Don’t Use MongoDB

Anonymous Post about MongoDb via pastebin

Below is the original anonymous post:

Don’t use MongoDB

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.

Why this matters

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.

Why is MongoDB popular?

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:

Don’t.

Why not?

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.

Right now the top answer on the Hacker News thread is 10gen’s CTO, Eliot Horowitz.

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