I dont understand how MongoDB can stay as a shiny tool given its long list of shortcomings. It is so elastic that I think it really has no true form (object cache, mq, relational??, key-value bucket).
Lately I haven't read any news about a company migrating to Mongo, but rather most were either departing from mongo because of some catastrophic outage or some mission impossible operation to handle the shortcomings, like this one.
In its current state it looks more like a prototyping tool that allows you to delay some tech decisions until your product matures, rather than a production tool. Or it is just me that wants at least 5 major versions on his production database.
Marketing. They marketed the hell out of it. Most developers I know have MongoDB mug.
> Lately I haven't read any news about a company migrating to Mongo, but rather most were either departing from mongo
They had some strange defaults to start with (to give them serious advantage in small silly benchmarks) like un-acknowledged writes. Yes you read that correctly, for years their default configuration was to throw write requests over the fence and assume they succeeded.
There are some horror stories of people's databases becoming silently corrupted. Those corrupted databases were then backed up and back ups were corrupted.
Eventually they had fixed their defaults but their reputation as an engineering company went down the hill ( I speak for myself here mostly).
From my experience a lot of people using MongoDB don't know what they are using. To them 'unacknowledged writes' and 'database level locks' sounds like Latin. They see short examples, cool mugs, other cool kids talking about how easy MongoDB is and they start using it.
or - mongo is great for some problems and not so great for other problems. if there is one thing you can figure out hanging out on HN long enough - it's cool to hate on mongodb
Tens of thousands of computer geeks all randomly chose to hate a product. Clearly a coincidence or unlucky alignment of stars, nothing to do with said product, of course.
It could just be the case that everyone complains about their database and the HN crowd uses a lot of MongoDB. There's certainly more total MySql/MSSQL griping, whether or not gripers/users is higher for MongoDB isn't really obvious.
I want to like Mongo, I'm all about "cutting my teeth on the bleeding edge", but I don't know what to think about MongoDB. Most conversations about it seem to go like this:
Lover: MongoDB is so much faster and easier than old school SQL!
Hater: It's fast and easy because it doesn't check if it's written data correctly, which leads to corrupt data.
Lover: Well you could always turn on the write lock.
Hater: If you turn on the write lock, it becomes slower than normal SQL databases.
Lover: Well you're just trying to use it the same way you use SQL. MongoDB is great for some problems and not so great for other problems.
Hater: What problems is MongoDB better at solving?
And then that's the end. I've never heard a convincing use case for this thing. If you can change that for me and give me one, I'd be delighted to listen.
Same reason MySQL sticks around when there are alternatives that make it harder to shoot yourself in the foot: Easy to get started, works well enough to make an MVP.
I think the premise of prototyping tool is pretty accurate. It makes easy to get something up and running and defer any operational knowledge until later. That being said, I don't see why people can't iterate their apps quickly on time-proven relational datastores. While having JavaScript through the stack is nice, I found writing Mongo Queries a PITA with all the curly-braces, etc...
Simple. There are some use cases for which MongoDB will be 5x faster than any other system. It is relatively unique among databases in that it is a document store as opposed to a relational system.
> There are some use cases for which MongoDB will be 5x faster than any other system.
This is a really bold claim that I very much doubt is true. For one, there are numerous other document stores that target the same sort of use cases as MongoDB. Second, there are benchmarks floating around that PostgreSQL used as a key value store is faster than MongoDB as is. I wouldn't be surprised if you saw similar things with other SQL databases.
Only with unacknowledged writes. If you ask for acks, it gets ridiculously slow. Much, much slower in every single way than Postgres with a fully indexed HSTORE. That's also fully ACID.
Of course Postgres is also relational. Have you tried using a document oriented store like MongoDB? I have. For some problems it just plain rocks.
That doesn't mean I would want to store financial transactions for a bank in a MongoDB, but it has its place and that place is clearly huge (judging by the number of people using it).
I love it because it is:
- simple to use
- fast enough (with safe writes, thank you!)
- simple to use
- simple to use
Did I mention it was simple to use, which allows me to focus on app instead of wrestling with DB?
Postgres' HSTORE is as document oriented as Mongo.
