Merge pull request #36 from alimanfoo/blosc_upgrade_20160721

upgrade c-blosc to 1.10.0; change default c-blosc compressor to lz4

Author: alimanfoo

c-blosc

@@ -1,6 +1,21 @@
 Release notes
 =============
 
+.. _release_1.1.0:
+
+1.1.0
+-----
+
+* The bundled Blosc library has been upgraded to version 1.10.0. The 'zstd'
+  internal compression library is now available within Blosc. See the tutorial
+  section on :ref:`tutorial_compression` for an example.
+* When using the Blosc compressor, the default internal compression library
+  is now 'lz4'.
+* The default number of internal threads for the Blosc compressor has been
+  increased to a maximum of 8 (previously 4).
+* Added convenience functions :func:`zarr.blosc.list_compressors` and
+  :func:`zarr.blosc.get_nthreads`.
+
 .. _release_1.0.0:
 
 1.0.0

docs/release.rst

@@ -21,8 +21,8 @@ example::
     >>> z = zarr.zeros((10000, 10000), chunks=(1000, 1000), dtype='i4')
     >>> z
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 317; ratio: 1261829.7; initialized: 0/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 313; ratio: 1277955.3; initialized: 0/100
       store: builtins.dict
 
 The code above creates a 2-dimensional array of 32-bit integers with
@@ -44,7 +44,7 @@ scalar value::
     >>> z[:] = 42
     >>> z
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
       nbytes: 381.5M; nbytes_stored: 2.2M; ratio: 170.4; initialized: 100/100
       store: builtins.dict
 
@@ -92,8 +92,8 @@ enabling persistence of data between sessions. For example::
     ...                chunks=(1000, 1000), dtype='i4', fill_value=0)
     >>> z1
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 317; ratio: 1261829.7; initialized: 0/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 313; ratio: 1277955.3; initialized: 0/100
       store: zarr.storage.DirectoryStore
 
 The array above will store its configuration metadata and all
@@ -116,8 +116,8 @@ Check that the data have been written and can be read again::
     >>> z2 = zarr.open('example.zarr', mode='r')
     >>> z2
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 2.3M; ratio: 163.8; initialized: 100/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 2.3M; ratio: 163.9; initialized: 100/100
       store: zarr.storage.DirectoryStore
     >>> np.all(z1[:] == z2[:])
     True
@@ -135,8 +135,8 @@ can be increased or decreased in length. For example::
     >>> z.resize(20000, 10000)
     >>> z
     zarr.core.Array((20000, 10000), float64, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 1.5G; nbytes_stored: 5.9M; ratio: 259.9; initialized: 100/200
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 1.5G; nbytes_stored: 5.7M; ratio: 268.5; initialized: 100/200
       store: builtins.dict
 
 Note that when an array is resized, the underlying data are not
@@ -151,20 +151,20 @@ which can be used to append data to any axis. E.g.::
     >>> z = zarr.array(a, chunks=(1000, 100))
     >>> z
     zarr.core.Array((10000, 1000), int32, chunks=(1000, 100), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 38.1M; nbytes_stored: 2.0M; ratio: 19.3; initialized: 100/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 38.1M; nbytes_stored: 1.9M; ratio: 20.0; initialized: 100/100
       store: builtins.dict
     >>> z.append(a)
     >>> z
     zarr.core.Array((20000, 1000), int32, chunks=(1000, 100), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 76.3M; nbytes_stored: 4.0M; ratio: 19.3; initialized: 200/200
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 76.3M; nbytes_stored: 3.8M; ratio: 20.0; initialized: 200/200
       store: builtins.dict
     >>> z.append(np.vstack([a, a]), axis=1)
     >>> z
     zarr.core.Array((20000, 2000), int32, chunks=(1000, 100), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 152.6M; nbytes_stored: 7.9M; ratio: 19.3; initialized: 400/400
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 152.6M; nbytes_stored: 7.6M; ratio: 20.0; initialized: 400/400
       store: builtins.dict
 
