Share your experience using Quest IT Security Search [EOL]

I am using Elastic Search not only for search purposes but for rendering on maps as well.

I have not searched any vectors so far, so I cannot provide you with the exact output of that.

I was not using vectors in Elastic Search because I was using a vector database. As I mentioned, I use other databases for that. I have not explored it because when it comes to the data, Elastic Search will become expensive. In that case, what I suggest to my clients is to go with PostgreSQL, a vector database, or any other vector database. They are a startup, which is the problem.

We are using streams.

My favorite feature is always aggregations and aggregators. You do not have to do multiple queries and it is always optimized for me.

I always got the perfect results because I am using full text search with aliases and keyword search, everything I am performing it. It always performs out of the box.

It is easy because I have been doing it for years. The last version I remember is 3.5 or 3.1 that I used. Since then, I have been following Elastic Search and the changes they do. For configuration, I have never seen any problem.

Elastic Search consumes lots of memory. You have to provide the heap size a lot if you want the best out of it. The major problem is when a company wants to use Elastic Search but it is at a startup stage. At a startup stage, there is a lot of funds to consider. However, their use case is that they have to use a pretty significant amount of data. For that, it is very expensive. For example, if you take OLTP-based databases in the current scenario, such as ClickHouse or Iceberg, you can do it on 4GB RAM also. Elastic Search is for analytical records. You have to do the analytics on it. According to me, as far as I have seen, people will start moving from Elastic Search sooner or later. Why? Because it is expensive. Another thing is that there is an open source available for that, such as ClickHouse. Around 2014 and 2012, there was only one competitor at that time, which was Solr. But now, not only is Solr there, but you can take ClickHouse and you have Iceberg also. How are we going to compete with them? There is also a fork of Elastic Search that is OpenSearch. As far as I have seen in lots of articles I am reading, users are using it as the ELK stack for logs and analyzing logs. That is not the exact use case. It can do more than that if used correctly. But as it involves lots of cost, people are shifting from Elastic Search to other sources.

When I am talking about pricing, it is not only the server pricing. It is the amount of memory it is using. The pricing is basically the heap Java, which is taking memory. That is the major problem happening here. If we have to run an MVP, a client comes to me and says, "Anurag, we need to do a proof of concept. Can we do it if I can pay a 4GB or 16GB expense?" How can I suggest to them that a minimum of 16GB is needed for Elastic Search so that your proof of concept will be proved? In that case, what I have to suggest from the beginning is to go with Cassandra or at the initial stage, go with PostgreSQL. The problem is the memory it is taking. That is the only thing.

I have been using Elastic Search since around 2012.

I have never seen any instabilities, even from the initial state.

I have checked it for a petabyte of records. It is scalable.

One person can do it, but when it comes to DevOps, we need a team always. Only if we have to manage Elastic Search, one person is fine.

Neutral

I have used Solr and MongoDB as direct alternatives. According to the situation, it basically happens based on what the client wants. Sometimes they want Cassandra in place of Elastic Search. Our thing is only to suggest them. When it comes to the server costing, they are always asking, "Can we move to another server?" For example, I was working with a lower attorney's application and we implemented Elastic Search. For AWS only, we had to take two instances of 32GB for Elastic Search. After a few months only, the client asked, "Anurag, is it possible if we can go to another source if the latency is reduced or if some concurrency will reduce?" In that case, we had to move to Cassandra. Alternatives, I do use them.

Elastic Search is working fine with streaming. I do not have any problem with that. I do not feel any problem with it because the library works well for the solution I am providing in Go. The libraries are healthy over there and it has worked well. I am satisfied with that. If there are some lags, I manage that. I have not used it. My review rating for Elastic Search is 9.5 out of 10.

My main use case for Glean Platform is consolidating marketing materials, as I work in DevOps Sales Ops. I set up this platform for the sales team to review material that might be useful for talking to prospects.

One specific example of how the sales team uses Glean Platform to find material for talking to prospects is when we had a prospect and Glean Platform was able to consolidate all the information that we had. That way it was easy to consolidate all the material and then create an email to send out.

The best features Glean Platform offers are mostly the view, which I think is very good.

What I find most helpful about the view is that the user interface is very good. It is very easy to chat with the chatbot and get all the material that I want in the way that I need it.

Glean Platform has positively impacted my organization by making it definitely very easy to consolidate a lot of the materials and easy to look at it all at once.

I don't have any complaints with Glean Platform actually.

I can't think of anything right now regarding needed improvements, even small things.

I rate it a nine because sometimes it's difficult to understand the integrations part, which is a little difficult to understand.

I have been using Glean Platform for four months now.

Glean Platform is stable.

The scalability of Glean Platform is very good, as it is able to scale across multiple instances.

The customer support of Glean Platform is really good.

I would rate the customer support a ten out of ten, as I think it's pretty good.

Positive

We did not previously use a different solution.

My experience with pricing, setup cost, and licensing has been pretty smooth actually, with no issues.

I have seen a return on investment, as I can't give you the exact number but we've been able to contact a lot of prospects just because of the marketing material that we've been able to consolidate with Glean Platform.

My experience with pricing, setup cost, and licensing has been pretty smooth actually, with no issues.

We did not evaluate other options before choosing Glean Platform, as this is the first time we're using this platform.

The advice I would give to others looking into using Glean Platform is definitely to give it a try. You'll never understand the full scope of it until you try it. I rate this product a nine out of ten overall.