Amazon OpenSearch Service Reviews

We use the solution as a login platform. We have a lot of microservices, and we get log records from there, which we host on Amazon OpenSearch.

There are a couple of cool features. The service provides vacuum storage for cold or ultra-warm data. We retrieve historical data with just a click of a button to move it from cold to hot or warm because it's already stored in the backend storage, S3.

Some configurations or settings are not accessible to end users, as OpenSearch Service is a managed service. It would be beneficial to have some level of customization available in the managed service, tailored to the specific use cases of the end users. Currently, there are strict controls. For instance, if you wish to adjust cluster settings or other parameters, it's challenging for AWS to modify them.

The real-time analytics provided by Amazon OpenSearch Service can significantly improve your decision-making processes. Initially, we struggled to determine the correct cluster size and monitor various metrics. You can easily observe metrics like JVM and CPU usage on the monitoring dashboard. This information helps choose the appropriate tool and understand its support and extension capabilities. It would be even better if the service included built-in alerting based on these metrics. If an issue arises, you must manually check the cluster's status. Implementing preconfigured alerts for critical metrics like JVM and CPU usage would significantly enhance the service's usability.

I have been using Amazon OpenSearch Service for 2 years.

The product is stable.

The solution's scalability is excellent. You can scale up with Ruby on Rails deployment. It's pretty effective. If you wish to upgrade anything, it's super efficient. For example, you can trigger a global deployment test, and you'll experience no downtime.

We receive good support from Amazon OpenSearch Service. You encounter difficulties resolving issues. They are satisfactory in terms of communication and response time.

Neutral

We couldn't find a suitable solution for authentication. Among the various options considered, the primary reason for choosing Amazon OpenSearch Service was our familiarity with AWS, as we already host our applications on this platform. Another significant factor was the high licensing costs. Upgrading to Elasticsearch's latest version incurs high licensing expenses. Also, the cost of using Splunk was high. Amazon OpenSearch Service was the most suitable option considering both the ecosystem and cost factors. Additionally, since Elasticsearch and OpenSearch are similar, transitioning to OpenSearch from Elasticsearch was relatively easy. This transition allows us to maintain a consistent user experience while remaining within the AWS ecosystem without incurring excessive costs.

Creating a cluster is hard, but sometimes, when you want to make a VPC web cluster, it is difficult to expose the dashboards. You can see the proxy settings if you want to integrate with Okta. We had a real challenge with setting up the integration with Okta. The cluster creation was not complex, whereas the integration took us months to resolve. Deployment took two to three months to complete.

The cluster setup is not complicated, especially if you want it taken care of with Okta and the dashboard exposed via proxy.

A managed service is more costly than a self-managed one. The cost isn't about monitoring; it also involves human resources and labor cost savings. For instance, if you previously used self-managed Elasticsearch, you need to handle all upgrade strategies and cluster management yourself. This incurred a significant cost, which makes tools like OpenSearch valuable. OpenSearch is designed to save your developers or users to manage the infrastructure.

It should be easier to integrate with other AWS services.

I recommend the solution, but it depends on your specific use case. For example, if you plan to use it for logging or centralized login setup. Thus, I suggest assessing your needs based on data characteristics. For example, if your data exhibits fluctuating volumes or varying patterns per day. Managing OpenSearch entails addressing various aspects, including site templates and lifecycle policies. Therefore, ensuring all necessary components are in place is crucial.

It's easy for a person to learn to use Amazon OpenSearch Service for the first time. There would be a learning curve, and it might take some time in terms of managing the internals of the OpenSearch cluster, but if you just want to use it as an end user, it's pretty intuitive. You can navigate around to see the options quickly.

Overall, I rate the solution a seven out of ten.

We use it to store and search for events that occur within our application, rather than traditional application logs.

It serves our needs well, but it's not a one-size-fits-all solution for all our problems.

It enables us to efficiently search and retrieve our event data, offering us a versatile approach to locate specific information within these logs. This flexibility allows us to conduct detailed searches and perform subsequent calculations.

