I don't know if it's the _most_ important feature, but I wouldn't build a serious site on top of anything that didn't have some sort of built-in sharding story.
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.
Postgres-XC is also in beta. Basically it's sharded PostgreSQL with full RI enforcement between shards, and seamless query integration. I assume they are working off the 9.2 codebase (hence the beta being the same time as Pg 9.2) but maybe it's only 9.1 (i.e. no JSON).
Postgres-XC is probably the most exciting PostgreSQL-related project out there. It promises full write-extensibility across the cluster without sacrificing consistency.
Fair, my statement was overly broad. Sites that are read-only or store blob data in something like S3 can often avoid sharding for quite a while and rely on machines to just get bigger over time.
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.
> 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 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.
" Sites that are read-only or store blob data in something like S3 can often avoid sharding"
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.
My definition of seriousness includes some relatively large scale. Your definition of seriousness appears to mean any site that is important to the person or business running it. Is that a fair assessment?
I think your definition is better, and I should've said "a potentially-large site" or something like that.
Yes, this. Thanks for listening. It's a pet peeve of mine because most people work on serious sites, and a minority of them have large scale data, but people often imply that everyone needs to solve massive scaling problems even if they will never have them.
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.
He said "serious site". He did not say "large site". For example, techcrunch is most certainly a serious site. It is NOT large scale and in need of 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.
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] looks like an example where the data didn't fit in RAM. Mongo works best when data fits in RAM or if you use SSD's. Yes, it's sub-optimal.
[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.
[2] When you look at [1] you'll notice that these exact problems are still prevalent.
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 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. "
What other databases did you go to the same lengths to make fail which handled them gracefully?
I don't think what I said was bullshit. So you wrote tests to make mongo fail, and you've seen cases where people run into problems with it. That still doesn't disprove my point. With postgres, you roll your own sharding. With mongo, you don't have to.
It seems you fall squarely into the bucket of 'people who haven't even tried' (and some other unfavorable buckets, but I'll leave that to your older self to judge).
Well that's just factually incorrect. MongoDB now has a per-database write lock and will have a per-collection write lock in the next version. So your halt under write-load statement is incorrect.
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.
MongoDB now has a per-database write lock and will have a per-collection write lock in the next version.
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.
I attended a talk by Instagram post-buyout. That's where I got the impression that sharding was not a huge obstacle for them (though it was significant). Keep in mind their entire data management team was 2 people I think.
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.
I would say most sites will do just fine without sharding. You can get very far by just scaling up with a more expensive database server and caching the most common read operations. Some of the largest websites in the world do not need to do more than this.
Hi, disclaimer - I work for ScaleBase, giving a true automated transparent sharding, so I live and breath sharding for 4 years now...
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.
I'm not sure why you said "a single database server". No one said that. We're talking about sharding across databases, and the vast majority of sites don't need that.
Having a master/slave setup is more than a single server, but it's NOT the same as sharding.
> I don't know if it's the _most_ important feature, but I wouldn't build a serious site on top of anything that didn't have some sort of built-in sharding story.
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.
