) regions of elements are defined (in a sea of undefined/padded values) may of course proceed regardless of the existence of alternative approaches for other use-cases. If there is a wish to make a sparse HDF5-API that can be efficient also for really sparse data, where most regions have size 1 (few pixels of an image or something like a nearly diagonal matrix), I would propose making sure that there is an option of buffered lower-level storage which is fast at accepting single-element updates (writes) and perhaps for reading can also provide an iterator over the defined elements in a chunk. Not sure whether HDF5 handles any chunk buffering now, I guess there could be a flush() or close()-method to let an application signal when it's suitable to commit a chunk to storage -- or have it automatically happen soon after the access pattern has proceeded to another chunk. Or should such buffering optimization rather be achieved by something like direct writing of application-compressed chunks, where the application would now prepare a buffer with the sparse-defined-region-headers etc. ready for copying to lower-level storage? |
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Thank you for reviewing RFC! I am not sure I understand the point about "API-compatibility with readers made for dense data". The proposal offers a programming model that allows to find defined elements within a specified region of the data set (H5Dget_defined) and to read them using regular H5Dread call. HDF5 will not read more data that is stored in the file, i.e., only defined elements. Readers of data (HDF5 application) do not need to be aware of a particular HDF5 storage mechanism, i.e., if it is contiguous, chunked or structured chunk storage. It means that introduction of new storage type in HDF5 will not affect reading applications when it encounters data stored using structured chunk storage. As RFCs point out, HDF5 provides users with many custom options for storing sparse data. The goal of the RFC is to propose a standard that doesn't depend on sparse data presentation in memory and doesn't require custom schema storage in order to find data in an HDF5 file. |
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This subthread might be a bit of a tangent, about me just explaining what I think markcmiller86 saw as an unnecessary/overreaching/overkill ambition in the RFC... But, I will explain what I meant by "API-compatibility with readers made for dense data". The RFC about sparse HDF5 dataset handling discusses the ways one will be able to find what regions are defined and get their contents, while maintaining the abstract concept of a multidimensional dataset (dataspace) where each index will either have a defined value or be undefined. I.e. the dataset still has some number of dimensions and a length of each dimension, so that the number of hypothetical elements can be computed (although most of them may be undefined and thus not occupying disk space). The sentence that "Readers of data (HDF5 application) do not need to be aware of a particular HDF5 storage mechanism" confirms my interpretation that "normal" calls to read some index or some hyperslab will still work. So the ambition seems to be that if there is an existing user program that reads an entire HDF5 dataset in a classic way, e.g. using H5Dread(..., file_space_id=H5S_ALL, ...), into an N-dimensional array of numbers (what I call a "dense representation"), then this program will through a future HDF5-library version be able to get its N-dimensional array even from a new kind of file where the low-level storage can be made in a sparse way. This will rely on a fill-value being chosen to put in the N-dimensional array where the sparse representation is undefined. This is what I mean by API-compatibility. Now, the point I saw raised by markcmiller86 was that this specific case of API-compatibility while nice in theory, might not be so important to existing sparse-data users in practice. Consider that the only users for which sparse data representation is completely essential are those for whom the dataspace is huge (so the N-dimensional array full of fill-values won't fit in memory or on disk) but the amount of defined elements is manageable. Such users would not have an existing program that builds any such plain N-dimensional array. Because we defined "such users" by the criterion that that N-dimensional array would be too large. So "such users" would have some kind of sparse representation also at the application level of the existing programs. The win by a HDF5-standard way of handling sparse data would of course still be that transfer between users would be easier, because future applications would not need to reinvent sparse representations, and users would not choose incompatible schemas for how they encode their sparse data into some combinations of traditional datasets. But this win would for "such users" seem achieved even if the new sparse-HDF5-dataset would have dropped compatibility with the traditional H5Dread(). I don't really want to argue much about this or claim that there would be any problem, this comment is just to hopefully clarify the reasoning behind the previous comment. If retaining H5Dread()-compatibility is not preventing a good file format design for sparse data use-cases it's of course a nice feature to have. Users of intermediate-sized arrays, that actually can fit in memory, will thanks to the compatibility be able to benefit from reduced disk space usage if their data is sparse (a kind of compression) while not having to change the rest of their programs (still using N-dimensional arrays with fill-values). |
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An unrelated thought when reading more about the implementation proposal is that the "erase" method might become rather complicated for handling the case when a user wishes to erase a few elements within what was previously defined (by one or more ROIs). Is it allowed for an implementation to keep the dense ROI-block and set some elements to zero (or whatever the fill-value is), or would the implementation have to break each ROI-block into two new blocks along affected dimensions in order to achieve the proper gap, so that the erased region becomes reported as undefined rather than just zero? Or does it make sense for implementations to let a small "anti-defining" region hide parts of an elsewhere defined region? It seems you could define this complication away if the API just promises that the erased elements will "appear to have the fill-value", and only in case an erased region completely covers a (chunk, ROI)-intersection does that (chunk, ROI)-intersection need to become marked as undefined. As I didn't read very carefully, it could of course be that I missed some ideas about how the erasing should work. |
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The goal of H5Derase API is to remove the locations and values themselves from storage. If the elements are not defined anymore, they cannot appear on the elements list when H5Dget_defined is called after the H5Derase call. When a region is read back by H5Dread call the erased elements will be filled with fill-value in the memory buffer that is provided by application. |
