I am asking a question that I assume does not have a simple black and white question but the principal of which I'm asking is clear.
Sample situation:
Lets say I have a collection of 1 million books, and I consistently want to always pull the top 100 rated.
Let's assume that I need to perform an aggregate function every time I perform this query which makes it a little expensive.
It is reasonable, that instead of running the query for every request (100-1000 a second), I would create a dedicated collection that only stores the top 100 books that gets updated every minute or so, thus instead of running a difficult query a 100 times every second, I only run it once a minute, and instead pull from a small collection of books that only holds the 100 books and that requires no query (just get everything).
That is the principal I am questioning.
Should I create a dedicated collection for EVERY query that is often
used?
Should I do it only for complicated ones?
How do I gauge which is complicated enough and which is simple enough
to leave as is?
Is there any guidelines for best practice in those types of
situations?
Is there a point where if a query runs so often and the data doesn't
change very often that I should keep the data in the server's memory
for direct access? Even if it's a lot of data? How much is too much?
Lastly,
Is there a way in MongoDB to cache results?
If so, how can I tell it to fetch the cached result, and when to regenerate the cache?
Thank you all.
Before getting to collection specifics, one does have to differentiate between "real-time data" vis-a-vis data which does not require immediate and real-time presenting of information. The rules for "real-time" systems are obviously much different.
Now to your example starting from the end. The cache of query results. The answer is not only for MongoDB. Data architects often use Redis, or memcached (or other cache systems) to hold all types of information. This though, obviously, is a function of how much memory is available to your system and the DB. You do not want to cripple the DB by giving your cache too much of available memory, and you do not want your cache to be useless by giving it too little.
In the book case, of 100 top ones, since it is certainly not a real time endeavor, it would make sense to cache the query and feed that cache out to requests. You could update the cache based upon a cron job or based upon an update flag (which you create to inform your program that the 100 have been updated) and then the system will run an $aggregate in the background.
Now to the first few points:
Should I create a dedicated collection for EVERY query that is often used?
Yes and no. It depends on the amount of data which has to be searched to $aggregate your response. And again, it also depends upon your memory limitations and btw let me add the whole server setup in terms of speed, cores and memory. MHO - cache is much better, as it avoids reading from the data all the time.
Should I do it only for complicated ones?
How do I gauge which is complicated enough and which is simple enough to leave as is?
I dont think anyone can really black and white answer to that question for your system. Is a complicated query just an $aggregate? Or is it $unwind and then a whole slew of $group etc. options following? this is really up to the dataset and how much information must actually be read and sifted and manipulated. It will effect your IO and, yes, again, the memory.
Is there a point where if a query runs so often and the data doesn't change very often that I should keep the data in the server's memory for direct access? Even if it's a lot of data? How much is too much?
See answers above this is directly connected to your other questions.
Finally:
Is there any guidelines for best practice in those types of situations?
The best you can do here is to time the procedures in your code, monitor memory usage and limits, look at the IO, study actual reads and writes on the collections.
Hope this helps.
Use a cache to store objects. For example in Redis use Redis Lists
Redis Lists are simply lists of strings, sorted by insertion order
Then set expiry to either a timeout or a specific time
Now whenever you have a miss in Redis, run the query in MongoDB and re-populate your cache. Also since cache resids in memory therefore your fetches will be extremely fast as compared to dedicated collections in MongoDB.
In addition to that, you don't have to keep have a dedicated machine, just deploy it within your application machine.
Related
I am working on a front end system for a radius server.
The radius server will pass updates to the system every 180 seconds. Which means if I have about 15,000 clients that would be around 7,200,000 entries per day...Which is a lot.
I am trying to understand what the best possible way to store and retrieve this data will be. Obviously as time goes on, this will become substantial. Will MongoDB handle this? Typical document is not much, something this
{
id: 1
radiusId: uniqueId
start: 2017-01-01 14:23:23
upload: 102323
download: 1231556
}
However, there will be MANY of these records. I guess this is something similar to the way that SNMP NMS servers handle data which as far as I know they use RRD to do this.
Currently in my testing I just push every document into a single collection. So I am asking,
A) Is Mongo the right tool for the job and
B) Is there a better/more preferred/more optimal way to store the data
EDIT:
OK, so just incase someone comes across this and needs some help.
