From 9bf3fc2fd3612a930c61cbb857834a4fd171962c Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Tue, 12 Nov 2024 17:37:12 -0800 Subject: [PATCH 01/17] Add guidance for CO HDF/NetCDF --- cloud-optimized-netcdf4-hdf5/index.qmd | 155 ++++++++++++++++++-- cloud-optimized-netcdf4-hdf5/references.bib | 33 +++++ 2 files changed, 179 insertions(+), 9 deletions(-) create mode 100644 cloud-optimized-netcdf4-hdf5/references.bib diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 9ab8c38..f0d987f 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -1,19 +1,156 @@ --- -title: Cloud-Optimized NetCDF4/HDF5 +title: Cloud-Optimized NetCDF/HDF +bibliography: references.bib --- -Cloud-optimized access to NetCDF4/HDF5 files is possible. However, there are no standards for the metadata, chunking and compression for cloud-optimized access for these file types. +## Background -::: {.callout-note} +::: callout-note +If you want to jump to cloud-optimized HDF/NetCDF solutions, you can skip the background and jump to the [checklist](#cloud-optimized-hdfnetcdf-checklist). +::: + +NetCDF and HDF were designed for disk access. As Matt Rocklin describes in [HDF in the Cloud: Challenges and Solutions for Scientific Data](https://matthewrocklin.com/blog/work/2018/02/06/hdf-in-the-cloud): "The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data." + +When accessing data on disk, small chunks were preferred since access was fast and accessing any part of a chunk required reading the entire chunk [@h5py_developers]. This same data, when stored on the cloud, does deliver good performance as well as many requests must be made for metadata and raw data. With the introduction of a network, fewer requests is preferred. + +However, there has been a lot of progress in establishing best practices for optimizing HDF5 and NetCDF-4 for the cloud. + +::: callout-note Note: NetCDF4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files with HDF5](https://docs.unidata.ucar.edu/netcdf-c/current/interoperability_hdf5.html). ::: -NetCDF4/HDF5 were designed for disk access and thus moving them to the cloud has borne little fruit. Matt Rocklin describes the issue in [HDF in the Cloud: Challenges and Solutions for Scientific Data](https://matthewrocklin.com/blog/work/2018/02/06/hdf-in-the-cloud): +## Details + +### Format + +To be considered cloud-optimized, the format should support chunking and compression. [NetCDF3](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html) and [HDF4 prior to v4.1](https://docs.hdfgroup.org/archive/support/products/hdf4/HDF-FAQ.html#18) do not support chunking and chunk-level compression, and thus cannot be reformatted to be cloud optimized. The lack of support for chunking and compression along with [other limitations](https://docs.hdfgroup.org/archive/support/products/hdf5_tools/h4toh5/h4vsh5.html) led to the development of NetCDF4 and HDF5. + +## Chunk Size + +There is no one-size-fits all chunk size and shape as use cases for different products vary. However, chunks should not be "too big" or "too small", because... + +#### When chunks are too small: + +- Extra metadata may increase file size. +- It takes extra time to look up each chunk. +- More network I/O is incurred because each chunk is stored and accessed independently (although contiguous chunks may be accessed by extending the byte range into one request). + +#### When chunks are too big: + +- An entire chunk must be read and decompressed to read even a small portion of the data. +- Managing large chunks in memory slows down processing and is more likely to exceed memory and chunk caches. + +A chunk size should be selected that is large enough to reduce the number of tasks that parallel schedulers have to think about (which affects overhead) but also small enough so that many of them can fit in memory at once. [The Amazon S3 Best Practices says the typical size for byte-range requests is 8-16MB](https://docs.aws.amazon.com/whitepapers/latest/s3-optimizing-performance-best-practices/use-byte-range-fetches.html). However, requests for data from contiguous chunks can be merged into 1 HTTP request, so chunks could be much smaller (one recommendation is 100kb to 2mb) [@jelenak2024]. + +## Chunk shape vs chunk size + +Recommended chunk size (total number of values per chunk) is generally based on the storage system's characteristics and its interaction with the data access library. That makes chunk size recommendations fairly universal when holding the storage system data access library (and its settings) constant. In contrast, chunk shape determines how values from different dimensions are grouped within each chunk. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as (A) 10 lat x 10 lon x 10 time, (B) 20 lat x 50 lon x 1 time, or (C) 5 lat x 5 lon x 40 time. Larger chunks in a given dimension improve read performance in that direction—e.g., (C) is best for time-series analysis, (B) for mapping, and (A) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of “aspect ratios” of chunks, adjusting relative chunk sizes to fit the expected balance of spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). + +A best practice to help determine both chunk size and shape would be to specify some "benchmark use cases" for the data. With these use cases in mind, evaluate what chunk shape and size is large enough such that the computation doesn't result in thousands of jobs and small enough that multiple chunks can be stored in-memory and a library’s buffer cache, such as [HDF5’s buffer cache](https://www.hdfgroup.org/2022/10/improve-hdf5-performance-using-caching/). + +## Consolidated Internal File Metadata + +Consolidated metadata (or metadata stored contiguously) is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what’s in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. + +::: {.callout-note} +For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. You can use the h5repack to reorganize the metadata. For example, the following line produces a new file out.h5 which uses the PAGE file space management strategy with a page size of 4MB: `$ h5repack -S PAGE -G 400000 in.h5 out.h5` [@jelenak2023] +::: + +::: {.callout-note} +_Lazy loading:_ To optimize use of the cloud, it should be possible to "lazily load" a file from cloud storage without an additional service. Lazy loading means deferring the reading of data values until they are needed. Usually, a library reads the metadata to provide a data structure to the user. When the user applies some filters and computation on the data, only then are data bits loaded from storage. +::: + +## Compression + +Compression is the process of minimizing the size of data stored on disk using an algorithm to reduce the overall size of the data. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. + +ADD IMAGE ->The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data. The only pragmatic way to read a chunk of data from an HDF file today is to use the existing HDF C library, which expects to receive a C FILE object, pointing to a normal file system (not a cloud object store) (this is not entirely true, as we’ll see below). -> ->So organizations like NASA are dumping large amounts of HDF onto Amazon’s S3 that no one can actually read, except by downloading the entire file to their local hard drive, and then pulling out the particular bits that they need with the HDF library. This is inefficient. It misses out on the potential that cloud-hosted public data can offer to our society. +NASA satellite data predominantly compressed with the zlib (a.k.a., gzip, deflate) method. However, other methods should be explored as a higher compression ratio is often possible, and in the case of HDF5, fills file pages better[@jelenak2023_report]. -To provide cloud-optimized access to these files without an intermediate service like [Hyrax](https://hdfeos.org/software/hdf5_handler.php) or the [Highly Scalable Data Service (HSDS)](https://www.hdfgroup.org/solutions/highly-scalable-data-service-hsds/), it is recommended to determine if the NetCDF4/HDF5 data you wish to provide can be used with kerchunk. Rich Signell provided some insightful examples and instructions on how to create a kerchunk reference file (aka `fsspec.ReferenceFileSystem`) for NetCDF4/HDF5 and the things to be aware of in [Cloud-Performant NetCDF4/HDF5 with Zarr, Fsspec, and Intake](https://medium.com/pangeo/cloud-performant-netcdf4-hdf5-with-zarr-fsspec-and-intake-3d3a3e7cb935). Note, the post is from 2020, so it's possible details have changed; however, the approach of using kerchunk for NetCDF4/HDF5 is still recommended. +## Data Product Usage Documentation (Tutorials and Examples) -Stay tuned for more information on cloud-optimized NetCDF4/HDF5 in future releases of this guide. +How users use the data is out of the producers' control. However, tutorials and examples can be starting points for many data product users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to leverage popular library's caching to optimize the experience for the user. + +For example, the following defaults will impact performance and are important to consider. +* HDF5 library: The size of the HDF5's chunk cache by default at time of writing is 1MB but is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. +* S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB, meaning at least that much data is fetched in each request. + +Here is some research done on caching for specific libraries and datasets that may be helpful in understanding the impact of caching and developing product guidance: + +- In this issue [Optimize s3fs read cache settings for the GEDI Subsetter](https://github.com/MAAP-Project/gedi-subsetter/issues/77) (findings to be formalized), Chuck Daniels found the "all" cache type (cache entire contents), a block size of 8MB and fill cache=True to deliver the best performance. +- In [HDF at the Speed of Zarr](https://docs.google.com/presentation/d/1iYFvGt9Zz0iaTj0STIMbboRKcBGhpOH_LuLBLqsJAlk/edit?usp=sharing), Luis Lopez demonstrates, using ICESat-2 data, the importance of using similar arguments with fsspec (blockcache instead of all, but the results in the issue above were not significantly different between these 2 options) as well as the importance of using nearly equivalent arguments in for h5py (raw data chunk cache nbytes and page_buff_size). + +Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in [@jelenak2024]. + +## Cloud-Optimized HDF/NetCDF Checklist + +Please consider the following when preparing HDF/NetCDF data for use on the cloud: + +- [ ] The format used supports consolidated metadata, chunking and compression (HDF5 and NetCDF4 do, but HDF4 and NetCDF3 do not). +- [ ] Consolidated metadata. See section below for checking for consolidated metadata. +- [ ] Chunk sizes that are not too big nor too small (100kb-16mb). See section below for how to check chunk sizes. +- [ ] An appropriate compression algorithm has been applied. +- [ ] Expected use cases for the data were considered when designing the chunk size and shape. +- [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries’ caching features. + +# How tos + +ADD ME - does this guidance work for NetCDF4 + +The examples below rely on the command line tools that come with the HDF5 library and as such require the HDF5 library package is installed on your system. While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. + +## How to check chunk size and shape + +Replace infile.h5 with a filename on your system and dataset_name with the name of a dataset in that file. + +``` bash +h5dump -pH infile.h5 | grep dataset_name -A 10 +``` + +## How to change the chunk size and shape + +``` bash +$ h5repack \ + # dataset:CHUNK=DIM[xDIM...xDIM] + /path/to/dataset:CHUNK=2000 infile.h5 outfile.h5 +``` + +## How to check for consolidated metadata + +To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. + +You can check the file space management strategy with the command line h5stat tool: + +``` bash +h5stat -S infile.h5 +``` + +This returns output such as: + +``` text +Filename: infile.h5 +File space management strategy: H5F_FSPACE_STRATEGY_FSM_AGGR +File space page size: 4096 bytes +Summary of file space information: + File metadata: 2157376 bytes + Raw data: 37784376 bytes + Amount/Percent of tracked free space: 0 bytes/0.0% + Unaccounted space: 802424 bytes +Total space: 40744176 bytes +``` + +Note the strategy: `File space management strategy: H5F_FSPACE_STRATEGY_FSM_AGGR`. The best choice for cloud-optimized access is `H5F_FSPACE_STRATEGY_PAGE`. + +## How to change the file space management strategy + +When repacking to use the PAGE file space management strategy, you will also need to specify a page size that will indicate the block size for metadata storage. This should be at least as big as the `File metadata` value returned from `h5stat -S`. + +``` bash +$ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 +``` + +# References + +::: {#refs} +::: diff --git a/cloud-optimized-netcdf4-hdf5/references.bib b/cloud-optimized-netcdf4-hdf5/references.bib new file mode 100644 index 0000000..c3e414a --- /dev/null +++ b/cloud-optimized-netcdf4-hdf5/references.bib @@ -0,0 +1,33 @@ +@manual{h5py_developers, + title = {Datasets}, + author = {{H5py Developers}}, + year = {n.d.