I have a fairly simple Akka.NET system that tracks in-memory state, but contains only derived data. So any actor can on startup load its up-to-date state from a backend database and then start receiving messages and keep their state from there. So I can just let actors fail and restart the process whenever I want. It will rebuild itself.
But... I would like to run across multiple nodes (mostly for the memory requirements) and I'd like to increase/decrease the number of nodes according to demand. Also for releasing a new version without downtime.
What would be the most lightweight (in terms of Persistence) setup of clustering to achieve this? Can you run Clustering without Persistence?
This not a single question, so let me answer them one by one:
So I can just let actors fail and restart the process whenever I want - yes, but keep in mind, that hard reset of the process is a lot more expensive than graceful shutdown. In distributed systems if your node is going down, it's better for it to communicate that to the rest of the nodes before, than requiring them to detect the dead one - this is a part of node failure detection and can take some time (even sub minute).
I'd like to increase/decrease the number of nodes according to demand - this is a standard behavior of the cluster. In case of Akka.NET depending on which feature set are you going to use, you may sometimes need to specify an upper bound of the cluster size.
Also for releasing a new version without downtime. - most of the cluster features can be scoped to a set of particular nodes using so called roles. Each node can have it's set of roles, that can be used what services it provides and detect if other nodes have required capabilities. For that reason you can use roles for things like versioning.
Can you run Clustering without Persistence? - yes, and this is a default configuration (in Akka, cluster nodes don't need to use any form of persistent backend to work).
Related
I set up a basic test topology with Petabridge Lighthouse and two simple test actors that communicate with each other. This works well so far, but there is one problem: Lighthouse (or the underlying Akka.Cluster) makes one of my actors the leader, and when not shutting the node down gracefully (e.g. when something crashes badly or I simply hit "Stop" in VS) the Lighthouse is not usable any more. Tons of exceptions scroll by and it must be restarted.
Is it possible to configure Akka.Cluster .net in a way that the rest of the topology elects a new leader and carries on?
There are 2 things to point here. One is that if you have a serious risk of your lighthouse node going down, you probably should have more that one -
akka.cluster.seed-nodes setting can take multiple addresses, the only requirement here is that all nodes, including lighthouses, must have them specified in the same order. This way if one lighthouse is going down, another one still can take its role.
Other thing is that when a node becomes unreachable (either because the process crashed on network connection is unavailable), by default akka.net cluster won't down that node. You need to tell it, how it should behave, when such thing happens:
At any point you can configure your own IDowningProvider interface, that will be triggered after certain period of node inactivity will be reached. Then you can manually decide what to do. To use it add fully qualified type name to followin setting: akka.cluster.downing-provider = "MyNamespace.MyDowningProvider, MyAssembly". Example downing provider implementation can be seen here.
You can specify akka.cluster.auto-down-unreachable-after = 10s (or other time value) to specify some timeout given for an unreachable node to join - if it won't join before the timeout triggers, it will be kicked out from the cluster. Only risk here is when cluster split brain happens: under certain situations a network failure between machines can split your cluster in two, if that happens with auto-down set up, two halves of the cluster may consider each other dead. In this case you could end up having two separate clusters instead of one.
Starting from the next release (Akka.Cluster 1.3.3) a new Split Brain Resolver feature will be available. It will allow you to configure more advanced strategies on how to behave in case of network partitions and machine crashes.
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've found different zookeeper definitions across multiple resources. Maybe some of them are taken out of context, but look at them pls:
A canonical example of Zookeeper usage is distributed-memory computation...
ZooKeeper is an open source Apache™ project that provides a centralized infrastructure and services that enable synchronization across a cluster.
Apache ZooKeeper is an open source file application program interface (API) that allows distributed processes in large systems to synchronize with each other so that all clients making requests receive consistent data.
I've worked with Redis and Hazelcast, that would be easier for me to understand Zookeeper by comparing it with them.
Could you please compare Zookeeper with in-memory-data-grids and Redis?
If distributed-memory computation, how does zookeeper differ from in-memory-data-grids?
If synchronization across cluster, than how does it differs from all other in-memory storages? The same in-memory-data-grids also provide cluster-wide locks. Redis also has some kind of transactions.
If it's only about in-memory consistent data, than there are other alternatives. Imdg allow you to achieve the same, don't they?
https://zookeeper.apache.org/doc/current/zookeeperOver.html
By default, Zookeeper replicates all your data to every node and lets clients watch the data for changes. Changes are sent very quickly (within a bounded amount of time) to clients. You can also create "ephemeral nodes", which are deleted within a specified time if a client disconnects. ZooKeeper is highly optimized for reads, while writes are very slow (since they generally are sent to every client as soon as the write takes place). Finally, the maximum size of a "file" (znode) in Zookeeper is 1MB, but typically they'll be single strings.
