I was wondering what sort of benefits a nosql option might have either in lieu of, or in conjunction with a rdbms for a typical MMORPG. In particular, I'm curious as to how data integrity is preserved under nosql.
I'll give a few examples where I'm kind of confused as to how server logic would operate:
Scenario 1: Let's say player A and B attack player C at the same time. A player_stats.kill_count is updated for whichever player kills player C. Normally, one might handle this AttackPlayerRequest in the following way:
Begin Transaction
{
attacker.health = newAttackerHealth
defender.health = newDefenderHealth
if defender.Health == 0
{
attacker.stats.kills += 1
}
}
Scenario 2: Player A sells an item to an NPC vendor, and quickly attempts to drop the same item from their inventory to the ground. (Even if you're UI disallows this, they can certainly just put the events on the wire).
This list obviously goes on and effects just about every player-player player-world interaction. One last piece of information is that the server is threaded as we don't want any particularly lengthy request to block other players from being able to do simple things in the game.
I guess my big question here is, am I misunderstanding something about nosql wherein this a trivial problem? If not, is this beyond the domain of what nosql options are meant to solve? Under a typical MMO, where might one inject nosql options to increase server performance?
This all depends on how your game operates.
NoSQL
Farmville on facebook uses NoSQL. Farmville acts like 1 enormous server that everyone is on. Let's say Farmville uses 3 computers in its database cluster. Computer 1 in San Diego. Computer 2 in New York. Computer 3 in Moscow. I login to Facebook from San Diego so I
connect to the database here. I level up and log out. Eventually Computer 1 Will tell computer 2 and 3 about how I leveled but it could be up to an hour before someone in Russia would see those changes to my account.
NoSQL scaling is as simple as adding another computer to the cluster and telling the website in that region to use that computer.
SQL
There are several methods of making SQL Work but I will explain a way to make it similar to NoSQL
Each Data-center will have 10 game servers, each with its own SQL Database + 1 Sub Master Database. The Sub Master Database shared the information between all 10 servers so if you login to server 1 and make character John Doe, then logout, Server 10 will have your character if you login to that server next.
Sub Master then shares its information with the Master Server at your HQ. The Master Server will then share John Doe to every other sub master at all the other data-centers, and those sub masters will update their game servers. This configuration would allow for you to login to server Weed in San Francisco, play your character, logout, login on server Vodka in Moscow, and still see your character.
Games like World of Warcraft use SQL but only certain parts of the database are shared. This allows for reduced Database size on each computer and also reducing hardware requirements.
In a real life situation each Sub Master would have a backup Sub Master, and your Master server would have a few backup servers in the event that one server goes down your entire network would not lock up.
I think MMO servers would do a lot of the stuff you've listed in memory. I don't think they flush all of those out into the database. I think more hard-set events like receiving new gear or changing your gear layout would be saved.
It's a lot of data, but not nearly as much as every single combat interaction.
MMORPGs that are worried about latency typically run on a single server, and store everything except object model information in RAM. Anything with battle environments are worried about latency.
Anything that uses HDD, be it an RDBMS or NoSQL, is a bad idea in battle environments. Updating and sharing object customization, position, movement vector, orientation vector, health, power, etc... data between 10,000 users on a single server via any mechanism that uses HHD is just silly if you are worried about latency. Even if you have 2 people fighting in a simple arena, latency is still an issue and you should run on a single server and keep everything in memory. Everything that will be rendered should be on your PC or in the memory of your PC as well.
MMORPGs that are not worried about latency can run on multiple servers and any database you wish (NoSQL, MySQL, etc...)
If you're making a game like farmville or neopets, then yes, NoSQL or MySQL become valid options.
To my understanding, nosql technology is something you would use in a batch job context, and not in a real-time context. As a matter of fact, the open source H-base implementation sits on top of the hadoop distributed file system (hdfs), which is primarily used for read-only situations.
The idea is that you 'seek' a record by iterating over the whole data set you are keeping 'on disk' (on a distributed file system actually). For petascale datasets it is infeasible to keep things in memory, so you utilize massive amounts of disk reads to be able to 'seek' the data at all. The distributed nature makes sure (or facilitates) that this occurs in a timely fashion.
