Searchkick
jq
Searchkick | jq | |
---|---|---|
10 | 54 | |
6,394 | 29,104 | |
- | 1.0% | |
7.3 | 9.3 | |
26 days ago | 6 days ago | |
Ruby | C | |
MIT License | 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.
Searchkick
- Searchkick: Intelligent Search Made Easy
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Most performant way to build an analytics dashboard from a relational database backend that only stores numeric values, where the data the end-user sees is "categorized" into numeric brackets (e.g. 60-79 = Med, 80-100 = High, etc)
I run a large scale production application that does something along these lines. If the data needs to be close to real-time, I'd say use `searchkick` + Elasticsearch, and use `searchkick`'s async feature to "stream" the data from your table to the ES index. Your dashboard will then just query from the ES index via searchkick.
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Postgres Full Text Search vs. the Rest
You're right, that's actually what we implemented, application-level hooks, but they needed development and maintenance effort that come for free with the adapter we're using for OpenSearch integration, which also comes with welcome features: synonyms, partial matches, and many others.
Spoiler, the adapter is Searchkick: https://github.com/ankane/searchkick
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Full-text Search with Elasticsearch in Rails
Searchkick
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How does elasticsearch work with a rails app that's already connected to a MySQL database.
Normally for Rails applications you would use a gem like searchkick since it greatly reduces the initial Elasticsearch complexity.
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Building a Workflow for Async Searchkick Reindexing
We lean heavily on Elasticsearch at CompanyCam. One of it's primary use cases is serving our highly filterable project feed. It is incredibly fast, even when you apply multiple filters to your query and are searching a largish data set. Our primary interface for interacting with Elasticsearch is using the Searchkick gem. Searchkick is a powerhouse and provides so many features out of the box. One place where we bump up against the edges is when trying to reindex a large collection.
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Swapping Elasticsearch for Meilisearch in Rails feat. Docker
Convinced? Ok read on and I’ll show you what switching from Elasticsearch to Meilisearch looked like for a real production app — ScribeHub. We also moved from Ankane’s excellent Searchkick gem to the first party meilisearch-rails gem and I’ll show you the changes there as well.
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Searching/Querying with Active Record Encryption
If you want to use a look-aside pattern (like you might have used with Searchkick + Elasticsearch), you should check out ActiveStash: https://github.com/cipherstash/activestash
- Full Text Searching in a MySQL database via rails.
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ransack VS Searchkick - a user suggested alternative
2 projects | 12 Aug 2021
Searchkick learns what your users are looking for. As more people search, it gets smarter and the results get better. It’s friendly for developers - and magical for your users. BONUS: it's written and supported by "ankane" who has flawless reputation amongst the Ruby community.
jq
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Data Science at the Command Line, 2nd Edition (2021)
Thanks, if anyone else is interested there is an explanation of this feature here: https://subtxt.in/library-data/2016/03/28/json_stream_jq And: https://github.com/jqlang/jq/wiki/FAQ#streaming-json-parser
The last time I tried, I think the reason I gave up on JQ for large inputs was that the throughput would max out at 7mb/s whereas the same thing with spark SQL on the same hardware (MacBook) would max out at 250mb/s. So I started looking into using other solutions for big data while I use jq in parallel for small data in multiple files.
I will test it out again cause this was 4-5 years ago when I last tested it, but I believe jaq is still preferred for large inputs. Still I prefer for big data to use Spark/Polars/clickhouse etc.
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Bytecode VMs in Surprising Places
Looks like you are correct https://github.com/jqlang/jq/blob/ed8f7154f4e3e0a8b01e6778de...
- Frawk: An efficient Awk-like programming language. (2021)
- Dehydrated: Letsencrypt/acme client implemented as a shell-script
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I turned my open-source project into a full-time business
I think like you. But also, one does not necessarily know beforehand that they will want to make money.
Like a project could be born out of pure generosity, but after the happy initial phase the project might get too heavy on the maintenance requirements, causing the author to approach burnout, and possibly deciding that they want to make money to continue pulling the cart forward.
However, here's something I do think: if you create something as Open Source, it should be out of a mentality of goodwill and for the greater good, regardless of how it ends up being used. OSS licenses do mean this with their terms. If you later get tired or burned out, you should just retire and allow the community to keep taking care of it. Just like it happened with the Jq tool [1].
[1]: https://github.com/jqlang/jq/releases/tag/jq-1.7
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How to load JSON data in PostgreSQL with the the COPY command
In this blog we'll see how to upload the JSON directly using PostgreSQL COPY command and using an utility called jq!
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How to Recover Locally Deleted Files From Github
And we can then make it easier to find the commit by filtering the response with jq.
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Essential Command Line Tools for Developers
Official Documentation: jqlang.github.io/jq
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Command line tools I always install on Ubuntu servers
To handle JSON files and JSON outputs in a script or format and highlight it, jq can be very handy. Many command line tools provide a json output, so you don't have to write a custom parser for a table a list in a terminal. Instead of that, you can use jq to get a specific value from the output or even modify the output. For more information, you can visit https://jqlang.github.io/jq/
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How I use Nix in my Elm projects
In some projects I've wanted to use HTTPie to test APIs and jq to work with some JSON data. Nix has been really helpful in managing those dependencies that I can't easily get from npm.
What are some alternatives?
chewy - High-level Elasticsearch Ruby framework based on the official elasticsearch-ruby client
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
ransack - Object-based searching.
jp - Validate and transform JSON with Bash
Elasticsearch Rails - Elasticsearch integrations for ActiveModel/Record and Ruby on Rails
gojq - Pure Go implementation of jq
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
Jolt - JSON to JSON transformation library written in Java.
Sunspot - Solr-powered search for Ruby objects
dasel - Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
elasticsearch-ruby - Ruby integrations for Elasticsearch
jmespath.py - JMESPath is a query language for JSON.