About

Find duplicate files and directories.

As other tools we use file hashes but additionally, we report duplicate directories as well, using a Merkle tree for directory hash calculation.

To increase performance, we use parallel hash calculation and optional limits on to-be-hashed data.

Install

From pypi:

$ pip3 install findsame

Dev install of this repo:

$ git clone ...
$ cd findsame
$ pip3 install -e .

The core part (package findsame and the CLI bin/findsame) has no external dependencies. If you want to run the benchmarks (see "Benchmarks" below), install:

$ pip3 install -r requirements_benchmark.txt

Usage

usage: findsame [-h] [-b BLOCKSIZE] [-l LIMIT] [-L AUTO_LIMIT_MIN] [-p NPROCS]
                [-t NTHREADS] [-o OUTMODE] [-v]
                file/dir [file/dir ...]

Find same files and dirs based on file hashes.

positional arguments:
  file/dir              files and/or dirs to compare

optional arguments:
  -h, --help            show this help message and exit
  -b BLOCKSIZE, --blocksize BLOCKSIZE
                        blocksize in hash calculation, use units K,M,G as in
                        100M, 218K or just 1024 (bytes) [default: 256.0K]
  -l LIMIT, --limit LIMIT
                        read limit (bytes or 'auto'), if bytes then same units
                        as for BLOCKSIZE apply, calculate hash only over the
                        first LIMIT bytes, makes things go faster for may
                        large files, try 500K [default: None], use 'auto' to
                        try to determine the smallest value necessary
                        automatically
  -L AUTO_LIMIT_MIN, --auto-limit-min AUTO_LIMIT_MIN
                        start value for auto LIMIT calculation when --limit
                        auto is used [default: 8.0K]
  -p NPROCS, --nprocs NPROCS
                        number of parallel processes [default: 1]
  -t NTHREADS, --nthreads NTHREADS
                        threads per process [default: 4]
  -o OUTMODE, --outmode OUTMODE
                        1: list of dicts (values of dict from mode 2), one
                        dict per hash, 2: dict of dicts (full result), keys
                        are hashes, 3: compact, sort by type (file, dir)
                        [default: 3]
  -v, --verbose         enable verbose/debugging output

The output format is json, see -o/--outmode, default is -o 3. An example using the test suite data:

$ cd findsame/tests
$ findsame data | jq .
{
  "dir:empty": [
    [
      "data/dir2/empty_dir",
      "data/dir2/empty_dir_copy",
      "data/empty_dir",
      "data/empty_dir_copy"
    ]
  ],
  "dir": [
    [
      "data/dir1",
      "data/dir1_copy"
    ]
  ],
  "file:empty": [
    [
      "data/dir2/empty_dir/empty_file",
      "data/dir2/empty_dir_copy/empty_file",
      "data/empty_dir/empty_file",
      "data/empty_dir_copy/empty_file",
      "data/empty_file",
      "data/empty_file_copy"
    ]
  ],
  "file": [
    [
      "data/dir1/file2",
      "data/dir1/file2_copy",
      "data/dir1_copy/file2",
      "data/dir1_copy/file2_copy",
      "data/file2"
    ],
    [
      "data/lena.png",
      "data/lena_copy.png"
    ],
    [
      "data/file1",
      "data/file1_copy"
    ]
  ]
}

This returns a dict whose keys are the path type (file, dir). Values are nested lists. Each sub-list contains paths having the same hash. A special case is file:empty and dir:empty which actually have the same hash (that of an empty string), which is not visible in this format. Use -o1 or -o2 in that case. More examples below.

Use jq for pretty-printing. Post-processing is only limited by your ability to process json (using jq, Python, ...).

Note that the order of key-value entries in the output from both findsame and jq is random.

Note that currently, we skip symlinks.

Performance

Parallel hash calculation

By default, we use --nthreads equal to the number of cores. See "Benchmarks" below.

Limit data to be hashed

Static limit

Apart from parallelization, by far the most speed is gained by using --limit. Note that this may lead to false positives, if files are exactly equal in the first LIMIT bytes. Finding a good enough value can be done by trial and error. Try 500K. This is still quite fast and seems to cover most real-world data.

Automatic optimal limit

We have an experimental feature where we iteratively increase LIMIT to find the smallest possible value. In every iteration, we increase the last limit by multiplying with config.cfg.auto_limit_increase_fac, with that re-calculate only the hash of files that have the same hash as others within the last LIMIT and check whether their new hashes are now different. This works but hasn't been extensively benchmarked. The assumption is that a small number of iterations on a subset of all files (those reported equal so far) converges quickly and is still faster than a non-optimal LIMIT or even no limit at all when you have many big files (as in GiB).