The principle is that you bring the processing to the distributed data, instead of pulling data from a very large database, sending it over the network and then process it on a local node. Pig (http://pig.apache.org/) and Hive (http://hive.apache.org/) are interfaces on top of hdfs, which allow for SQL-like queries, but in the background, they use mapreduce jobs. Interaction is slow, but this is not the point. The point is that the immense data set can be processed at all.
For gaming, this is obviously not the way to go. So you would expect that data to be fetched from the distributed file system, cached on a server, and used during a session, When the game has finished, all data would be committed back to the data set. (At least, that's how I imagine it would go).
Hdfs data sets are mostly processed at a certain point in time, after which the results are being published in one batch. For example, in a social network site, you would compile all connection data on a daily basis, search for all new connections between people, and when the operation has finished for all people in the data set, the results would be published in the typical 'X is now conected to Y' messages. This does not happen in real-time.
Related
I often hear about eventual consistency in different speeches about NoSQL, data grids etc.
It seems that definition of eventual consistency varies in many sources (and maybe even depends on a concrete data storage).
Can anyone give a simple explanation what Eventual Consistency is in general terms, not related to any concrete data storage?
Eventual consistency:
I watch the weather report and learn that it's going to rain tomorrow.
I tell you that it's going to rain tomorrow.
Your neighbor tells his wife that it's going to be sunny tomorrow.
You tell your neighbor that it is going to rain tomorrow.
Eventually, all of the servers (you, me, your neighbor) know the truth (that it's going to rain tomorrow), but in the meantime the client (his wife) came away thinking it is going to be sunny, even though she asked after one or more of the servers (you and me) had a more up-to-date value.
As opposed to Strict Consistency / ACID compliance:
Your bank balance is $50.
You deposit $100.
Your bank balance, queried from any ATM anywhere, is $150.
Your daughter withdraws $40 with your ATM card.
Your bank balance, queried from any ATM anywhere, is $110.
At no time can your balance reflect anything other than the actual sum of all of the transactions made on your account to that exact moment.
The reason why so many NoSQL systems have eventual consistency is that virtually all of them are designed to be distributed, and with fully distributed systems there is super-linear overhead to maintaining strict consistency (meaning you can only scale so far before things start to slow down, and when they do you need to throw exponentially more hardware at the problem to keep scaling).
Eventual consistency:
Your data is replicated on multiple servers
Your clients can access any of the servers to retrieve the data
Someone writes a piece of data to one of the servers, but it wasn't yet copied to the rest
A client accesses the server with the data, and gets the most up-to-date copy
A different client (or even the same client) accesses a different server (one which didn't get the new copy yet), and gets the old copy
Basically, because it takes time to replicate the data across multiple servers, requests to read the data might go to a server with a new copy, and then go to a server with an old copy. The term "eventual" means that eventually the data will be replicated to all the servers, and thus they will all have the up-to-date copy.
Eventual consistency is a must if you want low latency reads, since the responding server must return its own copy of the data, and doesn't have time to consult other servers and reach a mutual agreement on the content of the data. I wrote a blog post explaining this in more detail.
Think you have an application and its replica. Then you have to add new data item to the application.
Then application synchronises the data to other replica show in below
Meanwhile new client going to get data from one replica that not update yet. In that case he cant get correct up date data. Because synchronisation get some time. In that case it haven't eventually consistency
Problem is how can we eventually consistency?
For that we use mediator application to update / create / delete data and use direct querying to read data. that help to make eventually consistency
When an application makes a change to a data item on one machine, that change has to be propagated to the other replicas. Since the change propagation is not instantaneous, there’s an interval of time during which some of the copies will have the most recent change, but others won’t. In other words, the copies will be mutually inconsistent. However, the change will eventually be propagated to all the copies, and hence the term “eventual consistency”. The term eventual consistency is simply an acknowledgement that there is an unbounded delay in propagating a change made on one machine to all the other copies. Eventual consistency is not meaningful or relevant in centralized (single copy) systems since there’s no need for propagation.
source: http://www.oracle.com/technetwork/products/nosqldb/documentation/consistency-explained-1659908.pdf
Eventual consistency means changes take time to propagate and the data might not be in the same state after every action, even for identical actions or transformations of the data. This can cause very bad things to happen when people don’t know what they are doing when interacting with such a system.