I ran it for a while in mongo, I was really not satisfied with performance. We can chalk this up to the hardware I was running on, perhaps my level of knowledge or the framework I was using. However I found a solution that works very well for me. InfluxDB pretty much handles all of this right out of the box, its a time series database which is effectively the data I am trying to store (https://github.com/influxdata/influxdb). Performance for me has been like night & day. Again, could all be my fault, just updating this.
EDIT 2:
So after a while I think I figured out why I never got the performance I was after with Mongo. I am using sailsjs as framework and it was searching by id using regex, which obviously has a huge performance hit. I will eventually try migrate back to Mongo instead of influx and see if its better.
15,000 clients updating every 180 seconds = ~83 insertions / sec. That's not a huge load even for a moderately sized DB server, especially given the very small size of the records you're inserting.
I think MongoDB will do fine with that load (also, to be honest, almost any modern SQL DB would probably be able to keep up as well). IMHO, the key points to consider are these:
Hardware: make sure you have enough RAM. This will primarily depend on how many indexes you define, and how many queries you're doing. If this is primarily a log that will rarely be read, then you won't need much RAM for your working set (although you'll need enough for your indexes). But if you're also running queries then you'll need much more resources
If you are running extensive queries, consider setting up a replica set. That way, your master server can be reserved for writing data, ensuring reliability, while your slaves can be configured to serve your queries without affecting the write reliability.
Regarding the data structure, I think that's fine, but it'll really depend on what type of queries you wish to run against it. For example, if most queries use the radiusId to reference another table and pull in a bunch of data for each record, then you might want to consider denormalizing some of that data. But again, that really depends on the queries you run.
If you're really concerned about managing the write load reliably, consider using the Mongo front-end only to manage the writes, and then dumping the data to a data warehouse backend to run queries on. You can partially do this by running a replica set like I mentioned above, but the disadvantage of a replica set is that you can't restructure the data. The data in each member of the replica set is exactly the same (hence the name, replica set :-) Oftentimes, the best structure for writing data (normalized, small records) isn't the best structure for reading data (denormalized, large records with all the info and joins you need already done). If you're running a bunch of complex queries referencing a bunch of other tables, using a true data warehouse for the querying part might be better.
As your write load increases, you may consider sharding. I'm assuming the RadiusId points to each specific server among a pool of Radius servers. You could potentially shard on that key, which would split the writes based on which server is sending the data. Thus, as you increase your radius servers, you can increase your mongo servers proportionally to maintain write reliability. However, I don't think you need to do this right away as I bet one reasonably provisioned server should be able to manage the load you've specified.
Anyway, those are my preliminary suggestions.
We have a collection that is potentially going to be very large.This collection used to store Bill releated data. So this is often used to reporting/Analytics purpose.
Please let me know the best approch to handle this large collection
1) Can I split and archive the old data(say 12 months period)?.But here old data is required to get analytic reports.I want to query this old data to show the sale comparion for past 2 yesrs.
2)can I have new collection with old data(12 months) .So for every 12 months i've to create new collection. For reports generation,I've to access all this documents to query. So this will cause performance problem?
3) Can I go for Sharding?
There are many variables to account for, the clearest being what hardware you use, how the data is structured, and how it is queried. A distributed network ought to be able to chew through your data faster than a single machine, but before diving into that solution I recommend generating an absurd amount of mock data comparable to what you are expecting, and then testing various approaches. Seriously. Create a bunch of data, and try to break things. It's fun! Soon enough you'll know more about what your problem requires than any website could tell you.
As for direct responses:
Perhaps, before archiving the data, appropriate stats summaries can be generated (or updated). Those summaries/simplifications can be used for sale comparisons without reloading all of the archived data they represent.
This strikes me as sensible. By splitting up the sales data, you have more control over how much data needs to be accessed. After all, a user won't always wish to see 3 years of data, they may only wish to see last week's.
Move to sharding when you actually need it. As is stated on the MongoDB site:
Converting an unsharded database to a sharded cluster is easy and seamless, so there is little advantage in configuring sharding while your data set is small.
You'll know it's time when your memory-map approaches the server's RAM limit. MongoDB supports reading and writing to databases too large to keep in memory, but I'm sure you already know that is SLOW.
I currently run a MySQL-powered website where users promote advertisements and gain revenue every time someone completes one. We log every time someone views an ad ("impression"), every time a user clicks an add ("click"), and every time someone completes an ad ("lead").
Since we get so much traffic, we have millions of records in each of these respective tables. We then have to query these tables to let users see how much they have earned, so we end up performing multiple queries on tables with millions and millions of rows multiple times in one request, hundreds of times concurrently.
