MyScaleDB

Enable every developer to build production-grade GenAI applications with powerful and familiar SQL.

What is MyScaleDB?

MyScaleDB is the SQL vector database that enables developers to build production-ready and scalable AI applications using familiar SQL. It is built on top of ClickHouse and optimized for AI applications and solutions, allowing developers to effectively manage and process massive volumes of data.

Key benefits of using MyScaleDB include:

Fully SQL-Compatible
- Fast, powerful, and efficient vector search, filtered search, and SQL-vector join queries.
- Use SQL with vector-related functions to interact with MyScaleDB. No need to learn complex new tools or frameworks – stick with what you know and love.
Production-Ready for AI applications
- A unified and time-tested platform to manage and process structured data, text, vector, JSON, geospatial, time-series data, and more. See supported data types and functions
- Improved RAG accuracy by combining vectors with rich metadata and performing high-precision, high-efficiency filtered search at any ratio¹.
Unmatched performance and scalability
- MyScaleDB leverages cutting-edge OLAP database architecture and advanced vector algorithms for lightning-fast vector operations.
- Scale your applications effortlessly and cost-effectively as your data grows.

MyScale Cloud provides fully-managed MyScaleDB with premium features on billion-scale data². Compared with specialized vector databases that use custom APIs, MyScale is more powerful, performant, and cost-effective while remaining simpler to use. This makes it suitable for a large community of programmers. Additionally, when compared to integrated vector databases like PostgreSQL with pgvector or ElasticSearch with vector extensions, MyScale consumes fewer resources and achieves better accuracy and speed for structured and vector joint queries, such as filtered searches.

Why MyScaleDB

Fully SQL compatible
Unified structured and vectorized data management
Millisecond search on billion-scale vectors
Highly reliable & linearly scalable
Hybrid search & complex SQL vector queries

See our documentation and blogs for more about MyScale’s unique features and advantages. Our open-source benchmark provides detailed comparison with other vector database products.

Why build MyScaleDB on top of ClickHouse?

ClickHouse is a popular open-source analytical database that excels at big data processing and analytics due to its columnar storage with advanced compression, skip indexing, and SIMD processing. Unlike transactional databases like PostgreSQL and MySQL, which use row storage and main optimzies for transactional processing, ClickHouse has significantly faster analytical and data scanning speeds.

One of the key operations in combining structured and vector search is filtered search, which involves filtering by other attributes first and then performing vector search on the remaining data. Columnar storage and pre-filtering are crucial for ensuring high accuracy and high performance in filtered search, which is why we chose to build MyScaleDB on top of ClickHouse.

While we have modified ClickHouse's execution and storage engine in many ways to ensure fast and cost-effective SQL vector queries, many of the features (#37893, #38048, #37859, #56728, #58223) related to general SQL processing have been contributed back to the ClickHouse open source community.

Quick Start

MyScale Cloud

The simplest way to use MyScaleDB is to create an instance on MyScale Cloud service. You can start from a free pod supporting 5M 768D vectors. Sign up here and checkout MyScaleDB QuickStart for more instructions.

Self-Hosted

Using MyScaleDB Docker Image

To quickly get a MyScaleDB instance up and running, simply pull and run the latest Docker image:

docker run --name myscaledb myscale/myscaledb:1.4

This will start a MyScaleDB instance with default user default and no password. You can then connect to the database using clickhouse-client:

docker exec -it myscaledb clickhouse-client

Using Docker Compose

Use the following recommended directory structure and the location of the docker-compose.yaml file:

❯ tree myscaledb

myscaledb
├── docker-compose.yaml
└── volumes
    1 directory, 1 file

Define the configuration for your deployment. We recommend starting with the following configuration in your docker-compose.yaml file, which you can adjust based on your specific requirements:

version: '3.7'

services:
  myscaledb:
    image: myscale/myscaledb:1.4
    tty: true
    ports:
      - '8123:8123'
      - '9000:9000'
      - '8998:8998'
      - '9363:9363'
      - '9116:9116'
    volumes:
      - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/data:/var/lib/clickhouse
      - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/log:/var/log/clickhouse-server
    deploy:
      resources:
        limits:
          cpus: "16.00"
          memory: 32Gb

Note: You can also customize the configuration file of MyScaleDB. Copy the /etc/clickhouse-server directory from your myscaledb container to your local drive, modify the configuration, and add a directory mapping to the docker-compose.yaml file to make the configuration take effect:
- ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/config:/etc/clickhouse-server

Use the following command to get it running:

cd myscaledb
docker compose up -d

Access the MyScaleDB command line interface using the following command.

docker exec -it myscaledb-myscaledb-1 clickhouse-client

You can now run SQL statements. See Executing SQL Queries.

