Amazon OpenSearch Service Reviews

It's a good log management platform. In terms of infrastructure management, it's good. 

Even when I check with the logs, I can easily create the filter accordingly, the logs. But different data sources. So, as per my understanding, my experience is also good.

Basically, we are using the application logs, for example, from the EC2 and Kubernetes clusters, which will be coming from AWS. We push that log to the Stack Logs and Elasticsearch; for Elasticsearch, we view in Kibana for selecting the logs.

Basically, we're just using the filters to understand the logs.

My requirement is fully fulfilled. Since it is fully filling my requirements, it fits our architecture.

I want to see a new feature in Amazon Elasticsearch Service that allows users to create default filters for filtered levels. 

This would make it easier for users to filter the data and find the information more quickly.

I have been using this solution for two years.  

I did experience a few lags. So we had to reset the servers and all other stuff because sometimes Elasticsearch is not responding properly. So, during that time, we just restarted the services. Probably, some kinds of metrics do not match the current requirements. That could be the reason.

I would rate the stability an eight out of ten. 

It is scalable enough for us. 

I have used ELK, Grafana, Prometheus, and all these things. 

Currently, we are using a different platform in a different location. 

We had a place where in Azure, we were using ELK Virtual. There, it's a different scenario. But if we go for Oracle Cloud, we are implementing everything in Kubernetes. So, Grafana is very easy to capture the logs from the Kubernetes level. So, we prefer Grafana in terms of other log management tools.

It is not difficult to setup this tool. It took us two days to deploy it. 

It is a good tool. Overall, I would rate the solution an eight 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