Fastest Message Queues: How to Measure, Compare, and Find the Best One
Message queues are one of the building blocks of a cloud-based computing strategy. Messaging queues allow pieces of computing architecture to communicate with one another, making them an essential part of executing a microservices strategy. Developers have many options of host providers for messaging queues, like RabbitMQ, Kafka, Redis, and Iron.io. Speed is one driving factor in the choice of a messaging queue, but there are different benchmarks to determine the speed — or level of latency — of a given solution. Speed is also just one way to assess what message queue host is right for you.
For many organizations, the fastest and best solution is Iron.io, whose superior performance and long list of elite features elevates it above the competition.
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What is a Message Queue?
One of the fundamental characteristics of a system built on microservices is the capacity to develop each component individually. This allows for customization and better scalability than with a traditional monolith.
But it also means that all of those microservices have to speak to one another. As the network grows, the pieces may have to speak over a greater area or incorporate more elements. There's a risk that a portion of the system might fail and result in the loss of workflows and processes.
The message broker allows for the optimization of this communication. The overall system has components that produce messages and those that consume messages.
The message may be to start a workflow, end a process, or any number of an almost infinite series of events. Typically, a message is just data with a header, followed by specific information or instructions.
Timing is important; there may be events scheduled for a later time, or just too many events for the destination component to handle. Some superior solutions, like Iron.io, have redundant/failover capabilities that prevent the loss of messages, even in the event of a failure in one part of the network.
The message broker ensures that the system can take in each new message and store it if it's not yet at a stage where it can be processed.
The message queue ensures that the system can continue to take on new inputs at an almost unlimited rate, even if the rest of the chain isn't ready to process those inputs just yet. A message queue is just that: a lineup of messages, which are pieces of data with a specific purpose.
What is Latency in a Messaging Queue?
Latency is the amount of delay in the time it takes for data to move from one place to another. In more formal networking terms, latency is the total time it takes for a piece of data to complete a circuit. A circuit is like a round trip to and from the data's initiation point. Latency is therefore synonymous with speed, one major factor in deciding which messaging queue is right for a business or organization.
The need for low latency in a messaging queue might be obvious, but the amount of acceptable latency can vary by use case. A network of retail shops that runs a rewards program might be fine with some delay between a customer purchase and a balance update on points. That retailer might be fine with some amount of latency in the messaging queue. By contrast, a multi-player online gaming platform that relies on real-time updates of information might need as low latency as possible.
Since a microservice network means parts have to constantly talk to one another, the speed at which they do so is very important — it can affect every aspect of the chain of computing.
What Benchmarks Can You Use to Evaluate a Messaging Queue?
Latency may seem like a straightforward measure of messaging queue capacity. But there are different factors that affect latency. For that reason, it is difficult to choose a queue host without context. To put it simply, instead of asking, "how much time does it take?," the better question is, "how much time does it take to do what I need it to do?"
To provide a full analysis, you can use a process called benchmarking. In networking, benchmarking is the use of specific tests to see how a system runs. Benchmark tests depend on what you're analyzing and the information you want to gain. You might look at the amount of data coming into your network now at low, moderate, or peak volume. You can think of this as the number of messages you ask your network to process at a given time.
Thankfully, you don't have to come up with your own benchmarks to determine the latency of a certain messaging queue. The OpenMessaging Benchmark Frameworkcan help. This framework has different tests that simulate workloads of different sizes, whether through a CPU or cloud system. The framework has developed these benchmarks with certain open source messaging queues in mind. You can take this framework as it is, or tweak it to fit your needs.
The framework's workload analysis has several different elements, such as:
- Message size
- Number of topics
- Number of subscriptions/producers per topic
- Rate of message creation
- Size of message backlog
You can run a test of a particular workload and see the resulting latency figures for each messaging queue host. That way, you can compare Apache Kafka to Apache RabbitMQ, or other platforms in consideration.
