At Shopee, I worked with numerous database schemas to find out which table columns belonged to which schema. We utilized Elastic Search to manage metadata for millions of tables, allowing us to search efficiently. Besides that, we used Logstash to put all the log files in Elastic Search for easy searchability.
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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.
Elasticsearch at big belgian bank
What do you like best about the product?
API, dev console, schema-less indexing, documentation
What do you dislike about the product?
changing java sdk, hiding some previously available features behind a subscription
What problems is the product solving and how is that benefiting you?
excellent vector search, good indexing/search performance, support complex searches
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.
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
Review of Elastic
What do you like best about the product?
APM feature, I like the APM feature in Elastic which helps to identify the endpoints failing or services which were not healthy at any point of time. The way it shows the failure transaction, latency throughput and mapping with services is useful in my daily works. The dependencies feature is great addon to identify what other services are being affected due to the issue.
What do you dislike about the product?
Searching for aged logs. In one of our clusters, it is hard for us to get the aged logs when we search with any pattern. Don't think this is fully due to Elastic it has more to do with our logs and tier configuration too. Also getting the logs and metrics of database server is something I feel hard.
What problems is the product solving and how is that benefiting you?
Solving unexpected Major outages. Elastic helped us to identify the outages before customer is impacted with APM metrics, error alerts, Machine learning jobs. With the alerts and monitoring, we are able to notice the behavior early and fix the issues. Due to fill log ingestion in elastic, it is helpful in even single customer issue analysis. The tracing of the logs is beneficial.
Elasticsearch: A Powerhouse for Search, but a Beast to Tame
What do you like best about the product?
Fast full text search and real-time capabilities
Scalable architecture
Versatile integrations
Flexible
Support
Scalable architecture
Versatile integrations
Flexible
Support
What do you dislike about the product?
Complexity in setup
Using OTEL
Licensing and vendor lock-in
Searching Large logs
Can't select log text and add it for quick search. (double click and add feature)
Doesn't distribute data evenly across the nodes. Thereby increasing costs when auto-scaled at this scale
Auto-scaling not working properly
Using OTEL
Licensing and vendor lock-in
Searching Large logs
Can't select log text and add it for quick search. (double click and add feature)
Doesn't distribute data evenly across the nodes. Thereby increasing costs when auto-scaled at this scale
Auto-scaling not working properly
What problems is the product solving and how is that benefiting you?
real-time analytics and Visibility of the systems through dashboards
Quick searches with unstructured data
Proactive monitoring thereby reducing MTTR benefiting business with reduced downtime
Scalable and reliable - 0% downtime
AI features - still exploring but so far impressive
ML features -
Quick searches with unstructured data
Proactive monitoring thereby reducing MTTR benefiting business with reduced downtime
Scalable and reliable - 0% downtime
AI features - still exploring but so far impressive
ML features -
Search efficiency improves with enhanced metadata and log management
What is our primary use case?
How has it helped my organization?
Elastic Search significantly improved my work. Previously, when searching for text that appears in the middle of strings, the process was time-consuming. Elastic Search enables efficient searching, enhancing system performance and responsiveness. I can also collect logs through Kafka, send them to Elastic Search, and create indices, thus managing logs and customizing searches easily.
What is most valuable?
Elastic Search provides features such as stemming and range-based queries to search log files efficiently. It allows filtering data easily by searching for specific words based on created indexes. This made searches very efficient, and it also allows for log collection through Kafka and helps with managing logs and customizing searches according to needs, such as grouping by dates or user IDs.
What needs improvement?
Elastic Search could improve in areas such as search criteria and query processes, as search times were longer prior to implementing Elastic Search. Elastic Search has limitations for handling huge amounts of data and updates, especially if updates are frequent. It doesn't handle big data scale efficiently, especially regarding data size and scale, compared to Apache Solr. It doesn't support real-time search effectively, as it refreshes the indexes every few seconds.
What do I think about the stability of the solution?
It is stable as many companies already use Elastic Search. In cloud scenarios, it manages well by scaling up or down based on peak traffic. Otherwise, similar functionality needs to be replicated in a private cloud, including backups.
What do I think about the scalability of the solution?
Elastic Search requires enhancements for handling huge amounts of data and updates. Segmenting or sharding data and complexities regarding the cluster can be issues. Updating in Elastic Search involves index computations and user dependencies. There might be issues regarding data size and scaling, but these can be tuned and improved.
Which other solutions did I evaluate?
I remember Apache Solr, which is generally used for much larger scale data compared to Elastic Search. Apache Solr is used by most companies, and while Elastic Search is very common, there are technologies similar to Elastic Search, though I'm not familiar with all the names.
What other advice do I have?
I have used Elastic Search, but I might not be aware of many internal details; I just used the API to create an index, manage data, and search. It's very useful. On a scale of 1-10, I rate it an eight.
Really amazing experience easy to use easy to understand and easy to analyse
What do you like best about the product?
choosing the cloud is easy and it works with vm's just as well as physical hardware
What do you dislike about the product?
it works with Vm but something it is not in real time , if you set an event it takes time
What problems is the product solving and how is that benefiting you?
really good tool compare to others like qradar and other tools in market and easy to implement and easy to use and set up , make rally good tool to analyse events
User optimizes data analysis with advanced search features and seeks expanded functionality
What is our primary use case?
I have been using it for a year. The main use cases involved implementing search functionality.
What is most valuable?
When discussing the features of Elastic Search, the full text search capabilities are particularly beneficial for handling large volumes of data.
The full text search capabilities in Elastic Search have proven to be extremely valuable for our operations.
Regarding AI integration, we have not yet implemented any AI-driven projects or initiatives using Elastic Search.
What needs improvement?
There are some features and functionality that could be enhanced in Elastic Search to improve its overall capabilities.
For how long have I used the solution?
I have been using Elastic Search for a year.
What do I think about the stability of the solution?
In terms of performance and stability, Elastic Search has proven to be a reliable solution.
What do I think about the scalability of the solution?
The environment includes multiple users utilizing Elastic Search across different locations.
Which solution did I use previously and why did I switch?
Before implementing Elastic Search, I had experience working with other search engines from different vendors.
How was the initial setup?
The implementation strategy involved specific steps during the setup process to ensure proper configuration.
What was our ROI?
The main benefits observed from using Elastic Search include improvements in operational efficiency, along with cost, time, and resource savings.
What other advice do I have?
I previously used Graylog.
I am currently working with Elastic Search as the primary solution.
My role is Senior DevOps engineer at UVIK Digital.
On a scale of 1 to 10, with 10 being the highest, I would rate Elastic Search as an 8 overall as a product and solution.
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