When I am making an Insert in a specified keyspace, I wants that the data is stored only in a specified node (or node list). The information contains in the insert, may be confidential and should not be distributed on any nodes.
I first thought about implementing my own AbstractReplicationStrategy, but it seams that the first node choose depends on the Token (selected by the partitioner) and not the implemented strategy.
How can I be sure the the information contains in a keyspace only comes where I allow this?
I don't think that it is possible to do what you are asking. Cassandra actively tries to maintain a certain number of replicas of each piece of data- even if you managed to force only a single node to store your insert (which is fairly straighforward), you'd have no control over which node that was (as you found, this is controlled by the partitioner), and if the node went down your data would be lost.
The short answer is that controlling replication is not the way to achieve data security- you should use proper security techniques such as encryption, segregated networks, controlled access, etc.
Related
Aerospike is a key store database with support for persistence.
But can I trust this persistence enough to use it as an database altogether?
As I understand it writes data to memory first and then persist it.
I can live with eventual consistency, but I don't want to be in a state where something was committed but due to machine failure it never got written down to the disk and hence can never be retrieved.
I tried looking at the various use cases but I was just curious about this one.
Also what guarantee does client.put provides as far as saving of a new record is concerned.
Aerospike provides a user configurable replication factor. Most people use 2, if you are really concerned, you can use 3 or even more. Size the cluster accordingly. For RF=3, put returns when 3 nodes have written data to the their write-block in memory which is asynchronously flushed to persistent layer. So it depends on what node failure pattern you are trying protect against. If you are worried about entire cluster crashing instantly, then you may have a case for 1 second (default) worth of lost data. The one second can be configured lower as well. Aerospike also provides rack aware configuration which protects against data loss if entire rack goes down. The put goes to nodes in different racks always. Finally Aerospike provides cross data center replication - its asynchronous but does give an option to replicate your data across geo. Of course, going across geo does have its latency. Finally, if you are totally concerned about entire cluster shutdown, you can connect to two separate clusters in your application and always push updates to two separate clusters. Of course, you must now worry about consistency if application fails between two writes. I don't know of anyone who had to resort to that.
I have an application that, for performance reasons, will have completely independent standalone instances in several Azure data centers. The stack of Azure IaaS and PaaS components at each data center will be exactly the same. Primarily, there will be a front end application and a database.
So let's say I have the application hosted in 4 data centers. I would like to have the data coming into each Azure SQL database replicate it's data asynchronously to all of the other 3 databases, in an eventually consistent manner. Each of these databases needs to be updatable.
Does anyone know if Active Geo-Replication can handle this scenario? I know I can do this using a VM and IaaS, but would prefer to use SQL Azure.
Thanks...
Peer-to-peer tranasaction replication supports what you're asking for, to some extent - I'm assuming that's what you're referring to when you mention setting it up in IaaS, but it seems like it would be self defeating if you're looking to it for a boost in write performance (and against their recommendations):
From https://msdn.microsoft.com/en-us/library/ms151196.aspx
Although peer-to-peer replication enables scaling out of read operations, write performance for the topology is like that for a single node. This is because ultimately all inserts, updates, and deletes are propagated to all nodes. Replication recognizes when a change has been applied to a given node and prevents changes from cycling through the nodes more than one time. We strongly recommend that write operations for each row be performed at only node, for the following reasons:
If a row is modified at more than one node, it can cause a conflict or even a lost update when the row is propagated to other nodes.
There is always some latency involved when changes are replicated. For applications that require the latest change to be seen immediately, dynamically load balancing the application across multiple nodes can be problematic.
This makes me think that you'd be better off using Active Geo Replication - you get the benefit of PaaS and not having to manage your own VMs, not having to manage TR, which gets messy, and if the application is built to deal with "eventual consistency" in the UI, you might be able to get away with slight delays in the secondaries being up to date.
At my current project I actively use redis for various purposes. There are 2 redis databases for current application:
The first one contains absolutely temporary data: how many users are online, who are online, various admin's counters. This db is cleared before the application starts by start-up script.