}, + note = {Version 3.7}, + howpublished = {\url{https://docs.h5py.org/en/stable/high/dataset.html}}, + institution = {H5py Documentation} +} + +@misc{jelenak2024, + author = {Aleksander Jelenak}, + title = {Cloud Optimized HDF5 Files (Slides)}, + year = {2024}, + month = {July 24}, + howpublished = {Slides, \url{https://drive.google.com/file/d/1qH4x_PydLKTEjWfqXobEMMKZc3FSLqf8/view?usp=sharing}}, + note = {Presented at the ESIP Summer Meeting, Asheville, NC} +} + +@misc{jelenak2023, + author = {Aleksander Jelenak}, + title = {Cloud-Optimized HDF5 Files – \#HUG23}, + year = {2023}, + month = {June}, + howpublished = {YouTube video, The HDF Group. \url{https://www.youtube.com/watch?v=bDH59YTXpkc}} +} + +@misc{jelenak2023_report, + author = {Aleksander Jelenak}, + title = {Cloud-Optimized HDF5 Files}, + year = {2023}, + howpublished = {\url{https://drive.google.com/file/d/1qH4x_PydLKTEjWfqXobEMMKZc3FSLqf8/view?usp=sharing}}, + institution = {The HDF Group} +} From dfb9666d7b98908edd1f3e2889c9ad1af604876c Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Thu, 14 Nov 2024 14:27:43 -0800 Subject: [PATCH 02/17] Add preview --- .github/workflows/preview.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/preview.yml b/.github/workflows/preview.yml index c674eb6..ead1239 100644 --- a/.github/workflows/preview.yml +++ b/.github/workflows/preview.yml @@ -3,7 +3,7 @@ name: Deploy PR previews on: pull_request: branches: - - main + - staging types: - opened - reopened From 4436dfa61031bde02c6faa6ac326ba81b7affffa Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Thu, 14 Nov 2024 15:57:02 -0800 Subject: [PATCH 03/17] Add images and refine content --- cloud-optimized-netcdf4-hdf5/index.qmd | 134 ++++++++++++-------- cloud-optimized-netcdf4-hdf5/references.bib | 32 +++++ images/chunk-shape-options.png | Bin 0 -> 189245 bytes images/quinn-why-not-compress.png | Bin 0 -> 245289 bytes images/why-hdf-on-cloud-is-slow.png | Bin 0 -> 196488 bytes 5 files changed, 112 insertions(+), 54 deletions(-) create mode 100644 images/chunk-shape-options.png create mode 100644 images/quinn-why-not-compress.png create mode 100644 images/why-hdf-on-cloud-is-slow.png diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index f0d987f..b721154 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -1,124 +1,134 @@ --- title: Cloud-Optimized NetCDF/HDF bibliography: references.bib +author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak +toc-depth: 3 --- -## Background - ::: callout-note -If you want to jump to cloud-optimized HDF/NetCDF solutions, you can skip the background and jump to the [checklist](#cloud-optimized-hdfnetcdf-checklist). +You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). ::: -NetCDF and HDF were designed for disk access. As Matt Rocklin describes in [HDF in the Cloud: Challenges and Solutions for Scientific Data](https://matthewrocklin.com/blog/work/2018/02/06/hdf-in-the-cloud): "The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data." +# Background + +NetCDF and HDF were originally designed with disk access in mind. As Matt Rocklin explains in [HDF in the Cloud: Challenges and Solutions for Scientific Data](https://matthewrocklin.com/blog/work/2018/02/06/hdf-in-the-cloud): + +>The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data. -When accessing data on disk, small chunks were preferred since access was fast and accessing any part of a chunk required reading the entire chunk [@h5py_developers]. This same data, when stored on the cloud, does deliver good performance as well as many requests must be made for metadata and raw data. With the introduction of a network, fewer requests is preferred. +![](../images/why-hdf-on-cloud-is-slow.png) -However, there has been a lot of progress in establishing best practices for optimizing HDF5 and NetCDF-4 for the cloud. +[@barrett2024] + +For storage on disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. ::: callout-note Note: NetCDF4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files with HDF5](https://docs.unidata.ucar.edu/netcdf-c/current/interoperability_hdf5.html). ::: -## Details +# Current Best Practices for Cloud-Optimized HDF5 and NetCDF-4 -### Format +## Format To be considered cloud-optimized, the format should support chunking and compression. [NetCDF3](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html) and [HDF4 prior to v4.1](https://docs.hdfgroup.org/archive/support/products/hdf4/HDF-FAQ.html#18) do not support chunking and chunk-level compression, and thus cannot be reformatted to be cloud optimized. The lack of support for chunking and compression along with [other limitations](https://docs.hdfgroup.org/archive/support/products/hdf5_tools/h4toh5/h4vsh5.html) led to the development of NetCDF4 and HDF5. ## Chunk Size -There is no one-size-fits all chunk size and shape as use cases for different products vary. However, chunks should not be "too big" or "too small", because... +There is no one-size-fits all chunk size and shape as use cases for different products vary. However, chunks should not be "too big" or "too small". -#### When chunks are too small: +### When chunks are too small: -- Extra metadata may increase file size. -- It takes extra time to look up each chunk. -- More network I/O is incurred because each chunk is stored and accessed independently (although contiguous chunks may be accessed by extending the byte range into one request). +- Extra metadata may increase file size. +- It takes extra time to look up each chunk. +- More network I/O is incurred because each chunk is stored and accessed independently (although contiguous chunks may be accessed by extending the byte range into one request). -#### When chunks are too big: +### When chunks are too big: -- An entire chunk must be read and decompressed to read even a small portion of the data. -- Managing large chunks in memory slows down processing and is more likely to exceed memory and chunk caches. +- An entire chunk must be read and decompressed to read even a small portion of the data. +- Managing large chunks in memory slows down processing and is more likely to exceed memory and chunk caches. A chunk size should be selected that is large enough to reduce the number of tasks that parallel schedulers have to think about (which affects overhead) but also small enough so that many of them can fit in memory at once. [The Amazon S3 Best Practices says the typical size for byte-range requests is 8-16MB](https://docs.aws.amazon.com/whitepapers/latest/s3-optimizing-performance-best-practices/use-byte-range-fetches.html). However, requests for data from contiguous chunks can be merged into 1 HTTP request, so chunks could be much smaller (one recommendation is 100kb to 2mb) [@jelenak2024]. -## Chunk shape vs chunk size +::: callout-note +Performance greatly depends on libraries used to access the data and how they are configured to cache data as well. +::: + +### Chunk shape vs chunk size + +The chunk size - the number of values per chunk, often multiplied by their data type's size in bytes to get a total size in bytes - must be differentiated from the chunk shape, which is the number of values stored along each dimension in a given chunk. Recommended chunk size depends on a storage system's characteristics and its interaction with the data access library. That makes chunk size recommendations fairly universal when holding the storage system data access library constant. + +In contrast, an optimal chunk shape is use case dependent. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as: -Recommended chunk size (total number of values per chunk) is generally based on the storage system's characteristics and its interaction with the data access library. That makes chunk size recommendations fairly universal when holding the storage system data access library (and its settings) constant. In contrast, chunk shape determines how values from different dimensions are grouped within each chunk. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as (A) 10 lat x 10 lon x 10 time, (B) 20 lat x 50 lon x 1 time, or (C) 5 lat x 5 lon x 40 time. Larger chunks in a given dimension improve read performance in that direction—e.g., (C) is best for time-series analysis, (B) for mapping, and (A) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of “aspect ratios” of chunks, adjusting relative chunk sizes to fit the expected balance of spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). +1. 10 lat x 10 lon x 10 time, +2. 20 lat x 50 lon x 1 time, or +3. 5 lat x 5 lon x 40 time. + +Larger chunks in a given dimension improve read performance in that direction: (3) is best for time-series analysis, (2) for mapping, and (1) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of "aspect ratios" of chunks, adjusting relative chunk sizes to fit the expected balance of spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). + +![chunk shape options](../images/chunk-shape-options.png) +[@shiklomanov2024] A best practice to help determine both chunk size and shape would be to specify some "benchmark use cases" for the data. With these use cases in mind, evaluate what chunk shape and size is large enough such that the computation doesn't result in thousands of jobs and small enough that multiple chunks can be stored in-memory and a library’s buffer cache, such as [HDF5’s buffer cache](https://www.hdfgroup.org/2022/10/improve-hdf5-performance-using-caching/). ## Consolidated Internal File Metadata -Consolidated metadata (or metadata stored contiguously) is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what’s in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. +Consolidated metadata is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what’s in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. -::: {.callout-note} -For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. You can use the h5repack to reorganize the metadata. For example, the following line produces a new file out.h5 which uses the PAGE file space management strategy with a page size of 4MB: `$ h5repack -S PAGE -G 400000 in.h5 out.h5` [@jelenak2023] -::: +For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. ::: {.callout-note} -_Lazy loading:_ To optimize use of the cloud, it should be possible to "lazily load" a file from cloud storage without an additional service. Lazy loading means deferring the reading of data values until they are needed. Usually, a library reads the metadata to provide a data structure to the user. When the user applies some filters and computation on the data, only then are data bits loaded from storage. +_Lazy loading:_ Lazy loading is a common term for first loading only metadata, and deferring reading of data values until computation requires them. ::: -## Compression +### Compression Compression is the process of minimizing the size of data stored on disk using an algorithm to reduce the overall size of the data. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. -ADD IMAGE +![why not compress](../images/quinn-why-not-compress.png) +[@quinn2024] NASA satellite data predominantly compressed with the zlib (a.k.a., gzip, deflate) method. However, other methods should be explored as a higher compression ratio is often possible, and in the case of HDF5, fills file pages better[@jelenak2023_report]. ## Data Product Usage Documentation (Tutorials and Examples) -How users use the data is out of the producers' control. However, tutorials and examples can be starting points for many data product users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to leverage popular library's caching to optimize the experience for the user. +How users use the data is out of the producers' control. However, tutorials and examples can be starting points for many data product users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to configure popular libraries for optimizing performance. + +For example, the following library defaults will impact performance and are important to consider: +* HDF5 library: The size of the HDF5's chunk cache by default is 1MB. This value is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. Learn more: [Improve HDF5 performance using caching](https://www.hdfgroup.org/2022/10/17/improve-hdf5-performance-using-caching/). +* S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB ([S3FS API docs](https://s3fs.readthedocs.io/en/stable/api.html#s3fs.core.S3FileSystem). +* Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in [@jelenak2024]. -For example, the following defaults will impact performance and are important to consider. -* HDF5 library: The size of the HDF5's chunk cache by default at time of writing is 1MB but is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. -* S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB, meaning at least that much data is fetched in each request. +### Additional research -Here is some research done on caching for specific libraries and datasets that may be helpful in understanding the impact of caching and developing product guidance: +Here is some additional research done on caching for specific libraries and datasets that may be helpful in understanding the impact of caching and developing product guidance: - In this issue [Optimize s3fs read cache settings for the GEDI Subsetter](https://github.com/MAAP-Project/gedi-subsetter/issues/77) (findings to be formalized), Chuck Daniels found the "all" cache type (cache entire contents), a block size of 8MB and fill cache=True to deliver the best performance. - In [HDF at the Speed of Zarr](https://docs.google.com/presentation/d/1iYFvGt9Zz0iaTj0STIMbboRKcBGhpOH_LuLBLqsJAlk/edit?usp=sharing), Luis Lopez demonstrates, using ICESat-2 data, the importance of using similar arguments with fsspec (blockcache instead of all, but the results in the issue above were not significantly different between these 2 options) as well as the importance of using nearly equivalent arguments in for h5py (raw data chunk cache nbytes and page_buff_size). -Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in [@jelenak2024]. - ## Cloud-Optimized HDF/NetCDF Checklist Please consider the following when preparing HDF/NetCDF data for use on the cloud: -- [ ] The format used supports consolidated metadata, chunking and compression (HDF5 and NetCDF4 do, but HDF4 and NetCDF3 do not). -- [ ] Consolidated metadata. See section below for checking for consolidated metadata. -- [ ] Chunk sizes that are not too big nor too small (100kb-16mb). See section below for how to check chunk sizes. +- [ ] The format supports consolidated metadata, chunking and compression (HDF5 and NetCDF4 do, but HDF4 and NetCDF3 do not). +- [ ] Metadata has been consolidated (see also [how-to-check-for-consolidated-metadata](#how-to-check-for-consolidated-metadata)). +- [ ] Chunk sizes that are not too big nor too small (100kb-16mb) (see also [how-to-check-chunk-size-and-shape](#how-to-check-chunk-size-and-shape)). - [ ] An appropriate compression algorithm has been applied. - [ ] Expected use cases for the data were considered when designing the chunk size and shape. - [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries’ caching features. # How tos -ADD ME - does this guidance work for NetCDF4 +The examples below require the HDF5 library package is installed on your system. While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. -The examples below rely on the command line tools that come with the HDF5 library and as such require the HDF5 library package is installed on your system. While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. +## Commands in brief: -## How to check chunk size and shape - -Replace infile.h5 with a filename on your system and dataset_name with the name of a dataset in that file. +* [`h5stat`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__s_t__u_g.html): stats from an existing HDF5 file. +* [`h5repack`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__r_p__u_g.html): write a new file with a new layout. +* [`h5dump`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__d_p__u_g.html): display objects from an HDF5 file -``` bash -h5dump -pH infile.h5 | grep dataset_name -A 10 -``` - -## How to change the chunk size and shape - -``` bash -$ h5repack \ - # dataset:CHUNK=DIM[xDIM...xDIM] - /path/to/dataset:CHUNK=2000 infile.h5 outfile.h5 -``` ## How to check for consolidated metadata -To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. +To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks [@jelenak2023]. You can check the file space management strategy with the command line h5stat tool: @@ -140,16 +150,32 @@ Summary of file space information: Total space: 40744176 bytes ``` -Note the strategy: `File space management strategy: H5F_FSPACE_STRATEGY_FSM_AGGR`. The best choice for cloud-optimized access is `H5F_FSPACE_STRATEGY_PAGE`. +Notice the strategy: `File space management strategy: H5F_FSPACE_STRATEGY_FSM_AGGR`. This is the default option. The best choice for cloud-optimized access is `H5F_FSPACE_STRATEGY_PAGE`. Learn more about the options in the HDF docs: [File Space Management (HDF Group)](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html). ## How to change the file space management strategy -When repacking to use the PAGE file space management strategy, you will also need to specify a page size that will indicate the block size for metadata storage. This should be at least as big as the `File metadata` value returned from `h5stat -S`. +You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repacking to use the PAGE file space management strategy, you will also need to specify a page size that will indicate the block size for metadata storage. This should be at least as big as the `File metadata` value returned from `h5stat -S`. ``` bash $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 ``` +## How to check chunk size and shape + +Replace infile.h5 with a filename on your system and dataset_name with the name of a dataset in that file. + +``` bash +h5dump -pH infile.h5 | grep dataset_name -A 10 +``` + +## How to change the chunk size and shape + +``` bash +$ h5repack \ + # dataset:CHUNK=DIM[xDIM...xDIM] + /path/to/dataset:CHUNK=2000 infile.h5 outfile.h5 +``` + # References ::: {#refs} diff --git a/cloud-optimized-netcdf4-hdf5/references.bib b/cloud-optimized-netcdf4-hdf5/references.bib index c3e414a..b02ecb2 100644 --- a/cloud-optimized-netcdf4-hdf5/references.bib +++ b/cloud-optimized-netcdf4-hdf5/references.bib @@ -31,3 +31,35 @@ @misc{jelenak2023_report howpublished = {\url{https://drive.google.com/file/d/1qH4x_PydLKTEjWfqXobEMMKZc3FSLqf8/view?usp=sharing}}, institution = {The HDF Group} } + +@misc{quinn2024, + author = {Patrick Quinn}, + title = {Computer Scientists, Your Planet Needs You: An Introduction to Access-Optimized Data}, + year = {2024}, + month = {July}, + day = {24}, + note = {Slide presentation given at ESIP July 2024}, + organization = {NASA ESDIS Architect}, + howpublished = {\url{https://docs.google.com/presentation/d/1SsJKX_yOPa1pzV6IjRAQPLC1SBROejH1/edit?usp=sharing}}, + email = {patrick.m.quinn@nasa.gov} +} + +@misc{barrett2024, + author = {Andy Barrett and Luis Lopez and Amy Steiker and Aleksander Jelenek and Jeff Lee}, + title = {Evaluating Cloud-Optimized HDF5 Solutions for NASA Earthdata: A Case Study with ICESat-2}, + organization = {NSIDC DAAC, HDF Group, NASA}, + howpublished = {\url{https://docs.google.com/presentation/d/1h57MkIZ7RulsOApzxjiaY-g4ImI2_GzK7F5tg0JPVr8/edit?usp=sharing}}, + year = {2024}, + month = {July}, + day = {24}, +} + +@misc{shiklomanov2024, + author = {Alexey Shiklomanov}, + title = {Rethinking the Software Architecture of GMAO Data Tools and Services}, + year = {2024}, + month = {May}, + day = {23}, + howpublished = {\url{https://docs.google.com/presentation/d/1KRkolrDokTM0b_SIBds6BrIxFSmgZNuYYYuYbUtWtno/edit?usp=sharing}}, + note = {Slide presentation given at GMAO Science Theme Meeting} +} diff --git a/images/chunk-shape-options.png b/images/chunk-shape-options.png new file mode 100644 index 0000000000000000000000000000000000000000..0180c8e7c26020ffcb2d2f6719b98a761c02f85b GIT binary patch literal 189245 zcmeFZF(~7?h@$^N$Ku5YkNQEx!+Ic z4>;F*U4Bpm_WrFkV~jD!+@Z?vWH3;PQJ+0~h9M^_sru|0T>Gjbu%8-gs5JD{=j$uyRl_89ZeQ)9J1RNuW-FfleefbB_cq@ePOSDCbszQG8oo~tdx`7^{SzW^ z=4*I6pIQl6#sB&1#TWRb|M{L>7!GjxuehOX%OEv@L zKVQxw|0;_6pD#-ay@b&G&#QQd{{Q|LXKwyTPF70cW2dFvzC2ji+}yOPy**f{zdBrc z<9587!tW^HddO5JT4O{?O8Wfy^T*r0@@ChgR*wr*i|UMw)!U1`xw*ODzkfG1HT^;5 zFflcqc%7A%)!h&uSzgY{=x$+*Pqx<9jAeSM!rr&9X=%eVB{~0WZPmFREi0u9hvqByuJ=VFVUaVH-9&Z;V^x2! zo+-m$*WrnwQ8c0Uz1&X(F9E)z8sGQsfR{b$C2|%HxX%C=6Duo^$9s2ktd-oHobH(! z&0%raPd}|^SaRlCy_<<-xCrIs$Uu~2rfzI(usJ^~ 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r>^C*3#ee=a>Z`>6Kk@(LQru(b_dXLe%_S#!0zR@*ijo!LMnV4ru#etg literal 0 HcmV?d00001 From 74fa255887d4f72557a790c26608ad4bc5ad4468 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Thu, 14 Nov 2024 16:07:30 -0800 Subject: [PATCH 04/17] Some refinements --- _quarto.yml | 2 +- cloud-optimized-netcdf4-hdf5/index.qmd | 25 ++++++++++++++----------- 2 files changed, 15 insertions(+), 12 deletions(-) diff --git a/_quarto.yml b/_quarto.yml index 7902204..a7e5105 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -48,7 +48,7 @@ website: contents: - kerchunk/intro.qmd - kerchunk/kerchunk-in-practice.ipynb - - section: Cloud-Optimized HDF5 and NetCDF + - section: Cloud-Optimized HDF/NetCDF contents: - cloud-optimized-netcdf4-hdf5/index.qmd - section: Cloud-Optimized Point Clouds (COPC) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index b721154..cd61190 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -1,5 +1,5 @@ --- -title: Cloud-Optimized NetCDF/HDF +title: Cloud-Optimized HDF/NetCDF bibliography: references.bib author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak toc-depth: 3 @@ -19,17 +19,19 @@ NetCDF and HDF were originally designed with disk access in mind. As Matt Rockli [@barrett2024] -For storage on disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. +For storage on disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. + +A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. ::: callout-note -Note: NetCDF4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files with HDF5](https://docs.unidata.ucar.edu/netcdf-c/current/interoperability_hdf5.html). +Note: NetCDF-4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files with HDF5](https://docs.unidata.ucar.edu/netcdf-c/current/interoperability_hdf5.html). ::: # Current Best Practices for Cloud-Optimized HDF5 and NetCDF-4 ## Format -To be considered cloud-optimized, the format should support chunking and compression. [NetCDF3](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html) and [HDF4 prior to v4.1](https://docs.hdfgroup.org/archive/support/products/hdf4/HDF-FAQ.html#18) do not support chunking and chunk-level compression, and thus cannot be reformatted to be cloud optimized. The lack of support for chunking and compression along with [other limitations](https://docs.hdfgroup.org/archive/support/products/hdf5_tools/h4toh5/h4vsh5.html) led to the development of NetCDF4 and HDF5. +To be considered cloud-optimized, the format should support chunking and compression. [NetCDF3](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html) and [HDF4 prior to v4.1](https://docs.hdfgroup.org/archive/support/products/hdf4/HDF-FAQ.html#18) do not support chunking and chunk-level compression, and thus cannot be reformatted to be cloud optimized. The lack of support for chunking and compression along with [other limitations](https://docs.hdfgroup.org/archive/support/products/hdf5_tools/h4toh5/h4vsh5.html) led to the development of NetCDF-4 and HDF5. ## Chunk Size @@ -76,7 +78,7 @@ Consolidated metadata is a key characteristic of cloud-optimized data. Client li For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. ::: {.callout-note} -_Lazy loading:_ Lazy loading is a common term for first loading only metadata, and deferring reading of data values until computation requires them. +_Lazy