Netcdf-4 Filter Support Changes

The netcdf-c library filters API in version 4.7.4 has been deprecated in favor of a modified version that unfortunately may cause incompatibilties for users.

The initial reason for the incompatible changes was to support the use of filters in the new NCZarr code. The changes were not completely thought out so it was decided to remove them and revert to previous behaviors. At some future point, the filter mechanism will be extended to support filters for NCZarr, but these will be proper extensions: the existing, reverted, filter API will continue to be supported with no user-visible modifications.

Unfortunately, some advanced users of netcdf filters may experience some compilation or execution problems for previously working code because of these reversions. In that case, please revise your code. Apologies are extended for any inconvenience. Note that it is possible to detect which mechanism is in place at build time.

In summary, the changes are of the following kinds:

  • Some functions were renamed for consistency.
  • Revert the way that the function nc_inq_var_filter was indicating no filters existed.
  • Some auxilliary functions for parsing textual filter specifications have been moved to netcdf_aux.h.
  • All of the "filterx" functions have been removed.
  • The undocumented function nc_filter_remove was deleted.

See the Github document for details.

Highlights From My Summer Internship With Unidata

Lauren Prox
Lauren Prox

During the beginning of my internship, I devoted a great deal of time learning how to use Git and Github to collaborate on software development projects. After gaining this experience, I began improving documentation for a variety of Unidata remote repositories. I started with the netCDF-C repository and then moved on to the MetPy, Siphon, and Python Training remote repositories. This work was significant as it ensured that software users were able to locate resources, properly download software, and learn how to operate the software via informational materials.

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Overview of Zarr Support in netCDF-C

[Updated 03/07/21] 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 [4].

The NCZarr Data Model

NCZarr uses a data model [4] that is, by design, similar to, but not identical with the Zarr Version 2 Specification [6].
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 ncinqvarchunking_ 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 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

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 nccreate_ or ncopen_. 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,s3" (see below). Specifying "file" is mostly used for testing, but is used to support directory tree or zipfile format storage. - Host: Amazon S3 defines two forms: Virtual and Path. + Virtual: the host includes the bucket name as in bucket.s3.<region> + 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> + 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 allowed.

  • mode=nczarr|zarr|s3|file|zip... -- 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, file, and zip tell the library what storage driver to use. The s3 is default] and indicates using Amazon S3 or some equivalent. The file format stores data in a directory tree. The zip format stores data in a local zip file. It should be the case that zipping a file format directory tree will produce a file readable by the zip storage format. The zarr mode tells the library to use NCZarr, but to restrict its operation to operate on pure Zarr Version 2 datasets.

NCZarr Map Implementation

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 [5] class.

In NCZarr, the corresponding type is called zmap. The zmap API essentially implements a simplified variant of the Amazon S3 API.

As with Amazon S3, keys are utf8 strings with a specific structure: that of a path similar to those of a Unix path with '/' as the separator for the segments of the path.

As with Unix, all keys have this BNF syntax: ```` key: '/' | keypath ; keypath: '/' segment | keypath '/' segment ; segment: ````

Obviously, one can infer a tree structure from this key structure. A containment relationship is defined by key prefixes. Thus one key is "contained" (possibly transitively) by another if one key is a prefix (in the string sense) of the other. So in this sense the key "/x/y/z" is contained by the key "/x/y".

In this model all keys "exist" but only some keys refer to objects containing content -- content bearing. An important restriction is placed on the structure of the tree, namely that keys are only defined for content-bearing objects. Further, all the leaves of the tree are these content-bearing objects. This means that the key for one content-bearing object should not be a prefix of any other key.

There several other concepts of note. 1. Dataset - a dataset is the complete tree contained by the key defining the root of the dataset. Technically, the root of the tree is the key /.nczarr, where .nczarr can be considered the superblock of the dataset. 2. Object - equivalent of the S3 object; Each object has a unique key and "contains" data in the form of an arbitrary sequence of 8-bit bytes.

The zmap API defined here isolates the key-value pair mapping code from the Zarr-based implementation of NetCDF-4. It wraps an internal C dispatch table manager for implementing an abstract data structure implementing the zmap key/object model.

Search: The search function has two purposes: 1. Support reading of pure zarr datasets (because they do not explicitly track their contents). 2. Debugging to allow raw examination of the storage. See zdump for example.

The search function takes a prefix path which has a key syntax (see above). The set of legal keys is the set of keys such that the key references a content-bearing object -- e.g. /x/y/.zarray or /.zgroup. Essentially this is the set of keys pointing to the leaf objects of the tree of keys constituting a dataset. This set potentially limits the set of keys that need to be examined during search.

The search function returns a limited set of names, where the set of names are immediate suffixes of a given prefix path. That is, if \ is the prefix path, then search returns all \ such that \/\ is itself a prefix of a "legal" key. This can be used to implement glob style searches such as "/x/y/" or "/x/y/*"

This semantics was chosen because it appears to be the minimum required to implement all other kinds of search using recursion. It was also chosen to limit the number of names returned from the search. Specifically 1. Avoid returning keys that are not a prefix of some legal key. 2. Avoid returning all the legal keys in the dataset because that set may be very large; although the implementation may still have to examine all legal keys to get the desired subset. 3. Allow for use of partial read mechanisms such as iterators, if available. This can support processing a limited set of keys for each iteration. This is a straighforward tradeoff of space over time.

As a side note, S3 supports this kind of search using common prefixes with a delimiter of '/', although the implementation is a bit tricky. For the file system zmap implementation, the legal search keys can be obtained one level at a time, which directly implements the search semantics. For the zip file implementation, this semantics is not possible, so the whole tree must be obtained and searched.


  1. S3 limits key lengths to 1024 bytes. Some deeply nested netcdf files will almost certainly exceed this limit.
  2. Besides content, S3 objects can have an associated small set of what may be called tags, which are themselves of the form of key-value pairs, but where the key and value are always text. As far as it is possible to determine, Zarr never uses these tags, so they are not included in the zmap data structure.

A Note on Error Codes:

The zmap API returns two distinguished error code: 1. NC_NOERR if a operation succeeded 2. NC_EEMPTY is returned when accessing a key that has no content.

Note that NC_EEMPTY is a new error code to signal to that the caller asked for non-content-bearing key.

This does not preclude other errors being returned such NCEACCESS or NCEPERM or NC_EINVAL if there are permission errors or illegal function arguments, for example. It also does not preclude the use of other error codes internal to the zmap implementation. So zmapfile, for example, uses NCENOTFOUND internally because it is possible to detect the existence of directories and files. This does not propagate outside the zmap_file implementation.

Zmap Implementatons

The primary zmap implementation is s3 (i.e. mode=nczarr,s3) and indicates that the Amazon S3 cloud storage -- or some related applicance -- is to be used. Another storage format uses a file system tree of directories and files (mode=nczarr,file). A third storage format uses a zip file (mode=nczarr,zip). The latter two are used mostly for debugging and testing. However, the file and zip formats are important because they is intended to match corresponding storage formats used by the Python Zarr implementation. Hence it should serve to provide interoperability between NCZarr and the Python Zarr. This has not been tested.

Examples of the typical URL form for file and zip are as follows. ```` file:///xxx/yyy/testdata.file#mode=nczarr,file file:///xxx/yyy/,zip ````

Note that the extension (e.g. ".file" in "testdata.file") is arbitraty, so this would be equally acceptable. ```` file:///xxx/yyy/testdata.anyext#mode=nczarr,file ````

As with other URLS (e.g. DAP), these kind of URLS can be passed as the path argument to ncdump, for example.

NCZarr versus Pure Zarr.

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. The primary effects of using pure zarr are described in the Translation Section.

Notes on Debugging NCZarr Access

The NCZarr support has a trace facility. Enabling this can sometimes give important information. Tracing can be enabled by setting the environment variable NCTRACING=n, where n indicates the level of tracing. A good value of n is 9.

Zip File Support

In order to use the zip storage format, the libzip [3] library must be installed. Note that this is different from zlib.

