Mirroring is replicating data between Kafka cluster, while Replication is for replicating nodes within a Kafka cluster.
Is there any specific use of Replication, if Mirroring has already been setup?
They are used for different use cases. Let's try to clarify.
As described in the documentation,
The purpose of adding replication in Kafka is for stronger durability and higher availability. We want to guarantee that any successfully published message will not be lost and can be consumed, even when there are server failures. Such failures can be caused by machine error, program error, or more commonly, software upgrades. We have the following high-level goals:
Inside a cluster there might be network partitions (a single server fails, and so forth), therefore we want to provide replication between the nodes. Given a setup of three nodes and one cluster, if server1 fails, there are two replicas Kafka can choose from. Same cluster implies same response times (ok, it also depends on how these servers are configured, sure, but in a normal scenario they should not differ so much).
Mirroring, on the other hand, seems to be very valuable, for example, when you are migrating a data center, or when you have multiple data centers (e.g., AWS in the US and AWS in Ireland). Of course, these are just a couple of use cases. So what you do here is to give applications belonging to the same data center a faster and better way to access data - data locality in some contexts is everything.
If you have one node in each cluster, in case of failure, you might have way higher response times to go, let's say, from AWS located in Ireland to AWS in the US.
You might claim that in order to achieve data locality (services in cluster one read from kafka in cluster one) one still needs to copy the data from one cluster to the other. That's definitely true, but the advantages you might get with mirroring could be higher than those you would get by reading directly (via an SSH tunnel?) from Kafka located in another data center, for example single connections down, clients connection/session times longer (depending on the location of the data center), legislation (some data can be collected in a country while some other data shouldn't).
Replication is the basis of higher availability. You shouldn't use Mirroring to handle high availability in a context where data locality matters. At the same time, you should not use just Replication where you need to duplicate data across data centers (I don't even know if you can without Mirroring/an ssh tunnel).
Related
We have been using ActiveMQ version 5.16.0 broker with single instances in production. Now we are planning to use cluster of AMQ brokers for HA and load distribution with consistency in message data. Currently we are using only one queue
HA can be achieved using failover but do we need to use the same datastore or it can be separated? If I use different instances for AMQ brokers then how to setup a common datastore.
Please guide me how to setup datastore for HA and load distribution
Multiple ActiveMQ servers clustered together can provide HA in a couple ways:
Scale message flow by using compute resources across multiple broker nodes
Maintain message flow during single node planned or unplanned outage of a broker node
Share data store in the event of ActiveMQ process failure.
Network of brokers solve #1 and #2. A standard 3-node cluster will give you excellent performance and ability to scale the number of producers and consumers, along with splitting the full flow across 3-nodes to provide increased capacity.
Solving for #3 is complicated-- in all messaging products. Brokers are always working to be completely empty-- so clustering the data store of a single-broker becomes an anti-pattern of sorts. Many times, relying on RAID disk with a single broker node will provide higher reliability than adding NFSv4, GFSv2, or JDBC and using shared-store.
That being said, if you must use a shared store-- follow best practices and use GFSv2 or NFSv4. JDBC is much slower and requires significant DB maintenance to keep running efficiently.
Note: [#Kevin Boone]'s note about CIFS/SMB is incorrect and CIFS/SMB should not be used. Otherwise, his responses are solid.
You can configure ActiveMQ so that instances share a message store, or so they have separate message stores. If they share a message store, then (essentially) the brokers will automatically form a master-slave configuration, such that only one broker (at a time) will accept connections from clients, and only one broker will update the store. Clients need to identify both brokers in their connection URIs, and will connect to whichever broker happens to be master.
With a shared message store like this, locks in the message store coordinate the master-slave assignment, which makes the choice of message store critical. Stores can be shared filesystems, or shared databases. Only a few shared filesystem implementations work properly -- anything based on NFS 4.x should work. CIFS/SMB stores can work, but there's so much variation between providers that it's hard to be sure. NFS v3 doesn't work, however well-implemented, because the locking semantics are inappropriate. In any case, the store needs to be robust, or replicated, or both, because the whole broker cluster depends on it. No store, no brokers.
