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Elasticsearch

Score8.7 out of 10

208 Reviews and Ratings

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What is Elasticsearch?

Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.

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Elasticsearch Reviews

What users are saying about Elasticsearch.
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Elasticsearch Overall Review

Incentivized
John AndersonView profile
Lead Application Engineer IV in Information Technology at Cox Communications (10,001+ employees employees)

Use Cases and Deployment Scope

We use Elasticsearch to analyze and visualize logs from various Engineering workflows. We have clusters defined for providing Application Performance Monitoring for a variety of Engineering applications, utilizing Beats and other processes to populate the data required for monitoring and analysis. We also capture metrics (for both servers and applications).

Pros

  • Log and data capture, via Beats
  • Visualization of data
  • Application monitoring

Cons

  • Some of the cluster management functions could be more intuitive.
  • It would be nice if it could be used for large data sets (streaming data)
  • Troubleshooting could be easier.

Return on Investment

  • Elasticsearch provides a convenient way to analyze data from various data sources.
  • Visualizations from data analysis provide easy guidance to management.
  • Elasticsearch reduces overall IT/Administrative costs

Alternatives Considered

Apache Druid, Splunk Enterprise and Grafana

Other Software Used

Apache Druid, Apache Kafka, Apache Spark, Grafana

Great search, aggregation and visualization products.

Julie ZhongView profile
Data analytics in Engineering at Netnordic (201-500 employees employees)

Use Cases and Deployment Scope

We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis and KPI. We also ingest the log from different API and investigate when there is a trouble. We also use transform and machine learning feature to detect anomalies.

Pros

  • Full text search
  • aggregation
  • anomaly detection
  • dashboard
  • canvas

Cons

  • SIEM
  • Ingest API
  • The performance for a large cluster
  • business analysis

Return on Investment

  • The license is quite expensive
  • The consultation and operation cost is also a high cost
  • The performance during the peak period is not stable enough but there isn't good temporary solution

Alternatives Considered

OpenSearch and Splunk Enterprise

Other Software Used

AWS CloudTrail, Apache Kafka, AWS Lambda

Elasticsearch is a tricky, but great data platform

Incentivized
Borislav TraykovView profile
DevOps Team Leader in Engineering at Cognyte (1001-5000 employees employees)

Pros

  • 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.

Cons

  • 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.

Most Important Features

  • Data persistence, indexing and querying at high speed
  • Scalability
  • Building reporting over data thanks to Kibana

Return on Investment

  • 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.

Alternatives Considered

Apache Solr and MongoDB

Other Software Used

Microsoft Teams, Docker, Sonatype Nexus Platform

Search begets Search - Navigating your data progressively

Incentivized
Keith LubellView profile
Chief Technology Officer in Information Technology at Berkery, Noyes & Co., LLC (51-200 employees employees)

Pros

  • Indexing text data
  • Aggregations allow users to progressively add search criteria to refine their searches
  • Find trends in our data with Aggregations
  • Integrate text Search our taxonomy Search

Cons

  • Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
  • Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
  • Schema changes require complete reindexing of an index

Most Important Features

  • Text Search (Natural Language Processing)
  • Integration with Couchbase
  • Integration with multiple platforms including dot net and nodejs
  • Aggregations and search done together

Return on Investment

  • Most of our investment is in programming hours which is expensive
  • Easy to set up nodes
  • Free version has a lot of the great basic features

Alternatives Considered

Apache Solr, Splunk Enterprise and Couchbase

Other Software Used

Microsoft SQL Server, MySQL, Couchbase, MongoDB, Docker, ASP.NET, Visual Studio IDE, Microsoft Visual Studio Code, Windows Server, Global Relay Archive

Elasticsearch OSS Review

Incentivized
Andrew MeyerView profile
Sr Linux Engineer in Information Technology at Panera Bread (501-1000 employees employees)

Pros

  • Database
  • Scalability
  • Deployment

Cons

  • Backup
  • Rest API browser
  • Remote management using utilities

Most Important Features

  • Scalability
  • Optimization
  • Rapid deployment

Return on Investment

  • Finding talent.
  • Using open source versus paid.
  • Management if it gets too big.

Other Software Used

Red Hat Enterprise Linux (RHEL), MongoDB, CentOS Stream