I'm reading up on consistency models but can't seem to understand the concept of causality in distributed systems. I've googled quite a lot but don't find a good explanation of the concept. People usually explain why causality is a good thing, but what is the basic concept?
Assuming you are asking about the basic notion of causal relationships among events in distributed systems, the following may help get you on the right track.
In the absence of perfectly synchronised clocks shared by all processes of a distributed system, Leslie Lamport introduced the notion of Logical Clocks. A Logical Clock affords the establishment of a partial order over the events occurring in a distributed system via the so-called happened-before relationship, a causal relationship.
To illustrate a bit further, events on the same machine can be ordered by relying on the local clock. However, this is not generally an option for events that cross process-boundaries. In particular, we use the following insight to establish a causal relationship over message passing events in the system: send(m) at process p occurs before receive(m) at process q. This enables us to establish a causal relationship among these events.
I am not sure how helpful my explanation is, but, if you have not already done so, Leslie Lamport's original paper Time, Clocks, and the Ordering of Events in a Distributed System should help clear things up for you. Next, you may want to look at Spanner: Google's Globally Distributed Database for a creative way to deal with the issue of time in a distributed system (TrueTime).
Hope this helps.
Taken from https://jepsen.io/consistency/models/causal
Causal consistency captures the notion that causally-related operations should appear in the same order on all processes—though processes may disagree about the order of causally independent operations.
For example, consider a chat between three people, where Attiya asks “shall we have lunch?”, and Barbarella & Cyrus respond with “yes”, and “no”, respectively. Causal consistency allows Attiya to observe “lunch?”, “yes”, “no”; and Barbarella to observe “lunch?”, “no”, “yes”. However, no participant ever observes “yes” or “no” prior to the question “lunch?”.
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In this picture, we can see saga is the one that implements transactions and cqrs implements queries. As far as I know, a transaction is a set of queries that follow ACID properties.
So can I consider CQRS as an advanced version of saga which increases the speed of reads?
Note that this diagram is not meant to explain what Sagas and CQRS are. In fact, looking at it this way it is quite confusing. What this diagram is telling you is what patterns you can use to read and write data that spans multime microservices. It is saying that in order to write data (somehow transactionally) across multiple microservices you can use Sagas and in order to read data which belongs to multiple microservices you can use CQRS. But that doesn't mean that Sagas and CQRS have anything in common. They are two different patterns to solve completely different problems (reads and writes). To make an analogy, it's like saying that to make pizzas (Write) you can use an oven and to view the pizzas menu (Read) you can use a tablet.
On the specific patterns:
Sagas: you can see them as a process manager or state machine. Note that they do not implement transactions in the RDBMS sense. Basically, they allow you to create a process that will take care of telling each microservice to do a write operation and if one of the operations fails, it'll take care of telling the other microservices to rollback (or compensate) the action that they did. So, these "transactions" won't be atomic, because while the process is running some microservices will have already modified the data and others won't. And it is not garanteed that whatever has succeed can sucessfully be rolled back or compensated.
CQRS (Command Query Responsibility Segregation): suggests the separation of Commands (writes) and Queries (Reads). The reason for that, it is what I was saying before, that the reads and writes are two very different operations. Therefore, by separating them, you can implement them with the patterns that better fit each scenario. The reason why CQRS is shown in your diagram as a solution for reading data that comes from multiple microservices is because one way of implementing queries is to listen to Domain Events coming from multiple microservices and storing the information in a single database so that when it's time to query the data, you can find it all in a single place. An alternative to this would be Data Composition. Which would mean that when the query arrives, you would submit queries to multiple microservices at that moment and compose the response with the composition of the responses.
So can I consider CQRS as an advanced version of saga which increases the speed of reads?
Personally I would not mix the concepts of CQRS and Sagas. I think this can really confuse you. Consider both patterns as two completely different things and try to understand them both independently.
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.
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.
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I'm looking for good examples of NoSQL apps that portray how to work with lack of transactionality as we know it in relational databases. I'm mostly interested in write-intensive code, as for mostly read-only code this is a much easier task. I've read a number of things about NoSQL in general, about CAP theorem, eventual consistency etc. However those things tend to concentrate on the database architecture for its own sake and not on the design patterns to use with it. I do understand that it's impossible to achieve full transactionality within a distributed app. This is exactly why I would like to understand where and how requirements should be lowered in order to make the task feasable.
EDIT:
It's not that eventual consistency is my goal on it's own. For the time being I don't really see how to use NoSQL to certain things that are write-intensive. Say: I have a simplistic auction system, where there are offers. In theory the first person to accept an offer wins. In practice I would like at least to guarantee that there is only a single winner and that people get their results in the same request. It's probably not feasable. But how to solve it in practice - maybe some requests could take longer than usual, because something went wrong. Maybe some requests should be automatically refreshed. It's just an example.
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
Availablility: 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 solution to your question? Probably 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)
There is more to talk about, but let me know if this was helpful and if you have any followup questions, we can continue from there...
[Continued...]
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.
The same can be said for most SQL solutions: they all implement indexes (which use b-trees under the hood), have relatively smart query optimizers which are based on known CS algorithms, all use in-memory caching to reduce disk IO. The differences are mostly in implementation, management experience, toolset support, etc
unfortunately I can't point to some central repository of wisdom which contains all you will need to know. In general, start with asking yourself what NoSQL characteristics you really need. That will guide you to choosing between a key-value store, a document store or a column store. (those are the 3 main categories of NoSQL offerings). And from there you can start comparing the various implementations.
[Updated again 4/14/2011]
OK here's the part which actually justifies the bounty..
I just found the following 120 page whitepaper on NoSQL systems. This is very close to being the "NoSQL bible" which I told you earlier doesn't exist. Read it and rejoice :-)
NoSQL Databases, Christof Strauch
There are many applications where eventual consistency is fine. Consider Twitter as a rather famous example. There's no reason that your "tweets" have to go out to all of your "followers" instantaneously. If it takes several seconds (or even minutes?) for your "tweet" to be distributed, who would even notice?
If you want non-web examples, any store-and-forward service (like email and USENET) would be require eventual consistency.
It's not impossible to get transactions or consistency in NoSQL. A lot of people define NoSQL in terms of a lack of transactions or as requiring eventual consistency at best, but this isn't accurate. There are transactional nosql products out there - consider tuple spaces, for example - that scale very well even while providing app consistency.
I am evaluating (no specific use cases), just trying to understand the NoSQL (non-relational) solutions in breadth.
So, I pretty much understand Dynamo in term of (taken from Dynamo paper):
Partitioning --> Consistent hashing
High availability --> Vector clocks
Handling temp failures --> Sloppy quorum
Failure recovery --> Merkel Trees
Membership and failure detection --> Gosisp protocol
My question is, what are the other ways of each of these 5 (may be more "problems") are dealed with in other solutions like:
Bigtable based systems,
Just key-value storage like Redis and BDB.
other hybrid systems.
Other important issues:
1) Secondary Indices: If you don't need them then you can probably find an acceptable way to use most datastores.
2) Multiple Data Centers: If you're dealing with multiple data centers then you may not be able to use a master-slave architecture. Multi-master systems are much more complicated.
3) Transactions: If you need to make transactions (multi-step operations that need to act like they're one step), you may have difficulties with many non-relational systems because they tend to sacrifice more than they strictly need to with respect to ACID (atomicity, consistency, isolation, and durability).
A wonderful place to learn about this is to read about the CAP theorem:
http://www.julianbrowne.com/article/viewer/brewers-cap-theorem
http://blog.nahurst.com/visual-guide-to-nosql-systems