awesome-mysql
awesome-postgres
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awesome-mysql | awesome-postgres | |
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1 | 5 | |
2,235 | 9,520 | |
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1.8 | 5.0 | |
2 days ago | 3 days ago | |
GNU General Public License v3.0 or later | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-mysql
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CSS Deep
shlomi-noach/awesome-mysql - A curated list of awesome MySQL software, libraries, tools and resources
awesome-postgres
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Ask HN: Best GitHub Repos
Personally love awesome Postgres - https://github.com/dhamaniasad/awesome-postgres
- A curated list of PostgreSQL software, libraries, tools and resources
- Free, open-source tools for Postgres
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What is the best postgres SQL client for beginners?
Was a nice list of tools here https://dhamaniasad.github.io/awesome-postgres/
What are some alternatives?
asyncpg - A fast PostgreSQL Database Client Library for Python/asyncio.
PyMySQL - MySQL client library for Python
psycopg2 - PostgreSQL database adapter for the Python programming language
mysqlclient - MySQL database connector for Python (with Python 3 support)
pymssql - Official home for the pymssql source code.
oursql - oursql is a set of MySQL bindings for python with a focus on wrapping the MYSQL_STMT API to provide real parameterization and real server-side cursors.
mysql-python - MySQLdb is a Python DB API-2.0 compliant library to interact with MySQL 3.23-5.1 (unofficial mirror)
apsw - Another Python SQLite wrapper
pgtt - pgtt is a time traveling tool for PostgreSQL to help speedup development and testing of various applications by enabling the user to easily travel between points in time. This can be useful when for example you have to test a certain mutation multiple times and want to quickly rollback to before the mutation to make changes to the behaviour and test again. This will save time and avoids setting up the data over and over again, especially in larger applications with complex data and flows.