ddev VS bleve

Debugging is pretty easy with something like Ray (https://myray.app/) or Laravel Herd (https://herd.laravel.com/, despite its name runs differnet php apps just fine).

When it coems to running different PHP versions, modern PHP development has largely moved to using Docker (ddev, for example: https://ddev.com/) for that, and there's plenty Docker images for old PHP versions so it's a non-issue. I'd also imagine it be difficult to get many other outdated programming languages via a package manager.

There's now the PHP Foundation, which while relatively new in its formation, has significantly boosted PHP development and I'd recommend donating if improving the language is important to you.

Do you already want to try the new version, which is currently in alpha state? With DDEV this is super simple, just paste one command into the terminal.

For development, I will still prefer to continue with DDEV (a tool that I highly recommend). But the adventure with SQLite was very interesting, it really helped me not to pollute my termux.

I use https://ddev.com for almost all of my web project development, which basically automates all of this. Per-project databases, web containers, plugins, etc, and it’s now using Traefik as its router.

I use it with ddev for local development.

https://ddev.readthedocs.io/

With the right combination of overlapping interests, a lot can get done and incredible things get build. See for example the Drupal book or DDEV--both are extremely active Drupal projects, with lots of community activity, outside of drupal.org.

You can go from 0 to 100 quickly with DDEV or Lando: - https://docs.lando.dev/ - https://ddev.readthedocs.io/

For local: have you tried DDEV? Or Lando? they seem pretty fast to me. https://github.com/drud/ddev

Lando works great with Linux, use DDEV for Mac. Both are Docker-based.

With DDEV you can create Docker PHP + NodeJS environments which run on every operating systems in the same way. These environment configuration can be shared via git which makes open source software DDEV a great and robust choice for team projects.

I don't have the answer to that, but the project has been alive for many years. Seems maybe you should find the answer since you are developing a competing solution? Also it might be a good reference project for solving similar problems to yours. They do have bench tests you could play with https://github.com/blevesearch/bleve/blob/master/query_bench_test.go

I know of https://github.com/blevesearch/bleve and I think there was another project for full text search that I can't find now.

I would be interested in such a testbed. I would also like to know how Bleve Search (https://github.com/blevesearch/bleve) turns out.

I have for many years now a small search engine project in my free-time pipeline, but I'm before crawling even and I intend to sit for searching part after some of that.

Haha. Seriously I can’t tell the difference between these libraries https://github.com/blevesearch/bleve

I've never worked on a project that encompasses as many computer science algorithms as a search engine. There are a lot of topics you can lookup in "Information Storage and Retrieval":

- Tries (patricia, radix, etc...)

- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)

- Consensus (raft, paxos, etc..)

- Block storage (disk block size optimizations, mmap files, delta storage, etc..)

- Probabilistic filters (hyperloloog, bloom filters, etc...)

- Binary Search (sstables, sorted inverted indexes, roaring bitmaps)

- Ranking (pagerank, tf/idf, bm25, etc...)

- NLP (stemming, POS tagging, subject identification, sentiment analysis etc...)

- HTML (document parsing/lexing)

- Images (exif extraction, removal, resizing / proxying, etc...)

- Queues (SQS, NATS, Apollo, etc...)

- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)

- Rate limiting (leaky bucket, windowed, etc...)

- Compression

- Applied linear algebra

- Text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)

- etc...

I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.

- https://github.com/quickwit-oss/tantivy

- https://github.com/valeriansaliou/sonic

- https://github.com/mosuka/phalanx

- https://github.com/meilisearch/MeiliSearch

- https://github.com/blevesearch/bleve

- https://github.com/thomasjungblut/go-sstables

A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. The problem is that search with good rankings often requires custom storage so calculations can be sharded among multiple nodes and you can do layered ranking without passing huge blobs of results between systems.

For those curious, I'm on my 3rd search engine as I keep discovering new methods of compactly and efficiently processing and querying results.

There isn't a one-size-fits all approach, but I've never worked on a project that encompasses as many computer science algorithms as a search engine.

- Tries (patricia, radix, etc...)

- Trees (b-trees, b+trees, merkle trees, log-structured merge-tree, etc..)

- Consensus (raft, paxos, etc..)

- Block storage (disk block size optimizations, mmap files, delta storage, etc..)

- Probabilistic filters (hyperloloog, bloom filters, etc...)

- Binary Search (sstables, sorted inverted indexes)

- Ranking (pagerank, tf/idf, bm25, etc...)

- NLP (stemming, POS tagging, subject identification, etc...)

- HTML (document parsing/lexing)

- Images (exif extraction, removal, resizing / proxying, etc...)

- Queues (SQS, NATS, Apollo, etc...)

- Clustering (k-means, density, hierarchical, gaussian distributions, etc...)

- Rate limiting (leaky bucket, windowed, etc...)

- text processing (unicode-normalization, slugify, sanitation, lossless and lossy hashing like metaphone and document fingerprinting)

- etc...

I'm sure there is plenty more I've missed. There are lots of generic structures involved like hashes, linked-lists, skip-lists, heaps and priority queues and this is just to get 2000's level basic tech.

- https://github.com/quickwit-oss/tantivy

- https://github.com/valeriansaliou/sonic

- https://github.com/mosuka/phalanx

- https://github.com/meilisearch/MeiliSearch

- https://github.com/blevesearch/bleve

A lot of people new to this space mistakenly think you can just throw elastic search or postgres fulltext search in front of terabytes of records and have something decent. That might work for something small like a curated collection of a few hundred sites.

Search in SQL databases is a tough beast to get it right. And given that we support MySQL and Postgres both, it gets even harder to support quirks of both of them.

In enterprise editions, the only addition is Elasticsearch. But in our open-source version, we do have support for https://github.com/blevesearch/bleve. Although, it's in beta, we have a lot of customers using it.

I am wondering if you have tried using it and didn't like it?