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I completely agree that this is the intended behaviour. I was trying to point out that, if I understood the idea of defined regions/blocks/(hyperslabs?), it just seems potentially quite a complex operation to update the file to carry out a removal request if the user is allowed to remove a subset within a defined region. Consider the following toy example of a datset with whose dataspace has two dimensions of lengths 2 and 7: Each single digit is a defined element and the "u" indicate undefined elements. If we now erase column 3 (both rows), my interpreation of the RFC was that the updated dataset would appear as in any future kind of reading. This may be by getting a list of the still defined elements/blocks/(hyperslabs?) or perhaps by a call that gives the full 2-by-7 array padded with some fill-value, e.g. NaN, 0 or -1 depending on the application. So far just clarifying the expected behaviour. I suppose that if the six defined elements were defined in one go, the low-level sparse representation in the HDF-file could in some case be like a hyperslab with an offset of (0, 2) that just defines the region Now, if the implementation and the HDF5-file is not allowed to use any fill-value (e.g. because the fill-value may be chosen by the reading application and vary between read-calls), the remove-operation can not simply replace the "2" and "5" elements by some other value. The remove-operation would have to remove the entire region and replace it by two smaller regions that contain the remaining elements. One region that defines An alternative to simplify this would be if we required that each sparse dataset had a fill-value specified as a dataset attribute, in the file, and which perhaps was not allowed to change after the dataset has been created. An element with the fill-value is equivalent to being undefined. Then the removal operation would be as simple as replacing elements by the fill-value. As a second step, the implementation can check whether any of the defining regions is full of fill-values, then that region defines nothing and can be removed completely. Another alternative would be if removal was not possible for arbitrary elements or selections, only for selections precisely matching one (or more) defining regions/(hyperslabs?). Then the implementation would be as simple as going through the list of element-defining regions and determine if it should be kept or dropped. If this is what the RFC intended, I didn't pick it up in my first reading (and didn't try to re-read now). Perhaps it could be achieved by the remove-method taking an argument that is an index within the list of definitions, not an arbitrary selection. Or document that an exception can be raised if given a selection that only partly covers some of the element-region-definitions. |
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When you remove 2 and 5 new serialized selection of the rest of the elements will be stored along with 4 numbers 1,3,4,6. What is important that if one requests defined elements, the library will give a selection that describes the locations of the defined elements. There are functions in HDF5 that allow to get the coordinates of the values from the space_id that is returned by H5Dget_defined. space_id used in H5Dread will give a data buffer with defined values in the order in which selection APIs will give points coordinates. I need to add examples to RFC to show how it will work.
You are correct. To mitigate the issue, we can easily store a sparse chunk as "dense" if the user provides threshold and fill value to use for the undefined elements. It is one of the optimizations we could add.
Some libraries use this approach, for example, to define missing values. The issue is that how one will know if a defined value will never be the same value as an attribute. Some times it is possible but not in the general case. |
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I saw some new comments here and it inspired me to ask some more questions about structured chunks. How will structured chunks be the same or different from normal chunks? Will they use the same buffer? Can a dataset be composed of both regular chunks and structured chunks or does a producer have to decide at the outset which kind of chunk will be used and then stick with that? Will a dataset using structured chunks be extendible in the same way normally chunked datasets are? Will there be a separate chunk buffer for structured chunks? Will new data sieving code need to be added to support partial requests on datasets composed of structured chunks? Can existing chunk buffering algorithms be applied to structured chunks? Is there a point at which a structured chunk becomse so close to fully dense that it should just be converted to a normal chunk and is/will that be possible? There are high level API routines to direct write (normal) chunks. Will there be same now for structured chunks too or can the existing routines handle the structured chunks? Will there be new functionality for |
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Good questions! Thank you!
Once can think about "normal" chunk as a chunk with only one section. Structured chunks will be using the same chunk indexing schemas as current chunks expect that different metadata will be stored in the used index (see Section 4 of File Format RFC), for example, when Version 2 B-tree is used for chunk indexing (it is used for datasets with more than one unlimited dimension), Record Layout has Structured Chunk Metadata record added to the regular chunk Record Layout Spec.
Not sure what do you mean. We are going to re-implement chunk cache that can be shared between chunked datasets (both regular and structured chunk). The whole I/O pipeline will be reworked as described in this RFC, section 6.
No, structured chunk is another way of storing data, it is a dataset creation property, i.e., one has to decided on the storage at the dataset creation time. |
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I think it will be reused and/or enhanced. |
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We decided to introduce new direct chunking routines. Once will be able to use old routines on read and then do all tedious work of "unpacking" sections. |
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Yes, the RFC just outlines some issues. See section 4.6 for |
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As I mentioned above it is an optimization, but chunk's metadata will still be structured chunk metadata. We cannot change creation property of an HDF5 object. |
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Hello, HDF5 community!
We have been working on adding support for sparse data into HDF5. While designing file format and API extensions we have given significant thought to a number of other potential HDF5 enhancements and noticed the commonalities with the sparse data problem. In particular, the idea of extending the concept of the chunk to contain multiple sections that describe different facets of the values stored in the chunk seems to be applicable to a number of problems, for example, to HDF5 variable-length data and non-homogeneous arrays. This in turn raises the problem of compressing these different sections efficiently, as different compression algorithms may be optimal for different sections.
Our approach to HDF5 modifications is documented in two RFCs:
RFC:File Format Changes for Enabling Sparse Storage in HDF5 and RFC:Programming Model to Support Sparse Data in HDF5.
We are looking for community feedback on our proposal. Especially, we are interested in the use cases for storing sparse and variable-length data in HDF5 and suggestions on HDF5 APIs. Please notice that our approach will finally enable compression of variable-length data and writing it in parallel.
I will present our work at HUG23 and, hopefully, at EHUG23.
Please add your comments to this ticket. You are also very welcome to contact me directly if you have any questions,concerns, and/or suggestions. RFCs have my contact information.
Thank you and I look forward to your feedback.
Elena
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