loading:_ Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. ::: ### Compression @@ -93,9 +95,10 @@ NASA satellite data predominantly compressed with the zlib (a.k.a., gzip, deflat How users use the data is out of the producers' control. However, tutorials and examples can be starting points for many data product users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to configure popular libraries for optimizing performance. For example, the following library defaults will impact performance and are important to consider: + * HDF5 library: The size of the HDF5's chunk cache by default is 1MB. This value is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. Learn more: [Improve HDF5 performance using caching](https://www.hdfgroup.org/2022/10/17/improve-hdf5-performance-using-caching/). * S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB ([S3FS API docs](https://s3fs.readthedocs.io/en/stable/api.html#s3fs.core.S3FileSystem). -* Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in [@jelenak2024]. +* Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in @jelenak2024. ### Additional research @@ -108,7 +111,7 @@ Here is some additional research done on caching for specific libraries and data Please consider the following when preparing HDF/NetCDF data for use on the cloud: -- [ ] The format supports consolidated metadata, chunking and compression (HDF5 and NetCDF4 do, but HDF4 and NetCDF3 do not). +- [ ] The format supports consolidated metadata, chunking and compression (HDF5 and NetCDF-4 do, but HDF4 and NetCDF-3 do not). - [ ] Metadata has been consolidated (see also [how-to-check-for-consolidated-metadata](#how-to-check-for-consolidated-metadata)). - [ ] Chunk sizes that are not too big nor too small (100kb-16mb) (see also [how-to-check-chunk-size-and-shape](#how-to-check-chunk-size-and-shape)). - [ ] An appropriate compression algorithm has been applied. @@ -117,13 +120,13 @@ Please consider the following when preparing HDF/NetCDF data for use on the clou # How tos -The examples below require the HDF5 library package is installed on your system. While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. +The examples below require the HDF5 library package is installed on your system. These commands will also work for NetCDF-4 While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. ## Commands in brief: -* [`h5stat`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__s_t__u_g.html): stats from an existing HDF5 file. -* [`h5repack`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__r_p__u_g.html): write a new file with a new layout. -* [`h5dump`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__d_p__u_g.html): display objects from an HDF5 file +* [`h5stat`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__s_t__u_g.html) prints stats from an existing HDF5 file. +* [`h5repack`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__r_p__u_g.html) writes a new file with a new layout. +* [`h5dump`](https://support.hdfgroup.org/documentation/hdf5/latest/_h5_t_o_o_l__d_p__u_g.html) displays objects from an HDF5 file. ## How to check for consolidated metadata From 430041fd3dba8398bcbf3809cb10c6a5460a56ac Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 09:20:44 -0800 Subject: [PATCH 05/17] Fix compression header level --- cloud-optimized-netcdf4-hdf5/index.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index cd61190..7e3a548 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -81,7 +81,7 @@ For HDF5 files, to consolidate metadata, files should be written with the paged _Lazy loading:_ Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. ::: -### Compression +## Compression Compression is the process of minimizing the size of data stored on disk using an algorithm to reduce the overall size of the data. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. From 6afc9a07fcf8b6fb518bd67415b49ac8e7c0d32c Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 09:49:04 -0800 Subject: [PATCH 06/17] Add note about compressed vs uncompressed and note that GEDI example is not cloud-optimized --- cloud-optimized-netcdf4-hdf5/index.qmd | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 7e3a548..d779ade 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -35,6 +35,12 @@ To be considered cloud-optimized, the format should support chunking and compres ## Chunk Size +### How to determine chunk size + +To estimate the uncompressed size, multiple the dimensions of the chunk by the number of bytes or the given data type. For example, given 3 dimensional chunks of 10x100x100 and a data type of `float64` (8 bytes), each uncompresed chunk will be 0.8 MB. + +### How to choose a chunk size + There is no one-size-fits all chunk size and shape as use cases for different products vary. However, chunks should not be "too big" or "too small". ### When chunks are too small: @@ -54,6 +60,12 @@ A chunk size should be selected that is large enough to reduce the number of tas Performance greatly depends on libraries used to access the data and how they are configured to cache data as well. ::: +::: note +When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: +1. **Compression variabiliaty:** Because of data variability, you cannot deterministically know the size of each compressed chunk. +2. **Memory considerations:** The uncompressed size determines how much memory must be available for reading and writing each chunk. +::: + ### Chunk shape vs chunk size The chunk size - the number of values per chunk, often multiplied by their data type's size in bytes to get a total size in bytes - must be differentiated from the chunk shape, which is the number of values stored along each dimension in a given chunk. Recommended chunk size depends on a storage system's characteristics and its interaction with the data access library. That makes chunk size recommendations fairly universal when holding the storage system data access library constant. @@ -69,11 +81,15 @@ Larger chunks in a given dimension improve read performance in that direction: ( ![chunk shape options](../images/chunk-shape-options.png) [@shiklomanov2024] -A best practice to help determine both chunk size and shape would be to specify some "benchmark use cases" for the data. With these use cases in mind, evaluate what chunk shape and size is large enough such that the computation doesn't result in thousands of jobs and small enough that multiple chunks can be stored in-memory and a library’s buffer cache, such as [HDF5’s buffer cache](https://www.hdfgroup.org/2022/10/improve-hdf5-performance-using-caching/). +A best practice to help determine both chunk size and shape would be to specify some "benchmark use cases" for the data. With these use cases in mind, evaluate what chunk shape and size is large enough such that the computation doesn't result in thousands of jobs and small enough that multiple chunks can be stored in-memory and a library's buffer cache, such as [HDF5's buffer cache](https://www.hdfgroup.org/2022/10/improve-hdf5-performance-using-caching/). + +### Additional chunk shape and size resources + +* [Chunking Data: Choosing Shapes](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) ## Consolidated Internal File Metadata -Consolidated metadata is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what’s in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. +Consolidated metadata is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what's in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. @@ -104,7 +120,7 @@ For example, the following library defaults will impact performance and are impo Here is some additional research done on caching for specific libraries and datasets that may be helpful in understanding the impact of caching and developing product guidance: -- In this issue [Optimize s3fs read cache settings for the GEDI Subsetter](https://github.com/MAAP-Project/gedi-subsetter/issues/77) (findings to be formalized), Chuck Daniels found the "all" cache type (cache entire contents), a block size of 8MB and fill cache=True to deliver the best performance. +- In this issue [Optimize s3fs read cache settings for the GEDI Subsetter](https://github.com/MAAP-Project/gedi-subsetter/issues/77) (findings to be formalized), Chuck Daniels found the "all" cache type (cache entire contents), a block size of 8MB and fill cache=True to deliver the best performance. NOTE: This is for non-cloud-optimized data. - In [HDF at the Speed of Zarr](https://docs.google.com/presentation/d/1iYFvGt9Zz0iaTj0STIMbboRKcBGhpOH_LuLBLqsJAlk/edit?usp=sharing), Luis Lopez demonstrates, using ICESat-2 data, the importance of using similar arguments with fsspec (blockcache instead of all, but the results in the issue above were not significantly different between these 2 options) as well as the importance of using nearly equivalent arguments in for h5py (raw data chunk cache nbytes and page_buff_size). ## Cloud-Optimized HDF/NetCDF Checklist @@ -116,7 +132,7 @@ Please consider the following when preparing HDF/NetCDF data for use on the clou - [ ] Chunk sizes that are not too big nor too small (100kb-16mb) (see also [how-to-check-chunk-size-and-shape](#how-to-check-chunk-size-and-shape)). - [ ] An appropriate compression algorithm has been applied. - [ ] Expected use cases for the data were considered when designing the chunk size and shape. -- [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries’ caching features. +- [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries' caching features. # How tos From e0bb5b06e643afdc6d36913b5275e848eaf66c98 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 10:09:52 -0800 Subject: [PATCH 07/17] Add example output of h5dump utility --- cloud-optimized-netcdf4-hdf5/index.qmd | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index d779ade..d9317ec 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -187,6 +187,26 @@ Replace infile.h5 with a filename on your system and dataset_name with the name h5dump -pH infile.h5 | grep dataset_name -A 10 ``` +`-p` shows the properties (dataset layout, chunk size and compression) of objects in the HDF5 file. `-H` prints the header information only. Together, we get metadata about the structure of the file. + +Example command with output and how to read it, in comments: + +```bash +$ h5dump -pH ~/Downloads/ATL03_20240510223215_08142301_006_01.h5 | grep h_ph -A 10 +DATASET "h_ph" { + DATATYPE H5T_IEEE_F32LE # Four-byte, little-endian, IEEE floating point + DATASPACE SIMPLE { ( 15853996 ) / ( H5S_UNLIMITED ) } # simple (n-dimensional) dataspace, this is a 1-dimensional array of length 15,853,996 + STORAGE_LAYOUT { + CHUNKED ( 10000 ) # Dataset is stored in chunks of 10,000 elements per chunk + SIZE 57067702 (1.111:1 COMPRESSION) # Compressed datasets compression ratio of 1.111 to 1 - only slightly compresed + } + FILTERS { + COMPRESSION DEFLATE { LEVEL 6 } # Compressed with deflate algorithm with a compression level of 6 + } + ... +``` + + ## How to change the chunk size and shape ``` bash From 0a0899df2ee0cc2d80e5f86d876bea8bbc7b6009 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 10:27:04 -0800 Subject: [PATCH 08/17] Add intro about zarr and kerchunk. --- cloud-optimized-netcdf4-hdf5/index.qmd | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index d9317ec..a9c9b94 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -1,10 +1,12 @@ --- title: Cloud-Optimized HDF/NetCDF bibliography: references.bib -author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak +author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak, Luis Lopez toc-depth: 3 --- +The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes, as metadata is stored alongside the data. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also want to consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). + ::: callout-note You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). ::: @@ -37,7 +39,7 @@ To be considered cloud-optimized, the format should support chunking and compres ### How to determine chunk size -To estimate the uncompressed size, multiple the dimensions of the chunk by the number of bytes or the given data type. For example, given 3 dimensional chunks of 10x100x100 and a data type of `float64` (8 bytes), each uncompresed chunk will be 0.8 MB. +The uncompressed chunk size is the multiple of the chunk dimensions and the number of bytes for the given data type. For example, given 3 dimensional chunks of 10x100x100 and a data type of `float64` (8 bytes), each uncompresed chunk will be 0.8 MB. ### How to choose a chunk size @@ -60,9 +62,10 @@ A chunk size should be selected that is large enough to reduce the number of tas Performance greatly depends on libraries used to access the data and how they are configured to cache data as well. ::: -::: note +::: callout-note When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: -1. **Compression variabiliaty:** Because of data variability, you cannot deterministically know the size of each compressed chunk. + +1. **Compression variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. 