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Elasticsearch Review
What do you like best about the product?
- Reliable at scale: sharding and replication deliver solid HA; rolling restarts and node recovery are predictable when procedures are followed.
- Great for observability: fast searches/aggregations and the Elastic stack make log/metrics/APM pipelines effective for troubleshooting.
- Good ops surface: rich APIs and CAT endpoints make it scriptable, monitorable, and easy to automate runbooks.
- Great for observability: fast searches/aggregations and the Elastic stack make log/metrics/APM pipelines effective for troubleshooting.
- Good ops surface: rich APIs and CAT endpoints make it scriptable, monitorable, and easy to automate runbooks.
What do you dislike about the product?
- Finicky to run well: JVM/heap sizing, shard counts, and segment merges need care—or they bite during peak.
- Changes can be risky: upgrades, reindexing, and rebalances can cause latency spikes without tight change control.
- Costly footprint: hot nodes are CPU/IO heavy; replicas and long retention drive storage costs; licensing/features add complexity.
- Changes can be risky: upgrades, reindexing, and rebalances can cause latency spikes without tight change control.
- Costly footprint: hot nodes are CPU/IO heavy; replicas and long retention drive storage costs; licensing/features add complexity.
What problems is the product solving and how is that benefiting you?
Elasticsearch lets us deliver fast, relevant search and discovery across our articles. New articles become searchable within seconds, and aggregations power features like “most read,” topic pages, and related-article widgets. Flexible analyzers handle titles, body text, tags, and authors without rigid schemas, while replicas keep article search available during node failures. Net result: lower query latency, higher reader engagement, and a simpler path from article publish to discovery.
Senior Software Engineer
What do you like best about the product?
Data ingestion, Dashboards, Snapshots (Frozen Tier)
What do you dislike about the product?
Kibana could be better in of pagination, remembering number of lines selected, instead of defaulting to 25 lines
What problems is the product solving and how is that benefiting you?
We currently have many use cases, like ingesting data from our Banking app which is then used for 3rd level support, customer queries, fraud detection, audit logs, system metrics and monitoring
great experience
What do you like best about the product?
I like best how Elasticsearch handles large amounts of data in a scalable and efficient manner, making it easier to manage and scale as my data grows. Additionally, the extensive community support and integration with other tools make it a valuable addition to my data processing workflow.
What do you dislike about the product?
While I appreciate the many benefits Elasticsearch provides, some drawbacks include the steep learning curve, potential complexity in management and maintenance, and the risk of performance overhead. Additionally, ensuring security configurations and managing data consistency issues are crucial to avoiding potential data loss or corruption.
What problems is the product solving and how is that benefiting you?
Elasticsearch is addressing my organization's problems with inefficient data searching, complex analysis, and security issues. By providing a scalable, secure, and efficient infrastructure for data processing and search, Elasticsearch has significantly improved our productivity, decision-making capacity, and overall competitiveness
Impressive Tool
What do you like best about the product?
I use Elastic on a daily basis, and the visualization and log exploration features are very enjoyable and user-friendly once you get to know the solution. Fleet allows for a simple way to add agents, even in offline implementations, and the documentation in this context is very good. Elastic SIEM is also pleasant to use, but it’s important to keep in mind the retention of Elastic events and processes, as they can take up a lot of storage. The support is very good, although the AI chat is not always useful since it can sometimes point to outdated articles.
What do you dislike about the product?
Overall, I’m very satisfied with Elastic, but the biggest downside for me is the documentation. It’s often unclear or incomplete, especially when it comes to Elastic Agent and all the integrations. This makes setup and troubleshooting more complicated than it should be. One of the challenges I faced is with log parsing in the TCP custom input integration. The documentation is not very clear, and it’s not always obvious which preprocessors can be used or how to configure them properly. Of course, I should be using pipelines, but since Elastic provides such a solution, it should be properly documented. Sometimes even when debugging pipelines, not everything is clear or easy to understand.
What problems is the product solving and how is that benefiting you?
It centralizes and indexes logs from multiple sources, allowing fast and efficient searching and analysis. It helps monitor services, quickly detect errors or anomalies, and speeds up troubleshooting, saving time and improving overall system reliability.
Powerful and Reliable Search & Analytics Platform
What do you like best about the product?
Elasticsearch is extremely fast, scalable, and reliable for handling large amounts of data. I’ve used it extensively for log management, search queries, and analytics, and it consistently delivers results in near real-time. Its flexibility with queries, index lifecycle management, and clustering makes it an essential part of our infrastructure. The ecosystem around Elasticsearch (APIs, integrations, documentation) makes it easy to extend and adapt to different use cases.