Please don’t implement business critical document data stores until you understand this concept well. Screwing up a document data store implementation is much harder to fix than a relational model because the fundamental things that are going to be screwed up simply cannot be fixed as the things that are required to fix it are just not present in the ecosystem. Refactoring the data of an inflight store is also much harder than the simple ETL transformations of a RDBMS.
Not all document stores are created equal. Some these days (MongoDB) do support transactions of a sort, but migrating datastores is likely comparable to the expense of re-implementation.
WARNING: Developers and even architects who do not know or understand the technology of a document data store and are afraid to admit that for fear of losing their jobs but have been classically trained in RDBMS and who only know ACID systems (how different can it be?) and who don’t know the technology or take the time to learn it, will miss design a document data store. They may also try and use it as a RDBMS or for things like caching. They will break down what should be atomic transactions which should operate on an entire document into “relational” pieces forgetting that replication and latency are things, or worse yet, dragging third party systems into a “transaction”. They’ll do this so their RDBMS can mirror their data lake, without regard to if it will work or not, and with no testing, because they know what they are doing. Then they will act surprised when complex objects stored in separate documents like “orders” have less “order items” than expected, or maybe none at all. But it won’t happen often, or often enough so they’ll just march forward. They may not even hit the problem in development. Then, rather than redesign things, they will throw “delays” and “retries” and “checks” in to fake a relational data model, which won’t work, but will add additional complexity for no benefit. But its too late now - the thing has been deployed and now the business is running on it. Eventually, the entire system will be thrown out and the department will be outsourced and someone else will maintain it. It still won’t work correctly, but they can fail less expensively than the current failure.
In simple English, we can say: Although your system may be in inconsistent states, the aim is always to reach consistency at some point for each piece of data.
Eventual consistency is more like a spectrum. On one end you have strong consistency and on other you have eventual consistency. In between there are levels like Snapshot, read my writes, bounded staleness. Doug Terry has a beautiful explanation in his paper on eventual consistency thru baseball
.
As per me eventual consistency is basically toleration to random data in random order every time you read from a data store. Anything better than that is a stronger consistency model. For example, a snapshot has stale data but will return same data if read again so it is predictable. Sometimes application can tolerate data which is stale for a given amount of time beyond which it demands consistent data.
If you look at meaning of consistency it relates more to uniformity or lack of deviation. So in non computer system terms it could mean toleration for unexpected variations. It could be very well explained thru ATM. An ATM could be offline hence divergent from account balance from core systems. However there is a toleration for showing different balances for a window of time. Once the ATM comes online, it can sync with core systems and reflect same balance. So an ATM could be said to be eventually consistent.
Eventual consistency guarantees consistency throughout the system, but not at all times. There is an inconsistency window, where a node might not have the latest value, but will still return a valid response when queried, even if that response will not be accurate. Cassandra has a ring system where your data is split up into different nodes:
Any of those nodes can act as the primary interface point for your application. So there is no single point of failure because any of those nodes can serve as your primary API point. But there is a trade-off here. Because any node can be primary, that data needs to be replicated amongst all of these nodes in order to stay up to date. So all of the other nodes needs to know what is where at all times and that means that as a trade-off for this architecture, we have eventual consistency. Because it takes time for that data to propagate throughout the ring, through every node in your system. So, as the data is written, it might be a little bit of time before you can actually read that data back you just wrote. Maybe data is written to one node, but you are reading it from a different node and that written data have not made it to that other node yet.
Let's say you back up your photos on your phone to the cloud every Sunday. If you check your photos on Friday on your cloud, you are not going to see the photos that were taken between Monday-Friday. You are still getting a response but not an updated response but if you check your cloud on Sunday night you will see all of your photos. So your data across phone and cloud services eventually reach consistency.
I am developing a JAVA based web application. The primary aim is to have inventory for products being sold on multiple websites called channels. We will act as manager for all these channels.
What we need is:
Queues to manage inventory updates for each channel.
Inventory table which has a correct snapshot of allocation on each channel.
Keeping Session Ids and other fast access data in a cache.
Providing a facebook like dashboard(XMPP) to keep the seller updated asap.
The solutions i am looking at are postgres(our db till now in a synchronous replication mode), NoSQL solutions like Cassandra, Redis, CouchDB and MongoDB.
My constraints are:
Inventory updates cannot be lost.
Job Queues should be executed in order and preferably never lost.
Easy/Fast development and future maintenance.
I am open to any suggestions. thanks in advance.