2. **Memory considerations:** The uncompressed size determines how much memory must be available for reading and writing each chunk. ::: From af730b64ac82e79dbe71b90b7543b145feda43bf Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 10:27:52 -0800 Subject: [PATCH 09/17] Modify --- cloud-optimized-netcdf4-hdf5/index.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index a9c9b94..6be4c23 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -5,7 +5,7 @@ author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak, Luis Lopez toc-depth: 3 --- -The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes, as metadata is stored alongside the data. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also want to consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). +The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes, as metadata is stored alongside the data. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). ::: callout-note You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). From 8bd58419f5560fe11b27f3d1f22d73410459a750 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 17:57:23 -0800 Subject: [PATCH 10/17] Modify --- cloud-optimized-netcdf4-hdf5/index.qmd | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 6be4c23..972d1b1 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -182,6 +182,10 @@ You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repac $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 ``` +.::: {.callout} + +::: + ## How to check chunk size and shape Replace infile.h5 with a filename on your system and dataset_name with the name of a dataset in that file. From ed0da8d28f731d971fe1426fe44c2f3905d2f41c Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 18:19:15 -0800 Subject: [PATCH 11/17] Add callout warning about limitations of h5repack --- cloud-optimized-netcdf4-hdf5/index.qmd | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 972d1b1..71a37be 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -7,7 +7,7 @@ toc-depth: 3 The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes, as metadata is stored alongside the data. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). -::: callout-note +::: {.callout-note} You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). ::: @@ -25,7 +25,7 @@ For storage on disk, small chunks were preferred because access was fast, and re A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. -::: callout-note +::: {.callout-note} Note: NetCDF-4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files with HDF5](https://docs.unidata.ucar.edu/netcdf-c/current/interoperability_hdf5.html). ::: @@ -58,11 +58,11 @@ There is no one-size-fits all chunk size and shape as use cases for different pr A chunk size should be selected that is large enough to reduce the number of tasks that parallel schedulers have to think about (which affects overhead) but also small enough so that many of them can fit in memory at once. [The Amazon S3 Best Practices says the typical size for byte-range requests is 8-16MB](https://docs.aws.amazon.com/whitepapers/latest/s3-optimizing-performance-best-practices/use-byte-range-fetches.html). However, requests for data from contiguous chunks can be merged into 1 HTTP request, so chunks could be much smaller (one recommendation is 100kb to 2mb) [@jelenak2024]. -::: callout-note +::: {.callout-note} Performance greatly depends on libraries used to access the data and how they are configured to cache data as well. ::: -::: callout-note +::: {.callout-note} When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: 1. **Compression variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. @@ -89,6 +89,7 @@ A best practice to help determine both chunk size and shape would be to specify ### Additional chunk shape and size resources * [Chunking Data: Choosing Shapes](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) +* The [dynamic_target_chunks_from_schema](https://github.com/jbusecke/pangeo-forge-recipes/blob/dynamic_chunks_2/pangeo_forge_recipes/dynamic_target_chunks.py#L230-L343) function written by [Julius Busecke](https://github.com/jbusecke) may help in determing a chunk shape based on a target size and dimension aspect ratio. ## Consolidated Internal File Metadata @@ -182,8 +183,12 @@ You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repac $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 ``` -.::: {.callout} +::: {.callout-warning} +* The HDF5 library needs to be configured to use the page aggregated files. When using the HDF5 library you can set [H5Pset_page_buffer_size](https://hdfgroup.github.io/hdf5/develop/group___f_a_p_l.html#title89) and for [h5py File objects](https://docs.h5py.org/en/stable/high/file.html) you can set `page_buf_size` when instantiating the File object. +* h5repack has some limitations: aggregation is fast but rechunking is slow. You may want to use the h5py library directly to repack. See an example of how to do so in NSIDC's cloud-optimized ICESat-2 repo: [optimize-atl03.py](https://github.com/nsidc/cloud-optimized-icesat2/blob/main/notebooks/optimize-atl03.py). +* The NetCDF library doesn't expose the low-level HDF5 API so one must first create the file with the NetCDF library and then repack it with h5repack or python. See: [Using the HDF5 file space strategy property Unidata/netcdf-c #2871](https://github.com/Unidata/netcdf-c/discussions/2871). +author credit: Luis Lopez ::: ## How to check chunk size and shape From 4af9da773bb54221f6f841f3623c3de03effaa12 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Mon, 16 Dec 2024 18:19:57 -0800 Subject: [PATCH 12/17] Add callout warning about limitations of h5repack --- cloud-optimized-netcdf4-hdf5/index.qmd | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 71a37be..3d4599f 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -184,8 +184,10 @@ $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 ``` ::: {.callout-warning} +## Library limitations + * The HDF5 library needs to be configured to use the page aggregated files. When using the HDF5 library you can set [H5Pset_page_buffer_size](https://hdfgroup.github.io/hdf5/develop/group___f_a_p_l.html#title89) and for [h5py File objects](https://docs.h5py.org/en/stable/high/file.html) you can set `page_buf_size` when instantiating the File object. -* h5repack has some limitations: aggregation is fast but rechunking is slow. You may want to use the h5py library directly to repack. See an example of how to do so in NSIDC's cloud-optimized ICESat-2 repo: [optimize-atl03.py](https://github.com/nsidc/cloud-optimized-icesat2/blob/main/notebooks/optimize-atl03.py). +* h5repack's aggregation is fast but rechunking is slow. You may want to use the h5py library directly to repack. See an example of how to do so in NSIDC's cloud-optimized ICESat-2 repo: [optimize-atl03.py](https://github.com/nsidc/cloud-optimized-icesat2/blob/main/notebooks/optimize-atl03.py). * The NetCDF library doesn't expose the low-level HDF5 API so one must first create the file with the NetCDF library and then repack it with h5repack or python. See: [Using the HDF5 file space strategy property Unidata/netcdf-c #2871](https://github.com/Unidata/netcdf-c/discussions/2871). author credit: Luis Lopez From a39e4940514ee623b72dfefa745104c1c67cefc5 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Tue, 17 Dec 2024 11:39:38 -0800 Subject: [PATCH 13/17] Incorporate comments on page size --- cloud-optimized-netcdf4-hdf5/index.qmd | 69 ++++++++++++++++--------- images/why-hdf-on-cloud-is-slow.png | Bin 196488 -> 1702348 bytes 2 files changed, 46 insertions(+), 23 deletions(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 3d4599f..8ac660e 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -5,7 +5,7 @@ author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak, Luis Lopez toc-depth: 3 --- -The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes, as metadata is stored alongside the data. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). +The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). ::: {.callout-note} You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). @@ -17,11 +17,15 @@ NetCDF and HDF were originally designed with disk access in mind. As Matt Rockli >The HDF format is complex and metadata is strewn throughout the file, so that a complex sequence of reads is required to reach a specific chunk of data. +## Why accessing HDF5 on the cloud is slow + +In the diagram below, `R0, ..., Rn` represents the number of metadata requests required. A large number of these requests slows down working with these files in the cloud. + ![](../images/why-hdf-on-cloud-is-slow.png) [@barrett2024] -For storage on disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. +When reading and writing data from disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. @@ -35,11 +39,33 @@ Note: NetCDF-4 are valid HDF5 files, see [Reading and Editing NetCDF-4 Files wit To be considered cloud-optimized, the format should support chunking and compression. [NetCDF3](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html) and [HDF4 prior to v4.1](https://docs.hdfgroup.org/archive/support/products/hdf4/HDF-FAQ.html#18) do not support chunking and chunk-level compression, and thus cannot be reformatted to be cloud optimized. The lack of support for chunking and compression along with [other limitations](https://docs.hdfgroup.org/archive/support/products/hdf5_tools/h4toh5/h4vsh5.html) led to the development of NetCDF-4 and HDF5. +## Consolidated Internal File Metadata + +Consolidated metadata is a key characteristic of cloud-optimized data and enables "lazy loading" (see `Note` below). Client libraries use file metadata to understand what's in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. + +For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy (see also [H5F_FSPACE_STRATEGY_PAGE](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html#strategies)). When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. + +::: {.callout-note} +### HDF5 File Space Management Strategies + +How data and metadata are organized in an HDF5 file is the result of the file space management strategy used when writing the file. An explanation of the different strategies can be found in [HDF Support: File Space Management](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html). + +Here are a few additional considerations for understanding and implementing the `H5F_FSPACE_STRATEGY_PAGE` strategy: + +* **Chunks vs. Pages:** In HDF5, datasets can be chunked, meaning the dataset is divided into smaller blocks of data that can be individually compressed. Pages, on the other hand, represent the smallest unit that HDF5 uses for reading and writing data. To optimize performance, the chunk size should ideally align with the page size or be a multiple thereof. However, a chunk does not have to fit within a single page. Misalignment leads to fragmented chunks spanning multiple pages, which increases read latency. Entire pages are read into memory when accessing chunks or metadata. Only the relevant data (e.g., a specific chunk) is decompressed or used, while other data in the page remains cached in its raw form. +* **Page Size Considerations:** In HDF5, the page size applies to both metadata and raw data. Therefore, the chosen page size should strike a balance: it must consolidate metadata efficiently while minimizing unused space in raw data chunks. Excess unused space can significantly increase file size, which is typically not a concern for I/O performance when accessing parts of a file. However, increased file size can become a concern for storage costs. +::: + +::: {.callout-note} +### Lazy loading +Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. +::: + ## Chunk Size ### How to determine chunk size -The uncompressed chunk size is the multiple of the chunk dimensions and the number of bytes for the given data type. For example, given 3 dimensional chunks of 10x100x100 and a data type of `float64` (8 bytes), each uncompresed chunk will be 0.8 MB. +The uncompressed chunk size is calculated by multiplying the chunk dimensions by the size of the data type. For example, a 3D chunk of 10x100x100 with a float64 data type (8 bytes) results in an uncompressed chunk size of 0.8 MB. ### How to choose a chunk size @@ -63,20 +89,22 @@ Performance greatly depends on libraries used to access the data and how they ar ::: ::: {.callout-note} +### Uncompressed Chunk Size + When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: -1. **Compression variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. +1. **Data variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. 