krakend-ce VS bleve

KrakenD: A high-performance open-source API Gateway. It helps application developers release features quickly by eliminating all the complexities of SOA architectures while offering a unique performance.

This is my first foray into NGINX, and it seems my desired use-case is a bit different/left field. We're attempting to utilize NGINXaaS (Azure PaaS) to act as an API Gateway for some of our on-premise APIs. We were trailing KrakenD prior for this, but are moving toward NGINX as there's a desire to move everything PaaS (where appropriate). Our current KrakenD instance is configured to host a different set of URI than what the backend is configured for, IE:

Check what configuration language (JSON/Yaml) and style (Declarative/Imperative) chosen API Gateway support. It is not so crucial but sometimes you might ask: Does it have a user-friendly GUI and drag&drop easy config option? Some open-source projects like Tyk, Krakend.io, and Apache APISIX have built-in no-code possibly visual editing dashboards. You can even import all your APIs descriptions from a JSON.

KrakenD and Lura https://github.com/krakendio/krakend-ce https://github.com/luraproject/lura

I was trying to look up the license for that project and the repo linked from the footer of their website is https://github.com/krakendio/krakend-ce#readme (Apache 2), but because I just did a web search for krakend there is also https://github.com/luraproject/lura#readme which says it's from the Linux Foundation (also Apache 2)

Is KrakenD some kind of generic term, or does that project just have a complex history?

https://github.com/krakendio/krakend-ce api gateway built using lura

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?