corresponding HDF5 operations.
re: github issue https://github.com/Unidata/netcdf-c/issues/908
also in reference to https://github.com/pydata/xarray/issues/2004
The netcdf-c library has implemented the nc_get_vars and nc_put_vars
operations as element at a time. This has resulted in very slow
operation.
This pr attempts to improve the situation for netcdf-4/hdf5 files
by using the slab operations provided by the hdf5 library. The new
implementation passes the get/put vars stride information down to
the hdf5 slab operations.
The result appears to improve performance significantly. Some simple
tests on large 2-D arrays shows speedups in excess of 150.
Misc. other changes:
1. fix bug in ncgen/semantics.c; using a list's allocated length
instead of actual length.
2. Added a temporary hook in the netcdf library plus a performance
test case (tst_varsperf.c) to estimate the speedup. After users
have had some experience with this, I will remove it, probably
after the 4.7 release.
The file docs/indexing.dox tries to provide design
information for the refactoring.
The primary change is to replace all walking of linked
lists with the use of the NCindex data structure.
Ncindex is a combination of a hash table (for name-based
lookup) and a vector (for walking the elements in the index).
Additionally, global vectors are added to NC_HDF5_FILE_INFO_T
to support direct mapping of an e.g. dimid to the NC_DIM_INFO_T
object. These global vectors exist for dimensions, types, and groups
because they have globally unique id numbers.
WARNING:
1. since libsrc4 and libsrchdf4 share code, there are also
changes in libsrchdf4.
2. Any outstanding pull requests that change libsrc4 or libhdf4
are likely to cause conflicts with this code.
3. The original reason for doing this was for performance improvements,
but as noted elsewhere, this may not be significant because
the meta-data read performance apparently is being dominated
by the hdf5 library because we do bulk meta-data reading rather
than lazy reading.
Fix#299
The conditions to make this error are the following:
* Two variables with different chunk sizes
* Both variables write on the same unlimited dimension
* The first variable has already written data when the second variable is created