2. **Memory considerations:** The uncompressed size determines how much memory must be available for reading and writing each chunk. ::: ### Chunk shape vs chunk size -The chunk size - the number of values per chunk, often multiplied by their data type's size in bytes to get a total size in bytes - must be differentiated from the chunk shape, which is the number of values stored along each dimension in a given chunk. Recommended chunk size depends on a storage system's characteristics and its interaction with the data access library. That makes chunk size recommendations fairly universal when holding the storage system data access library constant. +The chunk size must be differentiated from the chunk shape, which is the number of values stored along each dimension in a given chunk. Recommended chunk size depends on a storage system's (such as S3) characteristics and its interaction with the data access library. In contrast, an optimal chunk shape is use case dependent. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as: 1. 10 lat x 10 lon x 10 time, -2. 20 lat x 50 lon x 1 time, or +2. 20 lat x 50 lon x 1 time, or, 3. 5 lat x 5 lon x 40 time. Larger chunks in a given dimension improve read performance in that direction: (3) is best for time-series analysis, (2) for mapping, and (1) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of "aspect ratios" of chunks, adjusting relative chunk sizes to fit the expected balance of spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). @@ -91,16 +119,6 @@ A best practice to help determine both chunk size and shape would be to specify * [Chunking Data: Choosing Shapes](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) * The [dynamic_target_chunks_from_schema](https://github.com/jbusecke/pangeo-forge-recipes/blob/dynamic_chunks_2/pangeo_forge_recipes/dynamic_target_chunks.py#L230-L343) function written by [Julius Busecke](https://github.com/jbusecke) may help in determing a chunk shape based on a target size and dimension aspect ratio. -## Consolidated Internal File Metadata - -Consolidated metadata is a key characteristic of cloud-optimized data. Client libraries use file metadata to understand what's in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. - -For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. - -::: {.callout-note} -_Lazy loading:_ Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. -::: - ## Compression Compression is the process of minimizing the size of data stored on disk using an algorithm to reduce the overall size of the data. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. @@ -112,12 +130,12 @@ NASA satellite data predominantly compressed with the zlib (a.k.a., gzip, deflat ## Data Product Usage Documentation (Tutorials and Examples) -How users use the data is out of the producers' control. However, tutorials and examples can be starting points for many data product users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to configure popular libraries for optimizing performance. +Tutorials and examples are starting points for many data users. These documents should include information on how to read data directly from cloud storage (as opposed to downloading over HTTPS) and how to configure popular libraries for optimizing performance. For example, the following library defaults will impact performance and are important to consider: * HDF5 library: The size of the HDF5's chunk cache by default is 1MB. This value is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. Learn more: [Improve HDF5 performance using caching](https://www.hdfgroup.org/2022/10/17/improve-hdf5-performance-using-caching/). -* S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB ([S3FS API docs](https://s3fs.readthedocs.io/en/stable/api.html#s3fs.core.S3FileSystem). +* S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB ([S3FS API docs](https://s3fs.readthedocs.io/en/stable/api.html#s3fs.core.S3FileSystem)). * Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in @jelenak2024. ### Additional research @@ -127,7 +145,7 @@ Here is some additional research done on caching for specific libraries and data - In this issue [Optimize s3fs read cache settings for the GEDI Subsetter](https://github.com/MAAP-Project/gedi-subsetter/issues/77) (findings to be formalized), Chuck Daniels found the "all" cache type (cache entire contents), a block size of 8MB and fill cache=True to deliver the best performance. NOTE: This is for non-cloud-optimized data. - In [HDF at the Speed of Zarr](https://docs.google.com/presentation/d/1iYFvGt9Zz0iaTj0STIMbboRKcBGhpOH_LuLBLqsJAlk/edit?usp=sharing), Luis Lopez demonstrates, using ICESat-2 data, the importance of using similar arguments with fsspec (blockcache instead of all, but the results in the issue above were not significantly different between these 2 options) as well as the importance of using nearly equivalent arguments in for h5py (raw data chunk cache nbytes and page_buff_size). -## Cloud-Optimized HDF/NetCDF Checklist +# Cloud-Optimized HDF/NetCDF Checklist Please consider the following when preparing HDF/NetCDF data for use on the cloud: @@ -140,7 +158,7 @@ Please consider the following when preparing HDF/NetCDF data for use on the clou # How tos -The examples below require the HDF5 library package is installed on your system. These commands will also work for NetCDF-4 While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. +The examples below require the HDF5 library package is installed on your system. These commands will also work for NetCDF-4. While you can check for chunk size and shape with h5py, h5py is a high-level interface primarily for accessing datasets, attributes, and other basic HDF5 functionalities. h5py does not expose lower-level file options directly. ## Commands in brief: @@ -151,7 +169,7 @@ The examples below require the HDF5 library package is installed on your system. ## How to check for consolidated metadata -To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks [@jelenak2023]. +To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write aggregate metadata and raw data into fixed size pages [@jelenak2023]. You can check the file space management strategy with the command line h5stat tool: @@ -183,10 +201,11 @@ You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repac $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 ``` +The HDF5 library needs to be configured to use the page aggregated files. When using the HDF5 library you can set [H5Pset_page_buffer_size](https://hdfgroup.github.io/hdf5/develop/group___f_a_p_l.html#title89) and for [h5py File objects](https://docs.h5py.org/en/stable/high/file.html) you can set `page_buf_size` when instantiating the File object. + ::: {.callout-warning} -## Library limitations +### Library limitations -* The HDF5 library needs to be configured to use the page aggregated files. When using the HDF5 library you can set [H5Pset_page_buffer_size](https://hdfgroup.github.io/hdf5/develop/group___f_a_p_l.html#title89) and for [h5py File objects](https://docs.h5py.org/en/stable/high/file.html) you can set `page_buf_size` when instantiating the File object. * h5repack's aggregation is fast but rechunking is slow. You may want to use the h5py library directly to repack. See an example of how to do so in NSIDC's cloud-optimized ICESat-2 repo: [optimize-atl03.py](https://github.com/nsidc/cloud-optimized-icesat2/blob/main/notebooks/optimize-atl03.py). * The NetCDF library doesn't expose the low-level HDF5 API so one must first create the file with the NetCDF library and then repack it with h5repack or python. See: [Using the HDF5 file space strategy property Unidata/netcdf-c #2871](https://github.com/Unidata/netcdf-c/discussions/2871). @@ -229,6 +248,10 @@ $ h5repack \ /path/to/dataset:CHUNK=2000 infile.h5 outfile.h5 ``` +# Closing Thoughts + +Many existing HDF5 and NetCDF4 collections use the default file space management strategy. While optimizing these collections would improve performance, it requires significant effort, including benchmark design and development, reprocessing and a deep understanding of HDF5 file space management and caching. When time and resources are limited, tools like [Kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/) offer a practical alternative. These tools don’t rechunk the data but instead consolidate metadata, resulting in notable performance improvements. + # References ::: {#refs} diff --git a/images/why-hdf-on-cloud-is-slow.png b/images/why-hdf-on-cloud-is-slow.png index b281e067d759bceaa9b8d55de94ef4137a5a176e..85b408790e924b7c8cd0ac52942c711ab4a50565 100644 GIT binary patch literal 1702348 zcmbrlbyyou*FQ`tv`BGlX_4S=#kF{F2<}diq_|u00tJFQv=Ah?28ZGf#a#*oTAWhc ze(8PRk6iEjSHI6*yP3(%oY~Br%{iYV8>y};kAp>yg@S^DqX3ivp`c*FP*Bjo7*CKb zzYeYOkPW4F($eY*($duGE{-;KU~3ctUdQ*i$i7(h zPJI2gT_iow=Kf*%A*%@mx+O%T!NP|3CWA}q)$XQbTx?{)W;hDvz+>KDwcnjy%Z@;y zDDMZ#?pplz(Z43OIfy6ktvx(c=6%_G>+i0`pg1yDwO z8aAnUK6du(h-2r_nE2;C@)<+pA0Cu)dOEf~rfz?CA}kQCO||Rv#p02LXC*)EINv%d zC?$`~a>*nm(VgSSmeIJ_;DILZ3fMm4VdYpqaug28NTh2gs2v%#>K)^W+x*FCoJmg1 zHH?Ysh~QNt74b{CN;fgDWmCmVo>j!JO{*Ot7@tXo$pG&Z{1Ci>svxgvwzt?cAvQv< z=|=)N!s*YV=|4~2UrABLH8rnJwiQ|5TX&!$+#ouj#!9!NtR`gFw$a##t(~K2d(oVc zawxA!(iWhcUJ)n0?^M$tc0aPu^>dN!dsvp?OB%6mt60+kw#THvr|=I^k0{Wn)pAg* 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r>^C*3#ee=a>Z`>6Kk@(LQru(b_dXLe%_S#!0zR@*ijo!LMnV4ru#etg From fa12e103bef8c220a31e17d18190ea1b18acd3de Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Tue, 17 Dec 2024 11:41:46 -0800 Subject: [PATCH 14/17] Add links --- cloud-optimized-netcdf4-hdf5/index.qmd | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 8ac660e..0a25fcd 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -116,7 +116,8 @@ A best practice to help determine both chunk size and shape would be to specify ### Additional chunk shape and size resources -* [Chunking Data: Choosing Shapes](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) +* [Unidata Blog: "Chunking Data: Choosing Shapes"](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) +* [HDF Support Site: "Chunking in HDF5"](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html) * The [dynamic_target_chunks_from_schema](https://github.com/jbusecke/pangeo-forge-recipes/blob/dynamic_chunks_2/pangeo_forge_recipes/dynamic_target_chunks.py#L230-L343) function written by [Julius Busecke](https://github.com/jbusecke) may help in determing a chunk shape based on a target size and dimension aspect ratio. ## Compression From b41046bbb5140deacddf1c431d38f98da61b3c69 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Wed, 18 Dec 2024 18:14:05 -0800 Subject: [PATCH 15/17] Self-review --- cloud-optimized-netcdf4-hdf5/index.qmd | 78 +++++++++++---------- cloud-optimized-netcdf4-hdf5/references.bib | 9 +++ 2 files changed, 50 insertions(+), 37 deletions(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index 0a25fcd..cda2a8e 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -1,11 +1,11 @@ --- title: Cloud-Optimized HDF/NetCDF bibliography: references.bib -author: Aimee Barciauskas, Alexey Shikmonalov, Alexsander Jelenak, Luis Lopez +author: Aimee Barciauskas, Alexey Shikmonalov, Luis Lopez toc-depth: 3 --- -The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes. If these formats are not a requirement, you may consider a cloud-native format like [Zarr](../zarr/intro.qmd). If these formats are a requirement, you may also consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). +The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes. If these formats are not a requirement, a cloud-native