duckdf
Typesense
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duckdf | Typesense | |
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3 | 129 | |
41 | 17,876 | |
- | 4.4% | |
0.0 | 9.8 | |
4 months ago | 9 days ago | |
R | C++ | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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.
duckdf
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DuckDB – in-process SQL OLAP database management system
Quite a while ago, when duckdb was just a duckling, I wrote an R package that supported direct manipulation of R dataframes using SQL.[1] duckdb was the engine for this.
The approach was never as fast as data.table but did approach the speed of dplyr for more complex queries.
Life had other things in store for me and I haven’t touched this library for a while now.
At the time there was no Julia connector for duckdb, but now that there is, I’d like to try this approach in that language.
[1] https://github.com/phillc73/duckdf
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ClickHouse as an alternative to Elasticsearch for log storage and analysis
Yeah, I agree sqldf is quite slow. Fair point.
As you've seen, duckdb registers an "R data frame as a virtual table." I'm not sure what they mean by "yet" either.
Of course it is possible to write an R dataframe to an on-disk duckdb table, if that's what you want to do.
There are some simple benchmarks on the bottom of the duckdf README[1]. Essentially I found for basic SQL SELECT queries, dplyr is quicker, but for much more complex queries, the duckdf/duckdb combination performs better.
If you really want speed of course, just use data.table.
[1] https://github.com/phillc73/duckdf
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Julia 1.6: what has changed since Julia 1.0?
That's a really good point that I'd not really thought about. I'd never really considered the difference between calling just functions versus macros.
Thinking about Query.jl and DataFramesMeta.jl, and I am for sure not an expert in either, I can't specifically speak to your `head` example, but other base functions can be combined with macros. For example, see the LINQ examples from DataFramesMeta.jl[1] where `mean` is being used. Or again the LINQ style examples in Query.jl[2], where `descending` is used in the first example, or `length` later in the Grouping examples.
Is that the kind of thing you meant?
For whatever reason, with the way my brain is wired, the LINQ style of query just works for me. I have never directly used LINQ, but do have some SQL experience. In fact, I wrote some dinky little wrapper functions[3] around duckdb[4] so I could directly query R dataframes and datatables with SQL using that backend, rather than sqldf[5].
[1] https://juliadata.github.io/DataFramesMeta.jl/stable/#@linq-...
[2] https://www.queryverse.org/Query.jl/stable/linqquerycommands...
[3] https://github.com/phillc73/duckdf
[4] https://duckdb.org/
[5] https://cran.r-project.org/web/packages/sqldf/index.html
Typesense
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Website Search Hurts My Feelings
There are actually plenty of non-ES products that are way easier to integrate and tune (and get better results with less effort).
- Typesense (https://github.com/typesense/typesense)
- Algolia
- Google Programmable Search Engine (https://programmablesearchengine.google.com/about/)
- Remote Machine Learning and Searching on a Raspberry Pi 5
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Open Source alternatives to tools you Pay for
Typesense - Open Source Alternative to Algolia
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DNS record "hn.algolia.com" is gone
If you like your penny take a look at Typesense https://typesense.org/ - nothing to complain here. Especially nothing complain about pricing.
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Vector databases: analyzing the trade-offs
I work on Typesense [1] (historically considered an open source alternative to Algolia).
We then launched vector search in Jan 2023, and just last week we launched the ability to generate embeddings from within Typesense.
You'd just need to send JSON data, and Typesense can generate embeddings for your data using OpenAI, PaLM API, or built-in models like S-BERT, E-5, etc (running on a GPU if you prefer) [2]
You can then do a hybrid (keyword + semantic) search by just sending the search keywords to Typesense, and Typesense will automatically generate embeddings for you internally and return a ranked list of keyword results weaved with semantic results (using Rank Fusion).
You can also combine filtering, faceting, typo tolerance, etc - the things Typesense already had.
[1] https://github.com/typesense/typesense
[2] https://typesense.org/docs/0.25.0/api/vector-search.html
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Creating an advanced search engine with PostgreSQL
For something small with a minimal footprint, I'd recommend Typesense. https://github.com/typesense/typesense
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Obsidian Publish full text search
I haven’t used Publish, but I’d assume you could use something like https://typesense.org/ to index and search the vault.
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DynamoDB search options
A cheaper option would be to use https://typesense.org. You can use DynamoDb streams to automatically load records. It has worked well for me.
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[Guide] A Tour Through the Python Framework Galaxy: Discovering the Stars
Try tigris | typesense for faster search
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Is it worth using Postgres' builtin full-text search or should I go straight to Elastic?
I’m also checking out Typesense as a possibility for replacing Elastic: https://typesense.org/
What are some alternatives?
tidyquery - Query R data frames with SQL
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
julia - The Julia Programming Language
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
loki - Like Prometheus, but for logs.
Apache Solr - Apache Lucene and Solr open-source search software
Makie.jl - Interactive data visualizations and plotting in Julia
meilisearch-laravel-scout - MeiliSearch integration for Laravel Scout
meilisearch-js-plugins - The search client to use Meilisearch with InstantSearch.
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.