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The tools allow efficient and fast synchronization of large directory trees from a Windows workstation to a Linux target machine. cdc_rsync* support efficient copy of files by using content-defined chunking (CDC) to identify chunks within files that can be reused. asset_stream_manager + cdc_fuse_fs support efficient streaming of a local directory to a remote virtual file system based on FUSE. It also employs CDC to identify and reuse unchanged data chunks.
CDC Indexer
This directory contains a CDC indexer based on our implementation of FastCDC.
Run the sample with Bazel:
bazel run -c opt //cdc_indexer -- --inputs '/path/to/files'
The CDC algorithm can be tweaked with a few compile-time constants for
experimentation. See the file indexer.h for preprocessor macros that can be
enabled, for example:
bazel build -c opt --copt=-DCDC_GEAR_TABLE=1 //cdc_indexer
At the end of the operation, the indexer outputs a summary of the results such as the following:
00:02 7.44 GB in 2 files processed at 3.1 GB/s, 50% deduplication
Operation succeeded.
Chunk size (min/avg/max): 128 KB / 256 KB / 1024 KB | Threads: 12
gear_table: 64 bit | mask_s: 0x49249249249249 | mask_l: 0x1249249249
Duration: 00:03
Total files: 2
Total chunks: 39203
Unique chunks: 20692
Total data: 9.25 GB
Unique data: 4.88 GB
Throughput: 3.07 GB/s
Avg. chunk size: 247 KB
Deduplication: 47.2%
160 KB ######### 1419 ( 7%)
192 KB ######## 1268 ( 6%)
224 KB ################### 2996 (14%)
256 KB ######################################## 6353 (31%)
288 KB ###################### 3466 (17%)
320 KB ########################## 4102 (20%)
352 KB ###### 946 ( 5%)
384 KB 75 ( 0%)
416 KB 27 ( 0%)
448 KB 7 ( 0%)
480 KB 5 ( 0%)
512 KB 1 ( 0%)
544 KB 4 ( 0%)
576 KB 2 ( 0%)
608 KB 3 ( 0%)
640 KB 3 ( 0%)
672 KB 3 ( 0%)
704 KB 2 ( 0%)
736 KB 0 ( 0%)
768 KB 0 ( 0%)
800 KB 1 ( 0%)
832 KB 0 ( 0%)
864 KB 0 ( 0%)
896 KB 0 ( 0%)
928 KB 0 ( 0%)
960 KB 0 ( 0%)
992 KB 0 ( 0%)
1024 KB 9 ( 0%)
For testing multiple combinations and comparing the results, the indexer also
features a flag --results_file="results.csv" which appends the raw data to the
given file in CSV format. Combine this flag with --description to label each
experiment with additional columns.