We're looking to move away from MySQL and to a key-value store or something along those lines. We need something that will let us store all these millions of rows, query them in milliseconds, and MOST IMPORTANTLY, use adhoc queries where we can query any single column, so we could do things like:
FROM leads WHERE country = 'US' AND user_id = 501 (the NoSQL equivalent, obviously)
FROM clicks WHERE ad_id = 1952 AND user_id = 200 AND country = 'GB'
etc.
Does anyone have any good suggestions? I was considering MongoDB or CouchDB but I'm not sure if they can handle querying millions of records multiple times a second and the type of adhoc queries we need.
Thanks!
With those requirements, you are probably better off sticking with SQL and setting up replication/clustering if you are running into load issues. You can set up indexing on a document database so that those queries are possible, but you don't really gain anything over your current system.
NoSQL systems generally improve performance by leaving out some of the more complex features of relational systems. This means that they will only help if your scenario doesn't require those features. Running ad hoc queries on tabular data is exactly what SQL was designed for.
CouchDB's map/reduce is incremental which means it only processes a document once and stores the results.
Let's assume, for a moment, that CouchDB is the slowest database in the world. Your first query with millions of rows takes, maybe, 20 hours. That sounds terrible. However, your second query, your third query, your fourth query, and your hundredth query will take 50 milliseconds, perhaps 100 including HTTP and network latency.
You could say CouchDB fails the benchmarks but gets honors in the school of hard knocks.
I would not worry about performance, but rather if CouchDB can satisfy your ad-hoc query requirements. CouchDB wants to know what queries will occur, so it can do the hard work up-front before the query arrives. When the query does arrive, the answer is already prepared and out it goes!
All of your examples are possible with CouchDB. A so-called merge-join (lots of equality conditions) is no problem. However CouchDB cannot support multiple inequality queries simultaneously. You cannot ask CouchDB, in a single query, for users between age 18-40 who also clicked fewer than 10 times.
The nice thing about CouchDB's HTTP and Javascript interface is, it's easy to do a quick feasibility study. I suggest you try it out!
Most people would probably recommend MongoDB for a tracking/analytic system like this, for good reasons. You should read the „MongoDB for Real-Time Analytics” chapter from the „MongoDB Definitive Guide” book. Depending on the size of your data and scaling needs, you could get all the performance, schema-free storage and ad-hoc querying features. You will need to decide for yourself if issues with durability and unpredictability of the system are risky for you or not.
For a simpler tracking system, Redis would be a very good choice, offering rich functionality, blazing speed and real durability. To get a feel how such a system would be implemented in Redis, see this gist. The downside is, that you'd need to define all the „indices” by yourself, not gain them for „free”, as is the case with MongoDB. Nevertheless, there's no free lunch, and MongoDB indices are definitely not a free lunch.
I think you should have a look into how ElasticSearch would enable you:
Blazing speed
Schema-free storage
Sharding and distributed architecture
Powerful analytic primitives in the form of facets
Easy implementation of „sliding window”-type of data storage with index aliases
It is in heart a „fulltext search engine”, but don't get yourself confused by that. Read the „Data Visualization with ElasticSearch and Protovis“ article for real world use case of ElasticSearch as a data mining engine.
Have a look on these slides for real world use case for „sliding window” scenario.
There are many client libraries for ElasticSearch available, such as Tire for Ruby, so it's easy to get off the ground with a prototype quickly.
For the record (with all due respect to #jhs :), based on my experience, I cannot imagine an implementation where Couchdb is a feasible and useful option. It would be an awesome backup storage for your data, though.
If your working set can fit in the memory, and you index the right fields in the document, you'd be all set. Your ask is not something very typical and I am sure with proper hardware, right collection design (denormalize!) and indexing you should be good to go. Read up on Mongo querying, and use explain() to test the queries. Stay away from IN and NOT IN clauses that'd be my suggestion.
It really depends on your data sets. The number one rule to NoSQL design is to define your query scenarios first. Once you really understand how you want to query the data then you can look into the various NoSQL solutions out there. The default unit of distribution is key. Therefore you need to remember that you need to be able to split your data between your node machines effectively otherwise you will end up with a horizontally scalable system with all the work still being done on one node (albeit better queries depending on the case).
You also need to think back to CAP theorem, most NoSQL databases are eventually consistent (CP or AP) while traditional Relational DBMS are CA. This will impact the way you handle data and creation of certain things, for example key generation can be come trickery.
Also remember than in some systems such as HBase there is no indexing concept. All your indexes will need to be built by your application logic and any updates and deletes will need to be managed as such. With Mongo you can actually create indexes on fields and query them relatively quickly, there is also the possibility to integrate Solr with Mongo. You don’t just need to query by ID in Mongo like you do in HBase which is a column family (aka Google BigTable style database) where you essentially have nested key-value pairs.
So once again it comes to your data, what you want to store, how you plan to store it, and most importantly how you want to access it. The Lily project looks very promising. The work I am involved with we take a large amount of data from the web and we store it, analyse it, strip it down, parse it, analyse it, stream it, update it etc etc. We dont just use one system but many which are best suited to the job at hand. For this process we use different systems at different stages as it gives us fast access where we need it, provides the ability to stream and analyse data in real-time and importantly, keep track of everything as we go (as data loss in a prod system is a big deal) . I am using Hadoop, HBase, Hive, MongoDB, Solr, MySQL and even good old text files. Remember that to productionize a system using these technogies is a bit harder than installing MySQL on a server, some releases are not as stable and you really need to do your testing first. At the end of the day it really depends on the level of business resistance and the mission-critical nature of your system.
Another path that no one thus far has mentioned is NewSQL - i.e. Horizontally scalable RDBMSs... There are a few out there like MySQL cluster (i think) and VoltDB which may suit your cause.
Again it comes to understanding your data and the access patterns, NoSQL systems are also Non-Rel i.e. non-relational and are there for better suit to non-relational data sets. If your data is inherently relational and you need some SQL query features that really need to do things like Cartesian products (aka joins) then you may well be better of sticking with Oracle and investing some time in indexing, sharding and performance tuning.
My advice would be to actually play around with a few different systems. However for your use case I think a Column Family database may be the best solution, I think there are a few places which have implemented similar solutions to very similar problems (I think the NYTimes is using HBase to monitor user page clicks). Another great example is Facebook and like, they are using HBase for this. There is a really good article here which may help you along your way and further explain some points above. http://highscalability.com/blog/2011/3/22/facebooks-new-realtime-analytics-system-hbase-to-process-20.html
Final point would be that NoSQL systems are not the be all and end all. Putting your data into a NoSQL database does not mean its going to perform any better than MySQL, Oracle or even text files... For example see this blog post: http://mysqldba.blogspot.com/2010/03/cassandra-is-my-nosql-solution-but.html
I'd have a look at;
MongoDB - Document - CP
CouchDB - Document - AP
Redis - In memory key-value (not column family) - CP
Cassandra - Column Family - Available & Partition Tolerant (AP)
HBase - Column Family - Consistent & Partition Tolerant (CP)
Hadoop/Hive - Also have a look at Hadoop streaming...
Hypertable - Another CF CP DB.
VoltDB - A really good looking product, a relation database that is distributed and might work for your case (may be an easier move). They also seem to provide enterprise support which may be more suited for a prod env (i.e. give business users a sense of security).
Any way thats my 2c. Playing around with the systems is really the only way your going to find out what really works for your case.
I'm building a system that tracks and verifies ad impressions and clicks. This means that there are a lot of insert commands (about 90/second average, peaking at 250) and some read operations, but the focus is on performance and making it blazing-fast.
The system is currently on MongoDB, but I've been introduced to Cassandra and Redis since then. Would it be a good idea to go to one of these two solutions, rather than stay on MongoDB? Why or why not?
Thank you
For a harvesting solution like this, I would recommend a multi-stage approach. Redis is good at real time communication. Redis is designed as an in-memory key/value store and inherits some very nice benefits of being a memory database: O(1) list operations. For as long as there is RAM to use on a server, Redis will not slow down pushing to the end of your lists which is good when you need to insert items at such an extreme rate. Unfortunately, Redis can't operate with data sets larger than the amount of RAM you have (it only writes to disk, reading is for restarting the server or in case of a system crash) and scaling has to be done by you and your application. (A common way is to spread keys across numerous servers, which is implemented by some Redis drivers especially those for Ruby on Rails.) Redis also has support for simple publish/subscribe messenging, which can be useful at times as well.
In this scenario, Redis is "stage one." For each specific type of event you create a list in Redis with a unique name; for example we have "page viewed" and "link clicked." For simplicity we want to make sure the data in each list is the same structure; link clicked may have a user token, link name and URL, while the page viewed may only have the user token and URL. Your first concern is just getting the fact it happened and whatever absolutely neccesary data you need is pushed.
Next we have some simple processing workers that take this frantically inserted information off of Redis' hands, by asking it to take an item off the end of the list and hand it over. The worker can make any adjustments/deduplication/ID lookups needed to properly file the data and hand it off to a more permanent storage site. Fire up as many of these workers as you need to keep Redis' memory load bearable. You could write the workers in anything you wish (Node.js, C#, Java, ...) as long as it has a Redis driver (most web languages do now) and one for your desired storage (SQL, Mongo, etc.)
MongoDB is good at document storage. Unlike Redis it is able to deal with databases larger than RAM and it supports sharding/replication on it's own. An advantage of MongoDB over SQL-based options is that you don't have to have a predetermined schema, you're free to change the way data is stored however you want at any time.
I would, however, suggest Redis or Mongo for the "step one" phase of holding data for processing and use a traditional SQL setup (Postgres or MSSQL, perhaps) to store post-processed data. Tracking client behavior sounds like relational data to me, since you may want to go "Show me everyone who views this page" or "How many pages did this person view on this given day" or "What day had the most viewers in total?". There may be even more complex joins or queries for analytic purposes you come up with, and mature SQL solutions can do a lot of this filtering for you; NoSQL (Mongo or Redis specifically) can't do joins or complex queries across varied sets of data.
I currently work for a very large ad network and we write to flat files :)
I'm personally a Mongo fan, but frankly, Redis and Cassandra are unlikely to perform either better or worse. I mean, all you're doing is throwing stuff into memory and then flushing to disk in the background (both Mongo and Redis do this).
If you're looking for blazing fast speed, the other option is to keep several impressions in local memory and then flush them disk every minute or so. Of course, this is basically what Mongo and Redis do for you. Not a real compelling reason to move.
All three solutions (four if you count flat-files) will give you blazing fast writes. The non-relational (nosql) solutions will give you tunable fault-tolerance as well for the purposes of disaster recovery.
In terms of scale, our test environment, with only three MongoDB nodes, can handle 2-3k mixed transactions per second. At 8 nodes, we can handle 12k-15k mixed transactions per second. Cassandra can scale even higher. 250 reads is (or should be) no problem.
The more important question is, what do you want to do with this data? Operational reporting? Time-series analysis? Ad-hoc pattern analysis? real-time reporting?
MongoDB is a good option if you want the ability to do ad-hoc analysis based on multiple attributes within a collection. You can put up to 40 indexes on a collection, though the indexes will be stored in-memory, so watch for size. But the result is a flexible analytical solution.
Cassandra is a key-value store. You define a static column or set of columns that will act as your primary index right up front. All queries run against Cassandra should be tuned to this index. You can put a secondary on it, but that's about as far as it goes. You can, of course, use MapReduce to scan the store for non-key attribution, but it will be just that: a serial scan through the store. Cassandra also doesn't have the notion of "like" or regex operations on the server nodes. If you want to find all customers where the first name starts with "Alex", you'll have to scan through the entire collection, pull the first name out for each entry and run it through a client-side regex.
I'm not familiar enough with Redis to speak intelligently about it. Sorry.
If you are evaluating non-relational platforms, you might also want to consider CouchDB and Riak.
Hope this helps.
Just found this: http://blog.axant.it/archives/236
Quoting the most interesting part:
This second graph is about Redis RPUSH vs Mongo $PUSH vs Mongo insert, and I find this graph to be really interesting. Up to 5000 entries mongodb $push is faster even when compared to Redis RPUSH, then it becames incredibly slow, probably the mongodb array type has linear insertion time and so it becomes slower and slower. mongodb might gain a bit of performances by exposing a constant time insertion list type, but even with the linear time array type (which can guarantee constant time look-up) it has its applications for small sets of data.
I guess everything depends at least on data type and volume. Best advice probably would be to benchmark on your typical dataset and see yourself.
According to the Benchmarking Top NoSQL Databases (download here)
I recommend Cassandra.
If you have the choice (and need to move away from flat fies) I would go with Redis. Its blazingly fast, will comfortably handle the load you're talking about, but more importantly you won't have to manage the flushing/IO code. I understand its pretty straight forward but less code to manage is better than more.
You will also get horizontal scaling options with Redis that you may not get with file based caching.
I can get around 30k inserts/sec with MongoDB on a simple $350 Dell. If you only need around 2k inserts/sec, I would stick with MongoDB and shard it for scalability. Maybe also look into doing something with Node.js or something similar to make things more asynchronous.
The problem with inserts into databases is that they usually require writing to a random block on disk for each insert. What you want is something that only writes to disk every 10 inserts or so, ideally to sequential blocks.
Flat files are good. Summary statistics (eg total hits per page) can be obtained from flat files in a scalable manner using merge-sorty map-reducy type algorithms. It's not too hard to roll your own.
SQLite now supports Write Ahead Logging, which may also provide adequate performance.
I have hand-on experience with mongodb, couchdb and cassandra. I converted a lot of files to base64 string and insert these string into nosql.
mongodb is the fastest. cassandra is slowest. couchdb is slow too.
I think mysql would be much faster than all of them, but I didn't try mysql for my test case yet.
i had a discussion with a coworker about the architecture of a program i'm writing and i'd like some more opinions.
The Situation:
The Program should update at near-realtime (+/- 1 Minute).
It involves the movement of objects on a coordinate system.
There are some events that occur at regular intervals (i.e. creation of the objects).
Movements can change at any time through user input.
My solution was:
Build a server that runs continously and stores the data internally.
The server dumps a state-of-the-program at regular intervals to protect against powerfailures and/or crashes.
He argued that the program requires a Database and i should use cronjobs to update the data. I can store movement information by storing startpoint, endpoint and speed and update the position in the cronjob (and calculate collisions with other objects there) by calculating direction and speed.
His reasons:
Requires more CPU & Memory because it runs constantly.
Powerfailures/Crashes might destroy data.
Databases are faster.
My reasons against this are mostly:
Not very precise as events can only occur at full minutes (wouldn't be that bad though).
Requires (possibly costly) transformation of data on every run from relational data to objects.
RDBMS are a general solution for a specialized problem so a specialized solution should be more efficient.
Powerfailures (or other crashes) can leave the Data in an undefined state with only partially updated data unless (possibly costly) precautions (like transactions) are taken.
What are your opinions about that?
Which arguments can you add for any side?
Databases are not faster. How silly... How can a database be faster than writing a custom data structure and storing it in memory ?? Databases are Generalized tools to persist data to disk for you so you don't have to write all the code to do that yourself. Because they have to address the needs of numerous disparate (and sometimes inconsistent) business functions (Persistency (Durability), Transactional integrity, caching, relational integrity, atomicity, etc. etc. ) and do it in a way that protects the application developer from having to worry about it so much, by definition it is going to be slower. That doesn't necessarilly mean his conclusion is wrong however.
Each of his other objections can be addressed by writing the code to address that issue yourself... But you see where that is going... At some point, the development efforts of writing the custom code to address the issues that are important for your application outweigh the performance hit of just using a database - which already does all that stuff out of the box... How many of these issues are important ? and do you know how to write the code necessary to address them ?
From what you've described here, I'd say your solution does seem to be the better option. You say it runs once a minute, but how long does it take to run? If only a few seconds, then the transformation to relational data would likely be inconsequential, as would any other overhead. most of this would take likely 30 seconds. This is assuming, again, that the program is quite small.
However, if it is larger, and assuming that it will get larger, doing a straight dump is a better method. You might not want to do a full dump every run, but that's up to you, just remember that it could wind up taking a lot of space (same goes if you're using a database).
If you're going to dump the state, you would need to have some sort of a redundancy system in place, along with quasi-transactions. You would want to store several copies, in case something happens to the newest version. Say, the power goes out while you're storing, and you have no backups beyond this half-written one. Transactions, you would need something to tell that the file has been fully written, so if something does go wrong, you can always tell what the most recent successful save was.
Oh, and for his argument of it running constantly: if you have it set to a cronjob, or even a self-enclosed sleep statement or similar, it doesn't use any CPU time when it's not running, the same amount that it would if you're using an RDBMS.
If you're writing straight to disk, then this will be the faster method over a database, and faster retrieval, since, as you pointed out, there is no overhead.
Summary: A database is a good idea if you have a lot of idle processor time or historical records, but if resources are a legitimate concern, then it can become too much overhead and a dump with precautions taken is better.
mySQL can now model spatial data.
http://dev.mysql.com/doc/refman/4.1/en/gis-introduction.html
http://dev.mysql.com/doc/refman/5.1/en/spatial-extensions.html
You could use the database to keep track of world locations, user locations, items locations ect.