Elasticsearch Reviews

Was able to demo its background use at a conference quickly.

Was able to index 100s of documents quickly.

Easy to create queries.

Shorter outages since it is easier to figure out root causes

Better visibility over your environment

Security keeping your data at your own environment

Investment is primarily the cost of developer time and maintenance time and with its cost being zero, Elasticsearch has a positive ROI.

I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.

One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.

On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.

Easy to maintain.

Simple to use.

Great monitoring out of the box for the product.

Improved decision making

Better log investigations have helped early action on issues

Helped in digitizing the organization value chain for maintaining the applications

It reduced our solutions' time-to-market, as it's very easy to set up.

It greatly boosted the user experience with near-instant search results.

Elasticsearch can give you insights based on predictability, to do a Capacity Plan for infrastructure metrics.

The Visualization and Dashboards can give you a real view of business KPIs.

With OOTB anomaly detection, you can see potential issues with systems.

Allowed us to give accurate, real-time feedback for RPA.

Able to see what works, and what doesn't work.

Allows us to identify consistent problems across 60+ different processes.

ElasticSearch was able to meet the high demands of our product when it mattered most.

Implementation of ElasticSearch was easy and quick, saving on the cost of implementation.

Managing ElasticSearch is very easy. With the right monitoring tools in place, it really is "set it and forget it".

(Negative) Expense: Just Time. Early on, I had issues getting it installed in an exotic distribution, so labor/hours invested.

(Positive) Configuration and Modularity: You don't *have* to implement a full ELK stack. In fact, you could just run one, two or three components. However, marrying up ES with something like Syslog-ng, you combine two very powerful, feature-rich software packages in their own right, into an amazingly powerful data collection and gathering tool.

(Positive) Shallow learning curve: if you can write your own Unix configuration files, you will be able to maintain and develop on Elasticsearch.

Time saving

Employee time

Innovation

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.

Improved the speed of our website

Improved the user experience by providing highly efficient text search

We're using it for logging, which makes it easy to query the errors and solve them

It takes time to understand the advanced queries in Elasticsearch

It has allowed fast searching on large datasets which allow our customers to conduct business in a timely and simple manner.

Quick to implement a powerful, effective search product that makes our user happy

Cheap, fast, and scalable - an ideal component of our stack

Full-text searches on certain tables have dropped by up to 98%. Searches that used to take upwards of 45 seconds to complete now take a fraction of a second. From a users perspective.

Taking the computational load off our servers has allowed us to decrease the number of Oracle cores we have saving us a lot of money in license fees.

The first and highest reason for switching to Elasticsearch was to speed up the queries that we had that were running slowly(full text search over millions of records). We had some Oracle searches that were taking upwards to 45 seconds. After switching to Elasticsearch those same exact queries were running under half a second. It was obvious to us what the return on the investment was there.

The first thing(unexpected but made sense) that we noticed when switching to elasticsearch was our database servers didn't need as many cores. As you pay for Oracle licensing by cores, this was a huge benefit. We dropped about 6 cores in our Oracle licensing as soon as we could after switching to ES.

Since we had such great success with searching one table we decided to include more tables into our searching to help with our database.

Elasticsearch has allowed us to shave off many (> 100 is some cases) milliseconds from our API response times.

Elasticsearch coupled with Kibana has allowed us a whole new level of visibility into our log data.

We have built out an entire service line using Elasticsearch.

Clients see their compliance audits eased by the Elasticsearch based products.

When we where initially exploring logging solutions, Splunk was the only vendor in town and they where extremely expensive ($60,000). We haven't revisited them since as ElasticSearch has accomplished all of our needs.

We haven't spent anything but Admin hours to maintain our ElasticSearch cluster. Right now we haven't incurred any cost of ownership as I have been maintaining the cluster myself.

We have a huge project to grow a new part of our business, but I am not sure if I can spend the time to really update cluster to support the new Logstash features & any syntax changes so I am reluctant to do so. Time is increasingly becoming scarce, so catering to the latest and greatest features that offer little to our organization isn't something we are interested in pursuing though we are going to need to update the ElasticStack eventually.

Since all of our metrics are in ElasticSearch, we have had nice trove of data to build our apps around, apps that require specific metrics. Prior to ElasticSearch, we had to build our own tools that handled that metric collection. The cost savings here is that we maintain a simple script that reports back information in our reporting interface vs rolling our own database metric solution that must be modified for every app we develop. That has equated to a huge saving in developer hours in our organization.

Positive impact. We could, for the first time, implement a really useful system to support our business users in working with our big data repositories.

Most of elasticsearch is free except few things which most of the organizations can live without or have a workaround. Not having to pay Splunk whole bunch of money is a huge ROI right there.

Indexing the logs and making it searchable has a huge impact on the way we operate. Developers no longer have to log in to the system to know what's happening. Especially when we have hundreds of servers, having a central place for all the logs is essential to operate the system.

It's really easy to set up and maintain even in a scale. Its hot and warm cluster notion is awesome. The self-maintenance makes a huge impact on the need for system admins.

Server cost and infrastructure management became easy.

Efficiency in data handling.

Less development time.

Sped up fault detection, diagnosis and recovery.

Facilitated getting insights on customer base.

Helped implement data-driven approach across the whole business.