Queues to manage inventory updates for each channel.
This is not necessarily a database issue. You might be better off looking at a messaging system(e.g. RabbitMQ)
Inventory table which has a correct snapshot of allocation on each channel.
Keeping Session Ids and other fast access data in a cache.
session data should probably be put in a separate database more suitable for the task(e.g. memcached, redis, etc)
There is no one-size-fits-all DB
Providing a facebook like dashboard(XMPP) to keep the seller updated asap.
My constraints are:
1. Inventory updates cannot be lost.
There are 3 ways to answer this question:
This feature must be provided by your application. The database can guarantee that a bad record is rejected and rolled back, but not guarantee that every query will get entered.
The app will have to be smart enough to recognize when an error happens and try again.
some DBs store records in memory and then flush memory to disk peridocally, this could lead to data loss in the case of a power failure. (e.g Mongo works this way by default unless you enable journaling. CouchDB always appends to the records(even a delete is a flag appended to the record so data loss is extremely difficult))
Some DBs are designed to be extremely reliable, even if an earthquake, hurricane or other natural disaster strikes, they remain durable. these include Cassandra, Hbase, Riak, Hadoop, etc
Which type of durability are your referring to?
Job Queues should be executed in order and preferably never lost.
Most noSQL solutions prefer to run in parallel. so you have two options here.
1. use a DB that locks the entire table for every query(slower)
2. build your app to be smarter or evented(client side sequential queuing)
Easy/Fast development and future maintenance.
generally, you will find that SQL is faster to develop at first, but changes can be harder to implement
noSQL may require a little more planning, but is easier to do ad hoc queries or schema changes.
The questions you probably need to ask yourself are more like:
"Will I need to have intense queries or deep analysis that a Map/Reduce is better suited to?"
"will I need to my change my schema frequently?
"is my data highly relational? in what way?"
"does the vendor behind my chosen DB have enough experience to help me when I need it?"
"will I need special feature such as GeoSpatial indexing, full text search, etc?"
"how close to realtime will I need my data? will it hurt if I don't see the latest records show up in my queries until 1sec later? what level of latency is acceptable?"
"what do I really need in terms of fail-over"
"how big is my data? will it fit in memory? will it fit on one computer? is each individual record large or small?
"how often will my data change? is this an archive?"
If you are going to have multiple customers(channels?) each with their own inventory schemas, a document based DB might have it's advantages. I remember one time I looked at an ecommerce system with inventory and it had almost 235 tables!
Then again, if you have certain relational data, a SQL solution can really have some advantages too.
I can certainly see how I could build a solution using mongo, couch, riak or orientdb with the given constraints. But as for which is the best? I would try talking directly DB vendors, and maybe watch the nosql tapes
Addressing your constraints:
Most NoSQL solutions give you a configurable tradeoff of consistency vs. performance. In MongoDB, for instance, you can decide how durable a write should be. If you want to, you can force the write to be fsync'ed on all your replica set servers. On the other extreme, you can choose to send the command and don't even wait for the server's response.
Executing job queues in order seems to be an application code issue. I'd say a timestamp in the db and an order by type of query should do for most applications. If you have multiple application servers and your queues need to be perfect, you'd have to use a truly distributed algorithm that provides ordering, but that is not a typical requirement, and it's very tricky indeed.
We've been using MongoDB for some time now, and I'm convinced this gives your app development speed a real boost. There's no big difference in maintenance, maintaining data is a pain either way. Not having a schema gives you added flexibility (lazy migrations), but it's more elaborate and requires some care.
In summary, I'd say you can do it both ways. The NoSQL is more code driven, and transactions and relational integrity are mostly managed by your code. If you're uncomfortable with that, go for a relational DB.
However, if you're data grows huge, you'll have to code some of this logic manually because you probably wouldn't want to do real-time joins on a 10B row database. Still, you can implement that with SQL as well.
A good way to find the boundary for different databases is to consider what you can cache. Data that can be cached and reconstructed at any time are a great way to start introducing a new layer, because there's no big risks there. Also, cached data usually doesn't keep any relations so you're not sacrificing any consistency here.
NoSQL is not correct for this application.
I mean, you can use it sure, but you will end up re-implementing a lot of what SQL offers for you. For example I see a lot of relations there. You also want ACID (although some NoSQL solutions do offer that).
