frameless
lantern
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| frameless | lantern | |
|---|---|---|
| 9 | 5 | |
| 869 | 646 | |
| -0.1% | 14.1% | |
| 8.1 | 9.6 | |
| 5 days ago | 6 days ago | |
| Scala | C | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
frameless
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for comprehension and some questions
I don't see how Spark is any "less controversial" when the Spark Delay instance for cats-effect takes an entire SparkSession implicitly.
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Why use Spark at all?
To add to this I lately have used Spark with frameless for compile time safety and it's an interesting library that works well with Spark.
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Guide for Apache Spark Setup, Job Optimisation, AWS EMR Cluster Configuration, S3, YARN and HDFS Optimisation
For type safety with dataframes, techniques like https://github.com/typelevel/frameless can be used.
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Spark scala v/s pyspark
The preferred way to write Spark programs is to use DataFrame API which is untyped and is essentially the same in Scala, C# and Python. It's a DSL that's used to describe AST of the computation and the end result is the same regardless of language. There's a library called Frameless (https://github.com/typelevel/frameless) that implements typed DataFrame API but it is not in wide use, it looked dead for quite some time (though now development seems to continue) and didn't play nice with IntelliJ IDEA last time I checked. Performance-wise there's no difference most of the time (since all the program does is create an AST) except when using UDFs - Python UDFs are significantly slower and you can't write "proper" UDFs in Python - ones that generate Java code.
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Does anyone here (intentionally) use Scala without an effects library such as Cats or ZIO? Or without going "full Haskell"?
Frameless is a nice way to grab some type safety back from Spark, and features opt-in Cats integration.
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Making the Spark DataFrame composition type safe(r)
Valid point! Have you seen the withColumnTupled API? It returns a typed tuple instead. This seems to satisfy your use case - the dataset preserves its type and doesn't require a new case class. This is kind of what you're suggesting but without case class generation. Though not sure whether attribute labels (names) are preserved in this case. It's also unclear whether this is good enough for wide tables.
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Recommendations for specializing in Spark (Scala)
I recommend using Frameless, which includes a Cats module. In general, I would encourage you to master “purely” functional programming first, because it’s foundational. Spark is a very specific technology, and probably not even the best in that class today—I would be very careful about trying to build a career around it.
lantern
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Are we at peak vector database?
Traditional DBs already kinda support vector DBs via pg_vector extensions and such.
There is a YC startup, latnern, that also built their own extension for postgres that is open source and is better for vector DB use cases: https://github.com/lanterndata/lantern
But yeah! Traditional DBs already support this, if you consider this extension to be part of Postgres.
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90x Faster Than Pgvector – Lantern's HNSW Index Creation Time
This extension is licensed under the Business Source License[0], which makes it incompatible with most DBaaS offerings. The BSL is a closed-source license. Good choice for Lantern, but unusable for everyone else.
Some Postgres offerings allow you to bring your own extensions, for instance Neon[1], where I work. I tried to look at AWS docs for you, but couldn't find anything about that. I did find Trusted Language Extensions[2], but that seems to be more about writing your own extension. Couldn't find a way to upload arbitrary extensions.
[0]: https://github.com/lanterndata/lantern/commit/dda7f064ca80af...
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Show HN: Lantern – a PostgreSQL vector database for building AI applications
Install and use our extension here` https://github.com/lanterndata/lantern
Features today + Coming soon
What are some alternatives?
Lantern
vector-search-class-notes - Class notes for the course "Long Term Memory in AI - Vector Search and Databases" COS 597A @ Princeton Fall 2023
spark-excel - A Spark plugin for reading and writing Excel files
usearch - Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
lantern_extras - Routines for generating, manipulating, parsing, importing vector embeddings into Postgres tables
azure-kusto-spark - Apache Spark Connector for Azure Kusto
react-semantic-search
bebe - Filling in the Spark function gaps across APIs
cats-effect - The pure asynchronous runtime for Scala
typeclassopedia - My tinkering to understand the typeclassopedia.
Laminar - Simple, expressive, and safe UI library for Scala.js