Unless he's planning to build sharding on postgres too, I think he's missing the point.
Not sure how sharding is the point of MongoDB, though - in most of the universe, sharding is a database architecture/schema-level thing, not a database-server level thing, and for good reason - it's pretty darn hard for a database server to shard effectively without some knowledge of the app layer (and which keys are likely to become hot).
Personally, if I really wanted a flexible-schema "document based" database, I'd have implemented this using the FriendFeed K/V + Index model ( http://backchannel.org/blog/friendfeed-schemaless-mysql ) plus Postgres's HStore functionality, storing K/V per document in an HStore rather than in one giant K/V table like FriendFeed. That way I wouldn't need to use V8 and JSON parsing to run queries, and the mythic MongoDB-style "sharding" would be just as easy (just distribute the document -> hstore table across shards keyed on ID again).
Such an approach wouldn't be horribly difficult to implement in SQL using a copy table and write triggers - almost identically to how SoundCloud's Large Hadron Migrator allows writes to occur over a MySQL InnoDB table that's locked for migration (but even simpler because the table schema can't conflict afterwords).
The entire problem is admittedly nontrivial, since if the application happens to be writing data to the shard under migration too quickly (or the shard being migrated to dies), the server can end up in a situation where the new shard is never able to catch up and become a master. However, the easy solution (give up and retry later) is Good Enough for most situations (and is pretty much how MongoDB works).
NoSQL seems to have three general claims (I'm not saying whether these are correct or not): (1) ease of administration in some cases; (2) different data model; (3) better performance or availability in some situations.
The author is clearly addressing the second, and you are clearly talking about the third.
On the other hand, old fashion MongoDB master slave configurations and now replica sets have been easy for me to set up when on just a few occasions I had to use a scaled out MongoDB setup.
With postgres you have to roll your own. If you want to bridge the gap from postgres to mongo, I think that's where you have to start.
There are many serious sites that don't need sharding.
That said, if your site grows in some way you didn't originally anticipate and you get to a point where you need to shard, but can only do so by changing data stores, then it's sad.
I think you're being too absolute. For instance, Instagram used sharding in postgres, and they didn't have to throw anything away or dedicate any huge engineering team to solve it.
Bullshit.
The sharding impl in MongoDB still[1] crumbles pitifully[2] under load. Regardless of sharding MongoDB still halts the world[3] under write-load.
Their map/reduce impl is a joke[4][5].
If you had done the slightest research you'd know that every single aspect that you need to scale out Mongo is either broken by design or so immature that you can't rely on it.
MongoDB may be fine as long as your working set fits into RAM on a single server. If you plan to go beyond that then you'd better start with a more stable foundation - or brace yourself for some serious pain.
[1] https://groups.google.com/group/mongodb-user/browse_thread/t...
[2] http://highscalability.com/blog/2010/10/15/troubles-with-sha...
[3] http://2.bp.blogspot.com/_VHQJkYQ5-dY/TUO3RAn8SNI/AAAAAAAABq...
[4] http://stackoverflow.com/a/3951871
[5] http://steveeichert.com/2010/03/31/data-analysis-using-mongo...
[2] is from a year and a half ago. It doesn't belong in a sentence that includes the word "still." I work at foursquare, btw. Those outages happened on my first and second days at the company. I wasn't so keen on mongo then either. We've gotten much better at administering it. Basically all our data is in mongo, and it has its flaws, but I'm still glad we use it.
[3] is also from a year and a half ago. Mongo 2.2 will have a per-database write lock, which is at least progress, even though it's obviously not enough. Since 2.0 (or 1.8?) it's also gotten better at yielding during long writes.
I have no experience with their mapreduce impl and can't speak to it.
I'd in fact be curious how exactly did you work around the sharding issues at 4square?
Remember I replied to someone who claimed it takes "no engineering effort" to scale MongoDB. That's not only obviously false, but last time I tried the sharding was so brittle that recommending it as a scaling path would border on malice.
