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The NetCDF NCZarr Implementation {#nczarr_head}

[TOC]

NCZarr Introduction {#nczarr_introduction}

Beginning with netCDF version 4.8.0, the Unidata NetCDF group has extended the netcdf-c library to provide access to cloud storage (e.g. Amazon S3 [1] ) by providing a mapping from a subset of the full netCDF Enhanced (aka netCDF-4) data model to a variant of the Zarr [4] data model that already has mappings to key-value pair cloud storage systems. The NetCDF version of this storage format is called NCZarr [2].

The NCZarr Data Model {#nczarr_data_model}

NCZarr uses a data model [2] that is, by design, similar to, but not identical with the Zarr Version 2 Specification [4]. Briefly, the data model supported by NCZarr is netcdf-4 minus the user-defined types and the String type. As with netcdf-4 it supports chunking. Eventually it will also support filters in a manner similar to the way filters are supported in netcdf-4.

Specifically, the model supports the following.

  • "Atomic" types: char, byte, ubyte, short, ushort, int, uint, int64, uint64.
  • Shared (named) dimensions
  • Attributes with specified types -- both global and per-variable
  • Chunking
  • Fill values
  • Groups
  • N-Dimensional variables
  • Per-variable endianness (big or little)

With respect to full netCDF-4, the following concepts are currently unsupported.

  • String type
  • User-defined types (enum, opaque, VLEN, and Compound)
  • Unlimited dimensions
  • Contiguous or compact storage

Note that contiguous and compact are not actually supported because they are HDF5 specific. When specified, they are treated as chunked where the file consists of only one chunk. This means that testing for contiguous or compact is not possible, the nc_inq_var_chunking function will always return NC_CHUNKED and the chunksizes will be the same as the dimension sizes of the variable's dimensions.

Enabling NCZarr Support {#nczarr_enable}

NCZarr support is enabled if the --enable-nczarr option is used with './configure'. If NCZarr support is enabled, then a usable version of libcurl must be specified using the LDFLAGS environment variable (similar to the way that the HDF5 libraries are referenced). Refer to the installation manual for details. NCZarr support can be disabled using the --disable-dap.

Accessing Data Using the NCZarr Prototocol {#nczarr_accessing_data}

In order to access a NCZarr data source through the netCDF API, the file name normally used is replaced with a URL with a specific format. Note specifically that there is no NC_NCZARR flag for the mode argument of nc_create or nc_open. In this case, it is indicated by the URL path.

URL Format

The URL is the usual scheme:://host:port/path?query#fragment format. There are some details that are important.

  • Scheme: this should be https or s3,or file. The s3 scheme is equivalent to "https" plus setting "mode=nczarr" (see below). Specifying "file" is mostly used for testing.
  • Host: Amazon S3 defines two forms: Virtual and Path.
    • Virtual: the host includes the bucket name as in bucket.s3.<region>.amazonaws.com
    • Path: the host does not include the bucket name, but rather the bucket name is the first segment of the path. For example s3.<region>.amazonaws.com/bucket
    • Other: It is possible to use other non-Amazon cloud storage, but that is cloud library dependent.
  • Query: currently not used.
  • Fragment: the fragment is of the form key=value&key=value&.... Depending on the key, the =value part may be left out and some default value will be used.

Client Parameters

The fragment part of a URL is used to specify information that is interpreted to specify what data format is to be used, as well as additional controls for that data format. For NCZarr support, the following key=value pairs are allowd.

  • mode=nczarr|zarr|s3|nz4|nzf... -- The mode key specifies the particular format to be used by the netcdf-c library for interpreting the dataset specified by the URL. Using mode=nczarr causes the URL to be interpreted as a reference to a dataset that is stored in NCZarr format. The modes s3, nz4, and nzf tell the library what storage driver to use. The s3 is default] and indicates using Amazon S3 or some equivalent. The other two, nz4 and nzf are again for testing. The zarr mode tells the library to use NCZarr, but to restrict its operation to operate on pure Zarr Version 2 datasets.
  • log=<output-stream>: this control turns on logging output, which is useful for debugging and testing. If just log is used then it is equivalent to log=stderr.

NCZarr Map Implementation {#nczarr_mapimpl}

Internally, the nczarr implementation has a map abstraction that allows different storage formats to be used. This is closely patterned on the same approach used in the Python Zarr implementation, which relies on the Python MutableMap [3] class. In NCZarr, the corresponding type is called zmap.

