Small files

Millions of small files are a huge problem on any filesystem. You may think /scratch, being a fast filesystem, doesn’t have this problem, but it’s actually worse here. Lustre (scratch) as like an object store, and stores files separately from medatata. This means that each file access requires multiple different network requests, and making a lot of files brings your research (and managing the cluster) to a halt. What counts as a lot? Your default quota is 1e6 files. 1e4 for a project is not a lot. 1e6 for a single project is.

You may have been directed here because you have a lot of files. In that case, welcome to the world of big data, even if your total size isn’t that much! (it’s not just size, but difficulty of handling using normal tools) Please read this and see what you can learn, and ask us if you need help.

This page is mostly done, but specific examples could be expanded.

See also:


The problem with small files

You know Lustre is high performance and fast. But, there is a relatively high overhead for accessing each file. Below, you can see some sample transfer rates, and you can see that total performance drops drastically when files get small. (These numbers were for the pre-2016 Lustre system, it’s better now but the same principle applies.) This isn’t just a problem when you are trying to read files, it’s also a problem when managing, moving, migrating, etc.

File size

Net transfer rate, many files of this size


1100 MB/s


990 MB/s






.04 MB/s

Why do people make millions of small files?

We understand there reasons people make lots of files: it’s convenient. Here are some of the common problems (and alternative solutions) people may be trying to solve with lots of files.

  • Flat files are universal format. If you have everything in its own file, then any other program can look at any data individually. It’s convenient. This is a fast way to get started and use things.

  • Compatibility with other programs. Same as above.

  • Ability to use standard unix shell tools. Maybe your whole preprocessing pipeline is putting each piece of data in its own file and running different standard programs on it. It’s the Unix way, after all. Using filesystem as your index. Let’s say you have a program that reads/writes data which is selected by different keys. It needs to locate the data for each key separately. It’s convenient to put all of these in their own files: this takes the role of a database index, and you simply open the file with the name of the key you need. But the filesystem is not a good index.

    • Once you get too many files, a database is the right tool for the job. There are databases which operate as single files, so it’s actually very easy.

  • Concurrency: you use filesystem as the concurrency layer. You submit a bunch of jobs, each job writes data to its own file. Thus, you don’t have to worry about problems with appending to the same file/database synchronization/locking/etc. This is actually a very common reason.

    • This is a big one. The filesystem is the most reliable way to join the output of different jobs (for example an array job), and it’s hard to find a better strategy. It’s reasonable to keep doing this, and combine job outputs in a second stage to reduce the number of files

  • Safety/security: the filesystem isolates different files from each other, so if you modify one, there’s less chance of corrupting any other ones. This goes right along with the reason above.

  • You only access a few files at a time in your day to day work, so you never realize there’s a problem. However, when we try to manage data (migrate, move, etc), then a problem comes up.

  • Realize that forking processes has similar overhead. Small reads are also non-ideal, but less bad(?).


  • Realize you will have to have to change you workflow. You can’t do everything with grep, sort, wc, etc. anymore. Congratulations, you have big data.

  • Consider right strategy for your program: a serious program should provide options for this.

    • For example, I’ve seen some machine learning frameworks which provide an option to compress all the input data into a single file that is optimized for reading. This is precisely designed for this type of case. You could read all the files individually, but it’ll be slower. So in this case, one should first read the documentation and see there’s a solution. One would take all the original files and make the processed input files. Then, take the original training data, package it together in one compressed archive for long-term storage. If you need to look at individual input files, you can always decompress one by one.

  • Split - combine - analyze

    • Continue like you have been doing: each (array?) job makes different output files. Then, after running, combine the outputs into one file/database. Clean up/archive the intermediate files. Use this combined DB/file to analyze the data in the long term. This is perhaps the easiest way to adapt your workflow.

  • HDF5: especially for numerical data, this is a good format for combining your results. It is like a filesystem within a file, you can still name your data based on different keys for individual access.

  • Unpack to local disk, pack to scratch when done.

    • Main article: Compute node local drives,

    • This strategy can be combined with many of the other strategies below

    • This strategy is especially good when your data is write-once-read-many. You package all of your original data into one convenient archive, and unpack it to the local disk when you need it. You delete it when you are done.

  • Use a proper database suitable for your domain (sqlite): Storing lots of small data where anything can be quickly findable and you can do computation efficiently is exactly what databases do. It can be difficult to have a general purpose database work for you, but there are a wide variety of special-purposes databases these days. Could one of them be suitable for storing the results of your computation for analysis?

    • Note that if you are really doing high-performance random IO, putting a database on scratch is not a good idea, and you need to think more.

    • Consider combining this with local disk: You can copy your pre-created database file to local disk and do all the random access you need. Delete when done. You can do modification/changes directly on scratch if you want.

  • key-value stores: A string key stores arbitrary data.

    • This is a more general database, basically. It stores arbitrary data for a certain key.

  • Read all data to memory.

    • A strategy for using many files. Combine all data into one file, read them all into memory, then do the random access in memory.

  • Compress them down when done.

    • It’s pretty obvious: when you are done with files, compress all of them into one. You have the archive and can always unpack when needed. You should especially at least do this when you are done with a project: if everyone did this, the biggest problems could be solved.

  • Make sure you have proper backups for large files, mutating files introduces risks!

    • If you do go using these strategies, make sure you don’t accidentally lose something you need. Have backups (even if it’s on scratch: backup your database files)

  • If you do have to keep many small flies, check the link above for lustre performance tuning.

  • If you have other programs that can only operate on separate files

    • This is a tough situation, investigate what you can do combining the strategies above. At least you can pack up when done, and possibly copying to local disk while you are accessing is a good idea.

  • MPI-I/O: if you are writing your own MPI programs, this can parallelize output

Specific example: HDF5 for numerical data, or some database

HDF5 is essentially a database for numerical data. You open a HDF5 file and access different data by path - the path is like a filename. There are libraries for accessing this data from all relevant programming languages.

If you have some other data that is structured, there are other databases that will work. For example, sqlite is a single-file, serverless database for relational data, and there are other similar things for time serieses or graphs.

Specific example: Unpacking to local disk

You can see examples at compute node local drives

Specific example: Key-value stores

Let’s say you have written all your own code and want an alternative to files. Instead, use a key-value database. You open one file, and store your file contents under different keys. When you need the data out, you request it by that key again. The keys take the place of filenames. Anytime you would open files, you just access from these key-value stores. You also have ways of dumping and restoring the data if you need to analyze it from different programs.

Performance tuning for small files

See here: Data storage on the Lustre file system