What do you dislike about the product?
Managing clusters at scale can sometimes be challenging, especially around balancing shards, force merge operations, and handling 429 rate-limit responses. While it’s very powerful, certain advanced operations require deep knowledge to avoid performance bottlenecks. That said, once tuned properly, it works extremely well and reliably.
What problems is the product solving and how is that benefiting you?
Elasticsearch helps us centralize and search through huge volumes of logs, metrics, and structured data in real time. It allows quick troubleshooting, better observability, and smarter analytics across our systems. By automating index lifecycle management and scaling clusters easily, it reduces operational overhead and keeps performance consistent. Overall, it improves visibility, decision-making, and efficiency for our teams.
The solution is modern and feature rich with extensive customization possibilities
What do you like best about the product?
The amount features present and you can do many custom things with it if something is not present out of the box, we really like the security monitoring features it provides
What do you dislike about the product?
Maintaining self managed deployments can be difficult, mapping conflicts and slow downs when ingesting many log sources can take a lot of time.
What problems is the product solving and how is that benefiting you?
Log collection and threat monitoring
Evaluation of Elasticsearch Efficiency Across Use Cases
What do you like best about the product?
The best thing I like about Elasticsearch is that its not limited to 1 or 2 features. I have been using ELK for implementing different use cases like the diverse search options like advanced relevance ranking, fuzzy search, autocomplete, and complex aggregations, analytics, monitoring.
The horizontal scaling feature eases the upgrade as data grows and query demands increase. Data ingestion, search queries, and cluster management can all be done via simple JSON-based API calls. Creating dashboards in Kibana can be quickly learnt and offers great insights on the metrics. It also much easier to connect using different languages with the official or community client libraries available.
We are also using Elasticsearch for real-time querying of logs and metrics for which ingestion is happening 24/7 and the dashboards are being monitored.
With the new AI features I see the use cases will continue to grow.
The horizontal scaling feature eases the upgrade as data grows and query demands increase. Data ingestion, search queries, and cluster management can all be done via simple JSON-based API calls. Creating dashboards in Kibana can be quickly learnt and offers great insights on the metrics. It also much easier to connect using different languages with the official or community client libraries available.
We are also using Elasticsearch for real-time querying of logs and metrics for which ingestion is happening 24/7 and the dashboards are being monitored.
With the new AI features I see the use cases will continue to grow.
What do you dislike about the product?
The one thing I dislike is sometimes the data is inconsistent and finding the reason for that is real pain because at one point it works perfectly fine and then shows incorrect data. One more thing I find confusing is the errors that are displayed when something goes wrong. The errors are not that insightful in some cases which leads to more time correcting them.
What problems is the product solving and how is that benefiting you?
We are storing Cloud based customer support data in Elasticsearch which is really huge and we have implemented real-time monitoring on top of it. It includes multiple complex dashboards and search options available to help the business person in monitoring and growing the business.
Simple but does the job well.
What do you like best about the product?
- Data enrichment
- Logs, Metrics and APM monitoring
- It has a built in Dashboards
- Plug and Play, deploy and start using with minimal configuration
- It has fleet management system for easy configuration.
- Logs, Metrics and APM monitoring
- It has a built in Dashboards
- Plug and Play, deploy and start using with minimal configuration
- It has fleet management system for easy configuration.
What do you dislike about the product?
- Not cheap compared to Coralogix.
- Elastic Agents needs re-enrollment to connect to the other Elastic Instance.
- Elastic Agents needs re-enrollment to connect to the other Elastic Instance.
What problems is the product solving and how is that benefiting you?
We mainly use Elasticsearch as observability/monitoring tool. It provides as visibility to see the health and performance of the systems/applications.
Fast Search Engine with a Learning Curve
What do you like best about the product?
Elasticsearch fast search performance. ability to perform full-text search, aggregations, and real-time analytics integrates with tools like Kibana, Logstash, and Beats and etc
What do you dislike about the product?
CCR is complex concept and considerable effort is needed for it
What problems is the product solving and how is that benefiting you?
logs analysis and reporting
a well matured tool with great community support.
What do you like best about the product?
We extensively use Elasticsearch for platform log aggregation and dashboarding. It works seamlessly, and we rarely encounter issues. We especially appreciate the autoscaling and lifecycle management features.
What do you dislike about the product?
Nothing specific to dislike. We extensivly use elastic for platform log agregation and dashboarding. Working seamlessly and rarely encounter isuses.
What problems is the product solving and how is that benefiting you?
platform log agregation and dashbords
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