format like [Zarr](../zarr/intro.qmd) should be considered. If HDF5/NetCDF-4 formats _are_ a requirement, consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). ::: {.callout-note} You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). @@ -19,13 +19,13 @@ NetCDF and HDF were originally designed with disk access in mind. As Matt Rockli ## Why accessing HDF5 on the cloud is slow -In the diagram below, `R0, ..., Rn` represents the number of metadata requests required. A large number of these requests slows down working with these files in the cloud. +In the diagram below, `R0, ..., Rn` represent metadata requests. A large number of these requests slows down working with these files in the cloud. ![](../images/why-hdf-on-cloud-is-slow.png) [@barrett2024] -When reading and writing data from disk, small chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. +When reading and writing data from disk, small blocks of metadata and raw data chunks were preferred because access was fast, and retrieving any part of a chunk involved reading the entire chunk [@h5py_developers]. However, when this same data is stored in the cloud, performance can suffer due to the high number of requests required to access both metadata and raw data. With network access, reducing the number of requests makes access much more efficient. A detailed explanation of current best practices for cloud-optimized HDF5 and NetCDF-4 is provided below, followed by a checklist and some how-to guidance for assessing file layout. @@ -41,35 +41,46 @@ To be considered cloud-optimized, the format should support chunking and compres ## Consolidated Internal File Metadata -Consolidated metadata is a key characteristic of cloud-optimized data and enables "lazy loading" (see `Note` below). Client libraries use file metadata to understand what's in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. +Consolidated metadata is a key characteristic of cloud-optimized data and enables "lazy loading" (see the `Lazy Loading` block below). Client libraries use file metadata to understand what's in the file and where it is stored. When metadata is scattered across a file (which is the default for HDF5 writing), client applications have to make multiple requests for metadata information. -For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy (see also [H5F_FSPACE_STRATEGY_PAGE](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html#strategies)). When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Further, only files using paged aggregation can use HDF5 library’s page buffer cache to reduce subsequent data access. +For HDF5 files, to consolidate metadata, files should be written with the paged aggregation file space management strategy (see also [H5F_FSPACE_STRATEGY_PAGE](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html#strategies)). When using this strategy, HDF5 will write data in pages where metadata is separated from raw data chunks. Note the page size should also be set, as the default size is 4096 bytes (or 4KB, [source](https://support.hdfgroup.org/documentation/hdf5/latest/group___f_c_p_l.html#gaab5e8c08e4f588e0af1d937fcebfc885)). Further, only files using paged aggregation can use the HDF5 page buffer cache -- a low-level library cache [@jelenak2022] -- to reduce subsequent data access. + +::: {.callout-note} +### Lazy loading +Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. +::: ::: {.callout-note} ### HDF5 File Space Management Strategies -How data and metadata are organized in an HDF5 file is the result of the file space management strategy used when writing the file. An explanation of the different strategies can be found in [HDF Support: File Space Management](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html). +HDF5 file organization—data, metadata, and free space—depends on the file space management strategy. Details on these strategies are in [HDF Support: File Space Management](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html). Here are a few additional considerations for understanding and implementing the `H5F_FSPACE_STRATEGY_PAGE` strategy: -* **Chunks vs. Pages:** In HDF5, datasets can be chunked, meaning the dataset is divided into smaller blocks of data that can be individually compressed. Pages, on the other hand, represent the smallest unit that HDF5 uses for reading and writing data. To optimize performance, the chunk size should ideally align with the page size or be a multiple thereof. However, a chunk does not have to fit within a single page. Misalignment leads to fragmented chunks spanning multiple pages, which increases read latency. Entire pages are read into memory when accessing chunks or metadata. Only the relevant data (e.g., a specific chunk) is decompressed or used, while other data in the page remains cached in its raw form. -* **Page Size Considerations:** In HDF5, the page size applies to both metadata and raw data. Therefore, the chosen page size should strike a balance: it must consolidate metadata efficiently while minimizing unused space in raw data chunks. Excess unused space can significantly increase file size, which is typically not a concern for I/O performance when accessing parts of a file. However, increased file size can become a concern for storage costs. -::: - -::: {.callout-note} -### Lazy loading -Lazy loading is a common term for first loading only metadata, and deferring reading of data values until required by computation. +* **Chunks vs. Pages:** In HDF5, datasets can be chunked, meaning the dataset is divided into smaller blocks of data that can be individually compressed (see also [Chunking in HDF5](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html)). Pages, on the other hand, represent the smallest unit HDF5 uses for reading and writing data. To optimize performance, chunk sizes should ideally align with the page size or be a multiple thereof. A chunk does not have to fit within a single page, however misalignment leads to chunks spanning multiple pages, which increases read latency. Entire pages are read into memory when accessing chunks or metadata. Only the relevant data (e.g., a specific chunk) is decompressed. +* **Page Size Considerations:** The page size applies to both metadata and raw data. Therefore, the chosen page size should strike a balance: it must consolidate metadata efficiently while minimizing unused space in raw data chunks. Excess unused space can significantly increase file size. File size is typically not a concern for I/O performance when accessing parts of a file. However, increased file size can become a concern for storage costs. ::: ## Chunk Size +As described in [Chunking in HDF5](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html), datasets in HDF5 can be split into chunks and stored in discrete compressed blocks. + ### How to determine chunk size -The uncompressed chunk size is calculated by multiplying the chunk dimensions by the size of the data type. For example, a 3D chunk of 10x100x100 with a float64 data type (8 bytes) results in an uncompressed chunk size of 0.8 MB. +The uncompressed chunk size is calculated by multiplying the chunk dimensions by the size of the data type. For example, a 3-dimensional chunk with dimension lengths 10x100x100 and a float64 data type (8 bytes) results in an uncompressed chunk size of 0.8 MB. + +::: {.callout-note} +### Uncompressed Chunk Size + +When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: + +1. **Data variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. +2. **Memory considerations:** The uncompressed size determines how much memory must be available for reading and writing each chunk. +::: ### How to choose a chunk size -There is no one-size-fits all chunk size and shape as use cases for different products vary. However, chunks should not be "too big" or "too small". +There is no one-size-fits all chunk size and shape as files, use cases, and storage systems vary. However, chunks should not be "too big" or "too small". ### When chunks are too small: @@ -82,32 +93,23 @@ There is no one-size-fits all chunk size and shape as use cases for different pr - An entire chunk must be read and decompressed to read even a small portion of the data. - Managing large chunks in memory slows down processing and is more likely to exceed memory and chunk caches. -A chunk size should be selected that is large enough to reduce the number of tasks that parallel schedulers have to think about (which affects overhead) but also small enough so that many of them can fit in memory at once. [The Amazon S3 Best Practices says the typical size for byte-range requests is 8-16MB](https://docs.aws.amazon.com/whitepapers/latest/s3-optimizing-performance-best-practices/use-byte-range-fetches.html). However, requests for data from contiguous chunks can be merged into 1 HTTP request, so chunks could be much smaller (one recommendation is 100kb to 2mb) [@jelenak2024]. +A chunk size should be selected that is large enough to reduce the number of tasks that parallel schedulers have to think about (which affects overhead) but also small enough so many can fit in memory at once. [The Amazon S3 Best Practices says the typical size for byte-range requests is 8-16MB](https://docs.aws.amazon.com/whitepapers/latest/s3-optimizing-performance-best-practices/use-byte-range-fetches.html). However, requests for data from contiguous chunks can be merged into 1 HTTP request, so chunks could be much smaller (one recommendation is 100kb to 2mb) [@jelenak2024]. ::: {.callout-note} Performance greatly depends on libraries used to access the data and how they are configured to cache data as well. ::: -::: {.callout-note} -### Uncompressed Chunk Size - -When designing chunk size, usually the size is for the _uncompressed_ chunk. This is because: - -1. **Data variability:** Because of data variability, you cannot deterministically know the size of each compressed chunk. -2. **Memory considerations:** The uncompressed size determines how much memory must be available for reading and writing each chunk. -::: - ### Chunk shape vs chunk size The chunk size must be differentiated from the chunk shape, which is the number of values stored along each dimension in a given chunk. Recommended chunk size depends on a storage system's (such as S3) characteristics and its interaction with the data access library. -In contrast, an optimal chunk shape is use case dependent. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as: +In contrast, an optimal chunk shape is _use case_ dependent. For a 3-dimensional dataset (latitude, longitude, time) with a chunk size of 1000, chunk shapes could vary, such as: 1. 10 lat x 10 lon x 10 time, 2. 20 lat x 50 lon x 1 time, or, 3. 5 lat x 5 lon x 40 time. -Larger chunks in a given dimension improve read performance in that direction: (3) is best for time-series analysis, (2) for mapping, and (1) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of "aspect ratios" of chunks, adjusting relative chunk sizes to fit the expected balance of spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). +Larger chunks in a given dimension improve read performance in that direction: (3) is best for time-series analysis, (2) for mapping, and (1) is balanced for both. Thus, chunk shape should be chosen based on how the data is expected to be used, as there are trade-offs. A useful approach is to think in terms of the chunks' aspect ratio, adjusting relative dimension lengths to fit the desired optimization for spatial versus time-series analyses (see [https://github.com/jbusecke/dynamic_chunks](https://github.com/jbusecke/dynamic_chunks)). ![chunk shape options](../images/chunk-shape-options.png) [@shiklomanov2024] @@ -118,16 +120,16 @@ A best practice to help determine both chunk size and shape would be to specify * [Unidata Blog: "Chunking Data: Choosing Shapes"](https://www.unidata.ucar.edu/blogs/developer/en/entry/chunking_data_choosing_shapes) * [HDF Support Site: "Chunking in HDF5"](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html) -* The [dynamic_target_chunks_from_schema](https://github.com/jbusecke/pangeo-forge-recipes/blob/dynamic_chunks_2/pangeo_forge_recipes/dynamic_target_chunks.py#L230-L343) function written by [Julius Busecke](https://github.com/jbusecke) may help in determing a chunk shape based on a target size and dimension aspect ratio. +* The [dynamic_chunks](https://github.com/jbusecke/dynamic_chunks) module by [Julius Busecke](https://github.com/jbusecke) may help in determing a chunk shape based on a target size and dimension aspect ratio. ## Compression -Compression is the process of minimizing the size of data stored on disk using an algorithm to reduce the overall size of the data. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. +Compression is the process of minimizing the size of data stored using an algorithm which can condense data through various methods. This can include scale and offset parameters which reduce the size of each byte that needs to be stored. There are many algorithms for compressing data and users can even define their own compression algorithms. Data product owners should evaluate what compression algorithm is right for their data. ![why not compress](../images/quinn-why-not-compress.png) [@quinn2024] -NASA satellite data predominantly compressed with the zlib (a.k.a., gzip, deflate) method. However, other methods should be explored as a higher compression ratio is often possible, and in the case of HDF5, fills file pages better[@jelenak2023_report]. +NASA satellite data is predominantly compressed with the zlib (a.k.a., gzip, deflate) method. However, other methods should be explored as a higher compression ratio is often possible, and in the case of HDF5, fills file pages better[@jelenak2023_report]. ## Data Product Usage Documentation (Tutorials and Examples) @@ -135,7 +137,9 @@ Tutorials and examples are starting points for many data users. These documents For example, the following library defaults will impact performance and are important to consider: -* HDF5 library: The size of the HDF5's chunk cache by default is 1MB. This value is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. Learn more: [Improve HDF5 performance using caching](https://www.hdfgroup.org/2022/10/17/improve-hdf5-performance-using-caching/). +* HDF5 library: + * **Chunk cache:** The size of the HDF5's chunk cache by default is 1MB. This value is configurable. Chunks that don't fit into the chunk cache are discarded and must be re-read from the storage location each time. See also: [Improve HDF5 performance using caching](https://www.hdfgroup.org/2022/10/17/improve-hdf5-performance-using-caching/) and [h5py documentation: Chunk cache](https://docs.h5py.org/en/stable/high/file.html#chunk-cache). + * **Page buffer size:** See [H5Pset_page_buffer_size](https://hdfgroup.github.io/hdf5/develop/group___f_a_p_l.html#title89) and [the page_buf_size argument to h5py.File](https://docs.h5py.org/en/stable/high/file.html). * S3FS library: The S3FS library is a popular library for accessing data on AWS's cloud object storage S3. It has a default block size of 5MB ([S3FS API docs](https://s3fs.readthedocs.io/en/stable/api.html#s3fs.core.S3FileSystem)). * Additional guidance on h5py, fsspec, and ROS3 libraries for creating and reading HDF5 can be found in @jelenak2024. @@ -155,7 +159,7 @@ Please consider the following when preparing HDF/NetCDF data for use on the clou - [ ] Chunk sizes that are not too big nor too small (100kb-16mb) (see also [how-to-check-chunk-size-and-shape](#how-to-check-chunk-size-and-shape)). - [ ] An appropriate compression algorithm has been applied. - [ ] Expected use cases for the data were considered when designing the chunk size and shape. -- [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries' caching features. +- [ ] Data product usage documentation includes directions on how to read directly from cloud storage and how to use client libraries' to optimize access. # How tos @@ -170,7 +174,7 @@ The examples below require the HDF5 library package is installed on your system. ## How to check for consolidated metadata -To be considered cloud-optimized, HDF5 files should be written with the paged file space management strategy. When using this strategy, HDF5 will write aggregate metadata and raw data into fixed size pages [@jelenak2023]. +To be considered cloud-optimized, HDF5 files should be written with the `PAGE` file space management strategy (see also [File Space Management Strategies](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/FileSpaceManagement.html#strategies)). When using this strategy, HDF5 will write aggregate metadata and raw data into fixed-size pages [@jelenak2023]. You can check the file space management strategy with the command line h5stat tool: @@ -196,7 +200,7 @@ Notice the strategy: `File space management strategy: H5F_FSPACE_STRATEGY_FSM_AG ## How to change the file space management strategy -You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repacking to use the PAGE file space management strategy, you will also need to specify a page size that will indicate the block size for metadata storage. This should be at least as big as the `File metadata` value returned from `h5stat -S`. +You can use the `h5repack` to reorganize the metadata [@jelenak2023]. When repacking to use the `PAGE` file space management strategy, you will also need to specify a page size that will indicate the block size for metadata storage. This should be at least as big as the `File metadata` value returned from `h5stat -S`. ``` bash $ h5repack -S PAGE -G 4000000 infile.h5 outfile.h5 @@ -208,7 +212,7 @@ The HDF5 library needs to be configured to use the page aggregated files. When u ### Library limitations * h5repack's aggregation is fast but rechunking is slow. You may want to use the h5py library directly to repack. See an example of how to do so in NSIDC's cloud-optimized ICESat-2 repo: [optimize-atl03.py](https://github.com/nsidc/cloud-optimized-icesat2/blob/main/notebooks/optimize-atl03.py). -* The NetCDF library doesn't expose the low-level HDF5 API so one must first create the file with the NetCDF library and then repack it with h5repack or python. See: [Using the HDF5 file space strategy property Unidata/netcdf-c #2871](https://github.com/Unidata/netcdf-c/discussions/2871). +* The NetCDF library doesn't expose the low-level HDF5 API so one must first create the file with the NetCDF library and then repack it with h5repack or python. You can also create NetCDF files with HDF5 libraries, however required NetCDF properties must be set. See also: [Using the HDF5 file space strategy property Unidata/netcdf-c #2871](https://github.com/Unidata/netcdf-c/discussions/2871). author credit: Luis Lopez ::: @@ -251,7 +255,7 @@ $ h5repack \ # Closing Thoughts -Many existing HDF5 and NetCDF4 collections use the default file space management strategy. While optimizing these collections would improve performance, it requires significant effort, including benchmark design and development, reprocessing and a deep understanding of HDF5 file space management and caching. When time and resources are limited, tools like [Kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/) offer a practical alternative. These tools don’t rechunk the data but instead consolidate metadata, resulting in notable performance improvements. +Many existing HDF5 and NetCDF4 collections use the default file space management strategy and with very small raw data chunks. While optimizing these collections would improve performance, it requires significant effort, including benchmark design and development, reprocessing and a deep understanding of HDF5 file space management and caching. When time and resources are limited, tools like [Kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/) offer a practical alternative. These tools don’t rechunk the data but instead consolidate metadata, resulting in notable performance improvements. # References diff --git a/cloud-optimized-netcdf4-hdf5/references.bib b/cloud-optimized-netcdf4-hdf5/references.bib index b02ecb2..54cf4c1 100644 --- a/cloud-optimized-netcdf4-hdf5/references.bib +++ b/cloud-optimized-netcdf4-hdf5/references.bib @@ -32,6 +32,15 @@ @misc{jelenak2023_report institution = {The HDF Group} } + +@misc{jelenak2022, + author = {Aleksander Jelenak}, + title = {Creating Cloud-Optimized HDF5 Files}, + year = {2022}, + howpublished = {\url{https://hdfeos.org/workshops/ws25/presentations/axj.pdf}}, + institution = {The HDF Group} +} + @misc{quinn2024, author = {Patrick Quinn}, title = {Computer Scientists, Your Planet Needs You: An Introduction to Access-Optimized Data}, From 80a61a0838bc033b6e607bd9b13038dfc592c2f0 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Fri, 20 Dec 2024 13:02:03 -0800 Subject: [PATCH 16/17] Add a sentence about services --- cloud-optimized-netcdf4-hdf5/index.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index cda2a8e..be17021 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -5,7 +5,7 @@ author: Aimee Barciauskas, Alexey Shikmonalov, Luis Lopez toc-depth: 3 --- -The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. This assumes one of those formats is a requirement, usually for archival purposes. If these formats are not a requirement, a cloud-native format like [Zarr](../zarr/intro.qmd) should be considered. If HDF5/NetCDF-4 formats _are_ a requirement, consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). +The following provides guidance on how to assess and create cloud-optimized HDF5 and NetCDF4 files. Cloud-optimized formats provide efficient subsetting without maintaining a service, such as [OpenDAP](https://www.opendap.org), [Hyrax](https://www.opendap.org/software/hyrax-data-server/) or [SlideRule](https://slideruleearth.io/). Often HDF5 and NetCDF-4 formats are a requirement, for archival purposes. If HDF5 and NetCDF-4 formats are not a requirement, a cloud-native format like [Zarr](../zarr/intro.qmd) should be considered. If HDF5/NetCDF-4 formats _are_ a requirement, consider zarr-readable chunk indexes such as [kerchunk](../kerchunk/intro.qmd) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/). ::: {.callout-note} You can skip the background and details by jumping to the [checklist](#cloud-optimized-hdfnetcdf-checklist). From a87945111212612ce0b182815e78ec050f6dacb0 Mon Sep 17 00:00:00 2001 From: Aimee Barciauskas Date: Fri, 20 Dec 2024 13:20:18 -0800 Subject: [PATCH 17/17] Remove sentence about chunk sizes bigger than page sizes. --- cloud-optimized-netcdf4-hdf5/index.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cloud-optimized-netcdf4-hdf5/index.qmd b/cloud-optimized-netcdf4-hdf5/index.qmd index be17021..215e158 100644 --- a/cloud-optimized-netcdf4-hdf5/index.qmd +++ b/cloud-optimized-netcdf4-hdf5/index.qmd @@ -57,7 +57,7 @@ HDF5 file organization—data, metadata, and free space—depends on the file sp Here are a few additional considerations for understanding and implementing the `H5F_FSPACE_STRATEGY_PAGE` strategy: -* **Chunks vs. Pages:** In HDF5, datasets can be chunked, meaning the dataset is divided into smaller blocks of data that can be individually compressed (see also [Chunking in HDF5](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html)). Pages, on the other hand, represent the smallest unit HDF5 uses for reading and writing data. To optimize performance, chunk sizes should ideally align with the page size or be a multiple thereof. A chunk does not have to fit within a single page, however misalignment leads to chunks spanning multiple pages, which increases read latency. Entire pages are read into memory when accessing chunks or metadata. Only the relevant data (e.g., a specific chunk) is decompressed. +* **Chunks vs. Pages:** In HDF5, datasets can be chunked, meaning the dataset is divided into smaller blocks of data that can be individually compressed (see also [Chunking in HDF5](https://support.hdfgroup.org/documentation/hdf5-docs/advanced_topics/chunking_in_hdf5.html)). Pages, on the other hand, represent the smallest unit HDF5 uses for reading and writing data. To optimize performance, chunk sizes should ideally align with the page size or be a multiple thereof. Entire pages are read into memory when accessing chunks or metadata. Only the relevant data (e.g., a specific chunk) is decompressed. * **Page Size Considerations:** The page size applies to both metadata and raw data. Therefore, the chosen page size should strike a balance: it must consolidate metadata efficiently while minimizing unused space in raw data chunks. Excess unused space can significantly increase file size. File size is typically not a concern for I/O performance when accessing parts of a file. However, increased file size can become a concern for storage costs. :::

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