Since the article is a resubmission, I hope you all will excuse a comment resubmission:

This is the 3rd or 4th time I've seen this article in the past few days so I've decided to post my take. I work with Mongo in a production scenario but I'm hesitant to post because these things tend to turn into a pointless argument. So let me stress: this is not an attack on the blog post, I'm hoping to improve the discussion here. Mongo has some real problems, these mentioned are not the problems. Here goes, written along with the article's segments.

- "It lies." Mongo used to have a default where a driver would fire off a write and not check that it succeeded with the server. This was very obviously a decision made to improve benchmark performance, though I imagine a benchmark with only default settings would be rather naive. Regardless, yes, the default was a stupid corporate decision but it is well known and should be apparent if you're deploying a Mongo cluster. Additionally, as the author notes, this default has changed and this entire point is no longer a concern.

- "Its Slow." A real point he raises is that its a little wonky you need to send a separate message for getLastError. I suspect this is an artifact of Mongo's historical internal structure. http://docs.mongodb.org/meta-driver/latest/legacy/mongodb-wi.... . If you look at the wire protocol, I think it is designed such that only the OP_QUERY and OP_GET_MORE message types get an OP_REPLY back. getLastError is a command, which are run through an OP_QUERY message. He notes that using this check affects performance. It does, but lets dig into this:
"Using this call requires doubling the cost of every write operation." If the author benchmarked this, I suspect he would find that it vastly more than doubles the latency of a single write operation from the client's perspective. My understanding is that when performing this kind of safe write, the driver sends an OP_INSERT, for which it doesn't have to wait for a reply, then immediately sends an OP_QUERY message (getLastError), on which it hangs waiting for the OP_REPLY. In other words, this is now slower because we've created a synchronous operation immediately after just firing off the insert command. Again, its a little wonky that we send Mongo two messages, but one is immediately after the other and that is vastly overshadowed by the fact that we now have to wait for a reply. I believe 1 synchronous send and receive is unavoidable to ensure a safe write in ANY system, and the argument about sending 2 messages really boils down to sending about 200 bytes over a socket vs 400 bytes, I personally don't worry about it.

- "It doesn't work pipelined / it doesn't work multithreaded." He is also missing the real complexity here, and this is where I have a problem with this blog post because this is not a theoretical discussion, if its content is true then it should just be proven rather than FUD launched into the world. As noted in the docs ( http://docs.mongodb.org/manual/reference/command/getLastErro.... ), getLastError applies for the socket over which the command was run, so its up to the driver to execute the getLastError on the same socket as the write, which is an implementation detail and a solved problem. The way the drivers do this in practice is you set the write concern and the driver takes care of the rest. If you run getLastError manually then it depends on the driver, but for Java the correct procedure is addressed at http://docs.mongodb.org/ecosystem/drivers/java-concurrency/ . So for the fastest possible safe performance, you multithread (which is effectively pipelining from the server's perspective) and run operations with the driver's thread-safe connection pool. Suffice to say people actually use these drivers in a multithreaded context in the real world, and they work.

- "WriteConcerns are broken." There are several relevant settings here, including write concern acknowledgement and fsync, the author is confused about how these map to the Java driver WriteConcern enum values. The acknowledgement setting (elegantly stored in a variable named "w", thanks 10gen) is the number of replicas that must confirm the write before the driver believes it has succeeded. I personally set this to 1, but you could potentially wait for the entire replica set to acknowledge. The fsync setting is whether or not this acknowledgement means that the server on these machines has completed the write in memory, or actually synced the data to disk. I set this to false for performance. There is an excellent StackOverflow answer on Java Mongo driver configuration at http://stackoverflow.com/questions/6520439/how-to-configure-.... .
The author also spends time noting that if you only ensure the write succeeds on a single machine, and you irreparably lose that machine before replication, then the data is lost. This is obviously true for every distributed system.

I've used a 4-shard Mongo in a production multithreaded environment that handled several hundred million writes. For part of this period our cluster was extremely unstable because of a serious data corrupting Mongo bug (more on this in a second). I haven't done a full audit but based on our logs, in about 6 months I've seen exactly 1 write go missing (which I believe was in the rollback log), so I'm personally not concerned about the things mentioned in the blog post. I've also been happy with performance as long as the data size comes under the memory limit. If your data exceeds memory, Mongo essentially falls on its face, though this is hard to avoid when a query requires disk accesses.

Mongo is not without its problems, however. As I mentioned, QA is a real concern, we hit a subtle bug when we upgraded to v2.2 that caused data corruption when profiling was turned on and the cluster was under high load. It was very difficult to debug, and basically should have been caught by 10gen before their release.

Another serious problem is that sharding configuration is still somewhat immature, and it seems like every new release is described as "well it used to be bad, we finally fixed it". Here is an example: you pick a shard key that can't be split into small enough chunks, and now shard balancing silently fails. Ok, so you pick a better shard key, but you can't migrate shard keys, so you have to drop the collection and start again. Except dropping a collection distributed across a cluster is buggy, so you can't recreate the collection with the same name and a different shard key. So you just pick a new name for your collection, and you have this weird broken state for the original one sitting around forever unless you completely blast your cluster and start from scratch. This sort of thing is not fun!

Mongo has many pros and cons, personally I think its real advantage is simplicity for developers which makes it worth putting up with the other stuff. Sorry for being long winded, hopefully this has been useful.