There is no reason you can't use both - keep relational data in relational databases, and non-relational data in key/value stores.
Quoting: http://gigaom.com/cloud/facebook-trapped-in-mysql-fate-worse-than-death/
There have been various attempts to
overcome SQL’s performance and
scalability problems, including the
buzzworthy NoSQL movement that burst
onto the scene a couple of years ago.
However, it was quickly discovered
that while NoSQL might be faster and
scale better, it did so at the expense
of ACID consistency.
Wait - am I reading that wrongly?
Does it mean that if I use NoSQL, we can expect transactions to be corrupted (albeit I daresay at a very low percentage)?
It's actually true and yet also a bit false. It's not about corruption it's about seeing something different during a (limited) period.
The real thing here is the CAP theorem which simply states you can only choose two of the following three:
Consistency (all nodes see the same data at the same time)
Availability (a guarantee that every request receives a response about whether it was successful or failed)
Partition
tolerance (the system continues to operate despite arbitrary message loss)
The traditional SQL systems choose to drop "Partition tolerance" where many (not all) of the NoSQL systems choose to drop "Consistency".
More precise: They drop "Strong Consistency" and select a more relaxed Consistency model like "Eventual Consistency".
So the data will be consistent when viewed from various perspectives, just not right away.
NoSQL solutions are usually designed to overcome SQL's scale limitations. Those scale limitations are explained by the CAP theorem. Understanding CAP is key to understanding why NoSQL systems tend to drop support for ACID.
So let me explain CAP in purely intuitive terms. First, what C, A and P mean:
Consistency: From the standpoint of an external observer, each "transaction" either fully completed or is fully rolled back. For example, when making an amazon purchase the purchase confirmation, order status update, inventory reduction etc should all appear 'in sync' regardless of the internal partitioning into sub-systems
Availability: 100% of requests are completed successfully.
Partition Tolerance: Any given request can be completed even if a subset of nodes in the system are unavailable.
What do these imply from a system design standpoint? what is the tension which CAP defines?
To achieve P, we needs replicas. Lots of em! The more replicas we keep, the better the chances are that any piece of data we need will be available even if some nodes are offline. For absolute "P" we should replicate every single data item to every node in the system. (Obviously in real life we compromise on 2, 3, etc)
To achieve A, we need no single point of failure. That means that "primary/secondary" or "master/slave" replication configurations go out the window since the master/primary is a single point of failure. We need to go with multiple master configurations. To achieve absolute "A", any single replica must be able to handle reads and writes independently of the other replicas. (in reality we compromise on async, queue based, quorums, etc)
To achieve C, we need a "single version of truth" in the system. Meaning that if I write to node A and then immediately read back from node B, node B should return the up-to-date value. Obviously this can't happen in a truly distributed multi-master system.
So, what is the "correct" solution to the problem? It details really depend on your requirements, but the general approach is to loosen up some of the constraints, and to compromise on the others.
For example, to achieve a "full write consistency" guarantee in a system with n replicas, the # of reads + the # of writes must be greater or equal to n : r + w >= n. This is easy to explain with an example: if I store each item on 3 replicas, then I have a few options to guarantee consistency:
A) I can write the item to all 3 replicas and then read from any one of the 3 and be confident I'm getting the latest version B) I can write item to one of the replicas, and then read all 3 replicas and choose the last of the 3 results C) I can write to 2 out of the 3 replicas, and read from 2 out of the 3 replicas, and I am guaranteed that I'll have the latest version on one of them.
Of course, the rule above assumes that no nodes have gone down in the meantime. To ensure P + C you will need to be even more paranoid...
There are also a near-infinite number of 'implementation' hacks - for example the storage layer might fail the call if it can't write to a minimal quorum, but might continue to propagate the updates to additional nodes even after returning success. Or, it might loosen the semantic guarantees and push the responsibility of merging versioning conflicts up to the business layer (this is what Amazon's Dynamo did).
Different subsets of data can have different guarantees (ie single point of failure might be OK for critical data, or it might be OK to block on your write request until the minimal # of write replicas have successfully written the new version)
The patterns for solving the 90% case already exist, but each NoSQL solution applies them in different configurations. The patterns are things like partitioning (stable/hash-based or variable/lookup-based), redundancy and replication, in memory-caches, distributed algorithms such as map/reduce.
