High Performant and Bottomless database
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Exabase provides support for these features:
- High performance
- Bottomless storage.
- Consistency.
exabase is embedded and scales as you scale your app. instances are light as they are tracked to use less memory and disk footprint.
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Exabase achieves a high degree of efficiency by employing the following techniques.
- Separation of concerns mechanism across tables. This allows for more efficiency by keeping each table manager in it own space.
- Exabase make an extensive use efficient algorithms for storage and query of data.
- Consistency and Durability in log files and other very important files is achieved through an ACID complaint data processing mechanism.
- Exabase achieves a high search query efficiency using a custom search indexing mechanism called Xtree.
Exabase support server-side Javascript runtime like:
- Nodejs.
- Bunjs.
- Denojs.
Native support to be written in the ig programming language. this is still a prototypal phase.
Install Exabase right on your project using npm.
npm i exabase@latest --save
The Exabase class accepts an object argument with the following options:
Exabase is queried with json. in the format
{
table: string;
operation?: {
dropTable?: boolean;
createTable?: boolean;
}
sort?: {
[x in keyof Partial<Model>]: "ASC" | "DESC";
};
get?: {
[field: string]: {
eq?: any;
lt?: any;
gt?: any;
lte?: any;
gte?: any;
like?: string;
};
};
insert?: Record<string, any>;
update?: Partial<Model>;
delete?: boolean;
get?: boolean;
count?: boolean;
skip?: number;
take?: number;
consistency?: "strong" | "eventual";
};const user = await db.query(
JSON.stringify({
table: "USER",
insert: { name: "james bond" },
})
);
const user2 = await db.query(JSON.stringify({ table: "USER", one: user._id }));
expect(user.name).toBe("james bond");
const user3 = (
await db.query(
JSON.stringify({
table: "USER",
search: { name: user.name },
})
)
)[0];
const user4 = await db.query(
JSON.stringify({
table: "USER",
update: { ...user, name: "gregs pola", age: 47 },
})
);
await db.query(JSON.stringify({ table: "USER", delete: user._id }));
const user5 = await db.query(JSON.stringify({ table: "USER", one: user._id }));
expect(user._id).toBe(user2._id);
expect(user._id).toBe(user3._id);
expect(user._id).toBe(user4._id);
expect(user4.name).toBe("greg pola");
expect(user5).toBe(undefined);import { Exabase } from "../dist/index.js";
const db = new Exabase();
await db.query(
JSON.stringify({
table: "USER",
execute: {
createTable: true,
},
})
);
await db.query(
JSON.stringify({
table: "CHILD",
execute: {
createTable: true,
},
})
);
const user = await db.query(
JSON.stringify({
table: "USER",
insert: {
age: i + 20,
name: "user name",
},
})
);
const kid = await db.query(
JSON.stringify({
table: "CHILD",
insert: {
age: 5,
name: "kid name",
},
})
);
user.kids.push(kid);
await db.query(
JSON.stringify({
table: "USER",
update: user,
})
);This benchmark is Exabase against sqlite.
Sqlite has a tiny footprint and off course really great performance with pure acidity and relational.
cpu: Intel(R) Celeron(R) CPU 4205U @ 1.80GHz runtime: bun 1.0.0 (x64-linux)
benchmark time (avg) (min … max) p75 p99 p995
---
SELECT _ FROM "Employee" Exabase 1.39 µs/iter (1.23 µs … 3.77 µs) 1.35 µs 3.77
µs 3.77 µs SELECT _ FROM "Employee" sqlite 270.73 µs/iter (187.72 µs … 3.24 ms)
267.24 µs 1.19 ms 1.48 ms 150X fasterLog caching is a basic LOG file level cache. this means, it stores the entire LOG(n) file of the table in memory, where n is the last active LOG file.
Open source.
telegram group.
If you contribute code to this project, you are implicitly allowing your code to be distributed under the Apache License. You are also implicitly verifying that all code is your original work.