Related options and defaults:

• --limit auto
• --auto-limit-min 8K = config.cfg.auto_limit_min
• config.cfg.auto_limit_increase_fac=2 (no cmd line so far)

Observations so far:

Convergence corner cases: When files are equal in a good chunk at file start and auto_limit_min is small, then the first few iterations show no change in files being equal (which we use to detect converged limit values). To circumvent early converge here, we iterate until the number of equal files changes. The worst case scenario is that auto_limit_min is already optimal. Since there is no way to determine that a priori, we will iterate until limit hits the biggest file size, which may be slow. That is why it is important to choose auto_limit_min small enough.

auto_limit_min: Don't use very small values such as 20 (that is 20 bytes). We found that this can converge to a local optimum (converged but too many equal files reported), depending in the structure of the headers of the files you compare. Stick with something like a small multiple of the blocksize of your file system (we use 8K).

Tests

Run nosetests3 (maybe apt install python3-nose before (Debian)).

Benchmarks

You may run the benchmark script to find the best blocksize and number threads and/or processes for hash calculations on your machine.

$ cd findsame/benchmark
$ ./clean.sh
$ ./benchmark.py
$ ./plot.py

This writes test files of various size to benchmark/files and runs a couple of benchmarks (runtime ~10 min for all benchmarks). Make sure to avoid doing any other extensive IO tasks while the benchmarks run, of course.

The default value of "maxsize" in benchmark.py (in the __main__ part) is only some MiB to allow quick testing. This needs to be changed to, say, 1 GiB in order to have meaningful benchmarks.

Bottom line:

• blocksizes below 512 KiB (--blocksize 512K) work best for all file sizes on most systems, even though the variation to worst timings is at most factor 1.25 (e.g. 1 vs. 1.25 seconds)
• multithreading (-t/--nthreads): up to 2x speedup on dual-core box -- very efficient, use NTHREADS = number of cores for good baseline performance (problem is mostly IO-bound)
• multiprocessing (-p/--nprocs): less efficient speedup, but on some systems NPROCS + NTHREADS is even a bit faster than NTHREADS alone, testing is mandatory
• we have a linear increase of runtime with filesize, of course

Tested systems:

• Lenovo E330, Samsung 840 Evo SSD, Core i3-3120M (2 cores, 2 threads / core)

• Lenovo X230, Samsung 840 Evo SSD, Core i5-3210M (2 cores, 2 threads / core)

  • best blocksizes = 256K
  • speedups: NPROCS=2: 1.5, NTHREADS=2..3: 1.9, no gain when using NPROCS+NTHREADS

• FreeNAS 11 (FreeBSD 11.0), ZFS mirror WD Red WD40EFRX, Intel Celeron J3160 (4 cores, 1 thread / core)

  • best blocksizes = 80K
  • speedups: NPROCS=3..4: 2.1..2.2, NTHREADS=4..6: 2.6..2.7, NPROCS=3..4,NTHREADS=4: 3

Output modes

Default (-o3)

The default output format is -o3 (same as the initial example above).

$ findsame -o3 data | jq .
{
  "dir:empty": [
    [
      "data/dir2/empty_dir",
      "data/dir2/empty_dir_copy",
      "data/empty_dir",
      "data/empty_dir_copy"
    ]
  ],
  "dir": [
    [
      "data/dir1",
      "data/dir1_copy"
    ]
  ],
  "file:empty": [
    [
      "data/dir2/empty_dir/empty_file",
      "data/dir2/empty_dir_copy/empty_file",
      "data/empty_dir/empty_file",
      "data/empty_dir_copy/empty_file",
      "data/empty_file",
      "data/empty_file_copy"
    ]
  ],
  "file": [
    [
      "data/dir1/file2",
      "data/dir1/file2_copy",
      "data/dir1_copy/file2",
      "data/dir1_copy/file2_copy",
      "data/file2"
    ],
    [
      "data/lena.png",
      "data/lena_copy.png"
    ],
    [
      "data/file1",
      "data/file1_copy"
    ]
  ]
}

Output with hashes (-o2)

$ findsame -o2 data | jq .
{
  "da39a3ee5e6b4b0d3255bfef95601890afd80709": {
    "dir:empty": [
      "data/dir2/empty_dir",
      "data/dir2/empty_dir_copy",
      "data/empty_dir",
      "data/empty_dir_copy"
    ],
    "file:empty": [
      "data/dir2/empty_dir/empty_file",
      "data/dir2/empty_dir_copy/empty_file",
      "data/empty_dir/empty_file",
      "data/empty_dir_copy/empty_file",
      "data/empty_file",
      "data/empty_file_copy"
    ]
  },
  "55341fe74a3497b53438f9b724b3e8cdaf728edc": {
    "dir": [
      "data/dir1",
      "data/dir1_copy"
    ]
  },
  "9619a9b308cdebee40f6cef018fef0f4d0de2939": {
    "file": [
      "data/dir1/file2",
      "data/dir1/file2_copy",
      "data/dir1_copy/file2",
      "data/dir1_copy/file2_copy",
      "data/file2"
    ]
  },
  "0a96c2e755258bd46abdde729f8ee97d234dd04e": {
    "file": [
      "data/lena.png",
      "data/lena_copy.png"
    ]
  },
  "312382290f4f71e7fb7f00449fb529fce3b8ec95": {
    "file": [
      "data/file1",
      "data/file1_copy"
    ]
  }
}

The output is one dict (json object) where all same-hash files/dirs are found at the same key (hash).