When you drill down into those patterns, the underlying algorithms are also fairly universal: version vectors, merckle trees, DHTs, gossip protocols, etc.
It does not mean that transactions will be corrupted. In fact, many NoSQL systems do not use transactions at all! Some NoSQL systems may sometimes lose records (e.g. MongoDB when you do "fire and forget" inserts rather than "safe" ones), but often this is a design choice, not something you're stuck with.
If you need true transactional semantics (perhaps you are building a bank accounting application), use a database that supports them.
First, asking if NoSql is 100% ACID 100% of the time is a bit of a meaningless question. It's like asking "Are dogs 100% protective 100% of the time?" There are some dogs that are protective (or can be trained to be) such as German Shepherds or Doberman Pincers. There are other dogs that could care less about protecting anyone.
NoSql is the label of a movement, and not a specific technology. There are several different types of NoSql databases. There are document stores, such as MongoDb. There are graph databases such as Neo4j. There are key-value stores such as cassandra.
Each of these serve a different purpose. I've worked with a proprietary database that could be classified as a NoSql database, it's not 100% ACID, but it doesn't need to be. It's a write once, read many database. I think it gets built once a quarter (or once a month?) and then is read 1000s of time a day.
There is a lot of different NoSQL store types and implementations. Every of them can solve trade-offs between consistency and performance differently. The best you can get is a tunable framework.
Also the sentence "it was quickly discovered" from you citation is plainly stupid, this is no surprising discovery but a proven fact with deep theoretical roots.
In general, it's not that any given update would fail to save or get corrupted -- these are obviously going to be a very big issue for any database.
Where they fail on ACID is in data retrieval.
Consider a NoSQL DB which is replicated across numerous servers to allow high-speed access for a busy site.
And lets say the site owners update an article on the site with some new information.
In a typical NoSQL database in this scenario, the update would immediately only affect one of the nodes. Any queries made to the site on the other nodes would not reflect the change right away. In fact, as the data is replicated across the site, different users may be given different content despite querying at the same time. The data could take some time to propagate across all the nodes.
Conversely, in a transactional ACID compliant SQL database, the DB would have to be sure that all nodes had completed the update before any of them could be allowed to serve the new data.
This allows the site to retain high performance and page caching by sacrificing the guarantee that any given page will be absolutely up to date at an given moment.
In fact, if you consider it like this, the DNS system can be considered to be a specialised NoSQL database. If a domain name is updated in DNS, it can take several days for the new data to propagate throughout the internet (depending on TTL configuration).
All this makes NoSQL a useful tool for data such as web site content, where it doesn't necessarily matter that a page isn't instantly up-to-date and consistent as long as it is reasonably up-to-date.
On the other hand, though, it does mean that it would be a very bad idea to use a NoSQL database for a system which does require consistency and up-to-date accuracy. An order processing system or a banking system would definitely not be a good place for your typical NoSQL database engine.
NOSQL is not about corrupted data. It is about viewing at your data from a different perspective. It provides some interesting leverage points, which enable for much easier scalability story, and often usability too. However, you have to look at your data differently, and program your application accordingly (eg, embrace consequences of BASE instead of ACID). Most NOSQL solutions prevent you from making decisions which could make your database hard to scale.
NOSQL is not for everything, but ACID is not the most important factor from end-user perspective. It is just us developers who cannot imagine world without ACID guarantees.
You are reading that correctly. If you have the AP of CAP, your data will be inconsistent. The more users, the more inconsistent. As having many users is the main reason why you scale, don't expect the inconsistencies to be rare. You've already seen data pop in and out of Facebook. Imagine what that would do to Amazon.com stock inventory figures if you left out ACID. Eventual consistency is merely a nice way to say that you don't have consistency but you should write and application where you don't need it. Some types of games and social network application does not need consistency. There are even line-of-business systems that don't need it, but those are quite rare. When your client calls when the wrong amount of money is on an account or when an angry poker player didn't get his winnings, the answer should not be that this is how your software was designed.
The right tool for the right job. If you have less than a few million transactions per second, you should use a consistent NewSQL or NoSQL database such as VoltDb (non concurrent Java applications) or Starcounter (concurrent .NET applications). There is just no need to give up ACID these days.
I would like all of my users to be able to read and write to the datastore very quickly. It seems like MongoDb has blazing reads, but the writes seem like they could be very very slow if the one master db needs to be located very far away from the client.