The second database is used for persistent data like user's ratings, user's friends, etc.
Everything seems to be correct and everybody is happy.
However, when I've started implementing a new functionality in my application, I discover that I need to intersect a set with user's friends with a set of online users. These sets stored in different redis databases, and I haven't found any possibility to do this task in redis, except changing application architecture and move all keys into one namespace(database).
Is there actually any way to perform some command in redis using data from multiple databases? Or maybe my use case of redis is wrong and I have to perform a fix of system architecture?
There is not. There is a command that makes it easy to move keys to another DB:
http://redis.io/commands/move
If you move all keys to one DB, make sure you don't have any key clashes! You could suffix or prefix the keys from the temp DB to make absolutely sure. MOVE will do nothing if the key already exists in the target DB. So make sure you act on a '0' reply
Using multiple DBs is definitely not a good idea:
A Quote from Salvatore Sanfilippo (the creator of redis):
I understand how this can be useful, but unfortunately I consider
Redis multiple database errors my worst decision in Redis design at
all... without any kind of real gain, it makes the internals a lot
more complex. The reality is that databases don't scale well for a
number of reason, like active expire of keys and VM. If the DB
selection can be performed with a string I can see this feature being
used as a scalable O(1) dictionary layer, that instead it is not.
With DB numbers, with a default of a few DBs, we are communication
better what this feature is and how can be used I think. I hope that
at some point we can drop the multiple DBs support at all, but I think
it is probably too late as there is a number of people relying on this
feature for their work.
https://groups.google.com/forum/#!msg/redis-db/vS5wX8X4Cjg/8ounBXitG4sJ
I'm creating a mobile app and it requires a API service backend to get/put information for each user. I'll be developing the web service on ServiceStack, but was wondering about the storage. I love the idea of a fast in-memory caching system like Redis, but I have a few questions:
I created a sample schema of what my data store should look like. Does this seems like it's a good case for using Redis as opposed to a MySQL DB or something like that?
schema http://www.miles3.com/uploads/redis.png
How difficult is the setup for persisting the Redis store to disk or is it kind of built-in when you do writes to the store? (I'm a newbie on this NoSQL stuff)
I currently have my setup on AWS using a Linux micro instance (because it's free for a year). I know many factors go into this answer, but in general will this be enough for my web service and Redis? Since Redis is in-memory will that be enough? I guess if my mobile app skyrockets (hey, we can dream right?) then I'll start hitting the ceiling of the instance.
What to think about when desigining a NoSQL Redis application
1) To develop correctly in Redis you should be thinking more about how you would structure the relationships in your C# program i.e. with the C# collection classes rather than a Relational Model meant for an RDBMS. The better mindset would be to think more about data storage like a Document database rather than RDBMS tables. Essentially everything gets blobbed in Redis via a key (index) so you just need to work out what your primary entities are (i.e. aggregate roots)
which would get kept in its own 'key namespace' or whether it's non-primary entity, i.e. simply metadata which should just get persisted with its parent entity.
Examples of Redis as a primary Data Store
Here is a good article that walks through creating a simple blogging application using Redis:
http://www.servicestack.net/docs/redis-client/designing-nosql-database
You can also look at the source code of RedisStackOverflow for another real world example using Redis.
Basically you would need to store and fetch the items of each type separately.
var redisUsers = redis.As<User>();
var user = redisUsers.GetById(1);
var userIsWatching = redisUsers.GetRelatedEntities<Watching>(user.Id);
The way you store relationship between entities is making use of Redis's Sets, e.g: you can store the Users/Watchers relationship conceptually with:
SET["ids:User>Watcher:{UserId}"] = [{watcherId1},{watcherId2},...]
Redis is schema-less and idempotent
Storing ids into redis sets is idempotent i.e. you can add watcherId1 to the same set multiple times and it will only ever have one occurrence of it. This is nice because it means you don't ever need to check the existence of the relationship and can freely keep adding related ids like they've never existed.
