integresql
fugue
| integresql | fugue | |
|---|---|---|
| 5 | 11 | |
| 715 | 1,887 | |
| 4.3% | 1.8% | |
| 8.9 | 6.4 | |
| 4 months ago | 22 days ago | |
| Go | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
integresql
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Mock unit test an API that uses postgres or integration test API with a "test" database?
For the case of PostgreSQL I've found IntegreSQL and its Javascript client really helpful because it can create a copy of the database per test case, which it helps to write integration tests with real DB calls.
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Mocking database calls without a library?
Mocking has some advantages, but so does using a real database, at work we use https://github.com/allaboutapps/integresql and I quite like the approach that integresql has, since it makes possible to have a fresh database with your dummy data for every test without impacting the execution speed (compared to dropping an re-creating the database).
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Ask HN: How do you test SQL?
Happy to hear that! When it comes to testing services that depend on PostgreSQL, this is still my preferred solution.
https://github.com/allaboutapps/integresql
disclaimer: author
- IntegreSQL – isolated PostgreSQL databases for integration tests
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Pg_tmp – Run tests on an isolated, temporary PostgreSQL database
I haven't had a change to try it yet, but IntegreSQL[0] looks like this on steroids. It allows you to create a template (runs migrations and seed dates), and then uses Postgres's built in cloning functionality to maintain a pool of fresh databases. They claim 500ms to clone a database without the pool, and that the pool pretty much hides the latency entirely.
[0]: https://github.com/allaboutapps/integresql
fugue
- FLaNK Stack Weekly 22 January 2024
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Please integrate it with Fugue.
https://github.com/fugue-project/fugue
- Fugue: A unified interface for distributed computing
- [Discussion] Open Source beats Google's AutoML for Time series
- Ask HN: How do you test SQL?
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Replacing Pandas with Polars. A Practical Guide
Fugue is an interesting library in this space , though I haven’t tried it
https://github.com/fugue-project/fugue
A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.
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The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
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[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
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What the Duck?!
I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.
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Pyspark now provides a native Pandas API
There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.
What are some alternatives?
flyway-spawn-demo - CI demo using Flyway and Spawn
modin - Modin: Scale your Pandas workflows by changing a single line of code
otj-pg-embedded - Java embedded PostgreSQL component for testing
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
testcontainers-go - Testcontainers for Go is a Go package that makes it simple to create and clean up container-based dependencies for automated integration/smoke tests. The clean, easy-to-use API enables developers to programmatically define containers that should be run as part of a test and clean up those resources when the test is done.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
spawn-demo - Demo application to show how Spawn can be integrated in Development and CI
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
entr - Run arbitrary commands when files change
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
localstripe - A fake but stateful Stripe server that you can run locally, for testing purposes.
xarray - N-D labeled arrays and datasets in Python