Dict values (-o1)

The format -o1 lists only the dict values from -o2, i.e. a list of dicts.

$ findsame -o1 data | jq .
[
  {
    "dir:empty": [
      "data/dir2/empty_dir",
      "data/dir2/empty_dir_copy",
      "data/empty_dir",
      "data/empty_dir_copy"
    ],
    "file:empty": [
      "data/dir2/empty_dir/empty_file",
      "data/dir2/empty_dir_copy/empty_file",
      "data/empty_dir/empty_file",
      "data/empty_dir_copy/empty_file",
      "data/empty_file",
      "data/empty_file_copy"
    ]
  },
  {
    "dir": [
      "data/dir1",
      "data/dir1_copy"
    ]
  },
  {
    "file": [
      "data/file1",
      "data/file1_copy"
    ]
  },
  {
    "file": [
      "data/dir1/file2",
      "data/dir1/file2_copy",
      "data/dir1_copy/file2",
      "data/dir1_copy/file2_copy",
      "data/file2"
    ]
  },
  {
    "file": [
      "data/lena.png",
      "data/lena_copy.png"
    ]
  }
]

More usage examples

Here we show examples of common post-processing tasks using jq. When the jq command works for all three output modes, we don't specify the -o option.

Count the total number of all equals:

$ findsame data | jq '.[]|.[]|.[]' | wc -l

Find only groups of equal dirs:

$ findsame -o1 data | jq '.[]|select(.dir)|.dir'
$ findsame -o2 data | jq '.[]|select(.dir)|.dir'
$ findsame -o3 data | jq '.dir|.[]'
[
  "data/dir1",
  "data/dir1_copy"
]

Groups of equal files:

$ findsame -o1 data | jq '.[]|select(.file)|.file'
$ findsame -o2 data | jq '.[]|select(.file)|.file'
$ findsame -o3 data | jq '.file|.[]'
[
  "data/dir1/file2",
  "data/dir1/file2_copy",
  "data/dir1_copy/file2",
  "data/dir1_copy/file2_copy",
  "data/file2"
]
[
  "data/lena.png",
  "data/lena_copy.png"
]
[
  "data/file1",
  "data/file1_copy"
]

Find the first element in a group of equal items (file or dir):

$ findsame data | jq '.[]|.[]|[.[0]]'
[
  "data/lena.png"
]
[
  "data/dir2/empty_dir"
]
[
  "data/dir2/empty_dir/empty_file"
]
[
  "data/dir1/file2"
]
[
  "data/file1"
]
[
  "data/dir1"
]

or more compact w/o the length-1 list:

$ findsame data | jq '.[]|.[]|.[0]'
"data/dir2/empty_dir"
"data/dir2/empty_dir/empty_file"
"data/dir1/file2"
"data/lena.png"
"data/file1"
"data/dir1"

Find all but the first element in a group of equal items (file or dir):

$ findsame data | jq '.[]|.[]|.[1:]'
[
  "data/dir1_copy"
]
[
  "data/lena_copy.png"
]
[
  "data/dir1/file2_copy",
  "data/dir1_copy/file2",
  "data/dir1_copy/file2_copy",
  "data/file2"
]
[
  "data/dir2/empty_dir_copy/empty_file",
  "data/empty_dir/empty_file",
  "data/empty_dir_copy/empty_file",
  "data/empty_file",
  "data/empty_file_copy"
]
[
  "data/dir2/empty_dir_copy",
  "data/empty_dir",
  "data/empty_dir_copy"
]
[
  "data/file1_copy"
]

And more compact:

$ findsame data | jq '.[]|.[]|.[1:]|.[]'
"data/file1_copy"
"data/dir1/file2_copy"
"data/dir1_copy/file2"
"data/dir1_copy/file2_copy"
"data/file2"
"data/lena_copy.png"
"data/dir2/empty_dir_copy/empty_file"
"data/empty_dir/empty_file"
"data/empty_dir_copy/empty_file"
"data/empty_file"
"data/empty_file_copy"
"data/dir2/empty_dir_copy"
"data/empty_dir"
"data/empty_dir_copy"
"data/dir1_copy"

The last one can be used to remove all but the first in a group of equal files/dirs:

$ findsame data | jq '.[]|.[]|.[1:]|.[]' | xargs cp -rvt duplicates/

jq trick: preserve color in less(1):

$ findsame data | jq . -C | less -R

Other tools

• fdupes
• findup from fslint