or client-side bugs can damage your broker, halt your publisher, crash your server, While a message is transient, it may still be source-of-truth data because it doesnt exist anywhere else yet, and if lost can never be recovered. performance, if the queues are always short, or if you have set a max-length policy. RPO defines the acceptable data loss window in the event of a disaster. Thousands of connections can be a heavy burden on a RabbitMQ server. since the index has to be rebuilt. Messages in transit might get lost in an event of a connection failure and need to be retransmitted. Types of data and the impact of their loss on businesses.
In order to achieve high availability, clients also need to be able to reconnect automatically in the event of a connection failure or a broker going offline. A common error made on CloudAMQP clusters is creating a new vhost but forgetting to enable an HA-policy for it. AP systems asynchronously replicate operations but this can cause data loss if a node is lost, this loss being due to replication lag. When using a load balancer, ensure that there is no affinity/stickiness to the balancing to ensure that when clients reconnect they are not always routed back to the same broker (which may now be down). Fig 8. but extending the throughput time. Set a Recovery from back-up can also take time - time to move backup files to their destination and time for the data system to recover from those files. for optimal performance and how to get the most stable cluster. How can I make RabbitMQ highly available and what architectures/practices are recommended for disaster recovery? Without an HA-policy, messages will not be synced between nodes. Make sure that you dont share channels between threads as most clients dont make channels thread-safe (it would have a serious negative effect on the performance impact). RAM, RabbitMQ starts flushing (page out) messages to disk. found in the control panel for your instances. This might slow down the server if you have thousands Fig 9. If you have about the same processing time all the time and network behavior remains the same, simply take the total round trip time and divide by the processing time on the client for each message to get an estimated prefetch value. AP systems can be built across multiple data centers but they increase the likelihood of data loss and also the size of the data loss window. rules based on the experience we have gained while working with RabbitMQ. An The upside is lower latency as operations can be immediately confirmed to clients. A message is simply data in-flight from a source to one or more destinations.
property will send messages which are either rejected, nacked or expired (with TTL) to the specified dead-letter-exchange. You will, therefore, get better performance if you split your queues into different cores, and into different nodes, if you have a RabbitMQ cluster. x-dead-letter-exchange As part of a Business Continuity Plan, an enterprise must decide on two key objectives with regard to disaster recovery. Separate the connections for publishers and consumers to achieve high throughput. This means that RabbitMQ won't send out the next message until after the round trip completes (deliver, process, acknowledge).
Start your managed cluster today. There are fundamental laws such as the speed of light and the CAP theorem which both have serious impacts on what kind of DR/HA solution we decide to go with. In order to make that balance, we need to take into consideration: First well cover the measurable objectives that define our acceptable data loss and unavailability time windows in the event of an incident, then well cover the considerations above. The downside is that there will be lag between the primary and secondary node, meaning that data loss can occur in the event that the primary is lost. Persistent data sticks around week after week and even year after year. When message m1 and m2 is published to E1 on DC1, those messages are routed to the queue on DC1 and also routed asynchronously to E1 in DC2 and enqueued on the queue there. Having many messages In the persistent/transient continuum, at the persistent end we have databases, at the transient end we have message queues (like RabbitMQ) and in the middle are distributed logs (e.g. Loss of a single failure domain does not impact availability or cause data loss. A feature called All database systems offer some kind of back-up feature. An RPO of 0 minutes is only achievable with a single cluster and so is not realistic in a multi-DC (or multi-region) scenario. The impact of failure on availability should either not be seen or be extremely low. queues as cores on the underlying node(s). If you are sending a lot of messages Try to keep your client as busy as possible. RabbitMQ and the RabbitMQ Logo are trademarks of VMware, Inc. As an organization, we have been working with RabbitMQ for a long time and Read more about the consistent hash exchange plugin However, you can leverage exchange federation or shovels which are an asynchronous message routing feature that work across clusters. The other common feature is replication. experience a hit to queue performance. x-dead-letter-routing-key A distributed log streams database data modifications to other systems. The Recovery Point Objective (RPO) determines the maximum time period of data loss a business can accept as the result of a major incident. First I want to go over Business Continuity Planning and frame our requirements in those terms. Using the pause_minoritypartition handling strategy makes mirrored queues favour consistency. A large prefetch count, on the other hand, could take lots of messages off the queue and deliver to one single consumer, keeping the other consumers in an idling state. When accessing the brokers of a cluster by their individual host names, ensure that all are provided to theclient so that it can attempt to reconnect to all nodes in the cluster until it finds a broker that is up and running. Giving a queue a name is important when you want to share the queue between producers and consumers, but it's not important if you are using temporary queues. Other values might be 1 hour or 24 hours, with higher values typically being easier to achieve at a lower implementation cost. In the example, we have a QoS prefetch setting of one (1). Dont open a channel each time you are publishing. availability and data safety. This duplication can be accommodated with a deduplication strategy. RabbitMQ offers two types of replicated queue: mirrored queues (HA queues) and quorum queues. RPO and RTO are a tradeoff between cost of implementation and cost of data loss/service interruption. Use the control panel in CloudAMQP to enable - or disable - plugins. 50 thousand messages. The reasons you should switch to Quorum Queues. RabbitMQ queues are bound to the node where they were first declared. Cookie Settings. Copyright 2007-2022 VMware, Inc. or its affiliates.
