Emdrive

Database management system for fast similarity search within metric spaces, written in Rust.

Data types

Name	Description	Size on disk	Value bounds
`UINT8`	unsigned 8-bit integer	1 byte	≥ 0 and < 2⁸
`UINT16`	unsigned 16-bit integer	2 bytes	≥ 0 and < 2¹⁶
`UINT32`	unsigned 32-bit integer	4 bytes	≥ 0 and < 2³²
`UINT64`	unsigned 64-bit integer	8 bytes	≥ 0 and < 2⁶⁴
`UINT128`	unsigned 128-bit integer	16 bytes	≥ 0 and < 2¹²⁸
`BOOL`	boolean value	1 byte	either `TRUE` (non-zero) or `FALSE` (zero)
`TIMESTAMP`	number of milliseconds since Unix epoch, saved in a signed 64-bit integer	8 bytes	≥ 2⁶³ ms before Unix epoch and < 2⁶³ ms after Unix epoch (around 292 million years in either direction)
`UUID`	UUID-like value	16 bytes	any sequence of 128 bits
`STRING(n)`	UTF-8 string	2+n bytes	≤ `n` characters, where `n` ≤ 2048

Emdrive types are non-nullable by default. They can made so simply by wrapping them in NULLABLE(). For instance, a nullable string of maximum length 20 is NULLABLE(STRING(20)).

Indexes

Name	Category	Description	Data types	Supported operators
`btree`	general	B+ tree	all	`=` (equality)
`emtree`	metric	EM-tree	depending on chosen metric	`@` (distance)

Metrics

Name	Description	Column types
`hamming`	Hamming distance	`UINT*`

Story

Let's imagine you're running an image search engine. As a fan of geese you called it Gaggle.
Being a search engine operator, you run a bot which crawls pages on the internet. Every time the bot sees an image, it computes a perceptual hash of it and saves it, along with some other metadata, to an Emdrive instance.

We'll be using database gaggle. A relevant table schema here may be:

Note that column hash is marked with METRIC KEY USING hamming!
While a primary key is B+ tree-based and allows for quick general lookups of rows, it's useless for distance queries. An EM-tree-based metric key does the job very well though. In this case, as we're comparing perceptual hashes in integer form, Hamming distance is the most relevant metric.

Oh, your bot has just seen a new image! Let's register it:

Now, look, a user just uploaded their image to see similar occurences of it from the internet. The search engine calculated that image's hash to be 0b00001011 (binary representation of decimal 11).
Let's check that against Emdrive. We'll be using the @ distance operator, which always returns a number and is exclusively supported for METRIC KEY columns.

It's a match! The image we saved previously has a similar hash, and we can now show it in search results.

`url`	`distance`
`"https://twixes.com/a.png"`	`3`

Data storage

Every table has a data file containing all its, well, data. Such data files are made up of pages.

Launch configuration

The following launch configuration settings are available for Emdrive instances. They are applied on instance launch from environment variables in the format EMDRIVE_${SETTING_NAME_UPPERCASE} (i.e. setting data_directory is set with variable EMDRIVE_DATA_DIRECTORY). If a setting's environment variable is not set, its default value will be used.

Name	Type	Default value	Description
`data_directory`	`STRING`	`"/var/lib/emdrive/data"`	Location of all data, including system tables
`http_listen_host`	`STRING`	`"127.0.0.1"`	Host on which the HTTP server will listen
`http_listen_port`	`UINT16`	`8824`	Port on which the HTTP server will listen

Search

SQL

HTTP interface

Benchmarks

Postgres	MySQL	ClickHouse	⚡️ Emdrive

Autogenerated IDs

Emdrive has no serial or auto-increment data type. For entity IDs, ULID is the recommended solution in Emdrive. It's UUID-like, meaning it fits into the UUID data type, and can be generated with function ULID().