The zmap model is a set of keys where each key maps to an object that can hold arbitrary data. The keys are assumed to have following BNF grammar.

key:   '/' segment
     | key '/' segment
     ;

This key structure induces a tree structure where each segment matches a node in the tree. This key/tree duality deliberately matches that of a typical file system path in e.g. linux. The key '/' is the root of the tree.

Datasets

Within the key induced tree, each dataset (in the netCDF sense) has a root which is specified by a specific key. All objects making up the dataset (see the section on NCZarr vs Zarr ) reside in objects (keys) below that dataset root key.

One restriction is that datasets cannot be nested in that no dataset root key can be a prefix of another dataset root key.

Zmap Implementatons

The primary zmap implementation is s3 (i.e. mode=nczarr,s3) and indicates that the Amazon S3 cloud storage is to be used. Other storage formats use a structured NetCDF-4 file format (mode=nczarr,nz4), or a directory tree (mode=nczarr,nzf) The latter two are used mostly for debugging and testing. However, the nzf format is important because it is intended to match a corresponding storage format used by the Python Zarr implementation. Hence it should serve to provide interoperability between NCZarr and the Python Zarr.

NCZarr versus Pure Zarr. {#nczarr_purezarr}

The NCZARR format extends the pure Zarr format by adding extra objects such as .nczarr and .ncvar. It is possible to suppress the use of these extensions so that the netcdf library can read and write a pure zarr formatted file. This is controlled by using mode=nczarr,zarr combination.

Notes on Debugging NCZarr Access {#nczarr_debug}

The NCZarr support has a logging facility. Turning on this logging can sometimes give important information. Logging can be enabled by using the client parameter "log" or "log=filename",or by setting the environment variable NCLOGGING. The first case will send log output to standard error and the second will send log output to the specified file. The environment variable is equivalent to log.

Amazon S3 Storage {#nczarr_debug}

The Amazon AWS S3 storage driver currently uses the Amazon AWS S3 Software Development Kit for C++ (aws-s3-sdk-cpp). In order to use it, the client must provide some configuration information. Specifically, the ~/.aws/config file should contain something like this.

[default]
output = json
aws_access_key_id=XXXX...
aws_secret_access_key=YYYY...

Addressing Style

The notion of "addressing style" may need some expansion. Amazon S3 accepts two forms for specifying the endpoint for accessing the data.

  1. Virtual -- the virtual addressing style places the bucket in the host part of a URL. For example:
https://<bucketname>.s2.<region>.amazonaws.com/
  1. Path -- the path addressing style places the bucket in at the front of the path part of a URL. For example:
https://s2.<region>.amazonaws.com/<bucketname>/

The NCZarr code will accept either form, although internally, it is standardized on path style. The reason for this is that the bucket name forms the initial segment in the keys.

Zarr vs NCZarr {#nczarr_vs_zarr}

Data Model

The NCZarr storage format is almost identical to that of the the standard Zarr version 2 format. The data model differs as follows.

  1. Zarr supports filters -- NCZarr as yet does not
  2. Zarr only supports anonymous dimensions -- NCZarr supports only shared (named) dimensions.
  3. Zarr attributes are untyped -- or perhaps more correctly characterized as of type string.

Storage Format

Consider both NCZarr and Zarr, and assume S3 notions of bucket and object. In both systems, Groups and Variables (Array in Zarr) map to S3 objects. Containment is modelled using the fact that the container's key is a prefix of the variable's key. So for example, if variable v1 is contained int top level group g1 -- _/g1 -- then the key for v1 is /g1/v. Additional information is stored in special objects whose name start with ".z". In Zarr, the following special objects exist.

  1. Information about a group is kept in a special object named .zgroup; so for example the object /g1/.zgroup.
  2. Information about an array is kept as a special object named .zarray; so for example the object /g1/v1/.zarray.
  3. Group-level attributes and variable-level attributes are stored in a special object named .zattr; so for example the objects /g1/.zattr and _/g1/v1/.zattr.

The NCZarr format uses the same group and variable (array) objects as Zarr. It also uses the Zarr special .zXXX objects.

However, NCZarr adds some additional special objects.

  1. .nczarr -- this is in the top level group -- key /.nczarr. It is in effect the "superblock" for the dataset and contains any netcdf specific dataset level information. It is also used to verify that a given key is the root of a dataset.