Well next see how these features can be used for high availability and disaster recovery. The goal is to keep the consumers saturated with work, but to minimize the client's buffer size so that more messages stay in Rabbit's queue and are thus available for new consumers or to just be sent out to consumers as they become free.. If an enterprise has its own fibre linking its data centers which is highly reliable and consistently low latency (like on-prem availability zones), then it might be an option. It could quickly become problematically to do this manually, without adding too much information about numbers of queues and their bindings into the publisher. If you specify RabbitMQ offers features to support high availability and disaster recovery but before we dive straight in Id like to prepare the ground a little. Fig 4.
any other topic related to RabbitMQ, don't hesitate to ask them Clustering across AZs in these clouds can offer higher availability than hosting a cluster in just one AZ. Because these values are a balance of the cost of implementation vs cost of service interruption - make sure you get the appropriate balance for the type of data that is stored and served. Some plugins might be great, but they also consume a lot of CPU or may use a high amount of RAM. If one of the bundled messages fails, will all messages need to be re-processed? With synchronous replication, the client only gets the confirmation that the operation has succeeded once it has been replicated to other nodes. Another recommendation for applications that often get hit by spikes of messages, The nodes are located in different availability zones and queues are automatically mirrored between availability zones. Simply fork the repository and submit a pull request. Synchronous replication provides data safety at the cost of availability and extra latency. TLS has a performance impact since all traffic has to be encrypted and decrypted. RabbitMQ has two features for schema replication. A consuming application that receives important messages should not acknowledge messages until it has finished with them so that unprocessed messages (worker crashes, exceptions, etc.) All rights reserved. These queue types use synchronous replication and provide data safety and high availability in the face of failures such as servers, disks and network. Ultimately the implementation will be a balance of cost of implementation vs the cost of data loss and service interruption. You choose an AP system when availability and/or latency is the most important consideration. auto-delete Alternatively, channels can be opened and closed more frequently, if required, and channels should be long-lived if possible, e.g. If you have questions about the contents of this guide or RabbitMQ stores transient data. With asynchronous replication, the client gets the confirmation that the operation has succeeded when it has been committed locally. These features were not built for an active-passive architecture and therefore do have some drawbacks. When setting up bundled messages, consider your bandwidth and architecture. Creating a CloudAMQP instance with two nodes, on the other hand, will get you half the performance compared to the same plan size for a single node. We go to great lengths to test new major versions before we release them to our customers. Each priority level uses an internal queue on the Erlang VM, which takes up some resources. Tanzu RabbitMQ offers the Schema Sync Plugin which actively replicates schema changes to a secondary cluster. The nodes are located in different availability zones and queues are automatically mirrored between availability zones. These limits affect the costs and feasibility of the RPO and RTO values we choose. For maximum performance, we recommend using VPC peering instead as the traffic is private and isolated without involving the AMQP client/server. Likewise, if a majority of nodes are down, the remaining brokers will pause themselves. a TTL policy of 28 days deletes queues that haven't been consumed from for 28 days. Caches and search engines typically store secondary data. Fig 5. Asynchronous replication provides higher availability, lower latency but at the cost of potential data loss in a fail-over - a good choice when RPO > 0 minutes. It is important to note that the concept of an AZ is not standardised and other cloud platforms may use this term more loosely. For high availability we recommend clustering multiple RabbitMQ brokers within a single data center or cloud region and the use of replicated queues. your system should be to maximize combinations of performance and availability Most RabbitMQ clients offer automatic reconnection features. Clustering requires highly reliable, low latency links between brokers. Messages are replicated to the passive data center. If an enterprise loses its most valuable persistent data it can kill the company. Persistent messages are heavier with regard to performance, as they have to be written to disk. Privacy Disaster Recovery typically refers to a response to a more major incident (a disaster) such as the loss of an entire data center, massive data corruption or any other kind of failure that could cause a total loss of service and/or data. Given a 24 hour TTL, a message might have been sitting in a queue in DC1 for 25 hours unconsumed when DC1 went down, and that same message will have been discarded from the same queue in DC2 an hour before.
