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diff --git a/tensorflow/core/lib/io/table_options.h b/tensorflow/core/lib/io/table_options.h
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+#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_