I've used it a couple of times in hobby projects and enjoyed not maintaining a schema. I read so many of these 'gotcha' style articles and for example one commenter here wants to have a manual "recently dirty" flag to combat the master / slave lag mentioned in the article. I know it's faster (tm) but once you have to take in to account all this low level stuff you have to worry about yourself wouldn't it just be better to rent/buy another rack of mysql servers and not worry about it?
I would have replied something similar if that was the question :-) (I use PG a lot these days).
Agreed on the first point (but I'm not sure you get exactly the same type of flexibility in all my use cases - I'll have to make a closer comparison).
For the second point, well not having to handle the schema for ETL jobs is sometimes fairly useful and removes a lot of cruft, that was part of my point (those ETL are code-based, only relying on MongoDB as a flexible store).
Very tempted to ask "In what way does postgres compare to an assembler and mongodb to a high level language?" but I think I'll just assume that you're trolling.
Besides, SQL sounds more high-level than map-reduce to me.
To elaborate on the semi-structured input point: Monogo and it's kin are great for EAV systems (http://en.wikipedia.org/wiki/Entity%E2%80%93attribute%E2%80%...), where your entities can have an arbitrary number of fields (often user defined). Trying to build this kind of system in a traditional RDBMs can be quite tricky.
Since we are a mysql shop, for this use-cases I serialize and store the form as xml in a CLOB column. For any field that needs to be searched on, I create an additional column.
Disadvantage of this is you can't run mysql queries against the data stored in the CLOB column.
So does PostgreSQL, but I have no idea about MySQL. You can run xpath queries against XML documents, and you can also index specific xpath queries (there is no general indexing infrastructure for XML in PostgreSQL). For JSON no path lookup functions are included in the core yet, but I assume there are extensions which add them.
Thanks for elaborating! This is much needed and spot on :-)
A long while back I built a somewhat complex survey app: I can confirm it's fairly more complicated to handle with a RDBMs, compared to a document store.
Very interesting presentation, though the title of "heralding the death of nosql" is either intentionally exaggerating, or indicates the author doesn't understand all the reasons why people go to nosql databases. In fact, the presentation demonstrates why: Postgres has tons of really fantastic, awesome features that next to nobody uses because they are hidden behind layers of SQL-type incantations and/or require various extensions.
The thing is that 10gen did a really, really good job at polishing the install process and documenting it to get people started.
No surprise to see the GIS part of MongoDB is built-in instead of an extension of some kind. I know a couple of people who used PG without even knowing there was a GIS extension.
I don't quite understand how the transition happened from no-json support to json support in those slides. How did the plv8js do this searching on fields?
IMO there is no rational explanation to this phenomena other than: people are different. Some get bored with stored procs and want same hassle but in another form.
Are you trying to give us an idea of what it's like to build an even moderate sized app without transactional consistency and with mapreduce? Because that is what it sounds like to me.
In my experience, it's not just throughput that's important, but also 99th percentile latency. If I understand fractal trees correctly, you sometimes need to rewrite all of your elements on disk. How do you do this without causing lag?
Basically, I think you're thinking of the COLA. What we implement does have a literal tree structure, with nodes and children and the whole thing, so at any point you're just writing out new copies of individual nodes, which are on the order of a megabyte. At no point do we have to rewrite a large portion of the tree, so there aren't any latency issues.
Thank you very much for the links. Is my understanding correct?
Essentially, a fractal tree is a B-Tree (or perhaps a B+Tree?) with buffers on each branch (per child). Operations get added to these buffers and when one becomes full, the operations get passed to the corresponding child node. Operations are applied when they reach the node that is responsible for the data concerned.
Not sure what Wu-Tang has to do with this, but....
Seriously though, is Mongo an ODB, or a document oriented database? ODBs/OODBs imply a much different use-case and functionality, and I think we ought not conflate the two.
Mongo is not an ODB in the traditional ODB sense. Mongo is just a document (or blob) database. ODB supports relationship, link traversal, inheritance, etc. Basically the whole shebang of the OO notions in a database.
PostgreSQL is the PAST and it will forever remain there until it solves its biggest problems.
It is the hardest out of all databases I've used to cluster, replicate, shard and manage. And the database world is moving towards scaling horizontally rather than vertically. I can push a button in say CouchDB to replicate and shard. Try doing that in PostgreSQL.
"And the database world is moving towards scaling horizontally rather than vertically."
I think that's an oversimplification. Two counterpoints:
* Database systems will always need to make heavy use of locality. The speed of light means that synchronizing access over long distances (even medium distances -- light only goes about a foot per nanosecond) will always be a challenge.
