Skip to main content

Delete Duplicate Files From the Command-line with .NET

Having run into a scenario where I had directories with many duplicate files, I just hacked up a simple command-line solution based on crypto signatures. It's the same idea used in source control systems like Git and Mercurial, basically the SHA-1 hash of a file's contents.

Sample usage:

DupDel.exe [target-directory]

The utility will recursively analyze any sub-directories under the target directory and build an index of all files based on their content. Once complete, duplicates are processed in an interactive manner where the user is presented with a choice of which duplicate to keep

Keep which of the following duplicates:
1. \Some foo.txt
2. \bar\some other foo.doc
>

The types of files under the target directory are not important, so you can pass in directories to documents, music files, pictures, etc. My computer churned through 30 GB of data in about 5 minutes, so it's reasonably fast.

Labels:

Comments

Clinty Clint said…
The link to DelDup is 404ing! :)
August 31, 2012 at 11:53 PM
Sandro Magi said…
Thanks! Turns out it was actually DupDel.zip.
September 1, 2012 at 9:24 AM
Post a Comment

Popular posts from this blog

async.h - asynchronous, stackless subroutines in C

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C! Features: It's 100% portable C. It requires very little state (2 bytes). It's not dependent on an OS. It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved. #include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground bre...
1 comment
Read more

Easy Automatic Differentiation in C#

I've recently been researching optimization and automatic differentiation (AD) , and decided to take a crack at distilling its essence in C#. Note that automatic differentiation (AD) is different than numerical differentiation . Math.NET already provides excellent support for numerical differentiation . C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries . Neither of these are suitable for learning how AD works. So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique. What is Automatic Differentiation? Simply put, automatic differentiation is a technique for calcu...
Post a Comment
Read more

Easy Reverse Mode Automatic Differentiation in C#

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading , this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important. Why Reverse Mode Automatic Differentiation? As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary! Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type: R → R N That is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario: R N → R That is, functions of many parameters t...
18 comments
Read more