I have used Mongo plenty and I really don't want to do it again. With its almost-safe writes it's extremely slow and no simpler to use than Postgres. Also much less flexible, basically a subset of Postgres' data model.
This is something that SQL database fans don't get. A small start-up can't afford to have a team of guys fiddling with incredibly obscure performance tuning settings.
A developer might not know how to optimize a slow-running Postgre query, but he knows how to cache data and denormalize in a manner that makes the query he wants to run fast. And a document-store plays nice with this.
Any simple relational beauty of SQL databases falls down once you get over a few million rows, and then the hideous hacks and bizarre settings start coming out. At that point you need a pile of domain-specific knowledge to make them play nice.
Mongo takes the domain-specific knowledge out of the equation and lets the developers apply their existing skill-set to the data.
I've you've got a thousand employees? Get a few DBAs and Postgre.
One thing you don't know or faced yet is that mongo fails, and sometimes it fails so hard that you would rather kick some rocks.
For example, if you start a background index generation on an eventually consistent replica set, indexing on secondary nodes are done foreground. Which means you only accept reads from slaves but slaves are unresponsive because of the index generation. In this state, if you try to do anything fancy your data will go corrupt. Only way out is to wait through the outage (which I find it pretty hard to do so). This is still not solved in 2.4, waiting for 2.6.
Replica sets with all secondaries which can't elect a primary because it lost a node, or the mostly random primary-secondary switches that drops all connections, seldomly primary reelecting itself meanwhile dropping connections for no apparent reasons. Mongo offers tin foil hats for integrity, consistency and reliability. So yeah, I'd rather examine and understand why an SQL query is slow. Because it is at least deterministic, which in mongo nothing really is.
Postgres supports free form json, XML or hstore document formats by the way, couchDb has its own specific features as a document db too. I still don't see why people want to go on with mongo this bad.
I've been running, on a five minute cron, kill -9 on the master mongo instance in our QA lab for a good long time. 24/7.
There's a program running that, 100 times per second, reads a document, MD5's the field, and writes it back. At the same time, it reads a file from the local filesystem, MD5's it, and writes it back. The document and the local filesystem file started with the same value.
After a few thousand kill -9's on the master instance, the local file and the mongo document are still identical.
I've been running MongoDB in production since 2010.
It's definitely possible to use Mongo in a way that isn't safe for your particular use case. But we're doing it correctly.
I haven't lost a bit of data in more than three years of MongoDB.
Mongo has a lot of limitations. We're currently researching various 'big data' solutions, because for us, Mongo doesn't fit that.
For ease of development (in dynamic languages, where your in-program data structures and in-database documents look almost identical), safety, and lack of headaches, MongoDB has been a consistent win for me and the teams I've been on.
Is your test producing a new document value 100 times a second, or just writing the same value back over and over again?
It sounds like it might be the latter, which is not a particularly stressful test (because you can't detect data rollback).
I'm more familiar with relational database internals, but I wouldn't be surprised if a DB just optimized out the unchanged-write entirely (they'd still need to read the current row value, but they don't have to invoke any data modification code once they see the value hasn't changed).
For a good test, you really want to simulate a power-loss, which you aren't getting when you do a process-level kill, because all the OS cache/buffers survives. You can do simulate this with a VM, or with a loopback device. I'd be amazed if MongoDB passed a changing-100-times-a-second test then. I'd be amazed if any database passed it. I'd even be amazed if two filesystems passed :-)
"A developer might not know how to optimize a slow-running Postgre query, but he knows how to cache data and denormalize in a manner that makes the query he wants to run fast"
And you can cache and denormalize with postgres just as easily, and it will probably perform better and not corrupt your data.
What mongo makes easy is things like replication and failover. It can go horribly wrong on that front, but until you see that, it is much easier to get up and running with replication and failover.
No, with Mongo you get a bunch hacks you need to do to maybe get indexed reads. I had to write a query optimiser that does lots of crazy things and simply rejects some entirely reasonably-looking queries because Mongo can't do them efficiently.
Not to mention the complete lack of durability. Even Redis is more durable.
Postgres is trivial to use. Is something slow (which will happen much later than with Mongo)? Add an index, done. I'm not a DBA, you don't need one.
