The XArray implementation that uses Zarr for storage
provides a mechanism to simulate named dimensions.
It does this by adding a per-variable attribute called
_ARRAY_DIMENSIONS. This attribute contains a list of names
to be matched against the shape values of the variable.
In effect a named dimension is created with the name
_ARRAY_DIMENSIONS(i) and length shape(i) for all i
in range 0..rank(variable).
Both read and write support is provided.
This XArray support is only invoked if the mode value
of "xarray" is defined. So for example, as in this URL.
````
https://s3.us-west-1.amazonaws.com/bucket/dataset#mode=nczarr,xarray,s3
````
Note that the "xarray" mode flag also implies mode flag "zarr", so the above
is equivalent to this URL.
````
https://s3.us-west-1.amazonaws.com/bucket/dataset#mode=nczarr,zarr,xarray,s3
````
The primary change to implement this was to unify the handling
of dimension references in libnczarr/zsync.
A test for this and other pure-zarr features was added as
nczarr_test/run_purezarr.sh
Other changes:
* Make sure distcheck leaves no files around.
* Change the special attribute flag DIMSCALEFLAG to HIDDENATTRFLAG
to support the xarray attribute.
* Annotate the zmap implementations with feature flags such as
WRITEONCE (for zip files).
If the user is opening a existing file for appending (NC_WRITE) in parallel and the file is in CDF5 format, the `NC_interpret_magic_number()` routine clears the `model->impl` setting of `NC_FORMATX_PNETCDF` which was set in `NC_omodeinfer` (See lines following the `done:` label in that routine which specifically set the `impl` if `useparallel` is true.)
This setting then gets overwritten when `NC_interpret_magic_number` is called which sets the `model->impl` back to `NC_FORMATX_NC3`. This can (did) cause problems with parallel output as the `NC3` format does not correctly handle parallel writing but the `PNETCDF` does.
Not sure if this is the best place for the test, but it did fix the parallel write issues I was seeing...
If you need more details on what is happening, let me know. But a restatement at a higher level is that I was calling `nc_open_par` with `NC_WRITE` and `NC_64BIT_DATA` mode and the existing file has `CDF5` for the magic number. However, the dispatcher was being set to `NC3_dispatch_table` instead of `NCP_dispatch_table` which is the dispatcher which had been chosen for the original creation of the file being appended to.
I was then getting zeroes in the data being written to the vars since NC3 wasn't correctly handling multiple MPI ranks writing to different parts of the same variable...
Primary Fixes:
* Add a whole variable optimization -- used in the rare case that nc_get/put_vara covers the whole of a variable and the variable has a single chunk.
* Fix chunking error when stride causes whole chunks to be skipped.
* Fix some memory leaks
* Add test cases
* Add one performance test to nczarr_test/. This uses the timer utils from unit_test: timer_utils.[ch].
* Move ncdumpchunks utility from ncdump to nczarr_test
Misc. Other Changes:
* Make check for aws libraries conditional on --enable-nczarr-s3
* Remove all but one bm tests from nczarr_test until they are working.
* Remove another dependency on HDF5 from supposedly non-HDF5 specific code; specifically hdf5_log_hdf5.
* Make the BAIL2 macro be hdf5 specific and replace elsewhere with an HDF5 independent equivalent.
* Move hdf5cache.c to libsrc4/nc4cache.c because it is used by nczarr.
* Modify unit_tests so that some of them are run even if using Windows.
* Misc. small bug fixes and refactors and memory leaks.
* Rename some conflicting tests for cmake.
* Attempted to make nc_perf work with cmake and failed.
cloud using a variant of the Zarr protocol and storage
format. This enhancement is generically referred to as "NCZarr".
The data model supported by NCZarr is netcdf-4 minus the user-defined
types and the String type. In this sense it is similar to the CDF-5
data model.
More detailed information about enabling and using NCZarr is
described in the document NUG/nczarr.md and in a
[Unidata Developer's blog entry](https://www.unidata.ucar.edu/blogs/developer/en/entry/overview-of-zarr-support-in).
WARNING: this code has had limited testing, so do use this version
for production work. Also, performance improvements are ongoing.
Note especially the following platform matrix of successful tests:
Platform | Build System | S3 support
------------------------------------
Linux+gcc | Automake | yes
Linux+gcc | CMake | yes
Visual Studio | CMake | no
Additionally, and as a consequence of the addition of NCZarr,
major changes have been made to the Filter API. NOTE: NCZarr
does not yet support filters, but these changes are enablers for
that support in the future. Note that it is possible
(probable?) that there will be some accidental reversions if the
changes here did not correctly mimic the existing filter testing.