I ran a few rather simple tests for common scenarios; high write-load, flapping mongod, kill -9/rejoin, temporary network partition, deliberate memory starvation. MongoDB failed terribly in every single one of them. The behavior would range from the cluster becoming unresponsive (temporary or terminally), over data-corruption (collection disappears or inaccessible with error), silent data-corruption (inconsistent query-results), to severe cluster imbalance, to crashes (segfault, "shard version not ok" and a whole range of other messages).
I didn't try very hard, it was terribly easy to trigger pathological behavior.
My take-home was that I most certainly don't want to be around when a large MongoDB deployment fails in production.
As such I'm a little disconcerted every time the Mongo scalability myth is reinstated on HN, usually by people who haven't even tried it beyond a single instance on their laptop.
I reiterate: /troll
What other databases did you go to the same lengths to make fail which handled them gracefully?
The map reduce implementation is quite new sure. But it is getting better and you can always link it up with Hadoop.
At the very least provide links that aren't nearly 2 years old.
That doesn't help when you need to make a bulk-update on a busy collection. Busy collections have a tendency towards being in need for bulk updates occasionally.
At the very least provide links that aren't nearly 2 years old.
The first link is 1 month old.
The other links also still describe the state of the art. Feel free to correct them factually if that is not true.
My point was that sharding is not an absolute "have it or not". Some features require major engineering efforts to get anywhere at all, but sharding is not one of them.
But if you think the overall effort is less with MongoDB then go for it.
Still too broad. Sorry, this is a pet peeve of mine, where tech people assume that everyone else has the same issues as them. For example, I worked on an ecommerce site that made over a mil a year. They had less than 10k products, and will never need sharding. They are not read-only, they have people updating their products on a daily basis through the site.
I think the opposite. Most serious sites will never need sharding.
Every large site on the internet has talked about strategies around sharding. At some point you are surely going to hit the physical limitations of one database on one server.
I think your definition is better, and I should've said "a potentially-large site" or something like that.
I don't agree. Almost every site that grows in ways that weren't anticipated (or at a scale that wasn't anticipated) will have to make technical changes. If you don't need to make any changes it's almost certainly the case that you originally over-engineered. If you're optimizing for cases that you don't anticipate, I don't know what to call it other than over-engineering. Facebook started simple and only made Cassandra when they needed to, Google didn't have BigTable when they started, etc etc.
http://highscalability.com/blog/2009/8/5/stack-overflow-arch... is the link I can find right now.
Having a master/slave setup is more than a single server, but it's NOT the same as sharding.
EDIT: Both run read-only slaves. I was only talking about no sharding.
The main problem is user/session concurrency. On one machine - it kills at some (near) point. A DB is doing much more for every write then reads (look at my blog here: http://database-scalability.blogspot.com/2012/05/were-in-big...). The limit is here and now, even 100 heavy writing sessions will choke the MySQL (or any SQL DB...) on any hardware.
Catch 22: Scale-out to repl slaves with R/W splitting? This can lower read load on the master DB, but read load can be better lowered by caching. The problem is writes and small supporting transactional reads, and slaves won't help. Distributing data (sharding?) is the only way to distribute write intensive load, and it also helps reads by putting them on smaller chunks, and parallelizing them is a sweet sweet bonus :)
As I see around (hundreds of medium-large sites) - there's no other way...
And one final word about the cloud: "one DB machine" is limited to a rather limited non-powerful virtualized compute and I/O space... In the cloud limits are here and now! Cloud is all about elasticity and scale-out.
Hope I helped! Doron
but it will be trivial if you do not do joins.
Postgres-XC is probably the most exciting PostgreSQL-related project out there. It promises full write-extensibility across the cluster without sacrificing consistency.
You know people say this, but in practice I find that simple hash bucketing with a redundant pair works surprisingly well, particularly in the cloud. Yes it isn't fancy, but it is trivial to manage and debug, and you can do a lot of optimizations given such a clear cut set of partitioning rules.
Your problems have to get really big before a more sophisticated mechanism really pays off in terms of avoiding headaches, and often the more sophisticated mechanisms actually cause more headaches before you get there.