meilisearch-js-plugins
tidyquery
| meilisearch-js-plugins | tidyquery | |
|---|---|---|
| 2 | 2 | |
| 455 | 167 | |
| 1.5% | - | |
| 8.5 | 0.0 | |
| 5 days ago | over 1 year ago | |
| TypeScript | R | |
| MIT License | Apache License 2.0 |
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.
meilisearch-js-plugins
-
How to integrate an extremely fast and relevant search into your Rails app using Meilisearch and React
We have achieved all this seamlessly, thanks to Meilisearch Rails and Instant Meilisearch. Meilisearch has integrations for almost every popular language or framework. Take a look at the complete list in the Meilisearch integration guides.
-
ClickHouse as an alternative to Elasticsearch for log storage and analysis
It is good. I can't find any CDC for Postgres for the incremental sync. And so I had to use the bulk update/sync and that causes performance issues occasionally. Also, some Algolia features are not available yet https://github.com/meilisearch/instant-meilisearch/issues/21...
tidyquery
-
Can "dplyr" code automatically be converted to SQL code?
tidyquery
-
ClickHouse as an alternative to Elasticsearch for log storage and analysis
> SQL is a perfect language for analytics.
Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.
It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.
In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.
This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.
[1] https://cran.r-project.org/web/packages/sqldf/index.html
[2] https://github.com/ianmcook/tidyquery
[3] https://github.com/phillc73/duckdf
What are some alternatives?
loki - Like Prometheus, but for logs.
duckdf - 🦆 SQL for R dataframes, with ducks
Apache Solr - Apache Lucene and Solr open-source search software
clickhousedb_fdw - PostgreSQL's Foreign Data Wrapper For ClickHouse
ClickHouse - ClickHouse® is a free analytics DBMS for big data
tidyquant - Bringing financial analysis to the tidyverse
meilisearch-react
tidyverse - Easily install and load packages from the tidyverse
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
tidylog - Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences