Things I wish I knew about MongoDB a year ago
snmaynard.com
snmaynard.com
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...
Let me guess. You would build a skyscraper using a trough and cement in a bucket ?
http://en.wikipedia.org/wiki/Object-relational_impedance_mis...
MongoDB is just ODB, and MySQL is just RDB.
Besides, postgres is the real future!
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.
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.
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.
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.
For many smaller queries, postgres does great on multi-core, and pgbouncer is a good connection pooler.
- dealing with semi-structured input (forms with some variability) and storing as a document, all while being able to query across the data
- used as a store to provide very flexible ETL jobs (with ability to upsert, filter/query, geonear etc)
For those situations, I would definitely use MongoDB again. As a RDBMS replacement, I wouldn't use it today.
If you need to store multiple forms types, it gets hairy very fast.
MongoDB allows to query inside the data (which is not a blob in that case).
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.
In what use cases does mongo kick postgres's ass?
- semi-structured input can be saved as hstore type or as json type;
- and for flexible jobs, you can use pretty much any popular language - PL/R, PL/Python, even PL/C if performance is really critical.
Besides, SQL sounds more high-level than map-reduce to me.
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).
so you could have:
create table form_results (
id serial primary key,
data json
);
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.
You can apply full text search on it, but that doesn't tell you if you're matching on a key or a value.
Here is a presentation (slides + video) I gave about a Ruby ETL, for instance. It illustrates the typical use cases I run into.
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.
If you have a use case that centres around storing document style data then MongoDB will be better suited.
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.
When you make changes so fast you need a liquid schema.
When you want to make your boss learn map-reduce so he can query the data.
When the application can take care of integration and not the database itself.
Does Mongo still have a global write lock?
[1] http://www.mongodb.org/display/DOCS/How+does+concurrency+wor...
> When you have so many writes that sharding isn't enough.
TokuDB does indexed insertions very fast. [1] We've even plugged ourselves in underneath MongoDB (just for fun) and we beat them too. [2]
> When you make changes so fast you need a liquid schema.
TokuDB supports lots of schema changes with zero downtime. [3]
> When you want to make your boss learn map-reduce so he can query the data.
Can't help you there, but I can make you not need to torture your boss that way.
> When the application can care of integration and not the database.
What if it didn't need to?
We also get fantastic compression [4], retain full transactional semantics, and lots of other fun stuff.
Email us if you're curious!
[1]: http://www.tokutek.com/resources/benchmark-results/benchmark...
[2]: http://www.tokutek.com/2012/08/10x-insertion-performance-inc...
[3]: http://www.tokutek.com/resources/benchmark-results/benchmark...
[4]: http://www.tokutek.com/resources/benchmark-results/benchmark...
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.
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.
A "fractal tree" is defined by our marketing team as "whatever it is we actually implement." If you want to talk cache-oblivious data structures, we can talk about things like the COLA and cache-oblivious streaming B-trees which have rigorous definitions in the literature. At some point, if you want to achieve a certain level of detail, you have to pick one or the other in order to continue the conversation.
I'm trying to experiment with these ideas on my side, but can't quite grok how large the buffers must be at each level of the tree.
Let's take a concrete example like: 2^40 (1T) records with an 8-byte key and an 8-byte value. In a traditional B-tree with a 8KiB block size and assuming links take 8 bytes too and assume for a while that blocks are completely full. So, that's 1G leaf blocks, a fanout of about 1K and hence & full 4-level trees: 1 root block, 1K level-2 blocks, 1M level-3 blocks, 1G leaf blocks.
In this setting, I understand that a fractal tree will attach a buffer to each internal node. How large will they be at level 1 (root), level 2, and level 3 ?
The degree of each internal node is way lower, because we want to use the space in internal nodes for buffers more than for keys. In a 16TB tree, we would probably have a tree of height 6 or 7.
What we restrict is the size of a whole internal node, so that any one buffer in it can get much more full than the others if it needs to. We want to pick a node size that makes sense for your media. 4MB is a good compromise for spinning disks between their typical bandwidth and seek time, and it's large enough to give the garbage collector a rest on SSDs.
Assume a node size that can hold 1M records in a buffer and 32 links (that's about 16 MiB more or less, more than what you suggest, but it simplifies the explanations).
Assume further that to operate on a node, you read it entirely in memory, modify it, sort it, then write it entirely on disk and that that takes about 1 second. This is pessimistic as there are surely more efficient ways to maintain sorted 1M nodes.
Now consider completely random insertions or 16-byte records and assume that the keys will spilt completely uniformly among the subtrees.
1. Every 1M insertions, you need to spill from level-1 (root buffer) to level-2, which generates about 32 IOs 2. Additionnally, every 32M insertions, you need to spill all level-2 buffers to level-3, which generates 3232 IOs n. every 32^(n-1) M.insertions, you need an additional 32^n IOs to spill from level-n to level-(n+1)
So, all in all, to insert 32^n M.records, you need a total of (n+1)32^(n+1) I/Os, which means 32(n+1) IO per 1M insertions.
For example, in a 16TiB data store, i.e. with n=8, you need 288 IOs per million insertions, or about 2400 random insertions/second at 1s/random IO.
In comparison, a B-Tree with a fanout 1024 and a block size 16KiB would generate about 4 random IOs per insertion. These IOs would be dominated by the seek time (~10ms), so you could get only 25 insertions per second.
On the other hand, a point query in this B-Tree would take 4 IOs instead of 8 random I/Os in the fractal tree. So, that's the tradeoff: much better insertion rate vs. slightly worse query time.
Note that a small fanout (about square root of the equivalent B-tree fanout) is important. If you take 1024 as fanout in the fractal tree, the tree has half the height, but the number of IOs per 1M insertions drops to 10245 instead of 32*9.
Have I got it right?
Flushing one buffer one level down costs O(1) I/Os. It moves B elements, so flushing one element one level down costs O(1/B) amortized I/Os. The tree has fanout O(1), so it has height O(log N). Therefore, each element must be flushed O(log N) times before it reaches its target leaf. So the cost to get one element down to its leaf is O((log N)/B) amortized I/Os.
Compare this with a B-tree, which costs O(log_B N) = O((log N)/(log B)) per insertion.
I'm pretty sure your math is right, though it attacks it from the other direction and I've only just skimmed it. It seems sound though.
The point query tradeoff is there in the theory, you're right, but it assumes a cold cache. In practice, most people have enough RAM for all their internal nodes, whether they're using a B-tree or a fractal tree, so either way it's typically 1 I/O per random point query in either data structure.
Be careful with asterisks here. :)
Now, what is the recommanded layout of the node buffers (the 4MiB buffers). I've read about Packed Memory Arrays, but don't quite get the complete idea. Can we really do better than read, merge, write ?
(A pity there's no preview button on this forum...)
How are fractal trees different?
This tweak to B-trees to support fast insertions works so well I am really surprised it has not become more common in practice. The only downside I can think of is that flushes down the tree when internal buffers get large may cause lower-level pages to become overfull, but if you allow more than one flush operations at each level you can avoid this problem. Of course, that's only important if you are paranoid about internal nodes actually fitting in a page; if you don't care about that, then the worst-case analysis picture looks much better since a flush operation causes at most one flush in the pages immediately below it in the tree structure.
When you actually go and implement the system, you find all these behaviors that really aren't expressed in the theory. There are lots of things we're still experimenting with. Flushing isn't too hard, if a flush makes another node way too big, you can just flush that one too. We don't allow our cascading flushes to fan out, to prevent latency-style problems, but they can flush all the way down to a leaf. There are some other fun problems around query strategies and concurrency control too. It's definitely a fun structure to play with.
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.
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.
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.
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).
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).
Riak is cool.
http://wiki.apache.org/cassandra/ArchitectureOverview#line-1...
As for me, it's mostly the quesion of perfomance, and application architecture, most time you don't want to wait until it's replicated to slaves.
[1]http://docs.basho.com/riak/latest/references/appendices/conc...
http://dev.mysql.com/doc/refman/5.5/en/replication-semisync....
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 ;-)
Cross-datacenter replication becomes a Really Bad Idea?
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.
There is also Calvin http://bit.ly/RGW9RY
http://www.postgresql.org/docs/9.1/static/warm-standby.html#...
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;
gordon, jeff
holmes, mycroft
holmes, sherlock
watson, john
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. :-)
Was a really nice surprise when I was building a location based web app.
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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.