Related: writing or reading to a Redis collection (e.g. List) that does not exist is the same as writing to an empty collection, i.e. A list gets created on-the-fly when you add an item to a list whilst accessing a non-existent list will simply return 0 results. This is a friction-free and productivity win since you don't have to define your schemas up front in order to use them. Although should you need to Redis provides the EXISTS operation to determine whether a key exists or a TYPE operation so you can determine its type.
Create your relationships/indexes on your writes
One thing to remember is because there are no implicit indexes in Redis, you will generally need to setup your indexes/relationships needed for reading yourself during your writes. Basically you need to think about all your query requirements up front and ensure you set up the necessary relationships at write time. The above RedisStackOverflow source code is a good example that shows this.
Note: the ServiceStack.Redis C# provider assumes you have a unique field called Id that is its primary key. You can configure it to use a different field with the ModelConfig.Id() config mapping.
Redis Persistance
2) Redis supports 2 types persistence modes out-of-the-box RDB and Append Only File (AOF). RDB writes routine snapshots whilst the Append Only File acts like a transaction journal recording all the changes in-between snapshots - I recommend adding both until your comfortable with what each does and what your application needs. You can read all Redis persistence at http://redis.io/topics/persistence.
Note Redis also supports trivial replication you can read more about at: http://redis.io/topics/replication
Redis loves RAM
3) Since Redis operates predominantly in memory the most important resource is that you have enough RAM to hold your entire dataset in memory + a buffer for when it snapshots to disk. Redis is very efficient so even a small AWS instance will be able to handle a lot of load - what you want to look for is having enough RAM.
Visualizing your data with the Redis Admin UI
Finally if you're using the ServiceStack C# Redis Client I recommend installing the Redis Admin UI which provides a nice visual view of your entities. You can see a live demo of it at:
http://servicestack.net/RedisAdminUI/AjaxClient/
I'm currently evaluating possible solutions to the follwing problem:
A set of data entries must be synchonized between multiple clients, where each client may only view (or even know about the existence of) a subset of the data.
Each client "owns" some of the elements, and the decision who else can read or modify those elements may only be made by the owner. To complicate this situation even more, each element (and each element revision) must have an unique identifier that is equal for all clients.
While the latter sounds like a perfect task for CouchDB (and a document based data model would fit my needs perfectly), I'm not sure if the authentication/authorization subsystem of CouchDB can handle these requirements: While it should be possible to restict write access using validation functions, there doesn't seem to be a way to authorize read access. All solutions I've found for this problem propose to route all CouchDB requests through a proxy (or an application layer) that handles authorization.
So, the question is: Is it possible to implement an authorization layer that filters requests to the database so that access is granted only to documents that the requesting client has read access to and still use the replication mechanism of CouchDB? Simplified, this would be some kind of "selective replication" where only some of the documents, and not the whole database is replicated.
I would also be thankful for directions to some detailed information about how replication works. The CouchDB wiki and even the "Definite Guide" Book are not too specific about that.
this begs for replication filters. you filter outbound replication based on whatever criteria you impose, and give the owner of the target unrestricted access to their own copy.
i haven't had the opportunity to play with replication filters directly, but the idea would be that each doc would have some information about who has access to it, and the filtering mechanism would then allow outbound replication of only those documents that you have access to. replication from the target back to the master would be unrestricted, allowing for the master to remain a rollup copy, and potentially multicast changes to overlapping sets of data.
What you are after is replication filters. According to Chris Anderson, it is a 0.11 feature.
"The current status is that there is
an API for filtering the _changes
feed. The replicator in 0.10 consumes
the changes feed, so the next step is
getting the replicator to use the
filter API.
There is work in progress on this, so
it should be fully ready to go in
0.11."
See the orginal post
Here is a new link to the some documentation about this:
http://blog.couchbase.com/what%E2%80%99s-new-apache-couchdb-011-%E2%80%94-part-three-new-features-replication
Indeed, as others have said, replication filters are the way to go for this. Here is a link with some information on using them.
One caveat I would add is that at scale replication filters can be extremely slow. More information about this and other nuances about couchdb can be found in this excellent blog post: "what every developer should know about couchdb". For large scale systems performing replication in the application layer has proven faster and more reliable.