fugue
testcontainers-dotnet
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| fugue | testcontainers-dotnet | |
|---|---|---|
| 11 | 16 | |
| 1,876 | 3,534 | |
| 2.3% | 3.2% | |
| 6.7 | 9.0 | |
| 7 days ago | 4 days ago | |
| Python | C# | |
| Apache License 2.0 | MIT License |
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.
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.
testcontainers-dotnet
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Integration tests with AWS S3 buckets using Localstack and Testcontainers
Testcontainers
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Integration Tests with In Memory DB vs Real DB on Docker
Like others said, it's better to test with an actual database. I recommend using Testcontainers (https://dotnet.testcontainers.org), you can even create multiple instances so your tests can run in parallel independently.
- Unit Testing
- Running untrusted (user-provided) Python code on ASP.NET/C# backend
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Integration tests for AWS serverless solution
To launch a container in code we will use Testcontainers. Testcontainers is a library that is built on top of the .NET Docker remote API and provides a lightweight implementation to support your test environment in all circumstances. This library supports pre-defined packages for containers or you can use your .dockerfile. We will use a pre-defined package for LocalStak. LocalStack is a cloud service emulator that runs in a single container for AWS service. LocalStack supports a growing number of AWS services.
- If i want to do testing CRUD should I use in memory or just do integration test where I use a seperate database?
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Do you guys mock everything in your Unit Tests?
Bogus - For creating fake data Verify - Snapshot testing for .NET MELT - For testing ILogger usage Stryker - Mutation Testing for .NET TestContainers - run docker programmatically in integration tests
- Testes de integração com containers
- What C# tools would you like to use that don't exist today?
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Ask HN: How do you test SQL?
.NET Shop using SQL Server here, but I think something similar to what we do can apply to any stack. We use TestContainers [1] to spin up a container with SQL Server engine running on it. Then use FluentMigrator [2] to provision tables and test data to run XUnit integration tests against. This has worked remarkably well.
[1] https://dotnet.testcontainers.org/
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
NUnit - NUnit Framework
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.
SpecFlow - #1 .NET BDD Framework. SpecFlow automates your testing & works with your existing code. Find Bugs before they happen. Behavior Driven Development helps developers, testers, and business representatives to get a better understanding of their collaboration
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
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.
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
testcontainers-python - Testcontainers is a Python library that providing a friendly API to run Docker container. It is designed to create runtime environment to use during your automatic tests.
xarray - N-D labeled arrays and datasets in Python
Docker.DotNet - :whale: .NET (C#) Client Library for Docker API
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
ephemeral-mongo - EphemeralMongo is a set of three NuGet packages wrapping the binaries of MongoDB 4, 5 and 6 built for .NET Standard 2.0.