Amazon CloudSearch vs. Amazon Elastic Compute Cloud (EC2) vs. Elasticsearch

Amazon Cloudsearch can be suitable for some queries that require fast data. For example, in our case, we used CloudSearch, in a tool called Global Search. That will search everything like names, emails and a lot of stuff in our application. If you want fast data and you have a simple query, Global Search isn't appropriate for you.

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Suitable for companies that are looking for performance at a competitive price, flexibility to switch instance type even with RI, flexibility to add-on IOPS, option to lower running cost with the regular introduction of new instance type that comes with higher performance but at a lower cost.

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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.

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• Really fast queries
• Good Reporting
• Reduce the cost of the server

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• Huge Diverse range of machine shapes are available which cater to our demand.
• Ability to combine the machines and integrate them with any other service in AWS (Ex: RDS, S3)
• Handling performance and scalability using auto-scaling, ELB configuration and high performance machine shapes.

Incentivized

• 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.

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• Can take some time to implement
• The initial configuration can be tricky
• Takes some time to update the values

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• The choices on AMIs, instance types and additional configuration can be overwhelming for any non-DevOps person.
• The pricing information should be more clear (than only providing the hourly cost) when launching the instance. AWS DynamoDB gives an estimated monthly cost when creating tables, and I would love to see similar cost estimation showing on EC2 instances individually, as not all developers gets access to the actual bills.
• The term for reserving instances are at least 12 months. With instance types changing so fast and better instances coming out every other day, it's really hard to commit to an existing instance type for 1 or more years at a time.

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• Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
• Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
• Schema changes require complete reindexing of an index

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We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

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You an start using EC2 instances immediately, is so easy and intuitive to start using them, EC2 has wizard to create the EC2 instances in the web browser or if you are code savvy you can create them with simple line in the CLI or using an SDK. Once you are comfortable using EC2, you can even automate the process.

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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.

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AWS's support is good overall. Not outstanding, but better than average. We have had very little reason to engage with AWS support but in our limited experience, the staff has been knowledgeable, timely and helpful. The only negative is actually initiating a service request can be a bit of a pain.

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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.

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Do not mix data and master roles. Dedicate at least 3 nodes just for Master

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I didn't investigate the best alternatives to CloudSearch, but did help with implementing this feature in our application. But from what i tested and used - Cloudsearch is very fast to get queries. Some negative points can be the time to implement this and some configurations that can be tricky.

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Amazon EC2 is super flexible compared to the PaaS offerings like Heroku Platform and Google App Engine since with Amazon EC2, we have access to the terminal. In terms of pricing, it's basically just the same as Google Compute Engine. The deciding factor is Amazon EC2's native integration with other AWS services since they're all in the same cloud platform.

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.

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• Positive Point - Reduced the server load
• Negative point - Not suitable for all queries
• Negative Point - Time to implement this feature

Incentivized

• It reduced the need for heavy on-premises instances. Also, it completely eliminates maintenance of the machine. Their SLA criteria are also matching business needs. Overall IAAS is the best option when information is not so crucial to post on the cloud.
• It makes both horizontal and vertical scaling really easy. This keeps your infrastructure up and running even while you are increasing the capacity or facing more traffic. This leads to having better customer satisfaction.
• If you do not choose your instance type suitable for your business, it may incur lots of extra costs.

Incentivized

• 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.

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