From the active/active documentation -
we have developed active/active high availability for queues
This solution still requires a RabbitMQ cluster, which means that it will not cope
seamlessly with network partitions within the cluster and, for that reason, is not
recommended for use across a WAN (though of course, clients can still connect from
as near and as far as needed)
What does it mean "not recommended for use across a WAN".
I cant understand this remark -
If I buy three machines on ec2 will I need to establish a domain controller/dns server?
What does this restriction mean? and why?
Replication is a time-sensitive application, this means that timing assumptions have to be done in order to get the distributed state synchronized across the replicas.
The Internet is an asynchronous network per definition, the network asynchronously evolves and there's no way to make assumptions on delivery times, neither in case MPLS (Multiprotocol Label Switching) paths are defined: the BGP (Border Gateway Protocol) introduces a lot of unpredictability, paths can be very unpredictable, and this translates in unpredictable latency.
According to above, unpredictable latency is a killer factor for Active-Active replication (i.e. mirroring the state synchronously among the replicas to reach a consistent distributed state).
Another problem to be taken into consideration consists in the Network Partitioning: in a set of replicas, one or more can be isolated creating "islands of non-consistent replicas": let's assume the replica set R = { R1, R2, R3, ..., RN }, for network connectivity reasons (e.g. BGP problems) a subset of replicas like {R1, R2, R3} may be isolated from the remaining ones. Network partitioning implies distributed state inconsistencies: the subset of replicas will be consistent, but globally they evolve independently towards a corrupted distributed state.
The CAP Theorem deals with the replication problem over the WAN (Wide Area Network, i.e. the Internet). It states:
Consistency, Availability and Partitioning cannot be achieved over the WAN or another asynchronous network, 2 out of 3 need to be chosen for large scale distributed systems (e.g. Availability and Network Partitioning for the well known NoSQL databases).
Coming back to the original question: according to above, that statement (from RabbitMQ documentation) tries to sum up in a pragmatic way the problems that I highlighted above (i.e. Active-Active replication cannot be achieved over the WAN). For this reason, if you need to replicate your Broker instances over the WAN, techniques like the Shoveling and Federation are commonly used in RabbitMQ deploys.
It means if you have 3 EC2 instances in your cluster, they should be in the same data center. Not US East and US West for example*. RabbitMQ uses Erlang's node communication and is pretty chatty. Low latency communication is critical to having a performant cluster.
*Ideally even the same subnet, but that's not always possible.
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.
Please be aware that I am a relative newbie to ActiveMQ.
I am currently working with a small cluster of ActiveMQ (version 5.15.x) nodes (< 5). I recently experimented with setting up the configuration to use "Shared File System Master Slave" with kahadb, in order to provide high availability to the cluster.
Having done so and seeing how it operates, I'm now considering whether this configuration provides the level of throughput required for both consumers/producers, as only one broker's ports are available at one time.
My question is basically two part. First, does it make sense to configure the cluster as highly available AND load balanced (through Network of Brokers)? Second, is the above even technically viable, or do I need to review my design consideration to favor one aspect over the other?
I had some discussions with the ActiveMQ maintainers in IRC on this topic a few months back.
It seems that they would recommend using ActiveMQ Artemis instead of ActiveMQ 5.
Artemis has a HA solution:
https://activemq.apache.org/artemis/docs/latest/clusters.html
https://activemq.apache.org/artemis/docs/latest/ha.html
The idea is to use Data Replication to allow for failover, etc:
When using replication, the live and the backup servers do not share the same data directories, all data synchronization is done over the network. Therefore all (persistent) data received by the live server will be duplicated to the backup.
And, I think you want to have at least 3 nodes (or some odd number) to avoid issues with split brain during network partitions.
It seems like Artemis can mostly be used as a drop-in replacement for ActiveMQ; it can still speak the OpenWire protocol, etc.
However, I haven't actually tried this yet, so YMMV.
In RabbitMQ, if two clusters are hosted on geographical different locations, then we can’t use Clustering. Then how to make them highly available I.e. if one site’s whole cluster goes down then the messages should be mirrored to other site and other site should be able to cater those messages. Note : sites are connected by WAN
See I can’t lose any message on the both sites. Publishing message to the right site can be taken care of, but if the messages are in queue(work queue) or messages are being processed by consumer and suddenly if the site goes down which includes the broker and consumer, how can those messages be catered by the other site. Like in a cluster if one node dies, the other one has all the messages mirrored and knows which were acknowledged, but how to achieve this on WAN, cause clustering cross WAN is not practical.
I think the question illustrates a conceptual problem with the design. To summarize,
There are two sites, connected via WAN
One site is the primary, while one is the active standby
There is a desire for complete replication of system state (total consistency) between site A and B, to include the status of messages in the queue and messages being processed.
Essentially, you want 100% consistency, availability, and partition tolerance. Such a design is not possible according to CAP Theorem. What RabbitMQ provides is either consistency and availability, with low partition tolerance via clustering, or availability and partition tolerance via federation or shovel. RabbitMQ does not deal very well with the case of needing consistency and partition tolerance, since message brokers really handle highly transient traffic.
Instead, what is needed is to fully scope the problem to something that can be solved using the available technologies. It sounds to me like the correct approach (since it's over a WAN) is to sacrifice availability for consistency and partition tolerance, and have your application handle the failover case. You may be able to configure RabbitMQ sufficiently in this regard - see https://www.rabbitmq.com/partitions.html.
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.
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).