* Multi-core means vertical scaling is back in (if by "vertical scaling" you mean "scaling on one box"), and probably for a while.
Postgres is doing an excellent job at both.
I do agree with the less-exaggerated point that postgres really needs to improve its multi-machine scaling. But it's far from a solved problem on any system under discussion.
I think you're referring to postgres's inability to use more than one core per query, which is true (or mostly true... there are quite a few helper processes that take on some of the work).
For many smaller queries, postgres does great on multi-core, and pgbouncer is a good connection pooler.
People still use SQLlite. Why? Because its suits a specific set of circumstances.
Postgres does the same---its an incredibly powerful database with a large suit of features that make application development both easy and sane. To the vast majority of projects where you won't outgrow a single server, it makes sense to use it.
One issue with MySQL in large databases is that schema changes are extremely expensive, so much so that you'll be making design decisions around it (e.g. how do we implement this feature without executing our two-day alter table statement). Not all RDBMS have this problem to such a degree but none can escape it entirely.
A lot of the gotchas that he notes are related to design trade-offs with different default behavior than an RDBMS typically would have. For example as your system gets large enough in MySQL you may find you have to do asynchronous replication as well, and then you will have similar problems with dirty reads.
> e.g. how do we implement this feature without executing our two-day alter table statement?
With MySQL being so prevalent, IMO this is the principal reason why the concept that RDBMS' are hard to work with exists. None of the other major platforms have this issue, but if a developer's only exposure is to MySQL, then the idea of schema alters are always fearsome.
This is not to say that ALTER TABLE statements are always quick - if constraint checks or default values are included, there can be long runtimes in PostgreSQL, MSSQL, Oracle, etc. - but significant downtime to add an empty nullable column is just plain stupid for a platform that has been around as long as MySQL.
MySQL has forced a major population segment of developers to toss the advantages of DB side validation and rich query languages for the purpose of _avoiding_MySQL_. Sure the whole object-relational impedance mismatch exists, SQL is hard, yada-yada, but I have never seen these reasons cause as much angst as taking a service offline to add a column to a table.
10gen should have a picture of Monty in their CFO's office.
An option is not a default, though, so using Postgres or MySQL absolutely would solve the problem of returning with success before persisting the data.
The contentious area isn't really that Mongo does this, it's because for whatever reason the people trying it don't expect Mongo to do this.
I think there are legitimate reasons to use a "NoSQL" solution rather than MySQL. I'm more interested to know in what use cases Mongo kicks it's competitors asses? What are it's competitors, even? I'll admit that the NoSQL world is a slightly blurry mess to me, with different products seemingly optimised for different cases.
My number one reason for choosing MongoDB is replication that just works out of the box and doesn't require either a read lock or shutting down the master to set up a new slave.
That's my understanding too, but MongoDB hit the sweet-spot for us, Riak couldn't handle the raw queries-per-second we needed without requiring extra hardware ($$).
Serious question: What database requires read locks or shutting down the master for setting up a new slave? Is it MySQL?
Both sounds like really weird requirements for replication which defeats half the purpose of having it. PostgreSQL has never had any of these two problems. Still working on improving usability but with the addition and improvements of pg_basbackup I would say it is almost there. Hopefully 9.3 will get timeline switching to simplify failover.
Map reduce across sharded servers comes to mind as an advantage. For that matter, horizontal scalability in general is a big advantage that many of the NoSQL data stores have over RDBMS databases.
The count({condition}) one is a worry. I'm guessing it is slow in the case where it has to page the index in in order to count it. I wonder if it is still a problem where the index is used a lot anyway. A fix in MongoDB would seem a lot better solution than having everyone implement their own hacky count-caching solution.
EDIT: Actually, looking at the bug reports, sounds like maybe lock contention on the index?
The master/slave replication problem seems bad but I think it can be worked around (for my particular project) with a flag on the user session ... if they've performed a write in the last 30 seconds, set slaveOkay = false. Users who are just browsing may experience a slight delay in seeing new documents but users who are editing stuff will see their edits immediately.
The inconsistent reads in replica sets is something we've come across with MySQL read slaves as well. I think it's a gotcha of that whole model of replication, rather than a MongoDB-specific issue.
I'm not aware of any database that solves this problem. Is there one? As far as I know, mysql reads must be distributed to the slaves at the application level, which has no knowledge of master/slave inconsistency. I suppose the time delta between master and slave can be queried, but that still doesn't protect from race conditions/inconsistent reads. This is actually why we chose to only utilize slaves for data redundancy rather than read throughput at my last company. Inconsistent reads weren't tolerable.