In any case, previously filter ids and parameters were of type
unsigned int. In order to support the more general zarr filter
model, this was all converted to char*. The old HDF5-specific,
unsigned int operations are still supported but they are
wrappers around the new, char* based nc_filterx_XXX functions.
This entailed at least the following changes:
1. Added the files libdispatch/dfilterx.c and include/ncfilter.h
2. Some filterx utilities have been moved to libdispatch/daux.c
3. A new entry, "filter_actions" was added to the NCDispatch table
and the version bumped.
4. An overly complex set of structs was created to support funnelling
all of the filterx operations thru a single dispatch
"filter_actions" entry.
5. Move common code to from libhdf5 to libsrc4 so that it is accessible
to nczarr.
Changes directly related to Zarr:
1. Modified CMakeList.txt and configure.ac to support both C and C++
-- this is in support of S3 support via the awd-sdk libraries.
2. Define a size64_t type to support nczarr.
3. More reworking of libdispatch/dinfermodel.c to
support zarr and to regularize the structure of the fragments
section of a URL.
Changes not directly related to Zarr:
1. Make client-side filter registration be conditional, with default off.
2. Hack include/nc4internal.h to make some flags added by Ed be unique:
e.g. NC_CREAT, NC_INDEF, etc.
3. cleanup include/nchttp.h and libdispatch/dhttp.c.
4. Misc. changes to support compiling under Visual Studio including:
* Better testing under windows for dirent.h and opendir and closedir.
5. Misc. changes to the oc2 code to support various libcurl CURLOPT flags
and to centralize error reporting.
6. By default, suppress the vlen tests that have unfixed memory leaks; add option to enable them.
7. Make part of the nc_test/test_byterange.sh test be contingent on remotetest.unidata.ucar.edu being accessible.
Changes Left TO-DO:
1. fix provenance code, it is too HDF5 specific.
* For URL paths, the new approach essentially centralizes all information
in the URL into the "#mode=" fragment key and uses that value
to determine the dispatcher for (most) URLs.
* The new approach has the following steps:
1. canonicalize the path if it is a URL.
2. use the mode= fragment key to determine the dispatcher
3. if dispatcher still not determined, then use the mode flags
argument to nc_open/nc_create to determine the dispatcher.
4. if the path points to something readable, attempt to read the
magic number at the front, and use that to determine the dispatcher.
this case may override all previous cases.
* Misc changes.
1. Update documentation
2. Moved some unit tests from libdispatch to unit_test directory.
3. Fixed use of wrong #ifdef macro in test_filter_reg.c
[I think this may fix an previously reported esupport query].
So, fixed the following:
1. Forgot to check for NC_FORMATX_PNETCDF case
in one of the switches in NC_infermodel.
2. Accidentally turned on both the NC_64BIT_OFFSET
and the NC_64BIT_DATA mode flags.
re: issue https://github.com/Unidata/netcdf-c/issues/1251
Assume that you have the URL to a remote dataset
which is a normal netcdf-3 or netcdf-4 file.
This PR allows the netcdf-c to read that dataset's
contents as a netcdf file using HTTP byte ranges
if the remote server supports byte-range access.
Originally, this PR was set up to access Amazon S3 objects,
but it can also access other remote datasets such as those
provided by a Thredds server via the HTTPServer access protocol.
It may also work for other kinds of servers.
Note that this is not intended as a true production
capability because, as is known, this kind of access to
can be quite slow. In addition, the byte-range IO drivers
do not currently do any sort of optimization or caching.
An additional goal here is to gain some experience with
the Amazon S3 REST protocol.
This architecture and its use documented in
the file docs/byterange.dox.
There are currently two test cases:
1. nc_test/tst_s3raw.c - this does a simple open, check format, close cycle
for a remote netcdf-3 file and a remote netcdf-4 file.
2. nc_test/test_s3raw.sh - this uses ncdump to investigate some remote
datasets.
This PR also incorporates significantly changed model inference code
(see the superceded PR https://github.com/Unidata/netcdf-c/pull/1259).
1. It centralizes the code that infers the dispatcher.
2. It adds support for byte-range URLs
Other changes:
1. NC_HDF5_finalize was not being properly called by nc_finalize().
2. Fix minor bug in ncgen3.l
3. fix memory leak in nc4info.c
4. add code to walk the .daprc triples and to replace protocol=
fragment tag with a more general mode= tag.
Final Note:
Th inference code is still way too complicated. We need to move
to the validfile() model used by netcdf Java, where each
dispatcher is asked if it can process the file. This decentralizes
the inference code. This will be done after all the major new
dispatchers (PIO, Zarr, etc) have been implemented.
