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May I ask some stupid questions? :/

I don't quite get the diagram of the Vespa Architecture. Is Vespa a middleware between database engine and query parser? This is what puzzles me.

If so, are there other such middlewares available for ie. PostgresSQL that allow hooking "Query Templating Models" (that is it?) generated via Machine-Learning Models? Is it way more complicated than that, or did they overengineer the problem into a monolith? EDIT: Looking at https://github.com/vespa-engine/vespa it seems that it is overengineered, or maybe it consists of individual micro-components like node.js, hmm more questions :(

Is GraphQL such middleware or lower-level?

Does Vespa replace custom Glue-Code between Backend and Frontend that generates such query-sets for content ranking/positioning?

Or what exactly does Vespa solve? I'm sorry, I've read the article, but can't say, yep that's what it is!

EDIT: How else could you solve what Vespa does using Rust, Go, or C/C++ libraries? A very simple or general direction would be immensely useful to understand Vespa =) The project makes the simultanous impression of an immense engineering feat and at the same time a huge code debt.

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1) Get PostgresSQL exntensions via "package manager" pgxnclient 1.1) pg_bouncer - For connetion pooling 1.2) yoke - As a high-availability cluster manager with auto-failover and automated cluster recovery 1.3) prestodb.io - Distributed SQL query engine for pgsql 1.4) pglogical - Logical streaming replication for using a publish/subscribe model 1.5) pg_lambda - To create your own AWS (meta) Lambda 1.6) pg_strom - To offload tasks to the GPU 1.7) zombodb - To utilize full-text searching via indexes backed by Elasticsearch 2) Put all together with pglogical and presto to seperate GPU/CPU intensive tasks. 2.1) "Build Missing Middleware" - To design/fuse a query visually that combines multiple backends 2.1.1) Create a binary data-stream by integrating pg_lambda, pg_strom, presto and zombodb 2.1.2) "Build Missing Middleware" - A tensor processing extension to use ML Model evaluations 2.1.3) "Use Missing Middleware" - For data-processing via Machine-Learning models 2.1.4) "Use Missing Middleware"- To output ML processed results into the database 2.2) Partition these queries using "pg_lambda + middleware" to create accelerated and fused query results

FEATURE VESPA ELASTIC SEARCH RELATIONAL DATABASES ACID transactions ••• Optimized for analytics ••• •• Optimized for serving ••• • •• Scalable ••• •• • Easy to operate at scale •• • Text search ••• •• • Machine learned ranking ••• • 2.1.2) - 2.1.4) Middleware logic container ••• 1.4) Live reconfiguration ••• 1.2)