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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.
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.
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.
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.
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.
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.
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.
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.
The main use case for Elastic Search is mainly for log management.
I appreciate the indexing capabilities and the speed of indexing in their product, which demonstrates how quickly logs are collected and stored. The search capabilities are also valuable.
The architecture of Elastic Search could be improved as it is complicated for most general users to build up the environment and maintain the cluster.
Currently, I do not have suggestions for additional functions that could be added to the product.
I have been working with Elastic Search for about two years.
I usually use Elastic Search on-premises, which introduces complexity in deployment. Using the cloud version would reduce the complexity of setting up.
I would rate the stability for Elastic Search as eight out of ten.
I would rate the scalability as eight.
I would rate technical support from Elastic Search as three out of ten.
The main issue is a general sum of all factors. Being based in Hong Kong means I can only assess the service in my region and cannot speak for other regions based on my experience.
Negative
I am currently working with multiple solutions including Elastic Search, Splunk, and Graylog.
The initial setup for Elastic Search is complex.
The real-time analytics capabilities depend on whether you use the paid version or open-source version.
I work with SME users of Elastic Search, though the solution can technically support enterprise customers.
I have not extensively used AI technology with Elastic Search.
I can recommend Elastic Search to other users.
The pricing for Elastic Search rates as four out of ten. Overall, I would rate Elastic Search as seven out of ten.
