WooriDB is a general purpose (EXPERIMENTAL) time serial database, which means it contains all entities registries indexed by DateTime. It is schemaless, key-value storage and uses its own query syntax that is similar to SparQL and Crux's Datalog.
If you want me to continue improving this project: Patreon link
Some other features are:
- Hashing keys content with
ENCRYPT
keyword. - Hashed values are filtered out and can only be checked with
CHECK
keyword. Ron
schemas for input and output.- JSON is supported via feature.
- Entities are indexed by
entity_name
(Entity Tree),DateTime
(Time Serial) andUuid
(Entity ID). Entity format is a HashMap where keys are strings and values are supportedTypes
. - Stores persistent data locally.
- Able to handle very large numbers when using the
P
suffix.- Ex:
98347883122138743294728345738925783257325789353593473247832493483478935673.9347324783249348347893567393473247832493483478935673P
.
- Ex:
- Configuration is done via environment variables.
- Authentication and Authorization via session token
- Conditional Update
- Some Relation Algebra
- Entity history
Woori
means our
and although I developed this DB initially alone, it is in my culture to call everything that is done for our community and by our community ours.
This project is hugely inspired by:
- Crux;
- Datomic;
- Prometheus
- SparQL.
- Database Internals
- Database System Concept
- Designing Data Intensive Application
- Professor Andy Pavlo Database classes.
- Zero Trust in Time Series Data?
To run WooriDB it is necessary to have Rust installed in the machine. There are two ways to do this:
- Go to rustup.rs and copy the command there, for unix it is
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
. - Clone WooriDB and execute
make setup
.
Release mode performance
:make release
in project root for performance optimization.Release mode size
:make run
in project root for size optimization.Debug mode
:make debug
in project root.
you can find the latest docker image at naomijub/wooridb. The current most stable tag is beta-8
. To execute the docker container run:
docker run -p 1438:1438 naomijubs/wooridb:beta-8 debug
for debug mode.docker run -p 1438:1438 -e AUTH_HASHING_COST=8 -e ADMIN=your-admin-id -e ADMIN_PASSWORD=your-admin-pswd naomijubs/wooridb:beta-8 run
for size optimization.docker run -p 1438:1438 -e AUTH_HASHING_COST=8 -e ADMIN=your-admin-id -e ADMIN_PASSWORD=your-admin-pswd naomijubs/wooridb:beta-8 release
for performance optimization.- All
-e/--env
can be replaced by a--env-file path/to/your/.env
. Your.env
file should contain the following fields:
HASHING_COST=16
PORT=1438
AUTH_HASHING_COST=8
ADMIN=your-admin-id
ADMIN_PASSWORD=your-admin-pswd
- Responses are in
RON
format. Support forJSON
is via--feature json
andEDN
will be done later by using features. - For now only persistent local memory is used. Support for
S3
,Postgres
andDynamoDB
will also be done later by using features. - Precise floats or numbers larger than f64::MAX/i128::MAX can be defined with an UPPERCASE
P
at the end.- Note: This type cannot be updated with
UPDATE CONTENT
. - Ex.:
INSERT {a: 98347883122138743294728345738925783257325789353593473247832493483478935673.9347324783249348347893567393473247832493483478935673P, } INTO my_entity
.
- Note: This type cannot be updated with
BLOB
will not be supported. Check out To BLOB or Not To BLOB: Large Object Storage in a Database or a Filesystem.- To configure hashing cost and port some environment variables are required:
HASHING_COST=16
PORT=1438
2,3 GHz Intel Core i9 8-Core 32 GB 2667 MHz DDR4
create_entity
time: [4.7008 ms 4.7367 ms 4.7725 ms]insert_entity
time: [12.647 ms 12.977 ms 13.308 ms]update_set_entity
time: [14.896 ms 15.087 ms 15.309 ms]update_content_entity
time: [14.871 ms 15.070 ms 15.307 ms]delete_entity
time: [5.3966 ms 5.4423 ms 5.4908 ms] - Filtered 400sevict_entity_id
time: [15.534 ms 15.623 ms 15.721 ms] - Filtered 400sevict_entity
time: [12.318 ms 12.416 ms 12.540 ms] - Filtered 400sselect_all
20 entities time: [6.3402 ms 6.4743 ms 6.6356 ms]select_all
10 entities time: [5.8318 ms 5.9682 ms 6.1340 ms]select_all
1 entity time: [5.1030 ms 5.1379 ms 5.1738 ms]history_10_registries_for_entity
time: [5.4936 ms 5.5328 ms 5.5725 ms]history_20_registries_for_entity
time: [6.0676 ms 6.1049 ms 6.1429 ms]
2,3 GHz Intel Core i9 8-Core 32 GB 2667 MHz DDR4
create_entity
time: [433.57 ns 435.00 ns 436.38 ns]inser_entity
time: [1.6349 us 1.6406 us 1.6463 us]select_all
time: [429.79 ns 431.05 ns 432.14 ns]select_args
time: [655.40 ns 657.53 ns 659.71 ns]
For ~10000 entries in a day, the file size will be 2.5 GB for registries (and for cached values - this may become an issue soon). After compression it can be reduced to 10% of this.
2,3 GHz Intel Core i9 8-Core 32 GB 2667 MHz DDR4
Config file:
config:
target: "http://localhost:1438"
phases:
- duration: 100
arrivalRate: 10
defaults:
headers:
Content-Type: "application/wql"
scenarios:
- flow:
- post:
url: "/wql/tx"
body: "INSERT {name: \"name\", last_name: \"last name\", age: 20, blood: 'O'} INTO person"
Contains 1000 registries of {name: \"name\", last_name: \"last name\", age: 20, blood: 'O'}
.
Config file:
config:
target: "http://localhost:1438"
phases:
- duration: 100
arrivalRate: 10
defaults:
headers:
Content-Type: "application/wql"
scenarios:
- flow:
- post:
url: "/wql/query"
body: "SELECT * FROM person"