  2. .nczgroup -- this is a parallel object to .zgroup and contains any netcdf specific group information. Specifically it contains the following.

    • dims -- the name and size of shared dimensions defined in this group.
    • vars -- the name of variables defined in this group.
    • groups -- the name of sub-groups defined in this group.

    These lists allow walking the NCZarr dataset without having to use the potentially costly S3 list operation.

  3. .nczvar -- this is a parallel object to .zarray and contains netcdf specific information. Specifically it contains the following.

    • dimrefs -- the names of the shared dimensions referenced by the variable.
    • storage -- indicates if the variable is chunked vs contiguous in the netcdf sense. 1 .nczattr -- this is parallel to the .zattr objects and stores the attribute type information.

Translation

With some constraints, it is possible for an nczarr library to read Zarr and for a zarr library to read the nczarr format.

The latter case, zarr reading nczarr is possible if the zarr library is willing to ignore objects whose name it does not recognized; specifically anthing beginning with .ncz.

The former case, nczarr reading zarr is also possible if the nczarr can simulate or infer the contents of the missing .nczXXX objects. As a rule this can be done as follows.

  1. .nczgroup -- The list of contained variables and sub-groups can be computed using the S3 list operation to list the keys "contained" in the key for a group. By looking for occurrences of .zgroup, .zattr, _.zarray to infer the keys for the contained groups, attribute sets, and arrays (variables). Constructing the set of "shared dimensions" is carried out by walking all the variables in the whole dataset and collecting the set of unique integer shapes for the variables. For each such dimension length, a top level dimension is created named ".zdim" where len is the integer length. The name is subject to change.
  2. .nczvar -- The dimrefs are inferred by using the shape in .zarray and creating references to the simulated shared dimension. netcdf specific information.
  3. .nczattr -- The type of each attribute is inferred by trying to parse the first attribute value string.

Compatibility {#nczarr_compatibility}

In order to accomodate existing implementations, certain mode tags are provided to tell the NCZarr code to look for information used by specific implementations.

Examples {#nczarr_examples}

Here are a couple of examples using the ncgen and ncdump utilities.

  1. Create an nczarr file using a local directory tree as storage. 
    ncgen -4 -lb -o "file:///home/user/dataset.nzf#mode=nczarr,nzf" dataset.cdl
  2. Display the content of an nczarr file using a local directory tree as storage. 
    ncdump "file:///home/user/dataset.nzf#mode=nczarr,nzf"
  3. Create an nczarr file using S3 as storage. 
    ncgen -4 -lb -o "s3://datasetbucket" dataset.cdl
  4. Create an nczarr file using S3 as storage and keeping to the pure zarr format. 
    ncgen -4 -lb -o "s3://datasetbucket#mode=zarr" dataset.cdl

References {#nczarr_bib}

[1] Amazon Simple Storage Service Documentation
[2] NetCDF ZARR Data Model Specification
[3] Python Documentation: 8.3. collections — High-performance container datatypes
[4] Zarr Version 2 Specification
[5] XArray Zarr Encoding Specification

Appendix A. Building NCZarr Support {#nczarr_build}

Currently only the following build cases are supported.

Operating System | Build System | NCZarr | S3 Support

Linux | Automake | yes | yes Linux | CMake | yes | yes Cygwin | Automake | yes | no OSX | Automake | unknown | unknown OSX | CMake | unknown | unknown Visual Studio | CMake | yes | tests fail

Note: S3 support includes both compiling the S3 support code as well as running the S3 tests.

Automake

There are several options relevant to NCZarr support and to Amazon S3 support. These are as follows.

  1. --enable-nczarr -- enable the NCZarr support. If disabled, then all of the following options are disabled or irrelevant.
  2. --enable-s3-sdk -- enable the use of the aws s3 sdk.
  3. --enable-s3-tests -- the s3 tests are currently only usable by Unidata personnel, so they are disabled by default.

A note about using S3 with Automake. Automake does not handle C++ libraries, so if S3 support is desired, and using Automake, then LDFLAGS must be properly set, namely to this.

LDFLAGS="$LDFLAGS -L/usr/local/lib -laws-cpp-sdk-s3"

The above assumes that these libraries were installed in '/usr/local/lib', so the above requires modification if they were installed elsewhere.