Tutorial

See Vector Search Documentation for how to create a SQL table with vector index and perform vector search. It's recommended to specify TYPE SCANN when creating a vector index in open source MyScaleDB.

Create a Table with Vector Column

-- Create a table with body_vector of length 384
CREATE TABLE default.wiki_abstract
(
    `id` UInt64,
    `body` String,
    `title` String,
    `url` String,
    `body_vector` Array(Float32),
    CONSTRAINT check_length CHECK length(body_vector) = 384
)
ENGINE = MergeTree
ORDER BY id;

Insert Data to Your Table

-- Insert data from parquet files on S3
INSERT INTO default.wiki_abstract SELECT * FROM s3('https://myscale-datasets.s3.ap-southeast-1.amazonaws.com/wiki_abstract_with_vector.parquet','Parquet');

Create the Vector Index

-- Build a SCANN vector index with Cosine metric on the body_vector
ALTER TABLE default.wiki_abstract ADD VECTOR INDEX vec_idx body_vector TYPE SCANN('metric_type=Cosine');

-- Query the index build progress from the `vector_indices` table
-- Wait until the index progress becomes `Built`
SELECT * FROM system.vector_indices;

Execute Vector Search

-- Perform vector search return the top-5 results
SELECT
  id,
  title,
  distance(body_vector, [-0.052, -0.0146, -0.0677, -0.0256, -0.0395, -0.0381, -0.025, 0.0911, -0.0429, -0.0592, 0.0017, -0.0358, -0.0464, -0.0189, -0.0192, 0.0544, -0.0022, -0.0292, -0.0474, -0.0286, 0.0746, -0.013, -0.0217, -0.0246, -0.0169, 0.0495, -0.0947, 0.0139, 0.0445, -0.0262, -0.0049, 0.0506, 0.004, 0.0276, 0.0063, -0.0643, 0.0059, -0.0229, -0.0315, 0.0549, 0.1427, 0.0079, 0.011, -0.0036, -0.0617, 0.0155, -0.0607, 0.0258, -0.0205, 0.0008, -0.0547, 0.0329, -0.0522, -0.0347, 0.0921, 0.0139, -0.013, 0.0716, -0.0165, 0.0257, -0.0071, 0.0084, -0.0653, 0.0091, 0.0544, -0.0192, -0.0169, -0.0017, -0.0304, 0.0427, -0.0389, 0.0921, -0.0622, -0.0196, 0.0025, 0.0214, 0.0259, -0.0493, -0.0211, -0.119, -0.0736, -0.1545, -0.0578, -0.0145, 0.0138, 0.0478, -0.0451, -0.0332, 0.0799, 0.0001, -0.0737, 0.0427, 0.0517, 0.0102, 0.0386, 0.0233, 0.0425, -0.0279, -0.0529, 0.0744, -0.0305, -0.026, 0.1229, -0.002, 0.0038, -0.0491, 0.0352, 0.0027, -0.056, -0.1044, 0.123, -0.0184, 0.1148, -0.0189, 0.0412, -0.0347, -0.0569, -0.0119, 0.0098, -0.0016, 0.0451, 0.0273, 0.0436, 0.0082, 0.0166, -0.0989, 0.0747, -0.0, 0.0306, -0.0717, -0.007, 0.0665, 0.0452, 0.0123, -0.0238, 0.0512, -0.0116, 0.0517, 0.0288, -0.0013, 0.0176, 0.0762, 0.1284, -0.031, 0.0891, -0.0286, 0.0132, 0.003, 0.0433, 0.0102, -0.0209, -0.0459, -0.0312, -0.0387, 0.0201, -0.027, 0.0243, 0.0713, 0.0359, -0.0674, -0.0747, -0.0147, 0.0489, -0.0092, -0.018, 0.0236, 0.0372, -0.0071, -0.0513, -0.0396, -0.0316, -0.0297, -0.0385, -0.062, 0.0465, 0.0539, -0.033, 0.0643, 0.061, 0.0062, 0.0245, 0.0868, 0.0523, -0.0253, 0.0157, 0.0266, 0.0124, 0.1382, -0.0107, 0.0835, -0.1057, -0.0188, -0.0786, 0.057, 0.0707, -0.0185, 0.0708, 0.0189, -0.0374, -0.0484, 0.0089, 0.0247, 0.0255, -0.0118, 