Links that are tunnels across the public internet are simply not viable. TTL and dead lettering can generate unforeseen negative performance effects, such as: A queue that is declared with the The plugin creates a hash of the routing key and spreads the messages out between queues that have a binding to that exchange. In the end it will be a balance of the downsides vs the business impact of data loss. Fail-over is often automated and fast causing little or no impact on clients. Acknowledgment has a performance impact, so for the fastest possible throughput, manual acks should be disabled. You can connect to RabbitMQ over AMQPS, which is the AMQP protocol wrapped in TLS.
Please be aware that we always have the most recommended version as the selected option (default) in the dropdown menu area where selection is made of the version for a new cluster. Some data systems use asynchronous replication for high availability that provide eventual consistency (with potential data loss). Typically not multi-DC friendly. There are three ways to delete a queue automatically. The limitation is that a cluster must be shut down entirely in order to make a back-up of its data directory. The operation is replicated in the background meaning that there is no additional latency for the client and no loss of availability if the network between the primary and secondary is down. The RabbitMQ default prefetch setting gives clients an unlimited buffer, meaning that RabbitMQ by default sends as many messages as it can to any consumer that looks ready to accept them. reuse the same channel per thread of publishing. The difference between replication and cross-cluster message routing is that replication involves replicating both enqueue and acknowledgement operations, whereas message routing is only about replicating the messages. Trademark Guidelines Good capacity planning goes hand-in-hand with Business Continuity Planning as both are required to achieve reliability and resiliency. of the queue so that it never gets larger than the max-length setting. Fig 3. Full back-ups can be made on a schedule, such as nightly or weekly and additionally can offer incremental backups on a higher cadence, such as every 15 minutes. The reliability and latency of the network between data centers is inferior to that of a LAN and building a CP system that has acceptable availability and latency is at best challenging and most likely infeasible. If you have one single or few consumers processing messages quickly, we recommend prefetching many messages at once. Quorum queues are CP and tolerate the loss of brokers as long as a quorum (majority) remains functioning.
Trademark Guidelines. We know how to configure current plans on CloudAMQP Keep in mind that the amount of messages per second is a way larger bottleneck than the message size itself. The page out process For each connection and channel performance, metrics have to be collected, analyzed and displayed. The important thing to remember about backups is that recovering from them involves potential data loss as there was potentially new data that entered the system since the last backup was made. Service interruption and/or data loss because data was not spread across failure domains. By declaring a queue with the x-message-ttl property, messages will be discarded from the queue if they haven't been consumed within the time specified. Two nodes will give you high availability since one node might crash or be marked as impaired, but the other node will still be up and running, ready to receive messages. The server acks when it has received a message from a publisher. If you cannot afford to lose any messages, make sure that your queue is declared as durable and that messages are sent with delivery mode "persistent". Passive DC2 accumulates messages, only removed by TTL or length limit policies. The fact that a message was consumed and removed from the queue is not included in federation or shovels (because that is not the purpose of those features). Because of this, they are not recommended for a production server. The handshake process for an AMQP connection is quite involved and requires at least 7 TCP packets (more, if TLS is used). a cache stores secondary data that can be re-hydrated from a persistent store. In the worst case, the server can crash because it is out of memory. Prefetch limits how many messages the client can receive before acknowledging a message. Round-trip time in this picture is in total 125ms with a processing time of only 5ms. in the queue might negatively affect the performance of the broker. The obvious answer is 0 seconds, meaning no data loss, but this can be hard to achieve and even when possible can have serious downsides like cost or performance. high-availability, Copyright 2007-2021 VMware, Inc. or its affiliates. At CloudAMQP we configure the RabbitMQ servers to accept and prioritize fast but secure encryption ciphers. If you have too many unacknowledged messages, you will run out of memory. Keep in mind that lazy queues will have the same effect on performance, even though you are sending transient messages. Availability and data loss may or may not overlap. best practices with your own RabbitMQ platform. Publish confirm is the same concept for publishing. exclusive queue automatically stored to disk, thereby minimizing the RAM usage,