Note also that if S3 support is enabled, then you need to have a C++ compiler installed because part of the S3 support code is written in C++.

CMake

The necessary CMake flags are as follows (with defaults)

  1. -DENABLE_NCZARR=on -- equivalent to the Automake --enable-nczarr option.
  2. -DENABLE_S3_SDK=off -- quivalent to the Automake --enable-s3-sdk option.
  3. -DENABLE_S3_TESTS=off -- equivalent to the Automake --enable-s3-tests option.

Note that unlike Automake, CMake can properly locate C++ libraries, so it should not be necessary to specify -laws-cpp-sdk-s3 assuming that the aws s3 libraries are installed in the default location. For CMake with Visual Studio, the default location is here:

C:/Program Files (x86)/aws-cpp-sdk-all

Testing S3 Support

The relevant tests for S3 support are nczarr_test/run_ut_mapapi.sh and nczarr_test/run_it_test2.sh.

Currently, by default, testing of S3 with NCzarr is supported only for Unidata members of the NetCDF Development Group. This is because it uses a specific bucket on a specific internal S3 appliance that is inaccessible to the general user. This is controlled by the --enable_s3_tests option.

However, an untested mechanism exists by which others may be able to run the tests. If someone else wants to attempt these tests, then they need to define the environment variable name NCS3PATH. The form of this variable is as follows:

NCS3PATH="https://<host>/<bucket>/<prefix>

This assumes a Path Style address (see above) where

  • host -- the complete host part of the url
  • bucket -- a bucket in which testing can occur without fear of damaging anything.
  • prefix - prefix of the key; the actual root, typically test, is appended to this to get the root key used by the test.

Example:

s3.us-west.amazonaws.com/testingbucket/segment1/segment2

If anyone tries to use this mechanism, it would be appreciated it any difficulties were reported to Unidata.

Appendix B. Building aws-sdk-cpp {#nczarr_s3sdk}

In order to use the S3 storage driver, it is necessary to install the Amazon aws-sdk-cpp library.

As a starting point, here are the CMake options used by Unidata to build that library. It assumes that it is being executed in a build directory, build say, and that build/../CMakeLists.txt exists.

cmake -DBUILD_ONLY=s3

The expected set of installed libraries are as follows:

  • aws-cpp-sdk-s3
  • aws-cpp-sdk-core

Appendix C. Amazon S3 Imposed Limits {#nczarr_s3limits}

The Amazon S3 cloud storage imposes some significant limits that are inherited by NCZarr (and Zarr also, for that matter).

Some of the relevant limits are as follows:

  1. The maximum object size is 5 Gigabytes with a total for all objects limited to 5 Terabytes.
  2. S3 key names can be any UNICODE name with a maximum length of 1024 bytes. Note that the limit is defined in terms of bytes and not (Unicode) characters. This affects the depth to which groups can be nested because the key encodes the full path name of a group.

Appendix D. Alternative Mechanisms for Accessing Remote Datasets

The NetCDF-C library contains an alternate mechanism for accessing data store in Amazon S3: The byte-range mechanism. The idea is to treat the remote data as if it was a big file. This remote "file" can be randomly accessed using the HTTP Byte-Range header.

In the Amazon S3 context, a copy of a dataset, a netcdf-3 or netdf-4 file, is uploaded into a single object in some bucket. Then using the key to this object, it is possible to tell the netcdf-c library to treat the object as a remote file and to use the HTTP Byte-Range protocol to access the contents of the object. The dataset object is referenced using a URL with the trailing fragment containing the string #mode=bytes.

An examination of the test program nc_test/test_byterange.sh shows simple examples using the ncdump program. One such test is specified as follows:

https://noaa-goes16.s3.amazonaws.com/ABI-L1b-RadC/2017/059/03/OR_ABI-L1b-RadC-M3C13_G16_s20170590337505_e20170590340289_c20170590340316.nc#mode=bytes

This mechanism generalizes to work with most servers that support byte-range access. Specifically, Thredds servers support such access using the HttpServer access method as can be seen from this URL taken from the above test program.

https://thredds-test.unidata.ucar.edu/thredds/fileServer/irma/metar/files/METAR_20170910_0000.nc#bytes

Point of Contact {#nczarr_poc}

Author: Dennis Heimbigner
Email: dmh at ucar dot edu
Initial Version: 4/10/2020
Last Revised: 6/8/2020