Taken together, this means that zookeeper is not meant to store for much data, and definitely not a cache. Instead, it's for managing heartbeats/knowing what servers are online, storing/updating configuration, and possibly message passing (though if you have large #s of messages or high throughput demands, something like RabbitMQ will be much better for this task).
Basically, ZooKeeper (and Curator, which is built on it) helps in handling the mechanics of clustering -- heartbeats, distributing updates/configuration, distributed locks, etc.
It's not really comparable to Redis, but for the specific questions...
It doesn't support any computation and for most data sets, won't be able to store the data with any performance.
It's replicated to all nodes in the cluster (there's nothing like Redis clustering where the data can be distributed). All messages are processed atomically in full and are sequenced, so there's no real transactions. It can be USED to implement cluster-wide locks for your services (it's very good at that in fact), and tehre are a lot of locking primitives on the znodes themselves to control which nodes access them.
Sure, but ZooKeeper fills a niche. It's a tool for making a distributed applications play nice with multiple instances, not for storing/sharing large amounts of data. Compared to using an IMDG for this purpose, Zookeeper will be faster, manages heartbeats and synchronization in a predictable way (with a lot of APIs for making this part easy), and has a "push" paradigm instead of "pull" so nodes are notified very quickly of changes.
The quotation from the linked question...
A canonical example of Zookeeper usage is distributed-memory computation
... is, IMO, a bit misleading. You would use it to orchestrate the computation, not provide the data. For example, let's say you had to process rows 1-100 of a table. You might put 10 ZK nodes up, with names like "1-10", "11-20", "21-30", etc. Client applications would be notified of this change automatically by ZK, and the first one would grab "1-10" and set an ephemeral node clients/192.168.77.66/processing/rows_1_10
The next application would see this and go for the next group to process. The actual data to compute would be stored elsewhere (ie Redis, SQL database, etc). If the node failed partway through the computation, another node could see this (after 30-60 seconds) and pick up the job again.
I'd say the canonical example of ZooKeeper is leader election, though. Let's say you have 3 nodes -- one is master and the other 2 are slaves. If the master goes down, a slave node must become the new leader. This type of thing is perfect for ZK.
Consistency Guarantees
ZooKeeper is a high performance, scalable service. Both reads and write operations are designed to be fast, though reads are faster than writes. The reason for this is that in the case of reads, ZooKeeper can serve older data, which in turn is due to ZooKeeper's consistency guarantees:
Sequential Consistency
Updates from a client will be applied in the order that they were sent.
Atomicity
Updates either succeed or fail -- there are no partial results.
Single System Image
A client will see the same view of the service regardless of the server that it connects to.
Reliability
Once an update has been applied, it will persist from that time forward until a client overwrites the update. This guarantee has two corollaries:
If a client gets a successful return code, the update will have been applied. On some failures (communication errors, timeouts, etc) the client will not know if the update has applied or not. We take steps to minimize the failures, but the only guarantee is only present with successful return codes. (This is called the monotonicity condition in Paxos.)
Any updates that are seen by the client, through a read request or successful update, will never be rolled back when recovering from server failures.
Timeliness
The clients view of the system is guaranteed to be up-to-date within a certain time bound. (On the order of tens of seconds.) Either system changes will be seen by a client within this bound, or the client will detect a service outage.
I am using redis version 2.8.3. I want to build a redis cluster. But in this cluster there should be multiple master. This means I need multiple nodes that has write access and applying ability to all other nodes.
I could build a cluster with a master and multiple slaves. I just configured slaves redis.conf files and added that ;
slaveof myMasterIp myMasterPort
Thats all. Than I try to write something into db via master. It is replicated to all slaves and I really like it.
But when I try to write via a slave, it told me that slaves have no right to write. After that I just set read-only status of slave in redis.conf file to false. Hence, I could write something into db.
But I realize that, it is not replicated to my master replication so it is not replicated to all other slave neigther.
This means I could'not build an active-active cluster.
I tried to find something whether redis has active-active cluster capability. But I could not find exact answer about it.
Is it available to build active-active cluster with redis?
If it is, How can I do it ?
Thank you!
Redis v2.8.3 does not support multi-master setups. The real question, however, is why do you want to set one up? Put differently, what challenge/problem are you trying to solve?
It looks like the challenge you're trying to solve is how to reduce the network load (more on that below) by eliminating over-the-net reads. Since Redis isn't multi-master (yet), the only way to do it is by setting up each app server with a master and a slave (to the other master) - i.e. grand total of 4 Redis instances (and twice the RAM).
