https://github.com/exasol/pyexasol

Exasol Python driver with low overhead, fast HTTP transport and compression
https://github.com/exasol/pyexasol

data-science database driver exasol exasol-integration python websocket-client

Last synced: 5 days ago
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Exasol Python driver with low overhead, fast HTTP transport and compression

• Host: GitHub
• URL: https://github.com/exasol/pyexasol
• Owner: exasol
• License: mit
• Created: 2018-03-08T14:12:14.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2025-01-14T10:52:48.000Z (13 days ago)
• Last Synced: 2025-01-20T23:34:39.749Z (6 days ago)
• Topics: data-science, database, driver, exasol, exasol-integration, python, websocket-client
• Language: Python
• Homepage: https://exasol.github.io/pyexasol/
• Size: 4.85 MB
• Stars: 72
• Watchers: 10
• Forks: 40
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
Pyexasol





    





    





    





    





    





PyEXASOL is the officially supported Python connector for [Exasol](https://www.exasol.com). It helps to handle massive volumes of data commonly associated with this DBMS.

You may expect significant performance improvement over ODBC in a single process scenario involving pandas.

PyEXASOL provides API to read & write multiple data streams in parallel using separate processes, which is necessary to fully utilize hardware and achieve linear scalability. With PyEXASOL you are no longer limited to a single CPU core.

---

* Documentation: [https://exasol.github.io/pyexasol/](https://exasol.github.io/pyexasol/index.html)

* Source Code: [https://github.com/exasol/pyexasol](https://github.com/exasol/pyexasol)

---

## PyEXASOL main concepts

- Based on [WebSocket protocol](https://github.com/exasol/websocket-api);

- Optimized for minimum overhead;

- Easy integration with pandas via HTTP transport;

- Compression to reduce network bottleneck;

## System requirements

- Exasol >= 7.1

- Python >= 3.9

## Getting started

Install PyEXASOL:

```

pip install pyexasol[pandas]

```

Run basic query:

```python

import pyexasol

C = pyexasol.connect(dsn='', user='sys', password='exasol')

stmt = C.execute("SELECT * FROM EXA_ALL_USERS")

for row in stmt:

    print(row)

```

Load data into `pandas.DataFrame`:

```python

import pyexasol

C = pyexasol.connect(dsn='', user='sys', password='exasol', compression=True)

df = C.export_to_pandas("SELECT * FROM EXA_ALL_USERS")

print(df.head())

```

You may set up `local config` to store your personal Exasol credentials and connection options:

```python

import pyexasol

C = pyexasol.connect_local_config('my_config')

stmt = C.execute("SELECT CURRENT_TIMESTAMP")

print(stmt.fetchone())

```

Connect to Exasol SAAS using OpenID token for authentication:

```python

import pyexasol

C = pyexasol.connect(dsn='', user='sys', refresh_token='')

stmt = C.execute("SELECT * FROM EXA_ALL_USERS")

for row in stmt:

    print(row)

```

## Created by

[Vitaly Markov](https://www.linkedin.com/in/markov-vitaly/), 2018 — 2022

Enjoy!

## Maintained by

[Exasol](https://www.exasol.com) 2023 — Today