0.0739, 0.0114, -0.0448, -0.016, -0.0836, 0.0107, 0.0067, -0.0535, -0.0186, -0.0042, 0.0582, -0.0731, -0.0593, 0.0299, 0.0004, -0.0299, 0.0128, -0.0549, 0.0493, 0.0, -0.0419, 0.0549, -0.0315, 0.1012, 0.0459, -0.0628, 0.0417, -0.0153, 0.0471, -0.0301, -0.0615, 0.0137, -0.0219, 0.0735, 0.083, 0.0114, -0.0326, -0.0272, 0.0642, -0.0203, 0.0557, -0.0579, 0.0883, 0.0719, 0.0007, 0.0598, -0.0431, -0.0189, -0.0593, -0.0334, 0.02, -0.0371, -0.0441, 0.0407, -0.0805, 0.0058, 0.1039, 0.0534, 0.0495, -0.0325, 0.0782, -0.0403, 0.0108, -0.0068, -0.0525, 0.0801, 0.0256, -0.0183, -0.0619, -0.0063, -0.0605, 0.0377, -0.0281, -0.0097, -0.0029, -0.106, 0.0465, -0.0033, -0.0308, 0.0357, 0.0156, -0.0406, -0.0308, 0.0013, 0.0458, 0.0231, 0.0207, -0.0828, -0.0573, 0.0298, -0.0381, 0.0935, -0.0498, -0.0979, -0.1452, 0.0835, -0.0973, -0.0172, 0.0003, 0.09, -0.0931, -0.0252, 0.008, -0.0441, -0.0938, -0.0021, 0.0885, 0.0088, 0.0034, -0.0049, 0.0217, 0.0584, -0.012, 0.059, 0.0146, -0.0, -0.0045, 0.0663, 0.0017, 0.0015, 0.0569, -0.0089, -0.0232, 0.0065, 0.0204, -0.0253, 0.1119, -0.036, 0.0125, 0.0531, 0.0584, -0.0101, -0.0593, -0.0577, -0.0656, -0.0396, 0.0525, -0.006, -0.0149, 0.003, -0.1009, -0.0281, 0.0311, -0.0088, 0.0441, -0.0056, 0.0715, 0.051, 0.0219, -0.0028, 0.0294, -0.0969, -0.0852, 0.0304, 0.0374, 0.1078, -0.0559, 0.0805, -0.0464, 0.0369, 0.0874, -0.0251, 0.0075, -0.0502, -0.0181, -0.1059, 0.0111, 0.0894, 0.0021, 0.0838, 0.0497, -0.0183, 0.0246, -0.004, -0.0828, 0.06, -0.1161, -0.0367, 0.0475, 0.0317]) AS distance
FROM default.wiki_abstract
ORDER BY distance ASC
LIMIT 5;

Community

We're committed to continuously improving and evolving MyScaleDB to meet the ever-changing needs of the AI industry. Join us on this exciting journey and be part of the revolution in AI data management!

Discord
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Get the latest MyScaleDB news or updates
- Follow @MyScaleDB on Twitter
- Follow @MyScale on LinkedIn
- Read MyScale Blog

Roadmap

Inverted index & performant keyword/vector hybrid search
Support more storage engines, e.g. ReplacingMergeTree
LLM observability with MyScaleDB
Data-centric LLM

License

MyScaleDB is licensed under the Apache License, Version 2.0. View a copy of the License file.

Credits

We give special thanks for these open-source projects, upon which we have developed MyScaleDB:

ClickHouse - A free analytics DBMS for big data.
Faiss - A library for efficient similarity search and clustering of dense vectors, by Meta's Fundamental AI Research.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors.
ScaNN - Scalable Nearest Neighbors library by Google Research.

See why metadata filtering is crucial for imporoving RAG accuracy here. ↩
The MSTG (Multi-scale Tree Graph) algorithm is provided through MyScale Cloud, achieving high data density with disk-based storage and better indexing & search performance on billion-scale vector data. ↩

XinYao1994/myscaledb