datafusion-server crate

Multiple session, variety of data sources query server implemented by Rust.

• Asynchronous architecture used by Tokio ecosystem
• Apache Arrow with DataFusion
  • Supports multiple data source with SQL queries
• Python plugin feature for data source connector and post processor

License

Copyright (c) 2022 - 2023 SAL Ltd. - https://sal.co.jp

Supported environment

• Linux
• BSD based Unix incl. macOS 10.6+
• SVR4 based Unix
• Windows 10+ incl. WSL 2

and other LLVM supported environment.

Build at local environment

Pre-require

• Rust Toolchain 1.70+ (Edition 2021) from https://www.rust-lang.org
• or the Docker official container image from https://hub.docker.com/_/rust

How to run

sh $ cargo init server-executer $ cd server-executer

Example of Cargo.toml

```toml [package] name = "server-executer" version = "0.1.0" edition = "2021"

[dependencies] datafusion-server = "0.8.7" ```

Example of src/main.rs

rust fn main() { datafusion_server::entry(); }

Example of config.toml

```toml

Configuration file of datafusion-server

[server] port = 4000 baseurl = "/" datadir = "./data" plugin_dir = "./plugins"

[session] defaultkeepalive = 3600 # in seconds

[log]

trace, debug, info, warn, error

level = "debug" ```

Debug build and run

sh $ cargo run

datafusion-server with Python plugins feature

Require Python interpreter v3.7+

How to run

Example of Cargo.toml

```toml [package] name = "server-executor" version = "0.1.0" edition = "2021"

[dependencies] datafusion-server = { version = "0.8.7", features = ["plugin"] } ```

Debug build and run

sh $ cargo run

Release build with full optimization

Example of Cargo.toml

```toml [package] name = "server-executor" version = "0.1.0" edition = "2021"

[profile.release] opt-level = 'z' strip = true lto = "fat" codegen-units = 1

[dependencies] datafusion-server = { version = "0.8.7", features = ["plugin"] } ```

Build for release

sh $ cargo build --release

Clean workspace

sh $ cargo clean

Usage

Multiple data sources with SQL query

• Can be used many kind of data source format (Parquet, JSON, ndJSON, CSV, ...).
• Data can be retrieved from the local file system and from external REST services.
  • Processing by JSONPath can be performed if necessary.
• Query execution across multiple data sources.
  • SQL query engine uses Arrow DataFusion.
  • Details https://arrow.apache.org/datafusion/user-guide/sql/index.html for more information.
• Arrow, JSON and CSV formats to response.

sh $ curl -X "POST" "http://localhost:4000/dataframe/query" \ -H 'Content-Type: application/json' \ -d $' { "dataSources": [ { "options": { "inferSchemaRows": 100, "hasHeader": true }, "name": "sales", "location": "superstore.csv", "format": "csv" } ], "query": { "sql": "SELECT * FROM sales" }, "response": { "format": "json" } }'