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Why small files are a curse for big datasets

Imagine you had a large dataset - millions of images or webpages - and aimed to pull it all down to your machine from a distant server. This is a frequent task if you want to spin up machines to train machine learning models or if you were using MapReduce to process a large dataset.

For our example, let's imagine a single machine needs to handle:
1.5 terabytes over 15 million files = 100KB per file

For downloading the dataset, the typical back of the envelope might be (dataset size) / (transfer speed). This would give us:

  • 1.5 TB / 100Mbps = 1.38 days
  • 1.5TB / 1Gbps = 3.33 hours

This is likely far from correct however, possibly by many orders of magnitude, due to the connection overhead of these small files. We'll look at how large an impact this has on a near optimal situation, retrieving files from Amazon S3, and consider how bad it can get in less optimal situations.

By the numbers...

For back of the envelope calculations, there's a small set of numbers you should know. Whilst the numbers do change over time, they don't change enough to strongly impact your equation.

When moving small files, our problems are most likely to come from:

  • 0.5 ms for a round trip within the same datacenter
  • 10 ms for a traditional disk seek
  • 150 ms for a round trip from California to the Netherlands Sydney, Australia

Why Sydney, Australia? First, the numbers are near equivalent, and second, let's just chalk it up to home sickness ^_^

Death by a thousand 15e6 cuts


In the optimal case, we only have to deal with 0.5 ms for a round trip in the same datacenter. Why is this important? A HTTP GET requires between one to three round trips to get rolling (DNS, TCP handshake, TLS tunnel). There are ways of saving time, such as using HTTP KeepAlive to not discard connections when they may be reused, but there's no way of getting around the minimum of one round trip request per file.

For more details on why HTTP is such a bad idea for short lived connections (TCP Slow Start, TCP being built for long lived flows, the verbosity of HTTP headers, etc), check out What to Expect from HTTP/2.

Let's imagine we're in an idealistic world where, once you've made a connection to the storage server, downloads are instantaneous and free. We're also super lucky in that we only need a single connection for each HTTP GET. Even in this dreamland, sequentially retrieving 15 million small records from a server in the same datacenter would take (0.5 ms * 15e6 =) 2.08 hours.

2.08 hours in connection overhead to get 15e6 million files sequentially at 0.5 ms per roundtrip

Now imagine that you're unlucky enough to have servers in California but live in Sydney, Australia...

625 hours in connection overhead to get 15e6 million files sequentially at 150 ms per roundtrip

Even in the optimal case, San Francisco is around 12,000 kilometres from Sydney, Australia. Even for light itself, it would take 40 ms to travel that distance.

Sure, you might be able to save a few miliseconds if we burrowed a hole through the middle of the earth, but...


You're super intelligent though, so you'll be parallelizing those downloads.

The connection overhead can be partially overcome by downloading many files in parallel. This technique is frequently used in web crawling for example, retrieving these small HTML files from dozens or hundreds of different servers at the same time.

Still, it takes time for a new connection to work out the maximum speed at which it can reliably send data - the TCP Slow Start problem mentioned earlier. It's also hard to perfectly parallelize an implementation, increasing complexity and load on both ends of the connection.

Luckily Amazon S3 is well optimized for the small file use case with your files partitioned over many servers for free. For best results, there's a set of best practices you can follow such as prefacing keys with a hash for assisting with load balancing.

As the real world is dark and full of terrors, you might be unlucky enough to have a spinning disk at the other end however. This decimates any of the advantages of your concurrent approach.

Given a spinning disk takes 10 ms to perform a random seek, that would limit you to retrieving approximately 100 files per second per disk. We can hope that there are either many hard drives or many servers but your luck is not recognised as currency in this establishment...

41.6 hours spent on random seeks on a spinning disk for 15e6 million files

Fixing the problem

Larger files

The easy and obvious solution is to get rid of small files by archiving / compressing them into a single larger file.

If you're transferring the files from one server to another, you can even do this in place, by running tar c some/dir | gzip - | ssh host2 tar xz.

Creating larger files sounds like a perfect solution except that it destroys your ability to perform random access - a deal breaker in many situations.

If you instead concatenate files together by keep an index of where a given file starts and ends, you can have your cake and eat it too. This tactic has been pursued in a number of different ways, including:

Whilst I leave the majority of the investigations up to the reader, I will state a few interesting advantages...

WARC files have the advantage of record level compression whilst still allowing for random lookups. This is possible as the gzip spec actually states that two or more gzip files stuck together should be interpreted as a single gzip file. As such, a WARC file is simply thousands of individually gzipped web pages all stuck together.

The Facebook file format is similar but doesn't use compression due to images already being compressed. Facebook have the additional challenge of files being created and deleted - something which a web archive doesn't need to worry about - so performs garbage collection by compacting haystacks where many of the files may no longer be required.

Anything from running tar on your directory all the way to a complex orchestration such as Facebook's haystack will help you however!


Benchmarking small files on Amazon S3

To see how this can impact you, I performed an experiment on Amazon S3 over 2,038 files. The total file size for this collection is 276MB - quite small given the connection speed available on the machine and the bandwidth provided by Amazon S3.

Experiments were performed both with aws s3 cp and TransferManager from the AWS Java SDK.

Interestingly whilst TransferManager runs in parallel itself, even adding the 2,038 files sequentially to the TransferManager was a bottleneck that required a quick call to Java 8's parallelStream to solve.

For transferring the 2,038 files naively:
2,038 files in 15.9 seconds = 138.87Mbps

Compared to transferring a single 276MB blob:
A single 276MB blob in 2.46 seconds = 897.56MBps

Extrapolated to our full 1.5 terabyte dataset with 15 million files, the transfer times for our dataset are:

  • 23.97 hours when we have our small file problem
  • 3.71 hours when composed into larger files

This is far closer to our initial back of the envelope of (dataset size) / (transfer speed)!


Please think about the small file problem, especially if you don't hate Australians who might be cursed with 150+ ms pings ^_^

Special thanks to Katey Nicosia for the postage stamp image =]