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.
Related
We would like to be able to read state inside a command use case.
We could get the state from event store for the specific aggregate, but what about querying aggregates by field(not id) or performing more complicated queries, that are not fitted for the event store?
The approach we were thinking was to use our read model for those cases as well and not only for query use cases.
This might be inconsistent, so a solution could be to have the latest version of the aggregate stored in both write/read models, in order to be able to tell if the state is correct or stale.
Does this make sense and if yes, if we need to get state by Id should we use event store or the read model?
If you want the absolute latest state of an event-sourced aggregate, you're going to have to read the latest snapshot (assuming that you are snapshotting) and then replay events since that snapshot from the event store. You can be aggressive about snapshotting (conceivably even saving a snapshot after every command), but you're giving away some write performance to make the read faster.
Updating the read model directly is conceivably possible, though that level of coupling is something that should be considered very carefully. Note also that you will very likely need some sort of two-phase commit to ensure that the read model is only updated when the write model is updated and vice versa. I strongly suggest considering why you're using CQRS/ES in this project, because you are quite possibly undermining that reason by doing this sort of thing.
In general, if you need a query for processing a particular command, it's likely that query will generally be the same, i.e. you don't need free-form query support. In that case, you can often have a read model that's tuned for exactly that query and which only cares about events which could affect that query: often a fairly small subset of the events. The finer-grained the read model, the easier it is to keep in sync (if it ignores 99% of events, for instance, it can't really fall that far behind).
Needing to make complex queries as part of command processing could also be a sign that your aggregate boundaries aren't right and could do with a re-examination.
Does this make sense
Maybe. Let's start with
This might be inconsistent
Yup, they might be. So what?
We typically respond to a query by sending an unlocked copy of the answer. In other words, it's possible that the actual information in the write model will change after this response is dispatched but before the response arrives at its destination. The client will be looking at a copy of the answer taken from the past.
So we might reasonably ask how much better it is to get information no more than one minute old compared to information no more than five minutes old. If the difference in value is pennies, then you should probably deploy the five minute version. If the difference is millions of dollars, then you're in a good position to negotiate a real budget to solve the problem.
For processing a command in our own write model, that kind of inconsistency isn't usually acceptable or wise. But neither of the two common answers require keeping the read and write models synchronized. The most common answer is to just work with the write model alone. The less common answer is to grab a snapshot out of a cache, and then apply any additional events to it to bring it up to date. The latter approach is "just" a performance optimization (first rule: don't.)
The variation that trips everyone up is trying to process a command somewhere else, enforcing a consistency rule on our data here. Once again, you need a really clear picture of how valuable the consistency is to the business. If it's really important, that may be a signal that the information in question shouldn't be split into two different piles - you may be working with the wrong underlying data model.
Possibly useful references
Pat Helland Data on the Outside Versus Data on the Inside
Udi Dahan Race Conditions Don't Exist
So, I am doing research on MongoDB, in line with upper management decision to embrace open source and to migrate existing product database from SQL Server to MongoDB and revamp the entire thing. Do note that our database should focus on data consistency and transactional guarantee.
And i discover this post: Click here. A summary of the post is as follow:
MongoDB claims to be strongly consistent, but a lot of evidence
recently has shown this not to be the case in certain scenarios (when
network partitioning occurs, which can happen under heavy load). This
means that you can potentially lose records that MongoDB has
acknowledged as "successfully written".
In terms of your application, if you have a need to have transactional
guarantees (meaning if you can't make a durable write, you need the
transaction to fail), you should avoid MongoDB. Example scenarios
where strong consistency and durability are essential include "making
a deposit into a bank account" or "creating a record of birth". To put
it another way, these are scenarios where you would get punched in the
face by your customer if you indicated an operation succeeded and it
didn't.
So, my question is as follow:
1) To what extend does "lost data" still valid in current version of MongoDB?
2) What approach can be take to ensure transactional guarantee in MongoDB?
I am pretty sure that if company like PayPal do use MongoDB, there is certainly a way of overcoming these issue.
The references in that post have been discussed here before (for example, here is one: MongoDB: Does Write Concern guarantee the write on primary plus atleast one secondary ). No need to duplicate your question.
The blog "Aphyr" mostly uses these articles to tout it's own tech (if you read the entire blog you will realise they have their own database which they market). Every database they show loses data except their own.
2) What approach can be take to ensure transactional guarantee in MongoDB?
I agree you should be handling database problems in client code, if not then how is your client side ever going to remain consistent in the event of partitions?
Since you are not Harry Potter (are you?) I will say that you need to check for exceptions thrown in your client code and react to them as needed.
1) To what extend does "lost data" still valid in current version of MongoDB?
As for the bug he mentions in 2.4.3: he fails (as I mention in the linked post) to state the bug reference so again, no comment still.
Besides 2 writes in 6,000? That's less data loss than I have seen in MySQL on a partition! So not too shabby.
I have not noticed such behaviour in my own app and, from small to extremely large sites, I have not noticed anyone reproduce benchmark type scenarios as displayed in that article, I doubt very much you will.
I am pretty sure that if company like PayPal do use MongoDB, there is certainly a way of overcoming these issue.
They would have tight coding to ensure consistency in distributed environments.
Of course, they would start by choosing the right tech for the situation...
Write Concern Reference
Write concern describes the level of acknowledgement requested from MongoDB for write operations to a standalone mongod or to replica sets or to sharded clusters. In sharded clusters, mongos instances will pass the write concern on to the shards.
https://docs.mongodb.org/v3.0/reference/write-concern/
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 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.