Common Messaging Queue Hosts
Once you've assessed your need for a messaging queue and know the acceptable amount of latency for your use case, you can start to compare specific hosts. This is an overview of some well-known players in the messaging queue space and what you need to know about each. It is also a starting point to find a high-performance messaging system with high-availability and functionality.
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RabbitMQ
RabbitMQ is a widely-used messaging queue host. It runs on open-source code and has a wide array of features. Specifically, it allows for distributed deployment, with local messaging clusters or federated across zones or between clusters. It has flexible message routing, message queuing, and multiple protocols that optimize your ability to have asynchronous messaging across a distributed network. RabbitMQ is one of the fastest message queues because it is lightweight, and therefore is quick and easy to deploy.
Apache Kafka
Apache Kafka boasts the capabilities of both a traditional messaging system and a distributed file system, as well as latency rates as low as 2ms (milliseconds). It also claims high levels of scalability and throughput, allowing for growth and expansion of message capacity as necessary. Like RabbitMQ, it is also an open source option, with a number of additional tools and add-ons that users can take advantage of. The impressive low latency rate makes Kafka one of the fastest message queue options.
Amazon SQS
Amazon SQS, as part of the AWS/Amazon cloud services ecosystem, promotes stringent security protocols as well as ease of use. This host is a fully managed service, so developers do not have to worry about regularly tweaking with the messaging protocols. Amazon has a cost-per-use pricing model for SQS. Its infinite scaling and high reliability mean users rarely experience delays, so Amazon SQS also gets a spot on the fastest message queue list.
Redis
Redis is another open-source messaging solution that features a robust throughput and scalability. Asynchronous systems rely on a solid messaging queue to function effectively. That is most evident when there are spikes in data flow. The messaging queue allows for flow management so the distributed system does not get overwhelmed. Redis promotes this aspect of the messaging queue technology as well as the solution's ability to prevent distributed system collapse when data fluctuates. Because of its ability to prevent collapse and keep things moving, Redis is also one of the fastest message queues.
Beanstalkd
Beanstalkd is a simple work queue developed for the sole purpose of job and message management. On its github, the developers call it a "to-do" list for an application. Jobs that you want to run later are added on to the to-do list. When they are ready to run, they are removed. Beanstalkd was made to ease the volume on a specific application, the "Causes on Facebook," application. Because speed and utility are the core drivers of Beanstalkd, it is one of the fastest message queues on the market.
Iron.io
Iron.io is an innovative player in the messaging queue arena. It is faster than two of the biggest players: Amazon SQS and RabbitMQ. This is important for anyone who relies on low latency to fulfill their business objectives. In addition, it has some prominent features:
- Push and pull queues
- Long polling
- Error queuing
- Alerts and triggers
- Cloud-based and scalable using high availability data centers
- Backups to ensure messaging continuation, specifically through the use of automated failover
- REST based API
Iron.io offers many additional benefits compared to Amazon SQS, Redis, Beanstalkd, RabbitMQ and other competitors such as ActiveMQ, Apache Pulsar and ZeroMQ.
- Push queues (Iron.io only)
- Webhook support (Iron.io only)
- Reporting and Analytics (Iron.io and RabbitMQ in some packages)
- Redundancy/Failover protection (Iron.io and AmazonSQS)
- Publish/Subscribe (pub/sub) (Iron.io, RabbitMQ and Redis)
- Elasticity (Iron.io and AmazonSQS)
- Unlimited queuing (Iron.io and AmazonSQS)
- SLA (Iron.io and Redis)
This is just a sample to show how Iron.io's messaging solution has an impressive suite of benefits that is more comprehensive than any others on the market. To see how Iron.io stacks up to its competition with its metrics, check out a comparison chart on the Iron.io website.
How Iron.io Helps
Iron.io also offers IronWorker, a complete background job solution with autoscaling algorithm. The combination of Iron.io's messaging queue solution MQ and IronWorker makes it an ideal partner for any organization looking to streamline and advance its cloud network infrastructure or distributed file systems. Iront.io has helped a number of customers to elevate their data processing in order to increase revenue. Check out these client case studies to learn how.
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