Amazon S3 Storage

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 awsaccesskey_id=XXXX... awssecretaccess_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: ``` ```

  2. Path -- the path addressing style places the bucket in at the front of the path part of a URL. For example:

``` ```

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

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 modeled using the fact that the container's key is a prefix of the variable's key. So for example, if variable v1 is contained in 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.
  4. .nczattr -- this is parallel to the .zattr objects and stores the attribute type information.


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 anything 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 search API 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.


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


The Xarray [7] Zarr implementation uses its own mechanism for specifying shared dimensions. It uses a special attribute named ''ARRAYDIMENSIONS''. The value of this attribute is a list of dimension names (strings). An example might be ["time", "lon", "lat"]. It is essentially equivalent to the .nczvar/dimrefs list, but stored as a specific variable attribute. It will be read/written if and only if the mode value "xarray" is specified. If enabled and detected, then these dimension names are used to define shared dimensions. Note that xarray implies pure zarr format.


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


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

Appendix A. Building NCZarr Support

Currently the following build cases are known to work.

Operating SystemBuild SystemNCZarrS3 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.


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-nczarr-s3 -- Enable NCZarr S3 support.
  3. --enable-nczarr-s3-tests -- the NCZarr S3 tests are currently only usable by Unidata personnel, so they are disabled by default.

A note about using S3 with Automake. 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++.


The necessary CMake flags are as follows (with defaults)

  1. -DENABLENCZARR=on -- equivalent to the Automake _--enable-nczarr option.
  2. -DENABLENCZARRS3=off -- equivalent to the Automake --enable-nczarr-s3 option.
  3. -DENABLENCZARRS3TESTS=off -- equivalent to the Automake _--enable-nczarr-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 ````

It is possible to install the sdk library in another location. In this case, one must add the following flag to the cmake command. ```` cmake ... -DAWSSDK_DIR=\ ```` where "AWSSDKDIR" is the path to the sdk installation. For example, this might be as follows. ```` cmake ... -DAWSSDK_DIR="c:\tools\aws-cpp-sdk-all" ```` This can be useful if blanks in path names cause problems in your build environment.

Testing S3 Support

The relevant tests for S3 support are in nczarr_test. They will be run if --enable-nczarr-s3-tests is on.

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.

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 following environment variables:


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.


```` NCZARRS3TEST_BUCKET=testbucket ````

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

Appendix B. Building aws-sdk-cpp

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

This library depends on libcurl, so you may to install that before building the sdk library.

Appendix C. Amazon S3 Imposed Limits

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 nctest/ shows simple examples using the ncdump program. One such test is specified as follows:

```` ````

Note that for S3 access, it is expected that the URL is in what is called "path" format where the bucket, noaa-goes16 in this case, is part of the URL path instead of the host.

The #mode=byterange 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.

```` ````

Byte-Range Authorization

If using byte-range access, it may be necessary to tell the netcdf-c library about the so-called secretid and accessid values. These are usually stored in the file ~/.aws/config and/or ~/.aws/credentials. In the latter file, this might look like this. ```` [default] awsaccesskey_id=XXXXXXXXXXXXXXXXXXXX awssecretaccess_key=YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY ````

__Point of Contact__

Author: Dennis Heimbigner
Email: dmh at ucar dot edu
Initial Version: 4/10/2020
Last Revised: 2/22/2021

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Enhancing the netCDF C++ Library and the Siphon Package

Aodhan Sweeney
Aodhan Sweeney

This summer at Unidata I worked on expanding functionality for both the netCDF C++ library and the Python data access tool Siphon. Previously, the netCDF C++ library was lacking important functionality that was included in other netCDF libraries. Fortunately, adding this functionality is a straightforward process. I created function wrappers in the C++ library that would call previously made functions in the C library. This allows those working in a C++ framework to continue to use the netCDF libraries without sacrificing additional functionality.

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This document defines the variant of the netcdf-c library API that can be used to read/write NCZarr dataset. Additionally, any special new flags or other parameter values are defined. It is expected that this document should be consistent with the NetCDF ZARR Data Model Specification [1].

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