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Wordmap is a simple way to lookup data directly from disk, bypassing RAM. It uses pread (no buffering), and takes advantage of SSD's constant seek time. The data is stored in equal size "cells" making it easy to calculate where things are located based on vectors.

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RSpec

Wordmap

A simple way to look up UTF-8 strings on disk by key(s) or index-powered queries without using any RAM.

Useful in cases where:

  • RAM is more important than data access speed (1-3k reads/sec depending on SSD)
  • Data is read-only (perhaps vendored with your repo)
  • Your dataset might have many values, but they are not outrageously long or varied in size (the biggest value defines the "cell" size in the wordmap, and all others are padded to it)

Installation

Note: Requires at least ruby 2.5 to support File#pread function.

Add this line to your application's Gemfile:

gem 'wordmap'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install wordmap

Usage

Before we can query a wordmap, we must create one.

Creating

Imagine you are storing fruit prices in cents, and fruits are also indexed by color and genus.

entries = { 'banana' => '14',     'lemon' => '49' }
color   = { 'banana' => 'yellow', 'lemon' => 'yellow' }
genus   = { 'banana' => 'musa',   'lemon' => 'citrus' }

Now given the above 3 hashes, you can create a wordmap like this:

Wordmap.create('path/to/fruits.wmap', entries, [:color, :genus]) do |index_name, key, value|
  if index_name == :color
    color[key]
  else
    genus[key]
  end
end

In the above code snippet we specify the path where wordmap will be stored, the data itself (entries), and the optional 3rd argument, which lists any indexes we'd like to create. If a 3rd argument is given, then you must also supply a block. In this block you will get each index_name, key, and value combination from your entries, and your job is to return the corresponding index key for that combination.

Querying

You can query a wordmap in 3 different ways.

1. By key(s)

fruits = Wordmap.new('path/to/fruits.wmap')
wordmap['banana'] # => ['14']

2. By query

fruits = Wordmap.new('path/to/fruits.wmap')

# Give me prices for banana and lemon.
fruits.query(%w[banana lemon]).to_a # => ["14", "49"]

# Give me prices for all yellow fruits.
fruits.query([:color, 'yellow']).to_a # => ["14", "49"]

# Give me prices for all citrus and musa fruits.
fruits.query([:genus, 'citrus', 'musa']).to_a # => ["14", "49"]

# Give me prices for all yellow citruses.
fruits.query([:genus, 'citrus'], [:color, 'yellow']).to_a # => ["49"]

# Out of lemon and banana, give me prices for only citrus ones.
fruits.query(%w[lemon banana], [:genus, 'citrus']).to_a # => ["49"]

Each query is an array of arrays (outer array is omitted in the examples, because it works either way). Inner arrays are treated like unions (everything in them is OR'ed). Outer array is treated as an intersection (results of inner arrays are AND'ed with one another). Order of arrays doesn't matter.

If an inner array starts with a symbol, then we're looking up an index of that name, otherwise — by key(s).

Tip: if you are only supplying 1 array (as in the first and second examples above), you can drop all array wrappers entirely.

fruits.query('banana', 'lemon')
fruits.query(:color, 'yellow')

Result format

The result is always a lazy enumerable of UTF-8 strings, which is why you see me call .to_a on each of them. Wordmap is trying to read files as lazily as possible.

Results order

The result values are in the order of how data is arranged in the wordmap's data file, which itself is based on lexicographical sorting of keys.

3. Sequentially

If you just want to read all values sequentially, you can treat a wordmap as an Enumerable.

fruits.select { |price| price.to_i < 40 } # => ["14"]

You have access to .each method, but it also accepts an argument. If you pass an integer, you can iterate over a single vector (a key dimension, see Multi-dimensional keys ). Since fruits is 1-dimensional array, you can only iterate over 0th dimension.

fruits.each.to_a # => ["14", "49"]
fruits.each(0).to_a # => ["banana", "lemon"]

You can pass a symbol to iterate sequentially over index keys.

fruits.each(:genus).to_a # => ["citrus", "musa"]

Multi-dimensional keys

In the above examples the keys are simply 'banana' and 'lemon' — strings. If you make your key an array of strings, that'd make a multi-dimensional key. This can come helpful for some data where 2 keys make sense (we have such use cases at Scott's). Internally, each dimension is a different vector. However if you go that route, keep in mind that all the "unused" key combinations will create gaps in the data file, therefore inflating its size. For example, if you make a key out of genus + name of a fruit, like %w[citrus lemon] and %w[musa banana], your file will become inflated with empty cells created for keys %w[citrus banana] and %w[musa lemon]. That space is taken (padded with null bytes) even though there are no values for these keys.

