FractalNerve

        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)

     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