Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
RabbitMQ
Score 9.0 out of 10
N/A
RabbitMQ, an open source message broker, is part of Pivotal Software, a VMware company acquired in 2019, and supports message queue, multiple messaging protocols, and more.
RabbitMQ is available open source, however VMware also offers a range of commercial services for RabbitMQ; these are available as part of the Pivotal App Suite.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
It is highly recommended that if you have microservices architecture and if you want to solve 2 phase commit issue, you should use RabbitMQ for communication between microservices. It is a quick and reliable mode of communication between microservices. It is also helpful if you want to implement a job and worker mechanism. You can push the jobs into RabbitMQ and that will be sent to the consumer. It is highly reliable so you won't miss any jobs and you can also implement a retry of jobs with the dead letter queue feature. It will be also helpful in time-consuming API. You can put time-consuming items into a queue so they will be processed later and your API will be quick.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
What RabbitMQ does well is what it's advertised to do. It is good at providing lots of high volume, high availability queue. We've seen it handle upwards of 10 million messages in its queues, spread out over 200 queues before its publish/consume rates dipped. So yeah, it can definitely handle a lot of messages and a lot of queues. Depending on the size of the machine RabbitMQ is running on, I'm sure it can handle more.
Decent number of plugins! Want a plugin that gives you an interface to view all the queues and see their publish/consume rates? Yes, there's one for that. Want a plugin to "shovel" messages from one queue to another in an emergency? Check. Want a plugin that does extra logging for all the messages received? Got you covered!
Lots of configuration possibilities. We've tuned over 100 settings over the past year to get the performance and reliability just right. This could be a downside though--it's pretty confusing and some settings were hard to understand.
It breaks communication if we don't acknowledge early. In some cases our work items are time consuming that will take a time and in that scenario we are getting errors that RabbitMQ broke the channel. It will be good if RabbitMQ provides two acknowledgements, one is for that it has been received at client side and second ack is client is completed the processing part.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
RabbitMQ is very easy to configure for all supported languages (Python, Java, etc.). I have personally used it on Raspberry Pi devices via a Flask Python API as well as in Java applications. I was able to learn it quickly and now have full mastery of it. I highly recommend it for any IoT project.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
I gave it a 10 but we do not have a support contract with any company for RabbitMQ so there is no official support in that regard. However, there is a community and questions asked on StackOverflow or any other major question and answer site will usually get a response.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
RabbitMQ has a few advantages over Azure Service Bus 1) RMQ handles substantially larger files - ASB tops out at 100MB, we use RabbitMQfor files over 200MB 2) RabbitMQ can be easily setup on prem - Azure Service Bus is cloud only 3) RabbitMQ exchanges are easier to configure over ASB subscriptions ASB has a few advantages too 1) Cloud based - just a few mouse clicks and you're up and running
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
Positive: we don't need to keep way too many backend machines around to deal with bursts because RabbitMQ can absorb and buffer bursts long enough to let an understaffed set of backend services to catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines around.
Negative: we've got many angry customers due to queues suddenly disappearing and dropping our messages when we try to publish to them afterward. Ideally, RabbitMQ should warn the user when queues expire due to inactivity but it doesn't, and due to our own bugs we've lost a lot of customer data as a result.
Positive: makes decoupling the web and API services from the deeper backend services easier by providing queues as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
