null
chispa
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null | chispa | |
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5 | 12 | |
1,737 | 508 | |
- | - | |
5.6 | 6.7 | |
2 months ago | 4 days ago | |
Go | Python | |
BSD 2-clause "Simplified" License | 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.
null
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JSON encoder/decoder supporting omitempty on structs
Use-case: working on PATCH requests, the body may or may not contain nullable values. I am using guregu/null and I can't use a pointer because if the json contains "null" as a value, the pointer will be set to nil in the struct, instead of a value representing the presence of null. In short I can't differentiate the absence of the field in the request from the presence of the field with a null value.
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Nilable - finally a way to stop using pointers just to get the nil state
https://github.com/guregu/null is an awesome package implementing most SQL scanner Interfaces plus JSON
- Golang backend with lots of raw SQL queries
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Is there a downside to treating possible null values in DB as pointers in struct?
Thereβs also this: https://github.com/guregu/null
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Gonion - Golang Client for querying Tor network data
Unfortunately in Go, switching to *bool makes the api a little more awkward to use since users that need to set true or false have to define a local variable then use a pointer to that. Another option would be something like null, but that adds a dependency to your currently-dependency-free project. If anyone has a better solution to this pattern, I'd love to hear it.
chispa
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Testing spark applications
Unit and e2e tests using a combination of pytest and chispa (https://github.com/MrPowers/chispa). Custom library to create random test data that fits schema with optional hardcoded overrides for relevant fields to test business logic.
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Spark open source community is awesome
here's a little README fix a user pushed to chispa
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Invitation to collaborate on open source PySpark projects
chispa is a library of PySpark testing functions.
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installing pyspark on my m1 mac, getting an env error
The other approach I've used is Poetry, see the chispa project as an example. Poetry is especially nice for projects that you'd like to publish to PyPi because those commands are built-in.
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Spark: local dev environment
- All Spark transformations are tested with pytest + chispa (https://github.com/MrPowers/chispa)
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Pyspark now provides a native Pandas API
Pandas syntax is far inferior to regular PySpark in my opinion. Goes to show how much data analysts value a syntax that they're already familiar with. Pandas syntax makes it harder to reason about queries, abstract DataFrame transformations, etc. I've authored some popular PySpark libraries like quinn and chispa and am not excited to add Pandas syntax support, haha.
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Show dataengineering: beavis, a library for unit testing Pandas/Dask code
I am the author of spark-fast-tests and chispa, libraries for unit testing Scala Spark / PySpark code.
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Tips for building popular open source data engineering projects
Blogging has been the main way I've been able to attract users. Someone searches "testing PySpark", they see this blog, and then they're motivated to try chispa.
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Ask HN: What are some tools / libraries you built yourself?
I built daria (https://github.com/MrPowers/spark-daria) to make it easier to write Spark and spark-fast-tests (https://github.com/MrPowers/spark-fast-tests) to provide a good testing workflow.
quinn (https://github.com/MrPowers/quinn) and chispa (https://github.com/MrPowers/chispa) are the PySpark equivalents.
Built bebe (https://github.com/MrPowers/bebe) to expose the Spark Catalyst expressions that aren't exposed to the Scala / Python APIs.
Also build spark-sbt.g8 to create a Spark project with a single command: https://github.com/MrPowers/spark-sbt.g8
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Open source contributions for a Data Engineer?
I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.
What are some alternatives?
csvutil - csvutil provides fast and idiomatic mapping between CSV and Go (golang) values.
spark-fast-tests - Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)
validator - Simple validation for Rust structs
spark-daria - Essential Spark extensions and helper methods β¨π²
react-leaflet-canvas-overlay - React Leaflet component similar to ImageOverlay and VideoOverlay
quinn - pyspark methods to enhance developer productivity π£ π― π
gh-token - Manage installation access tokens for GitHub apps from your terminal π»
lowdefy - The config web stack for business apps - build internal tools, client portals, web apps, admin panels, dashboards, web sites, and CRUD apps with YAML or JSON.
firebase-rules - A type-safe Firebase Real-time Database Security Rules builder. Compose and re-use common rules. Reference constants used throughout the project. Catch any errors and typos. Auto-completion.
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
trdsql - CLI tool that can execute SQL queries on CSV, LTSV, JSON, YAML and TBLN. Can output to various formats.
dagster - An orchestration platform for the development, production, and observation of data assets.