Fast, Flexible, and Innovative—Elasticsearch at Its Best
What do you like best about the product?
I appreciate its speed, flexibility, and innovation.
What do you dislike about the product?
There isn’t much to dislike about Elastic Search.
What problems is the product solving and how is that benefiting you?
It’s helping us improve our search platform and making it better overall.
Elasticsearch: The Best Engine for Fast Data Search and Analysis
What do you like best about the product?
Elasticsearch is the best platform/engine to analyze and search your data. With the AI capabilities Elastic is developing, it becomes even more powerful. Besides the company offers an excellent support.
I cannot imagine the current internet and technological world without Elasticsearch.
What do you dislike about the product?
Documentation is sometimes hard to follow and navigating it feels confusing.
What problems is the product solving and how is that benefiting you?
You just put your data in Elasticsearch, and it can produce value. No matter if the data comes from old databases, files, logs, etc. Once it´s in Elasticsearch you extract all the value and knowledge from it.
Elasticsearch unifies multi-platform insights with powerful log search
What do you like best about the product?
Elasticsearch help to gather information from multiple platforms. Providing a single view for searching UI, search effectively from massive log data
What do you dislike about the product?
So far, we do not use much advance features in Elastic at this moment. When we have to use a certain feature in Elastic. We have to study the methodology and check from community for case reference. Also, there is less reference cases or examples that I cannot find easily if I want to arrange integration between Elasticsearch with third party application such as Oracle DB / Fortigate Firewall etc.
What problems is the product solving and how is that benefiting you?
For Telcom internal use: usually operator has many IoT device and application such as switch, router, server, VM and also many log file generated from them. The inventory is large and complex. We have use Elasticsearch to summarize the view to keep record and search these devices log. Also, with some known behavior or threshold for potential fault issue, we have set the alarm mechanism to trigger support team for troubleshooting for quick respond. In conclude, it helps me for inventory, reporting, monitoring and troubleshooting.
Easy to Use, Seamless GCP Integration with Zero Issues
What do you like best about the product?
The platform is very easy to use and very easy to integrate with GCP. We were able to get it to work directly in our tool with 0 issues.
What do you dislike about the product?
Expensive to scale. We have a lot of data we use to search and elastic just costs a lot so we need to set up lifecycle management
What problems is the product solving and how is that benefiting you?
search and full text lookup. We are in ecomm and customers need to look through products
Powerful and Scalable Search Engine with Excellent Performance
What do you like best about the product?
What I like most about Elasticsearch is its speed and flexibility. It can handle very large volumes of data while still delivering fast and accurate search results. The query DSL is powerful and allows complex filtering and aggregation, which makes it suitable for many use cases beyond simple search. It also scales very well and integrates easily with other tools in the Elastic ecosystem.
What do you dislike about the product?
The main downside is the learning curve. Getting the most out of Elasticsearch requires a good understanding of mappings, indexing strategies, and performance tuning. It can also be resource-intensive, especially for smaller teams or projects, and may feel overkill for simple search needs.
What problems is the product solving and how is that benefiting you?
Elasticsearch solves the problem of searching, analyzing, and exploring large and complex datasets in near real time. It allows us to centralize data from multiple sources and query it efficiently. This has significantly improved performance, reduced response times, and enhanced the overall user experience by providing fast and relevant search results.
Blazingly Fast, Feature-Rich Elasticsearch with Top-Notch Documentation
What do you like best about the product?
It simply works as expected and is blazingly fast. Using the ELK stack has been a life changer as well. Lots of features have been added over the years (working with Elasticsearch for a lot of years now). Worth mentioning is that the documentation is top notch. Very well structured, easy to understand and with lots of examples.
What do you dislike about the product?
In all these years that I have been using Elasticsearch, I did not find a single thing I actually missed. It's a complete package that delivers all that I am looking for.
What problems is the product solving and how is that benefiting you?
We use the ELK stack daily for monitoring, logging but especially also as search engine on our main pages. The whole customer search for our bank is based on Elasticsearch.
Search and aggregations have transformed how I manage and visualize complex real estate data
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Elastic Search since around 2012.
What do I think about the stability of the solution?
I have never seen any instabilities, even from the initial state.
What do I think about the scalability of the solution?
I have checked it for a petabyte of records. It is scalable.
How are customer service and support?
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.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Elastic is good but the costs cannot be predicted
What do you like best about the product?
It is a tool that supports a community which generates many improvements and helps in support.
What do you dislike about the product?
When it is licensed and used in the cloud, the costs are not clear, making payments difficult and managing consumption.
What problems is the product solving and how is that benefiting you?
It is used as a repository for searches, whether from our SIEM solution or NOC.
Great Experience with Elastic Search
What do you like best about the product?
It was easy to set up, and it was just as easy to get started right away.
What do you dislike about the product?
It’s a little slow when indexing bulk records from a CSV file.
What problems is the product solving and how is that benefiting you?
Elasticsearch helped us set up typo-resistant, faster searches to meet our clients’ search needs.
Search performance has transformed large-scale intent discovery and hybrid query handling
What is our primary use case?
My use case has evolved over time with Elastic Search. Initially, we started with it as a searching solution. Before Elastic Search, our primary source of truth was SQL databases, the traditional RDBMS. We thought about taking the data from the traditional RDBMS because they were not able to cater to the scale that we wanted to achieve, so we migrated the data from MySQL, keeping it as the primary source of truth, but for the searching mechanism and wildcard searches, we migrated to Elastic Search.