Couchdb seems that it has slow reads, but how about the writes in the case when the client is very far away from the master.
With couchdb, we can have multiple masters, meaning we can always have a write node close to the client. Could couchdb actually be faster for writes than mongodb in the case when our user base is spread very far out geographically?
I would love to use mongoDb due to its blazing fast speed, but some of my users very far away from the only master will have a horrible experience.
For worldwide types of systems, wouldn't couchDb be better. Isn't mongodb completely ruled out in the case where you have users all around the world?
MongoDb, if you're listening, why don't you do some simple multi-master setups, where conflict resolution can be part of the update semantic?
This seems to be the only thing standing in between mongoDb completely dominating the nosql marketshare. Everything else is very impressive.
Disclosure: I am a MongoDB fan and user, i have zero experience with CouchDB.
I have a heavy duty app that is very read write intensive. I'd say reads outnumber writes by a factor of around 30:1. The way mongo is designed reads are always going to be much faster than writes the trick (in my experience) is to make your writes so efficient that you can dedicate a higher percentage of your system resources to the writes.
When building a product on top of mongo the key thing to remember is the _id field. This field is automatically generated and added to all of your JSON objects it will look something like 47cc67093475061e3d95369d when you design your queries (Find's) try and query on this field wherever possible as it contains the machine location (and i think also disk location??? - i should check this) where the object lives so when you use a find or update using this field will really speed up your machine. Consider this in the design of your system.
Example:
2 of the clusters in my database are "users" and "posts". A user can create multiple posts. These two collections have to reference each other alot in the implementation of my app.
In each post object i store the _id of the parent user.
In each user object i store an array of all the posts the user has authored.
Now on each user page I can generate a list of all the authored posts without a resource stressful query but with a direct look up of the _id. The bigger the mongo cluster the bigger the difference this is going to make.
If you're at all familiar with oracle's physical location rowids you may understand this concept only in mongo it is much more awesome and powerful.
I was scared last year when we decided to finally ditch MySQL for mongo but I can tell you the following about my experience:
- Data porting is always horrible but it went as well as I could have imagined.
- Mongo is probably the best documented NoSQL DB out there and the Open Source community is fantastic.
- When they say fast and scalable there not kidding, it flies.
- Schema design is very easy and much more natural and orderly than key/value type db's in my opinion.
- The whole system seems designed for minimal user complexity, adding nodes etc is a breeze.
Ok, seriously I swear mongo didn't pay me to write this (I wish) but apologies for the love fest.
Whatever your choice, best of luck.
Here is a detailed article that 10gen has created, and gives examples of when you should choose MongoDB or CouchDB, with reasons as well.
http://www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
Edit
The above link was removed, but can be viewed here: http://web.archive.org/web/20120614072025/http://www.mongodb.org/display/DOCS/Comparing+Mongo+DB+and+Couch+DB
Your question as of now, is full with speculation and guessing.
...why can't we opt out of consistency for certain writes, so long as we're sure that the person that wrote the data will be able to read it consistently, whereas others will observe eventual consistency
What if those writes effect other writes? What if those writes would prevent other people from doing stuff. It's hard to tell the possible side effect if since you didn't tell us any specifics.
My main suggestion to you is that you do some testing. Unless you've tested it, speculation about bottle necks is a complete waste of time. You don't need to test it via remote machines, set up some local DBs and add some artificial lag, then run your tests.
This way you can test the different options you've got, see where MongoDB is better, or where CouchDB excels at. Then you can either take one of them and go with the contras, or you can try and tweak your Database Model it self and do more tests.
Nobody here will be able to give you a general solution to your specific problem (well unless you give us all your code and you pay us for working on it :P ) databases aren't easy especially if you need to scale them under certain requirements.
For worldwide types of systems, wouldn't couchDb be better. Isn't mongodb completely ruled out in the case where you have users all around the world?
MongoDB supports sharding. So you don't need a single master. In fact, it looks like you have a ready shard key (region).
MongoDB also supports replica sets along with sharding. So if you need to run in multiple data centers (DCs) you put a master and one of the replicas in the same DC. In fact, they also suggest adding a 3rd node to a separate DC as a hot backup failover.
You will have to drill into the more detailed configuration of MongoDB, but you can definitely control where data is stored and you can prioritize that other replicas in a DC are "next in line" for promotion to Master.
