Скачать или смотреть Optimize Your File Hashing: Calculating MD5 Hash of Large Numbers of Files Recursively

Discover how to enhance performance when `calculating MD5 hashes` of multiple files using Java. Learn effective methods for optimizing file processing!
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This video is based on the question https://stackoverflow.com/q/64846266/ asked by the user 'Somjit' ( https://stackoverflow.com/u/2715083/ ) and on the answer https://stackoverflow.com/a/64848609/ provided by the user 'Olivier Pellier-Cuit' ( https://stackoverflow.com/u/5661651/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Optimize Your File Hashing: Calculating MD5 Hash of Large Numbers of Files Recursively

Processing large numbers of files can often become a bottleneck for developers and system administrators. In particular, calculating checksums, such as MD5 hashes, for numerous files under a root directory can take considerable time—especially when dealing with many large files.

If you've encountered a similar issue in your Java applications, where hashing a substantial number of files proves sluggish, you're not alone.

The Problem

In this scenario, you’ve been using Java's Files.walk() method to traverse files within a directory to calculate their MD5 hashes. The method is indeed straightforward, but it may not be performing as efficiently as you'd expect, especially when you have an old Intel Core i3 CPU processing around 300 images of 5-10 MB each.

Even with the use of parallel streams, you observed that the processing time remained unchanged. The challenge is to figure out how to enhance this process and reduce the time taken to generate MD5 hashes.

Your Current Approach

Your existing code for calculating the MD5 hash looks like this:

[[See Video to Reveal this Text or Code Snippet]]

In this code:

You collect all files under a specified depth.

Filter out directories to focus only on files.

Utilize a parallel stream to possibly speed up the hashing process.

However, as you've found, the parallelization does not yield the expected gains.

A Better Solution

To improve performance, the key is to collect the output of Files.walk(...) into a list first, allowing the streams to be parallelized effectively. Here’s a revised approach:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the New Approach

Collecting First:

By collecting the files into a list, you create a fixed-size collection. This approach allows Java to efficiently determine how to split the work across multiple threads.

Switching to Parallel:

After collecting the directory contents, switching to a parallel stream can effectively use multiple cores of your CPU, enhancing processing speed. Users with multi-core CPUs have reported reductions in processing time by as much as five times on SSDs.

Mapping Hashes:

The map(FileHash::getHash) function remains unchanged. Each file’s path gets its hash calculated.

Final Thoughts

Optimizing how you calculate MD5 hashes is pivotal when dealing with large datasets. By correctly utilizing Java streams and understanding the implications of parallel processing, you can achieve significant performance improvements.

In summary, remember to collect your file paths first before applying parallel processing. This simple adjustment can dramatically enhance your application’s efficiency. Now, go ahead and try it out!