Anatomy

For those interested, here's some high level implementation and structure overview.

Staying out of RAM

When you initialize a wordmap object in ruby, it opens a few file descriptors, and reads a few integers of metadata from each file. Nothing else is loaded.

When making a look up, wordmap seeks and reads just the needed bytes in the file using File#pread function. This avoids any caching or preloading of data into RAM.

Structure

A wordmap on disk is just a directory with a few files in it. The files are formatted in a content addressable way similar to "words" in computer memory.

data file

The data file is where your entries are stored. When a wordmap is created, it iterates through your input hash of data, and finds the longest entry. This entry determines the size of a single cell in the data file, which means that all other entries are padded to this size. (A cell is just a padded entry. It's like a spreadsheet where all cells must be equal length.) Once we dump all the cells with your entires into the data file, we can easily find each cell by its sequential index, because it's just index times cell size.

For example, let's take solar system's planet names. The longest name is 7 chars, so all other names are left-padded to 7 chars. Here I'm padding with spaces, but in wordmap they'd be padded with null bytes instead.

Mercury  Earth   MarsJupiter Saturn Uranus Neptune

Now to find the 3rd item, we can just 2 * 7 = 14. We seek to 14th byte position and read 7 bytes to get Mars. Then we trim the padding to get Mars.

The important part is the order of data in this file. When a wordmap is created, all the keys are sorted lexicographically, and for every key, entry is written in the order of how the corresponding keys are sorted. This means that if we know index of where a key is positioned sequentially, we also know index of where the cell is in the data file.

vec files

Vector files are structured the same as data file, but they store keys instead of entries. If you used a 1-dimensional key, then it creates just one vector file. Since this file is sorted, we can apply binary-search to find a key in this file, and then seek to corresponding position in the data file to find the entry.

For multi-dimensional keys, multiple vector files are created (one per dimension). Let's say we have a 2-dimensional key (a key that's an array of 2 strings). The first vector will contain all the first strings, and second all the second strings of all keys. Now when wordmap is doing a lookup by key, it will first bsearch the first vector, then bsearch the second vector. The 2 found positions are then multiplied by entry's cell size and added together to get the exact location of the cell in the data file.

Metadata

Data and vector files each have a couple of numbers at the beginning that specify cells' bytesize and count. This is the only part that wordmap reads into RAM when instantiated. Having read these 2 integers, we can derive 2 additional pieces of information:

  1. the bytesize of the metadata itself, so that we can skip over it
  2. how many cells we should read every time we read a lot of cells (to optimize sequential reads)

The latter is always trying to be near ~10kb per read (unless a single cell is longer than 10kb, then it's using single cell's size).

Indexes

Indexes are just recursively-nested wordmaps inside the wordmap you create. These nested wordmaps have index keys as the keys, and lists of locations as values. The values of indexes are invisible to the end user, but since this section is about anatomy, it makes sense to mention them.

The locations are stored as a comma-separated list of delta encoded sorted integers and ranges. For example, if we are storing locations 1,3,5,6,7,8,12,15 the stored value will look like this: 1,2,2+3,4,3. You can unpack this value by saying "first position is 1, second position is 1 + 2 = 3, third position is 3 + 2 = 5, now add 3 more successively: 6,7,8, then 8 + 4 = 12, and 12 + 3 = 15".

When looking up a query, wordmap produces lazy iterators for unioning and intersecting data. These iterators lazily walk indexed locations, or keys in a vector file, and return each found entry from the data file.

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/wordmap. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

Code of Conduct

Everyone interacting in the Wordmap project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

About

Wordmap is a simple way to lookup data directly from disk, bypassing RAM. It uses pread (no buffering), and takes advantage of SSD's constant seek time. The data is stored in equal size "cells" making it easy to calculate where things are located based on vectors.

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