RabbitMQ does support back-upsbut the support is limited and therefore not commonly used. Fail-overs are not as fast as quorum queues and there exist edge cases where the fail-over can take minutes or hours for extremely large queues. We have, due to popular demand, created a diagnostic tool for RabbitMQ that is Rack awareness adds additional resiliency to replicated data stores. at once (e.g. Note that its important to consume from all queues. You will not suddenly The client can either ack the message when it receives it, or when the client has completely processed the message. Bad architecture design decisions With mirrored queues you can specify the list of nodes it should be spread across. You will also get pause minority; a partition handling strategy in a three-node cluster that protects data from being inconsistent due to net-splits. Quorum based CP systems can tolerate the failure or network isolation of a minority of nodes and delivery availability without data loss and are a good high availability solution. nodes in the cluster after a restart. You should ideally only have one connection per process, and then use a channel per thread in your application. Data type continuums and typical data store usage. Finally well look at the RabbitMQ features available to us and their pros/cons. Recovery Point Objective (RPO) defines the time period of data loss that is acceptable in the event of a disaster. Setting the RabbitMQ Management statistics rate mode to detailed has a serious performance impact and should not be used in production. We recommend two plugins that will help you if you have multiple nodes or a single node cluster with multiple cores: The consistent hash exchange plugin allows you to use an exchange to load-balance messages between queues. If you have many consumers and/or long processing time, we recommend you to set the prefetch count to one (1) so that messages are evenly distributed among all your workers. Fig 1. The obvious answer might be 0 seconds, meaning a seamless failover with continued availability, but this might not be technically achievable and if it were then the costs might be far higher than your enterprise is willing to pay. Please note that you should disable lazy queues if you require really high
It is recommended that each process only creates one TCP connection, using multiple channels in that connection for different threads. If you consume on the same TCP connection, the server might not receive the message acknowledgments from the client, thus effecting the consume performance. Copyright 2011-2022 CloudAMQP. Quorum queues do not use RabbitMQs traditionalpartition handling strategiesbut use their own failure detector that is both faster to detect partitions and failures but also less prone to false positives, this allows them to deliver the fastest fail-overof the replicated queue types. the routing key of the message with be changed when dead lettered. How to handle the payload (message size) of messages sent to RabbitMQ is a common question among users. Perhaps one of the most significant changes in RabbitMQ 3.8 was the new queue This is a replicated queue to provide high This is where data modifications are streamed from one node to another, meaning that the data now resides in at least two locations. Instead, you should let the server choose a random queue name instead of making up your own names, or modify the RabbitMQ policies. The goal when designing Asynchronous replication and backups are typically the solution for Disaster Recovery. be affected negatively if you have too many queues. a microservice stores some small amount of data in a database that belongs to another service. Privacy and The best practice is to reuse connections and multiplex a connection between threads with channels. Now lets look at RabbitMQs features, framed against what we have covered so far. The consistent hash exchange plugin can be used if you need to get the maximum use of many cores in your cluster. Fig 2. This is a feature of data systems that ensure that data is replicated across racks or availability zones or data centers, basically any type of failure domain in your infrastructure. No confirmed writes will be lost, but any brokers on the minority side lose availability and if there are multiple partitions where no majority exists then the entire cluster becomes unavailable. Additionally, it is time-consuming to restart a cluster with many messages loss of performance capacity due losing hot data, waiting for an operation to be replicated adds latency, If the network is down between the nodes then availability may be lost, If the secondary node(s) is down then availability may be lost, AP - Availability in a partitioned network, CP - Consistency in a partitioned network, High Availability is the ability to remain available in the face of failures, Disaster Recovery is the ability to recover from disaster with bounded data loss and unavailability. So stay tuned because RabbitMQ is changing fast. RabbitMQ can apply back pressure on the TCP connection when the publisher is sending too many messages for the server to handle. As an example, exchange E1 has a single binding to a quorum queue in DC1. In this post I am going to cover perhaps the most commonly asked question I have received regarding RabbitMQ in the enterprise. In most use cases it is sufficient to have no more than 5 priority levels. Classic queues can be mirrored and configured for either