acidity

This is not a fly.io specific question but I always wonder how in these globally distributed system, how are databases handled?

I understand you can put your application server in any location but generally there is only one storage so are these application servers doing cross region database calls?

Having only worked with single cluster setup web apps, I am always curious about this part.

Is the answer always - use a managed replicated database and send read queries to one near your location and all write queries goes to the primary instance?

Thanks. My initial question arose where does products like SnowFlake/RedShift falls into this. They seem to be coined as datawarehouse but seems they do the same as OLAP work. But looking at Snowflake docs I read:

"""Currently there is no option similar to an SSAS cube in Snowflake. Once data is loaded into the databases Snowflake allows to query the data in a way similar to traditional OLTP databases. For data aggregations the Snowflake library offers rich sets of in-built functions. Together with UDFs, SPs and Materialized Views we can build custom solutions for precomputed data aggregations. For data analysis we still have to rely upon third party tools. Snowflake provides a variety of different connectors to access its database objects from other analytical tools. There are plans in near future to introduce an integrated tool for data aggregations, analysis and reporting."""

So they say, its not really an OLAP.

And how exactly it differs from OLTP? Is a web app OLTP?

Can you expand on this? We are just starting on Helm so curious to know your thoughts.

As a side note, is there something similar for building a distributed application (could be a very simple NoSQL DB or maybe some stream processor).

Newbie question but will this help me also write desktop apps?

As an Indian who did MS in US. The parent is 100% correct.

>>> Weird. OK, I tried it with some other parameters, and it suddenly took 15 seconds at 100% CPU, with PostgreSQL doing some linear scan through data. Using EXPLAIN I found that with full production data the query planner was doing something idiotic in some cases. I learned how to impact the query planner, and then this query went back to taking only a few milliseconds for any input. With this one change to influence the query planner (to actually always use an index I had properly made), things became dramatically faster. Basically the load on the database server went from 100% to well under >>> 5%.

I am actually interested in this part. Figuring out issues with EXPLAIN is one of my favorite things.

This is so cool. I am actually planning to start working on something similar but for wet shaving market. Basically, I was trying to setup something where user can choose their after shave pairing based on scent details.

Do you have plans to open source your algorithm?