The simple scenario is when each app updates only a mutually-exclusive subset of the database's keys. In that scenario this kind of setup may actually be beneficial (at least in the short term). If, however, both apps can touch all keys or if even just one key is "shared" for writes between the apps, then you'll need to bake locking/conflict resolution/etc... logic into your apps to consolidate local master and slave differences (and that may be a bit of an overkill). In either case, however, you'll end up with too many (i.e. more than 1) Redises, which means more admin effort at the very least.
Also note that by colocating app and database on the same server you're setting yourself for near-certain scalability failure. What will happen when you need more compute resources for your apps or Redis? How will you add yet another app server to the mix?
Which brings me back to the actual problem you are trying to solve - network load. Why exactly is that an issue? Are your apps so throughput-heavy or is the network so thin that you are willing to go to such lengths? Or maybe latency is the issue that you want to resolve? Be the case as it may be, I recommended that you consider a time-proven design instead, namely separating Redis from the apps and putting it on its own resources. True, network will hit you in the face and you'll have to work around/with it (which is what everybody else does). On the other hand, you'll have more flexibility and control over your much simpler setup and that, in my book, is a huge gain.
Redis Enterprise has had this feature for quite a while, but if you are looking for an open source solution KeyDB is a fork with Active Active support (called Active Replica).
Setting it up is just a little more work than standard replication:
Both servers must have "active-replica yes" in their respective configuration files
On server B execute the command "replicaof [A address] [A port]"
Server B will drop its database and load server A's dataset
On server A execute the command "replicaof [B address] [B port]"
Server A will drop its database and load server B's dataset (including the data it just transferred in the prior step)
Both servers will now propagate writes to each other. You can test this by writing to a key on Server A and ensuring it is visible on B and vice versa.
https://github.com/JohnSully/KeyDB/wiki/KeyDB-(Redis-Fork):-Active-Replica-Support
My understanding could be amiss here. As I understand it, Couchbase uses a smart client to automatically select which node to write to or read from in a cluster. What I DON'T understand is, when this data is written/read, is it also immediately written to all other nodes? If so, in the event of a node failure, how does Couchbase know to use a different node from the one that was 'marked as the master' for the current operation/key? Do you lose data in the event that one of your nodes fails?
This sentence from the Couchbase Server Manual gives me the impression that you do lose data (which would make Couchbase unsuitable for high availability requirements):
With fewer larger nodes, in case of a node failure the impact to the
application will be greater
Thank you in advance for your time :)
By default when data is written into couchbase client returns success just after that data is written to one node's memory. After that couchbase save it to disk and does replication.
If you want to ensure that data is persisted to disk in most client libs there is functions that allow you to do that. With help of those functions you can also enshure that data is replicated to another node. This function is called observe.
When one node goes down, it should be failovered. Couchbase server could do that automatically when Auto failover timeout is set in server settings. I.e. if you have 3 nodes cluster and stored data has 2 replicas and one node goes down, you'll not lose data. If the second node fails you'll also not lose all data - it will be available on last node.
If one node that was Master goes down and failover - other alive node becames Master. In your client you point to all servers in cluster, so if it unable to retreive data from one node, it tries to get it from another.
Also if you have 2 nodes in your disposal you can install 2 separate couchbase servers and configure XDCR (cross datacenter replication) and manually check servers availability with HA proxies or something else. In that way you'll get only one ip to connect (proxy's ip) which will automatically get data from alive server.
Hopefully Couchbase is a good system for HA systems.
Let me explain in few sentence how it works, suppose you have a 5 nodes cluster. The applications, using the Client API/SDK, is always aware of the topology of the cluster (and any change in the topology).
When you set/get a document in the cluster the Client API uses the same algorithm than the server, to chose on which node it should be written. So the client select using a CRC32 hash the node, write on this node. Then asynchronously the cluster will copy 1 or more replicas to the other nodes (depending of your configuration).
Couchbase has only 1 active copy of a document at the time. So it is easy to be consistent. So the applications get and set from this active document.
In case of failure, the server has some work to do, once the failure is discovered (automatically or by a monitoring system), a "fail over" occurs. This means that the replicas are promoted as active and it is know possible to work like before. Usually you do a rebalance of the node to balance the cluster properly.
The sentence you are commenting is simply to say that the less number of node you have, the bigger will be the impact in case of failure/rebalance, since you will have to route the same number of request to a smaller number of nodes. Hopefully you do not lose data ;)
You can find some very detailed information about this way of working on Couchbase CTO blog:
http://damienkatz.net/2013/05/dynamo_sure_works_hard.html
Note: I am working as developer evangelist at Couchbase