# ZS: a file format for compressed sets¶

ZS is a simple, read-only, binary file format designed for distributing, querying, and archiving arbitarily large data sets (up to tens of terabytes and beyond) – so long as those data sets can be represented as a set of arbitrary binary records. Of course it works on small data sets too. You can think of it as a replacement for files stored in tab- or comma-separated format – each line in such a file becomes a record in a ZS file. But ZS has a number of advantages over these traditional formats:

• ZS files are small: ZS files (optionally) store data in compressed form. The 3-gram counts from the 2012 US English release of the Google N-grams are distributed as a set of gzipped text files in tab-separated format, and take 1.3 terabytes of space. Uncompressed, this data set comes to more than 9 terabytes (and would be even more if loaded into a database). The same data in a ZS file with the default settings (bzip2 compression) takes just 0.8 terabytes – more than 35% smaller than the current distribution format, and 11x smaller than the raw data.
• Nonetheless, ZS files are fast: ZS files allow decompression to be parallelized over multiple CPUs, which is not possible with traditional compression formats like gzip. And this is important, because decompression is a slow and inherently serial operation. Using a fast compute server for measurements, we found that gunzip can spit out 3-gram data at ~180 MiB/s. Google distributes these counts in many separate files; one of these, for example, contains just the 3-grams that begin with the letters “th”. On our compute server, decompressing just this one file takes 47 minutes.

Bzip2 decompression on its own is quite a bit slower than gunzip, but with 8 CPUs and using Python’s relatively crude parallelization facilities, the same server can decompress our smaller ZS file at 210 MiB/s, and a careful implementation should scale nearly linearly with the number of CPUs. And of course, we have the option of choosing many different locations on the space/speed tradeoff curve: if we used gzip compression in our ZS file, it’d be roughly the same size as the current distribution format, but would decompress multiple times faster; or with LZMA compression (which will probably become the ZS default soon), decompression will be roughly twice as fast as for bzip2, and produce even smaller files.

• In fact, ZS files are really, REALLY fast: Suppose we want to know how many different Google-scanned books published in the USA in 1955 used the phrase “this is fun”. ZS files have a limited indexing ability that lets you quickly locate any arbitrary span of records that fall within a given sorted range, or share a certain textual prefix. This isn’t as nice as a full-fledged database system that can query on any column, but it can be extremely useful for data sets where the first column (or first several columns) are usually used for lookup. Using our example file, finding the “this is fun” entry takes 5 disk seeks and ~20 milliseconds of CPU time – something like 80 ms all told. (And hot cache performance – e.g., when performing repeated queries in the same file – is even better. The answer is 27 books.) When this data is stored as gzipped text, then only way to locate an individual record, or span of similar records, is start decompressing the file from the beginning and wait until the records we want happen to scroll by, which in this case – as noted above – could take more than 45 minutes. Here, using ZS is ~35,000x faster.

• ZS files contain rich metadata: In addition to the raw data records, every ZS file contains a set of structured metadata in the form of an arbitrary JSON document. You can use this to store information about this file’s record format (e.g., column names), notes on data collection or preprocessing steps, recommended citation information, or whatever you like, and be confident that it will follow your data where-ever it goes.

• ZS files are network friendly: Suppose you know you just want to look up a few individual records that are buried inside that 0.8 terabyte file, or want a large span of records that are still much smaller than the full file (e.g., all 3-grams that begin “this is”). With ZS, you don’t have to actually download the full 0.8 terabytes of data; given a URL to the file, the ZS tools can efficiently locate and fetch just the parts of the file you need. Of course if you need to make a large number of queries then it’ll be faster (and kinder to whoever’s hosting the file!) to just download it. But there’s no point in throwing around gigabytes of data to answer a kilobyte question.

Try it yourself:

\$ zs dump --prefix='this is fun\t' http://bolete.ucsd.edu/njsmith/google-books-eng-us-all-20120701-3gram.zs
this is fun	1729	1	1
this is fun	1848	1	1
...
this is fun	2008	435	420
this is fun	2009	365	352

• ZS files are ever-vigilant: Computer hardware is simply not reliable, especially on scales of years and terabytes. I’ve dealt with RAID cards that would occasionally flip a single bit in the data that was being read from disk. How confident are you that this won’t be a bit that changes your results? Standard text files provide no mechanism for detecting data corruption. Gzip and other traditional compression formats provide some protection, but it’s only guaranteed to work if you read the entire file from start to finish and then remember to check the error code at the end, every time. ZS, by contrast, protects every bit of data with 64-bit CRC checksums, and the software we distribute will never show you any data that hasn’t first been double-checked for correctness. (Fortunately, the cost of this checking is negligible; all the times quoted above include these checks). If it matters to you whether your analysis gets the right answer, then ZS is a good choice.

• Relying on the ZS format creates minimal risk: The ZS file format is simple and fully documented; an average programmer with access to standard libraries could write a working decompressor in a few hours. The reference implementation is BSD-licensed, undergoes exhaustive automated testing (>98% coverage) after every checkin, and includes an exhaustive file format validator, so you can confirm that your files match the spec and be confident that they will be readable by any compliant implementation.

• ZS files have a name composed entirely of sibilants: How many file formats can say that?

This manual documents the reference implementation of the ZS file format, which includes both a command-line zs tool for manipulating ZS files and a fast and featureful Python API, and also provides a complete specification of the ZS file format in enough detail to allow independent implementations.

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