My experience with the relevancy of search results in Elastic Search includes both traditional keywords and full-text search. In the supply chain industry, with millions of orders and customers such as CMA CGM, Maersk, or Kuehne+Nagel, filtering out those orders was essential, using a shipment number, transportation order number, or an origin or destination number. In the gaming industry at FDJ United, full-text searches make more sense to understand gaming intent. For example, when a user searches for 'I really want to play action games', we break down that full-text query, use custom text analyzers, and derive the intent behind the user's query in combination with a vector database alongside Elastic Search.
My assessment of the effectiveness of hybrid search, combining vector and text searches, shows that Elastic Search is remarkable for text-based searches. I have explored other solutions, but none can beat Elastic Search in that area. When I combine hybrid searches with vector databases, they store the mathematical representation of the data. For instance, to find the top 10 closest proximity based on a query, the vector database uses cosine similarity on the available data and suggests the top 10 results while Elastic Search can keep the metadata, enabling quick access to the entire database based on derived intent.
I have utilized trusted GenAI experiences related to semantic search and text-based search in my current project using Elastic Search. My go-to solution for text-based searches will always be Elastic Search, but for semantic search, I am trying to build a solution that emphasizes system-level understanding agents. For example, if a new engineer queries the agent for a system explanation, it scans all the relevant data and provides a comprehensive analysis of the service, contextualizing inputs to reduce hallucination, controlled temperatures for the LLM model, and reducing nucleus sampling. As for knowledge preservation, I use a vector database to store significant outputs generated by the LLM, depending on user preferences regarding the gravity of the analyses performed.
What is most valuable?
The best features of Elastic Search that I appreciate include its capability for eventual consistent systems where you do not need hard consistency, and it scales very smoothly. For wildcard searches and regex patterns, it really scales massively. It offers ILM, indexation lifecycle management, which allows you to enable a search for a span of six months for the data fed into the system while moving the rest to a new cluster. The structure of the inverted index document facilitates its core features, and I find how Elastic Search understands, indexes, and creates mappings for your data to be remarkable.
What needs improvement?
While Elastic Search is a good product, I see areas for improvement, particularly regarding the misconception that any amount of data can simply be dumped into Elastic Search. When creating an index, careful consideration of data massaging is essential. Elastic Search stores mappings for various data types, which must remain below a certain threshold to maintain functionality. Users need to throttle the number of fields for searching to avoid overloading the system and ensure that the design of the document is efficient for the Elastic Search index. Additionally, I suggest utilizing ILM periodically throughout the year to manage data shuffling between clusters, preventing hotspots in the distribution of requests across nodes.
For how long have I used the solution?
I have been using Elastic Search for more than six years.
What do I think about the stability of the solution?
In terms of stability, I would rate it eight out of ten regarding downtime, bugs, and glitches.
What do I think about the scalability of the solution?
For scalability, I assign it a ten out of ten.
How are customer service and support?
I would rate Elastic Search's technical support as nine out of ten.
Which solution did I use previously and why did I switch?
Before Elastic Search, our primary source of truth was SQL databases, the traditional RDBMS.
How was the initial setup?
Estimating the return on investment from Elastic Search is nuanced; however, I can share that initially, search times from traditional RDBMS were around two to three seconds, and with Elastic Search, we reduced that to 50 milliseconds, indicating a significant improvement.
What about the implementation team?
Assessing the complexity of deploying Elastic Search, I have a gray area because a separate DevOps team handles that aspect, but from my experience writing code and utilizing its features, I find it not complex at all.
What was our ROI?
Estimating the return on investment from Elastic Search is nuanced; however, I can share that initially, search times from traditional RDBMS were around two to three seconds, and with Elastic Search, we reduced that to 50 milliseconds, indicating a significant improvement.
What's my experience with pricing, setup cost, and licensing?
On the subject of pricing, Elastic Search is very cost-efficient. You can host it on-premises, which would incur zero cost, or take it as a SaaS-based service, where the expenses remain minimal.
Which other solutions did I evaluate?
When comparing Elastic Search to other vendors and products, I have recently explored Algolia, which is also a fully managed service. Elastic Search offers a choice between hosting on-premises or as a fully managed service, which has been beneficial compared to other solutions.
In my company's relationship with the vendor, I have always worked in product-based companies using Elastic Search, often as part of solutions from companies such as Manhattan Associates and in the gaming sector. For B2B industries, they sold to large clients such as Maersk and CMA CGM while my current company, Agoda, operates in the B2C space.
What other advice do I have?
Elastic Search does require some maintenance, especially when considering features such as ILM if you want to enjoy its capabilities. Maintenance tasks depend on the established data pipeline and may introduce some friction.
Currently, we are not using Elastic streams for log ingestion; previously, we utilized the ELK and EFK stacks with Logstash for log ingestion and Kibana for visualization. I also observe a trend where companies migrate to Grafana Loki instead of ELK.
Regarding integration aspects, Elastic Search has exposed REST APIs for all its services, making it easy to integrate with third-party models or endpoints regardless of the underlying infrastructure, as any modern development language can interact with these REST services.
I have not used the attack discovery feature.
My deployment of Elastic Search is on-premises.
At Agoda, we handle over 1.2 billion searches daily, facilitated by Elastic Search.
While I have been at my current company for four months, I am still getting to know my colleagues; however, I know there is a dedicated team focused on Elastic Search. This team exposes a service that acts as an intermediary for communication between Elastic Search and other services.
In my department, there are more than 100 people, whereas the overall organization consists of thousands, exceeding 10,000.
I would rate this review overall as a nine out of ten.