Riak does. You say, when writing, "please don't return until this data is replicated on 2 servers." And when reading, "please only return a successful read if this data is read from 2 servers."
So you have R = 2, W = 2, R+W = 4, and if your replication (N) val is 3, you're fine (you're always going to get consistency if R+W > N).
Cassandra does, you can write with a write consistency of W and read with a read consistency of R, and as long as their sum is greater than the replication factor (number of copies to store across the cluster) you have consistent reads. W + R > N.
As far as I can tell, WriteConcerns don't protect from inconsistent reads in all cases. It looks like the most conservative setting is Majority, but even then there is no assurance that reads won't occur during the replication, nor that they won't occur to one of the minority of non-replicated servers.
You can set it to the total number of slaves you have and ensure data is on all of them. Normally that slows writes down enough that it's undesirable, though.
No, Riak is nontransactional. But neither is mongo if it's important here. Riak is apparently getting some kind of strong consistency though. Calvin looks interesting for a nosql with distributed transactions.
This is also something we desperately needed at my last company, but we couldn't find FOSS that supported it. mysql offers XA, but I've heard mixed reviews.
Shameless plug (hey Tokutek is doing it), in VoltDB replication is synchronous so it doesn't have this problem.
Latency in the current version is nothing to write home about, but in V3 latency with replication is 600-1000 microseconds. Group commit to disk is every 1-2 milliseconds.
V3 also allows reads to be load balanced across replicas and masters so you gain some additional read capacity from replication. V3 also routes transactions directly to the node with the data so you don't use capacity forwarding transactions inside the cluster.
You get to keep transactions to. Now go figure out what you don't get to keep ;-)
You don't have to give up cross DC replication if you do it asynchronously, but you lose cross shard consistency when there is a dirty fail over. This effects distributed transactions and series of single part transactions that depend on each other across different shards.
What Volt supports right now is actually asynchronous replication that does preserve cross shard consistency, but that is not going to last.
You can do synchronous multi-DC replication, but then you have Spanner and the associated latency of multiple data-center quorums.
One way to resolve it is to mark that user or session (or even just request) "sticky to the master" for long enough to cover your normal replication delay.
When we saw it before, ensuring that a given request which issued a write also read from the master was sufficient. (sub-second replication delay).
If I'm reading your description right, this is hardly mongo-specific. Try it in mysql, for example:
(index is [:last, :first])
select first from names
where last in ('gordon','holmes','watson')
order by first;
An index is an ordering by which a search may be performed -
to illustrate, the index for my small table looks pretty much like this:
gordon, jeff
holmes, mycroft
holmes, sherlock
watson, john
Unless the first key is restricted to a single value, it can't order by the second key without performing at least a merge-sort. They aren't in that order in the index.
The post reads as a series of criticisms about mongo. I don't love mongo, but I'm not aware of any data store that can perform that type of query purely from an index.
Now, the description was vague enough that he could have been describing a real bug I'm not aware of - at one point I've seen MySQL decide to use an index for sorting instead of for filtering when that query plan was 500x slower. If mongo has a bug like that one, disregard my comment please. :-)
That was our use-case as well. And it works fine for this but just in the application layer. We are not using Mongo for data storage (at least we are not trusting it to hold it for long)
I think it's generally full of gotchas similar to that of SQL databases like MySQL and Oracle. In fact, most of the issues mentioned in this article, like delayed replication, indexed queries and using 'explain' are issues I've had to deal with in MySQL and Oracle. Most of these databases are fine out of the box for small scale use, but when you scale up you have to deal with these 'gotchas' like indexing, partitioning, bulk loading, and having to profile everything etc.
Yes, but only because it has ~infinite marketing hype.
It isn't and shouldn't be a general replacement for a RDBMS; it makes some interesting sacrifices for performance that you have to understand before using it. But it is very much a quality product; it makes some easy things very easy and some very hard things possible.
In what use cases does mongo kick mysql's ass?
I've used it a couple of times in hobby projects and enjoyed not maintaining a schema. I read so many of these 'gotcha' style articles and for example one commenter here wants to have a manual "recently dirty" flag to combat the master / slave lag mentioned in the article. I know it's faster (tm) but once you have to take in to account all this low level stuff you have to worry about yourself wouldn't it just be better to rent/buy another rack of mysql servers and not worry about it?
Look forward to learning something...