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/* Copyright 2015 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_LIB_IO_TABLE_OPTIONS_H_
#define TENSORFLOW_LIB_IO_TABLE_OPTIONS_H_
#include <stddef.h>
namespace tensorflow {
namespace table {
// DB contents are stored in a set of blocks, each of which holds a
// sequence of key,value pairs. Each block may be compressed before
// being stored in a file. The following enum describes which
// compression method (if any) is used to compress a block.
enum CompressionType {
// NOTE: do not change the values of existing entries, as these are
// part of the persistent format on disk.
kNoCompression = 0x0,
kSnappyCompression = 0x1
};
// Options to control the behavior of a table (passed to Table::Open)
struct Options {
// Approximate size of user data packed per block. Note that the
// block size specified here corresponds to uncompressed data. The
// actual size of the unit read from disk may be smaller if
// compression is enabled. This parameter can be changed dynamically.
size_t block_size = 262144;
// Number of keys between restart points for delta encoding of keys.
// This parameter can be changed dynamically. Most clients should
// leave this parameter alone.
int block_restart_interval = 16;
// Compress blocks using the specified compression algorithm. This
// parameter can be changed dynamically.
//
// Default: kSnappyCompression, which gives lightweight but fast
// compression.
//
// Typical speeds of kSnappyCompression on an Intel(R) Core(TM)2 2.4GHz:
// ~200-500MB/s compression
// ~400-800MB/s decompression
// Note that these speeds are significantly faster than most
// persistent storage speeds, and therefore it is typically never
// worth switching to kNoCompression. Even if the input data is
// incompressible, the kSnappyCompression implementation will
// efficiently detect that and will switch to uncompressed mode.
CompressionType compression = kSnappyCompression;
};
} // namespace table
} // namespace tensorflow
#endif // TENSORFLOW_LIB_IO_TABLE_OPTIONS_H_
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