One glaring issue was with our mapping configuration as the system accepted the data we posted, but after a few months, when we attempted complex queries, we realized the date formatting had become problematic. Modifying the mapping at that point proved to be a costly and time-consuming task.

I have been working with it for four years.

It provides a high level of stability.

We cater to approximately a thousand clients, and each of these clients represents training organizations with a varying number of actual users, typically around four users per client since they pertain to trainers alongside their students. These clients vary in size, with some having as many as four hundred thousand students, while others have just around a hundred. We estimate that our platform serves a user base of at least twenty million individuals. The scalability of the system is a notable strength. It can be readily scaled, particularly when using it as a managed service.

The initial installation has been straightforward, and the system is functioning smoothly.

We don't manually deploy Elasticsearch in AWS, but we make use of a managed service called OpenSearch. We initially set up our OpenSearch cluster around four years ago, and it has been functioning seamlessly ever since, so we haven't had to go through any complex deployment processes.

My advice is to approach it as a tool that requires a learning curve. While it might seem easy at first, it's worth investing the time to understand it thoroughly because what appears simple initially can become complex and challenging later on. Overall, I would rate it eight out of ten.

We are using Elasticsearch for embedded management and infrastructure management. Our environment includes instances with different tools and components from different companies.

In case there is a failure, Elastic manages everything well, and there no major downtime. There may be alternative instances created at the same time.

The configuration should be more straightforward because we had to select a lot of things. This included the CPU and the configuration required for the alternative instance. We would prefer that whatever we have configured for EC2 is just copied, and alternate operations required at the user's end can be done with just a simple click. You should not have to enter the details again, such as what memory is required or what CPUs are needed.

We have been using the Amazon Elasticsearch Service for almost a year. I have been working with several Amazon services for about the same length of time.

Definitely, this solution is very scalable.

We are a very big organization. We have approximately 50,000 employees across the globe.

Most of them are using AWS services, so I'm sure they are also very versed in Elasticsearch.

Technical support is good and we get a very timely response.

We were using a Linux-based solution but it was really complicated. AWS has now made our life easy. It's a really helpful product.

The initial setup was straightforward. We followed the AWS guidelines for the process and it worked. I think that the installation took between 10 and 15 minutes.

We are currently using the on-premises deployment but we are planning to implement the SaaS solution. We plan to continue using this solution in the future.

The deployment was done by our cloud operations team.

We have an operations team of more than 20 members to deploy and maintain this solution.

Compared to other cloud platforms, it is manageable and not very expensive.

I would rate this solution an eight out of ten.

We use the Amazon Elasticsearch systems for Java programming. It gives us the charges according to the use cases. If this solution is not used, it will not charge the client. In addition, we can upgrade it from microprocessor to mini, and from mini to micro anytime.

One valuable feature is that it takes the easy steps to upgrade the processing power of the computer. In addition, if we have to bind any type of the public IP or change the domain, it is instantly available in the help guide. The help guide is easy to access for the amendment. The help guide provides a step by step guide.  In most of the cases they provide us the code for the Java and the JavaScript. This sample code is helpful. For example, if we want to use the AWS Lambda function, we can use the help guide to complete the task.

There is the problem with the database. If we want to use the database of the MySQL on body and other things, Amazon only provides the host to run to our applications bias, but there is no option to manage the database within the Elasticsearch product.

The stability of this product is good. We are always informed whenever the server is down.

If I had to give the scalability a number rating, I would give it a 9. I can scale with a single login, and I am able to get the server or update the server at any time.  Furthermore, I can consider multiple servers and can easily manage the load balancing using the multiple server on a  single login.

We previously used GoDaddy, which has limited processing powers.

It was very easy. We just created an elastic server, created an access suggestion from the command line and then set up the complete project using the command line on the local disc. It is so easy. We really appreciate Amazon!

You only pay for what you use. This is a nice benefit.

We considered Google Firebase, but after doing our research, we chose Amazon Elasticsearch.

They have the good documentation in the help text and that is the reason the Amazon Elasticsearch is the perfect solution for the current market