At this point however, you're well into the details of MongoDB and you'll need to dig around and "play" quite a bit. However, you'll need lots of "play time" for any solution that's really going to handle masters across data centers.
I'm currently trying to pick a database vendor.
I'm just seeking some personal opinions from fellow database developers out there.
My question is especially targeted towards people who:
1) have used Main Memory DB (MMDB) that supports replicating to disk (hybrid) before (i.e. ExtremeDB)
or
2) have used Versant Object Database and/or Objectivity Database and/or Progress ObjectStore
and the question is really: if you could recommend a database vendor, based on your experience, that would suit my application.
My application is a commercial real-time (read: high-performance) object-oriented C++ GIS kind of app, where we need to do a lot of lat/lon search (i.e. given an area, find all matching targets within the area...R-Tree index).
The types of data that I would like to store into the database are all modeled as objects and they make use of std::list and std::vector, so naturally, Object Database seems to make sense. I have read through enough articles to convince myself that a traditional RDBMS probably isnt what I'm really looking for in terms of
performance (joins or multiple
tables for dynamic-length data like
list/vector)
ease of programming
(impedance mismatch)
However, in terms of performance,
Input data is being fed into the system at about 40 MB/s.
Hence, the system will also be doing insert into the database at the rate of roughly 350 inserts per second (where each object varies from 64KB to 128KB),
Database will consistently be searched and updated via multiple threads.
From my understanding, all of the Object DBs I have listed here use cache for storing database objects. ExtremeDB claims that since it's designed especially for memory, it can avoid overhead of caching logic, etc. See more by googling: Main Memory vs. RAM-Disk Databases: A Linux-based Benchmark
So..I'm just a bit confused. Can Object DBs be used in real-time system? Is it as "fast" as MMDB?
Fundamentally, I difference between a MMDB and a OODB is that the MMDB has the expectation that all of its data is based in RAM, but persisted to disk at some point. Whereas an OODB is more conventional in that there's no expectation of the entire DB fitting in to RAM.
The MMDB can leverage this by giving up on the concept that the persisted data doesn't necessarily have to "match" the in RAM data.
The way anything with persistence is going to work, is that it has to write the data to disk on update in some fashion.
Almost all DBs use some kind of log for this. These logs are basically "raw" pages of data, or perhaps individual transactions, appended to a file. When the file gets "too big", a new file is started.
Once the logs are properly consolidated in to the main store, the logs are discarded (or reused).
Now, a crude, in RAM DB can exist simply by appending transactions to a log file, and when it's restarted, it just loads the log in to RAM. So, in essence, the log file IS the database.
The downside of this technique is the longer and more transactions you have, the bigger your log/DB is, and thus the longer the DB startup time. But, ideally, you can also "snapshot" the current state, which eliminates all of the logs up to date, and effectively compresses them.
In this manner, all the routine operations of the DB have to manage is appending pages to logs, rather than updating other disk pages, index pages, etc. Since, ideally, most systems don't need to "Start up" that often, perhaps start up time is less of an issue.
So, in this way, a MMDB can be faster than an OODB who has a different contract with the disk, maintaining logs and disk pages. In this way, an OODB can be slower even if the entire DB fits in to RAM and is properly cached, simply because you incur disk operations outside of the log operations during normal operations, vs a MMDB where these operations happen as a "maintenance" task, which can be scheduled during down time and/or quiet time.
As to whether either of these systems can meet you actual performance needs, I can't say.
The back ends of databases (reader and writer processes, caching, lock managing, txn log files, ACID semantics) are the same, so RDBs and OODB are actually very similar here. The difference is the interface to the application programmer. Is your data model complicated, consists of lots of classes with real inheritance relationships? Then OO is good. Is it relatively flat and simple? Then go RDB. What is the nature of the relationships? Is it pointer-like and set like? Then go RDB. Is is more complicated, like (ordered) list, array, map? Then you should go OO. Also, do you have a stand-alone application with no need to integrate with other apps? Then OO is ok. Do you have to share data with other apps (i.e. several apps access the same database)? Then that's a deal-breaker for OO, and you should stick with RDB. Is the schema of your database stable or do you expect it to evolve frequently? OODBs are bad ad schema evolution, so if you expect frequent changes, stick with RDBs.