Database management system for fast similarity search within metric spaces, written in Rust.
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 microseconds since Unix epoch, saved in a signed 64-bit integer | 8 bytes | ≥ 2⁶³ µs before Unix epoch and < 2⁶³ µs after Unix epoch (around 292 000 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))
.
Name | Category | Description | Data types | Supported operators |
---|---|---|---|---|
btree |
general | B+ tree | all | = (equality) |
emtree |
metric | EM-tree | depending on chosen metric | @ (distance) |
Name | Description | Column types |
---|---|---|
hamming |
Hamming distance | UINT* |
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:
CREATE TABLE photos_seen (
hash UINT8 METRIC KEY USING mtree(hamming),
url STRING(2048) PRIMARY KEY,
width UINT32,
height UINT32,
seen_at TIMESTAMP
);
Note that column
hash
is marked withMETRIC 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:
INSERT INTO photos_seen (hash, url, width, height, seen_at)
VALUES (0b11001111, 'https://twixes.com/a.png', 1280, 820, '2077-01-01T21:37');
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.
SELECT url, hash @ 0b00001011 AS distance FROM photos_seen WHERE distance < 4;
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 |
$EMDRIVE_DATA_DIRECTORY # /var/lib/emdrive/data by default
└── gaggle/ # database
└── photos_seen/ # table
└── 0 # core table data
Every table has a data
file containing all its, well, data. Such data
files are made up of pages.
The following launch configuration settings are available for Emdrive instances.
They are applied on instance launch from environment variables in the format EMDRIVE_${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 |
Postgres | MySQL | ClickHouse | ⚡️ Emdrive |
---|
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()
.