elasticsearch-mapper-attachments VS Typesense

Elasticsearch - www.elastic.co/

The increasing complexity of modern systems led to the rise of AIOps (Artificial Intelligence for IT Operations) and observability practices. AIOps leveraged machine learning algorithms to automate problem detection, analysis, and resolution. Observability focused on gaining insights into system behaviour through metrics, logs, and traces. As a result, tools like Prometheus, Grafana, and ELK stack (Elasticsearch, Logstash, Kibana) gained popularity.

My only experience with NetFlow collection is on my home firewall/router running pfSense Community Edition, which is free to download and can be installed on a wide assortment of X86 hardware. I installed the Softflowd package, which exports NetFlow data to a dedicated Elasticsearch/Logstash/Kibana (ELK) server on my LAN. I believe Security Onion and ElastiFlow also can be NetFlow collectors.

Elasticsearch, Logstash, and Kibana (ELK) Stack: An open source suite of tools for log management and analysis, providing real-time insights into security events.

Recently I have taken an interest in big data. https://neo4j.com/ , https://cassandra.apache.org/ , https://clickhouse.com/, https://www.elastic.co/ - are all databases I have experience with. Neo4j and Cassandra only as a hobby, but Clickhouse I have used in production, and Elasticsearch I have used for some 7 years now.

Buy an enterprise-class, wired router like the Negate 2100 ($349 USD), which runs pfSense, and configure the Deco AXE5400 device(s) to operate in Access Point Mode. Then install the Softflowd package through the pfSense web UI. Softflowd will collect and export NetFlow data to a NefFlow collector, which is the separate computer/VM/container referred to above, running software like Security Onion, ElastiFlow, or Elasticsearch/Logstash/Kibana (ELK).

If you’re unaware, elastic search is some like enterprise level search shit. They just put it in a theme. https://www.elastic.co

systemctl status kibana ● kibana.service - Kibana Loaded: loaded (/etc/systemd/system/kibana.service; enabled; vendor preset: enabled) Active: active (running) since Tue 2023-03-28 09:40:05 UTC; 33min ago Docs: https://www.elastic.co Main PID: 3168 (node) Tasks: 11 (limit: 9432) Memory: 303.3M CPU: 35.190s CGroup: /system.slice/kibana.service └─3168 /usr/share/kibana/bin/../node/bin/node /usr/share/kibana/bin/../src/cli/dist --logging.dest=/var/log/kibana/kibana.log --pid.file=/run/kibana/kibana.pid "--deprecation.skip_deprecated_settings[0]=logging.dest" Mar 28 09:40:05 wazuh systemd[1]: Started Kibana.

Disregarding props-drilling technique in favor of a more reliable and elegant solution we looked for inspiration elsewhere. Another project of ours .find was using Typesense/Algolia components, which looked a bit like black-box/magic, but at the same time provided a clean approach to build complex and highly customizable solutions.

Have you ever tried to look up something, only to realise your search engine doesn't recognise your typos? Typesense to the rescue! It's a fast, typo-tolerant search engine built for an easier browsing experience. The latest version comes with new features such as built-in conversational search, image search, voice search, analytics, and more. Dive into the release notes for the full list of changes and enhancements.

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/)

Typesense - Open Source Alternative to Algolia

If you like your penny take a look at Typesense https://typesense.org/ - nothing to complain here. Especially nothing complain about pricing.

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

For something small with a minimal footprint, I'd recommend Typesense. https://github.com/typesense/typesense

I haven’t used Publish, but I’d assume you could use something like https://typesense.org/ to index and search the vault.

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.