 .. _tutorial_compress:
@@ -188,17 +188,24 @@ functions. For example::
 
     >>> z = zarr.array(np.arange(100000000, dtype='i4').reshape(10000, 10000),
     ...                chunks=(1000, 1000), compression='blosc',
-    ...                compression_opts=dict(cname='lz4', clevel=3, shuffle=2))
+    ...                compression_opts=dict(cname='zstd', clevel=3, shuffle=2))
     >>> z
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 3, 'cname': 'lz4', 'shuffle': 2}
-      nbytes: 381.5M; nbytes_stored: 17.6M; ratio: 21.7; initialized: 100/100
+      compression: blosc; compression_opts: {'clevel': 3, 'cname': 'zstd', 'shuffle': 2}
+      nbytes: 381.5M; nbytes_stored: 3.1M; ratio: 121.1; initialized: 100/100
       store: builtins.dict
 
 The array above will use Blosc as the primary compressor, using the
-LZ4 algorithm (compression level 3) internally within Blosc, and with
+Zstandard algorithm (compression level 3) internally within Blosc, and with
 the bitshuffle filter applied.
 
+A list of the internal compression libraries available within Blosc can be
+obtained via::
+
+    >>> from zarr import blosc
+    >>> blosc.list_compressors()
+    ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd']
+
 In addition to Blosc, other compression libraries can also be
 used. Zarr comes with support for zlib, BZ2 and LZMA compression, via
 the Python standard library. For example, here is an array using zlib
@@ -270,8 +277,8 @@ array with thread synchronization::
     ...                 synchronizer=zarr.ThreadSynchronizer())
     >>> z
     zarr.sync.SynchronizedArray((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 317; ratio: 1261829.7; initialized: 0/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 313; ratio: 1277955.3; initialized: 0/100
       store: builtins.dict; synchronizer: zarr.sync.ThreadSynchronizer
 
 This array is safe to read or write within a multi-threaded program.
@@ -285,8 +292,8 @@ provided that all processes have access to a shared file system. E.g.::
     ...               synchronizer=synchronizer)
     >>> z
     zarr.sync.SynchronizedArray((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 317; ratio: 1261829.7; initialized: 0/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 313; ratio: 1277955.3; initialized: 0/100
       store: zarr.storage.DirectoryStore; synchronizer: zarr.sync.ProcessSynchronizer
 
 This array is safe to read or write from multiple processes.
@@ -350,13 +357,13 @@ data. E.g.::
     >>> a = np.arange(100000000, dtype='i4').reshape(10000, 10000).T
     >>> zarr.array(a, chunks=(1000, 1000))
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=C)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 26.1M; ratio: 14.6; initialized: 100/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 26.3M; ratio: 14.5; initialized: 100/100
       store: builtins.dict
     >>> zarr.array(a, chunks=(1000, 1000), order='F')
     zarr.core.Array((10000, 10000), int32, chunks=(1000, 1000), order=F)
-      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'blosclz', 'shuffle': 1}
-      nbytes: 381.5M; nbytes_stored: 10.0M; ratio: 38.0; initialized: 100/100
+      compression: blosc; compression_opts: {'clevel': 5, 'cname': 'lz4', 'shuffle': 1}
+      nbytes: 381.5M; nbytes_stored: 9.5M; ratio: 40.1; initialized: 100/100
       store: builtins.dict
 
 In the above example, Fortran order gives a better compression ratio. This
@@ -460,12 +467,12 @@ Configuring Blosc
 
 The Blosc compressor is able to use multiple threads internally to
 accelerate compression and decompression. By default, Zarr allows
-Blosc to use up to 4 internal threads. The number of Blosc threads can
-be changed, e.g.::
+Blosc to use up to 8 internal threads. The number of Blosc threads can
+be changed to increase or decrease this number, e.g.::
 
     >>> from zarr import blosc
     >>> blosc.set_nthreads(2)
-    4
+    8
 
 When a Zarr array is being used within a multi-threaded program, Zarr
 automatically switches to using Blosc in a single-threaded