This is not the case for most of the NoSQL databases where you'll pay for lack of certain features either by a) having to write a lot of code, or b) bad-to-crippling performance for use cases it wasn't meant to solve.
So, unless you're already very clear on what your exact use case is going why the spend time analysing before even getting your project off the ground?
Can you give a common example of these? This article is referring to issues related to row vs column data stores, not sql vs nosql.
* Column stores like BQ and Keen don't let you efficiently slice and dice data by factors other than time. If you're slicing by customer or product, your queries become incredibly slow and expensive. You start writing hacky shit like figuring out when your customer's first sale was so you can narrow the time slightly, but that barely helps.
* MongoDB doesn't do joins. So you denormalize big chunks of your data, and now you have update problems because 1) you have to hunt all that down and 2) you don't have transactions that span collections. Also the aggregation language is tedious compared to SQL, requiring you to do most of the work of a query planner yourself.
* Some other person in this thread said MongoDB was faster than Postgres, but I found quite the opposite to be true. For the same real-world workload, basic aggregations on an index, we found Postgres to be much faster than Mongo. No idea what that other person is talking about.
Because if you don't know what you want you are almost guaranteed to pick the wrong technology.
That's one way to look at it...but a bit shortsighted.
Requirements can and do change, and a well designed model in an RDBMS will be far more extensible than a similar one in NoSQL document store. So RDBMS' aren't the "wrong" technology, they the safest bet; not to mention most modern relational DBs already out-perform mongo, so the point is sort of moot anyway.
Because that sounds like magic.
Also MongoDB destroys any RDBMS (minimum 10x faster) if you have embedded structures instead of joining against 10 tables in a normalised design. Hence the importance of understanding your query patterns and domain before selecting the database.
Real world example: Consider an Order table and a Visit table; conversion rates aggregate orders over visits. In Mongo you can denormalize some of the Visit data into Order, but what happens when you change the logic for computing conversion ratios? Or you want conversion ratios broken down by web browser, source tag, or any of the other data elements that live in Visit but you didn't denormalize ahead of time?
I would argue that since both Mysql and PostgreSQL are JSON document stores with mostly the same capabilities when it comes to querying and aggregation I don't see the advantage of using MongoDB at all.
I wouldn't even use MongoDB for caching when redis does a better job at it. Logs? I don't see why logs cannot be shoved into a RDBMS. Prototyping? create a table with a JSON field and a primary key. Distributed file system? I don't know any business which uses gridFS as a CDN, full text search? PostgreSQL does it better. So what is the job your are talking about? PostgreSQL is so much powerful for analytics because of the power of SQL.
I agree with your overall argument of PostgreSQL & MySQL >> MongoDB(for querying and aggregation). But in all the experiences I’ve had doing analytical work with both; Postgres easily comes out ahead. If you’re starting from a blank slate, I’d definitely recommend it over MySQL. Just update/feature addition rate along with the better community quality are enough for me to prefer Postgres over MySQL.
With two engineers starting from scratch, we launched a product in three months that was making millions (per month, in profit) by six. When I hear "done a prototype and evaluated the product", I think you operate on a very different kind of timeframe. We needed a customer-facing analytics solution ASAP for the sales that our customers were already making.
This is why Postgres would have been the right choice from the start (mea culpa). It may not be the best solution, but it will be an adequate solution and get you through enough scale that you can worry about the million other holes to backfill. I thought I was being clever with BigQuery; it looked great on paper. Keen has better marketing but really the same problems. Mongo at least I was familiar with going into it, and that solution worked for a couple months, right up until the queries got complicated. Which in retrospect they were always going to.
In a runaway startup you're not going to be an expert in everything, or have time to plan out an optimal solution. Pick technologies that you can be confident will be "good enough for now" and give you time to find the boundaries of your particular problem domain. Postgres is a good axe to start with.
On one hand you complain about using technologies before you have done a prototype and evaluated the product. Then you blindly tell startups to just use MySQL/PostgreSQL without having any idea of their use case or whether it matches their query patterns.
If you are a startup the right way to go is to document your use case, understand what queries those use cases demand and then find the right database that satisfies it e.g. don't pick MongoDB if you are doing lots of joins and don't pick PostgreSQL if you are doing wide-table feature engineering type analytics.
Right tool for the right job.