It isn't a popular opinion on HN, but I will still advise for Oracle or SQL Server in terms of tooling, cluster scaling, server side programming and DB drivers.

Then again, we work with customers whose Oracle and SQL Server licenses costs aren't an issue.

If you take the issues of open-source and licensing out of the equation (both important issues in their own right, but not related to the point at hand) then Oracle and SQL Server are both ridiculously good.

I personally try to avoid them (due to the cost, and the lock-in) but they are astoundingly performant and featureful RDBMSs with a huge amount of support and documentation behind them.

I guess some took it personally, although I mentioned I do like Postgres.

I just happen to like the other ones even more, since I was lucky to be able to use them in a few projects.

2) "server side programming" - as you have the source code with Postgresql and their a plugins for most major programming languages I don't buy this.

3) If you of spent the same on Enterprisedb or CitusDB I can guarantee you would get similar polish and support. People don't.

Something else you might be interested in:

https://www.linkedin.com/pulse/mongodb-32-now-powered-postgr...

I have always been a Postgres fan, but now I find it very difficult to imagine a problem MongoDB would be better suited for.

Inspired from Postgrest linked above, it automatically builds a GraphQL API by reflecting on postgres schema.

1 - http://docs.sequelizejs.com/en/latest/

1 - http://github.com/Vincit/objection.js

2 - http://knexjs.org

https://github.com/tgriesser/bookshelf/issues/720

For instance, at my current startup, we employ at least 7 different databases (including PG). And, I don't say that to brag - each has a specific use for the problem at hand.

You have to consider the needs and trade offs of your specific project. And, if you can, try to isolate your storage behind some interface. Because often your needs will change.

(I say this with 20+ years of experience and over a dozen commercially-successful products in my belt)

Data has more value when combined, so a wise database choice also depends on what you already have. You can combine data from different systems (e.g. FDWs, which postgres has great support for), but something is usually lost along the way.

I think postgres is a good default, because it is good for a lot of things. But sure, if you make an informed decision otherwise, there's nothing wrong with that.

It doesn't matter whether you have your needs clearly defined, Postgresql is a jack of all trades and a master of many.

An abstracted interface for your datalayer is a must have.

Many startups and projects begin with a single db and grow into new dbs as the business requirements change.

Obviously if you know your data model well enough you can foresee a lot of these requirements and pick the right tool for the job.

However, you will usually need to pick a db to start with and hope that it will accommodate as many of those unknowns as possible.

I believe that Postgres is the best choice in that scenario, as it is extremely mature and has an incredible amount of flexibility.

Somehow that makes me feel better that at least, I'm on the right path.

The reason why Postgres is so popular here is because if you want to have an open source solution, Postgres is the best available. I'm sure there are better proprietary solutions, but even though it is free, PG is pretty darn close to them.

Why? Especially after point #1 and assuming the document-store was a good fit for the data model.

Many of the NoSQL essentially takes us back to 60s before Codd came up with relational model[1] These ideas tend to come back once in a while [2][3], but so far nothing is better than relational model.

NoSQL still makes sense in many cases (generally when your specific use case does not need all guarantees of ACID), you can get in return higher performance or horizontal scalability.

MongoDB is aim to be generic database but rides on the wave of NoSQL "coolness". It was created by people who had no experience in databases and are learning as they go[4]. As they started adding things that are essential for database they realized it's not that simple.

Currently MongoDB is outperformed by Postgres. In fact there is an application called ToroDB which provides protcol compatibility for Postgres that emulates MongoDB and even that outperforms Mongo. Mongo also doesn't scale well horizontally, so essentially you don't really don't get any significant advantage.

[1] https://en.wikipedia.org/wiki/Hierarchical_database_model

[2] https://en.wikipedia.org/wiki/Object_database

[3] https://en.wikipedia.org/wiki/XML_database

[4] For example they started with mmap'ed memory regions to store the data. Did not initially use fsync() to make sure data is saved on disk. In a way it reminds me of MySQL several years ago. It's much better now than it was in the past, but it has a lot of warts left of from the past.

MongoDB: for when you don't need consistency, availability, or partition tolerance.

There are some really good NoSQL products out there. I seriously think RethinkDB is on par with Postgres. I've also used Cassandra and BerkeleyDB and they're both decent. But unless some core part of your business logic is pathological to implement in SQL (like Reddit's comment trees) you should go with Postgres.

my favorite quote: "the relational model is rooted depth in the set theory and math is hard to fool"

it was on a post on how all NoSQL are ending up having to reinvent having, group and join clauses on their API because that's what apps do with data

I hate hearing absolutist dogma like this. Some things are faster in Postgres, some things are faster in Mongo. We are migrating our analytics db from Mongo to PG but we hit a wall because SELECT COUNT(DISTINCT x)) performs abysmally in Postgres. Mongo's aggregation framework is an immature PITA but it performs this little trick well enough that we're pretty much stuck keeping Mongo around unless we want to use MSSQL or Oracle or something else with a better (and much more expensive) query planner.

We still love (and prefer) Postgres but it is not a pareto improvement. There are always tradeoffs, and this kind of fanboyism just speaks to inexperience.