availability (AP) or consistency (CP). post on quorum queues and mirrored queues, The available tools for redundancy/availability and their limitations, The types of data and the associated costs to the business if lost. available for all dedicated instances in CloudAMQP. In order to mitigate the problems of duplication, you can apply either message TTL policies on the passive cluster that remove messages after a time period or queue length limits that remove messages when the queue length reaches the limit. Acknowledgments let the server and clients know when to retransmit messages. are using Quorum Queues by default. Messages sent to the exchange are consistently and equally distributed across many queues, based on the routing key of the Back-ups can be slower to recover with, but have other benefits such as being able to travel back in time. In order to avoid losing messages in the broker, you need to be prepared for broker restarts, broker hardware failure, or broker crashes. synchronous replication - replicated queues, asynchronous cluster-to-cluster message routing - exchange federation and shovels. Disable busy polling in RabbitMQ if you have many will not keep up with the speed of the publishers all the time, we recommend When you create a CloudAMQP instance with one node, you will get one single node with high performance, because messages dont need to be mirrored between multiple nodes. Any at-least-once message queue/bus will cause duplicates from time to time, so this may already be something that is taken care of. Publish confirm also has a performance impact, however, keep in mind that its required if the publisher needs at-least-once processing of messages. on the queue. Also, queues will continue to grow as messages accumulate. It can however increase costs if the cloud provider charges for cross AZ data transfer. In this environment, the use of quorum (majority) based replication algorithms by CP systems can make unavailability a rare event. queue in the cluster. The postings on this site are by individual members of the It also takes time to sync messages between Messages, exchanges, and queues that are not durable and persistent will be lost during a broker restart. Both fall within the realm of Business Continuity. However, if you bundle multiple messages you need to keep in mind that this might affect the processing time. These are admittedly, imperfect solutions given that they could actually cause message loss. Leased links from providers are better but also a risky choice. AP systems continue to be available despite not reaching the desired redundancy level. RabbitMQ has many features on its road-map, including real asynchronous replication support for disaster recovery, data recovery tooling, rack awareness and more. Make sure that you are using the latest recommended version of client libraries. The CPU and RAM usage may also usually takes time and blocks the queue from processing messages when there are Once read, the message is destroyed. The RabbitMQ sharding plugin does the partitioning of queues automatically; i.e., once you define an exchange as sharded, then the supporting queues are automatically created on every cluster node and messages are sharded accordingly. that cover best practices recommended by the RabbitMQ community. For high performance, the best practice is to use transient messages. A too high value may keep one consumer busy, while other consumers are being kept in an idling state. With quorum queues you must currently create the queue with an initial group size of 1 and then add members on the nodes to achieve the desired spread. The image below illustrates a long idling time. The CAP theorem states that in the event of a network partition, either you can have consistency or availability but not both. This is the only form of replication that offers no data loss, but it comes at a price: Synchronous replication is typically the solution for high availability within a data center or cloud region. The quorum queue leader synchronously replicates the new enqueues and the acknowledgements. series, we are going to share all this knowledge so you can follow the DC2 has a federation link to DC1 and a federated exchange E1 which also has a single binding to a quorum queue. Disaster Recovery attempts to avoid permanent partial or total failure or loss of a system and usually involves building a redundant system that is geographically separated from the main site. its source is available on GitHub. When designing both a High Availability and Disaster Recovery strategy, we need to take into account some fundamental limits such as the speed of light and the CAP theorem. Availability zones are essentially data centers that are connected by ultra reliable, low latency links but not geographically separated. Most regions in AWS, Azure and GCP offer multiple availability zones. The Recovery Time Objective (RTO) determines the maximum time period of unavailability, aka the time it takes to recover and be operational again. Fig 7. RabbitMQ documentation. You choose a CP system when consistency is the most important consideration. Tags: All pre-fetched messages are removed from the queue and invisible to other consumers. current plans on CloudAMQP and all our For business continuity plans that require multiple data centers, with geographical separation in an active-passive architecture there are challenges. 
