Overall Satisfaction with Elasticsearch
We use Elasticsearch (Elastic for short, but that includes Kibana & LogStash so the full ELK kit) for 3 major purposes:
- product data persistence - as JSON objects.
- as log storage - different components produce log files in different formats + logs from other systems like the OSes and even some networking appliances.
- as test automation results storage & reporting platform - this is an implementation we glimpsed from an old Trivago blog post.
- Data persistence & retriveval
- Data indexing
- Metrics & reporting over data thanks to its query language & Kibana visualization
- Flexibility of data sources - a lot of existing "beats" + ability to push custom data easily
- Very scalable - although a minimum of 3 nodes is advised, even a 1-node installation can work great for some use cases.
- Licensing - this is big issue with a lot of companies that try to embed Elasticsearch as a part of their products and not have to expose that explicitly or deal with licensing complications.
- Security - this is not a feature enabled by default so installations can go and be unsecure & thus exploited without anyone noticing.
- Having security turned off can be beneficial for some performance optimizations though.
- Cluster restructuring/upgrading - if you need to do a rolling cluster upgrade, node roles and data replication is handled in a complicated & tricky way so you need to have knowledge & experience to survive such an operation with your data & cluster to be operational after it.
- Data persistence, indexing and querying at high speed
- Scalability
- Building reporting over data thanks to Kibana
- Greatly reduced data-in-transit and at-rest overheads
- Provided us with a truly scalable solution for our data
- Kibana offers a reporting platform based on our custom queries. Extremely useful for reports from automated test executions.
- Apache Solr and MongoDB
Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
Do you think Elasticsearch delivers good value for the price?
Yes
Are you happy with Elasticsearch's feature set?
Yes
Did Elasticsearch live up to sales and marketing promises?
Yes
Did implementation of Elasticsearch go as expected?
Yes
Would you buy Elasticsearch again?
No