In my experience, it's easier to get good throughput from a shared file store than a shared database although, of course, there are many factors to consider. Poor network connectivity will make it hard to get good throughput with any kind of shared store (or any kind of cluster, for that matter).
When using individual message stores, it's typical to put the brokers into some kind of mesh, with 'network connectors' to pass messages from one broker to another. Both brokers will accept connections from clients (there is no master), and the network connections will deal with the situation where messages are sent to one broker, but need to be consumed from another.
Clients' don't necessarily need to specify all brokers in their connection URIs, but generally will, in case one of the brokers is down.
A mesh is generally easier to set up, and (broadly speaking) can handle more client load, than a master-slave with shared filestore. However, (a) losing a broker amounts to losing any messages that were associated with it (until the broker can be restored) and (b) the mesh interferes with messaging patterns like message grouping and exclusive consumers.
There's really no hard-and-fast rule to determine which configuration to use. Many installers who already have some sort of shared store infrastructure (a decent relational database, or a clustered NFS, for example) will tend to want to use it. The rise in cloud deployments has had the effect that mesh operation with no shared store has become (I think) a lot more popular, because it's so symmetric.
There's more -- a lot more -- that could be said here. As a broad question, I suspect the OP is a bit out-of-scope for SO. You'll probably get more traction if you break your question up into smaller, more focused, parts.
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'm a newbie to Redis and I was wondering if someone could help me to understand if it can be the right tool.
This is my scenario:
I have many different nodes, everyone behaving like a master and accepting clients connections to read and write a few geographical data data and the timestamp of the incoming record.
Each master node could be hosted onto a drone that only randomly get in touch and can comunicate with others, accordind to network conditions; when this happens they should synchronize their data according to their age (only the ones more recent than a specified time).
Is there any way to achieve this by Redis or do I have to implement this feature at application level?
I tried master/slaves configuration without success and I was wondering if Redis Cluster can somewhat meet my neeeds.
I googled around, but what I found had not an answer good for me
https://serverfault.com/questions/717406/redis-multi-master-replication
Using Redis Replication on different machines (multi master)
Teo, as a matter of fact, redis don't have a multi master replication.
And the cluster shard it's data through different instances. Say you have only two redis instances. Instance1 will accept store and retrieve instance1 and instance2 data. But he will ask for, and store in, instance2 every key that does not belong to his shard.
This is not, I think, really what you want. You could give a try to PostgreSQL+BDR as PostgreSQL supports nosql store and BDR provides a real master master replication (https://wiki.postgresql.org/wiki/BDR_Project) if that's really what you need.
I work with both today (and also MongoDB). Each one with a different goal. Redis would provide a smaller overhead and memory use, fast connection and fast replication. But it won't provide multi master (if you really need it).
I'm reading about production topology for the Analytics part of Worklight 6.2.
https://www-01.ibm.com/support/knowledgecenter/api/content/SSZH4A_6.2.0/com.ibm.worklight.monitor.doc/monitor/t_setting_up_production_cluster.html
It explains that nodes can act both as Master Node or as Data Node or only as one of them.
My question is why we should configure dedicated nodes, Master OR Data instead of configuring all the nodes for both Master AND Data.
I assume the the node (only one) acting as master will provide worst performance in its Data role but on the other hand the configuration will be simpler and the high availability will be higher.
Thank you.
Your assumption is correct.
A master node is responsible for handling communication between the data nodes. The data nodes will be responsible for indexing data. Having dedicated master and data nodes will allow them to focus their processing time and memory on their specific tasks. However, as you mentioned, in some cases its not worth doing this to complicate the configuration.
Another reason is that its not necessary to put a master node on a high performing machine. You can reserve the better machines for the data nodes.
The analytics console uses Elasticsearch under the covers. It would be worth looking up the benefits and drawbacks of choosing master and data nodes in Elasticsearch since it is an open source library and there are several resources available for it.
Edit:
As you can imagine, there is no one size fits all configuration. The configuration depends on several factors such as:
How long you wish to keep data stored
How many machines you have to dedicate to analytics
How verbose your client logs have been set
Your preferences between availability and performance
In my personal tests, I typically keep each node as a data and master node. Its possible that in the future we will document how the different configurations affect performance.
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