Also MSSQL's query planner isn't better than Postgres', I work with both. Postgres does have its quirks though, especially with the MVCC row expiry.

Meh. Postgres' planner doesn't know how to generate a skip-scan/loose index scan for DISTINCT. You can write it yourself, but it's a bit painful: https://wiki.postgresql.org/wiki/Loose_indexscan

If you have a low cardinality that can be a huge efficiency difference.

    EXPLAIN ANALYZE SELECT COUNT(DISTINCT(calc)), calc FROM price_history GROUP BY calc;
                                                          QUERY PLAN                                                           
    -------------------------------------------------------------------------------------------------------------------------------
     GroupAggregate  (cost=10713.04..11381.06 rows=10 width=5) (actual time=1010.383..1073.263 rows=11 loops=1)
       Group Key: calc
       ->  Sort  (cost=10713.04..10935.68 rows=89056 width=5) (actual time=1010.321..1049.189 rows=89041 loops=1)
             Sort Key: calc
             Sort Method: external merge  Disk: 1392kB
             ->  Seq Scan on price_history  (cost=0.00..3391.56 rows=89056 width=5) (actual time=0.007..20.516 rows=89041 loops=1)
     Planning time: 0.074 ms
     Execution time: 1076.521 ms
    (8 rows)

    EXPLAIN ANALYZE SELECT COUNT(DISTINCT(calc)), calc FROM price_history GROUP BY calc;
                                                                         QUERY PLAN                                                                          
    -------------------------------------------------------------------------------------------------------------------------------------------------------------
     GroupAggregate  (cost=0.29..2804.82 rows=10 width=5) (actual time=0.117..47.381 rows=11 loops=1)
       Group Key: calc
       ->  Index Only Scan using price_history_calc_idx on price_history  (cost=0.29..2359.52 rows=89041 width=5) (actual time=0.054..18.579 rows=89041 loops=1)
             Heap Fetches: 83
     Planning time: 0.208 ms
     Execution time: 47.416 ms
    (6 rows)

An alternative is PipelineDB, which is built on Postgres (and drop-in compatible, supposedly) and should provide an efficient implementation for those kinds of queries: https://www.pipelinedb.com/

I've never used it, though.

https://github.com/aggregateknowledge/postgresql-hll ?

or even

https://www.periscopedata.com/blog/hyperloglog-in-pure-sql.h... ?

    SELECT COUNT(*) FROM (SELECT DISTINCT x FROM table) AS temp;

    SELECT COUNT(DISTINCT x))

This sounds exactly like what MySQL development looked like to me early in its rise to popularity.

The main problem someone is thinking about may be solved by a document DB, but then they also have 1000 other problems they didn't think about that are a horrible fit for a document DB.

And due to the nature of databases, where data has greater value when combined, using a new database system for every application isn't a great option.

SQL is great for business data, and passable-to-good for everything else. So the only way to beat it is by being way better at some specific thing, and finding users where that specific thing is so important that it's OK if the data is on an island.

IMHO, unless you have a situation such that Mongo's horizontal scaling is actually required, it's better to use Postgres even if you're doing document-store stuff.

http://cryto.net/~joepie91/blog/2015/07/19/why-you-should-ne...

The only time I would recommend Mongo is for storing geo-spatial data, as it has several built ins that make it much easier to work with. Even then I find it a lot more convenient to keep that data in Postgres and replicate it to Mongo.

However if the write load is very high then Mongo is better suited as the intial store. I then replicate to other dbs.

You can of course improve Mongo's speed by using weaker write concern, but you can also disable WAL in Postgres too.

However I found scaling write load past a single server's limits more difficult using Postgres than MongoDB.

Fortunately that is part of what the OP addresses in his post.

The biggest selling point of NoSQL databases really is increasing performance and horizontal scalability due to removing some guarantees that RDBS provides. With MongoDB you get neither of the advantages.

Generally with Postgres you can't scale easily, unless you only do reads, but postgres outperforms MongoDB. You can get some marginal improvement by running multiple MongoDB nodes, but a single Postgres will still outperform it.

Also with benchmarks like this, it is often overlooked that in a relational database you often can store data in a smarter way and get extra boost of performance.

For example in my previous job we had 3 MongoDB which were used for mapping IP address to a zip code. Those databases were 12GB each. They run on a beefy instances in AWS because they wanted to make sure all the data could fit in RAM.

I did a POC and put the same data in Postgres and it essentially was just 600MB of data. All queries were sub milisecond, on smallest instance.

How come? Well in Postgres I stored ranges of IPs using ip4r extension. And put an GiST index which works with ranges. Mongo did not understand IP addresses so what they essentially did is they computed every possible IPv4 address and stored it into 64 bit integer, then they placed an index which probably was bigger than the data itself.

Their solution also won't scale with IPv6.

[1] http://www.datastax.com/wp-content/themes/datastax-2014-08/f...

[1] http://protogrid.com

Our framework only has a mysql adapter for now, but it should be pretty easy to add a postgres one.

Generally, if you can, you should consider supporting Postgres in your framework. It's a much saner and robuster database from an Ops point of view (replication doesn't fail as often) and more flexible from a Dev view (performance is generally more predictable, much wider feature set with document storage / PostGIS / etc., …).