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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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
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.
Elastic search review
What do you like best about the product?
New features rollout is very impressive.
What do you dislike about the product?
Data ingeston process at times is conplex
What problems is the product solving and how is that benefiting you?
Search Products with a lowest possible latency. Compliance for e-commerce products.
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 -
One of the best product to host large volumes of data for any kind of analysis
What do you like best about the product?
Faster and easier indexing helps us to load Tera bytes of data and use it for analysis and predictive analysis.
What do you dislike about the product?
There is nothing to dislike here about this fantastic product
What problems is the product solving and how is that benefiting you?
Search engine and log analysis
Amazing Search Platform
What do you like best about the product?
Ease of use. Quick to setup and get it running. API driven or most of the functionality. Ease of integration with applications.
What do you dislike about the product?
Search Crawlers. Some configurations are manual and are not driven via APIs.
Vector Search is slow.
Vector Search is slow.
What problems is the product solving and how is that benefiting you?
We use Elastic to solve multiple problems including:
Website Search
Search Curations
Plain data search with indexes which powers multiple user experience / websites
Website Search
Search Curations
Plain data search with indexes which powers multiple user experience / websites
Fast and reliable search engine with excellent scalability
What do you like best about the product?
Elasticsearch provides extremely fast and powerful search capabilities, even on very large datasets. I like how flexible it is with indexing and querying structured as well as unstructured data. Its ability to handle full-text search, filtering, and aggregations makes it ideal for analytics and real-time monitoring. Integration with Kibana adds strong visualization support, helping us easily explore trends and patterns. The distributed nature of Elasticsearch ensures scalability, making it suitable for high-volume production systems. It is also very easy to integrate with different applications and data pipelines, which makes adoption smooth across teams. Implementation is straightforward, with clear documentation and community support that reduces the learning curve. Customer support is also excellent. In my organization, we use it very frequently as all the logs, service traces, and errors are centralized in Elasticsearch for debugging and monitoring.
What do you dislike about the product?
While Elasticsearch is powerful, it can be resource-intensive and requires careful configuration to avoid performance bottlenecks. Setting up clusters and managing shard allocation can sometimes be tricky for beginners. Query syntax, while flexible, can feel complex for new users. Also, as the data size grows, managing indexes and optimizing queries requires ongoing effort.
What problems is the product solving and how is that benefiting you?
In my organization, we use Elasticsearch along with Kibana to centralize and analyze application logs, API traces, and service dependencies. It helps us monitor system health through dashboards that track latency, 4xx/5xx errors, and performance metrics in real time. This setup makes troubleshooting much faster and improves observability across services. The ability to visualize data directly in Kibana allows our teams to detect issues proactively, optimize performance, and ensure smooth customer experiences. We also rely on Elasticsearch’s alerting features to get notified of anomalies or spikes, which reduces downtime and supports faster incident resolution. Its scalability ensures that as our traffic and data volume grow, our monitoring remains efficient without performance degradation. Overall, Elasticsearch with Kibana has become a critical part of our monitoring and observability stack.
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
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