I've kept quiet for awhile for various political reasons, but I now feel a kind of social responsibility to deter people from banking their business on MongoDB.
Our team did serious load on MongoDB on a large (10s of millions of users, high profile company) userbase, expecting, from early good experiences, that the long-term scalability benefits touted by 10gen would pan out. We were wrong, and this rant serves to deter you from believing those benefits and making the same mistake we did. If one person avoid the trap, it will have been worth writing. Hopefully, many more do.
Note that, in our experiences with 10gen, they were nearly always helpful and cordial, and often extremely so. But at the same time, that cannot be reason alone to supress information about the failings of their product.
Databases must be right, or as-right-as-possible, b/c database mistakes are so much more severe than almost every other variation of mistake. Not only does it have the largest impact on uptime, performance, expense, and value (the inherit value of the data), but data has inertia. Migrating TBs of data on-the-fly is a massive undertaking compared to changing drcses or fixing the average logic error in your code. Recovering TBs of data while down, limited by what spindles can do for you, is a helpless feeling.
Databases are also complex systems that are effectively black boxes to the end developer. By adopting a database system, you place absolute trust in their ability to do the right thing with your data to keep it consistent and available.
To be fair, it must be acknowledged that MongoDB is popular, and that there are valid reasons for its popularity.
- It is remarkably easy to get running
- Schema-free models that map to JSON-like structures have great appeal to developers (they fit our brains), and a developer is almost always the individual who makes the platform decisions when a project is in its infancy
- Maturity and robustness, track record, tested real-world use cases, etc, are typically more important to sysadmin types or operations specialists, who often inherit the platform long after the initial decisions are made
- Its single-system, low concurrency read performance benchmarks are impressive, and for the inexperienced evaluator, this is often The Most Important Thing
Now, if you're writing a toy site, or a prototype, something where developer productivity trumps all other considerations, it basically doesn't matter what you use. Use whatever gets the job done.
But if you're intending to really run a large scale system on Mongo, one that a business might depend on, simply put:
1. MongoDB issues writes in unsafe ways by default in order to win benchmarks
If you don't issue getLastError(), MongoDB doesn't wait for any confirmation from the database that the command was processed. This introduces at least two classes of problems:
- In a concurrent environment (connection pools, etc), you may have a subsequent read fail after a write has "finished"; there is no barrier condition to know at what point the database will recognize a write commitment
- Any unknown number of save operations can be dropped on the floor due to queueing in various places, things outstanding in the TCP buffer, etc, when your connection drops of the db were to be KILL'd or segfault, hardware crash, you name it
2. MongoDB can lose data in many startling ways
Here is a list of ways we personally experienced records go missing:
- They just disappeared sometimes. Cause unknown.
- Recovery on corrupt database was not successful, pre transaction log.
- Replication between master and slave had gaps in the oplogs, causing slaves to be missing records the master had. Yes, there is no checksum, and yes, the replication status had the slaves current
- Replication just stops sometimes, without error. Monitor your replication status!
3. MongoDB requires a global write lock to issue any write
Under a write-heavy load, this will kill you. If you run a blog, you maybe don't care b/c your R:W ratio is so high.
4. MongoDB's sharding doesn't work that well under load
Adding a shard under heavy load is a nightmare. Mongo either moves chunks between shards so quickly it DOSes the production traffic, or refuses to more chunks altogether.
This pretty much makes it a non-starter for high-traffic sites with heavy write volume.
5. mongos is unreliable
The mongod/config server/mongos architecture is actually pretty reasonable and clever. Unfortunately, mongos is complete garbage. Under load, it crashed anywhere from every few hours to every few days. Restart supervision didn't always help b/c sometimes it would throw some assertion that would bail out a critical thread, but the process would stay running. Double fail.
It got so bad the only usable way we found to run mongos was to run haproxy in front of dozens of mongos instances, and to have a job that slowly rotated through them and killed them to keep fresh/live ones in the pool. No joke.
6. MongoDB actually once deleted the entire dataset
MongoDB, 1.6, in replica set configuration, would sometimes determine the wrong node (often an empty node) was the freshest copy of the data available. It would then DELETE ALL THE DATA ON THE REPLICA (which may have been the 700GB of good data) AND REPLICATE THE EMPTY SET. The database should never never never do this. Faced with a situation like that, the database should throw an error and make the admin disambiguate by wiping/resetting data, or forcing the correct configuration. NEVER DELETE ALL THE DATA. (This was a bad day.)
They fixed this in 1.8, thank god.
7. Things were shipped that should have never been shipped
Things with known, embarrassing bugs that could cause data problems were in "stable" releases--and often we weren't told about these issues until after they bit us, and then only b/c we had a super duper crazy platinum support contract with 10gen.
The response was to send up a hot patch and that they were calling an RC internally, and then run that on our data.
8. Replication was lackluster on busy servers
Replication would often, again, either DOS the master, or replicate so slowly that it would take far too long and the oplog would be exhausted (even with a 50G oplog).
We had a busy, large dataset that we simply could not replicate b/c of this dynamic. It was a harrowing month or two of finger crossing before we got it onto a different database system.
But, the real problem:
You might object, my information is out of date; they've fixed these problems or intend to fix them in the next version; problem X can be mitigated by optional practice Y.
Unfortunately, it doesn't matter.
The real problem is that so many of these problems existed in the first place.
Database developers must be held to a higher standard than your average developer. Namely, your priority list should typically be something like:
- Don't lose data, be very deterministic with data
- Employ practices to stay available
- Multi-node scalability
- Minimize latency at 99% and 95%
- Raw req/s per resource
10gen's order seems to be, #5, then everything else in some order. #1 ain't in the top 3.
These failings, and the implied priorities of the company, indicate a basic cultural problem, irrespective of whatever problems exist in any single release: a lack of the requisite discipline to design database systems businesses should bet on.
Please take this warning seriously.