TrustRadius
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.https://media.trustradius.com/product-logos/vL/Vh/UHT9H0QQVEZZ.pngAn amazing search engineElasticsearch is being used for multiple purposes in multiple projects: centralized log management, APM, Metrics Collection as a TSDB, and as a replacement for traditional OLAP databases. It provides a high-performance indexing and search engine, which has become an invaluable tool addressing hard problems that would otherwise be very difficult to solve.,Ingress and indexing. Searching. Aggregations.,Aggregations on top of other aggregations. Encryption at rest. Has a performance penalty when using inked documents.,9,It reduced our solutions' time-to-market, as it's very easy to set up. It greatly boosted the user experience with near-instant search results.,MongoDB,Visual Studio IDE, GitHub, Amazon CloudWatchElasticsearch, centralized logs and anomaly detection, easily deployed.Elasticsearch has been a big help for us. We used to work with Application Performance Management Tools that need another layer of visualization and data treatment, and with Elasticsearch we have delivered better insights for our customers. We use Elasticsearch at our Technology &amp; Services Department to address these issues for our customers: - Customized Dashboards. - Anomaly Detection. - Metrics Predictability.,Anomaly detection. It can find patterns over a wide variety of metrics and values. Behind the walls, Elasticsearch has a robust distributed architecture to support queries and data processing, and it is easy to maintain and scale. Elasticsearch has a new Elastic Cloud SaaS solution which is very easy to deploy, set up, and scale with all features and more. Elasticsearch has an important security layer to separate access to data and dashboards.,If you want to explode Elasticsearch's capabilities, you need to have a medium-high SQL and Database knowledge. The user interface is heavy in Java requirements, and sometimes you can get some lag displaying heavy results for heavy queries. It will be helpful if you can construct Logstash queries with a drag&drop based user interface.,10,Elasticsearch can give you insights based on predictability, to do a Capacity Plan for infrastructure metrics. The Visualization and Dashboards can give you a real view of business KPIs. With OOTB anomaly detection, you can see potential issues with systems.,Splunk Enterprise,Slack, Freshsales CRM, Microsoft Visual Studio CodeWorks well but is difficult to set up and manageWe currently use it to log the details of our RPA processes as they run through their production and development environments. They log back checkpoints, statues and error messages back to the Kibana database we use in conjunction with Elasticsearch.,Powerful beats modules. Later number of input/output pipelines. Open documentation.,Documentation is often incomplete. Forums are very full but misleading. The programs don't work well together. They have different methodology and flavors in each. Different configurations in each element make it difficult to use.,6,Allowed us to give accurate, real-time feedback for RPA. Able to see what works, and what doesn't work. Allows us to identify consistent problems across 60+ different processes.,IBM FileNet Content Manager, MySQL, IBM Watson Analytics, IBM Watson Commerce InsightsElasticsearch - A catalyst for application maintenance and log managementElasticsearch has been phenomenal in upgrading the log management capabilities at my organization. Elasticsearch, along with Kibana, has provided a wide range of capabilities for our IT teams to investigate issues and create live monitoring environments. This is being utilized as a central tool for all of our apps organization-wide. Before Elasticsearch, our teams were finding a hard time investigating issues, tracking the root cause, and resolving them. Elasticsearch have greatly reduced the investigation time for us.,It's an Open Source tool Elasticsearch extends its visualization and analytics capabilities through Kibana, which is a powerful tool Elasticsearch provides 3rd party integration facilities using REST API,Search capabilities can be further improved with a much faster response time on historical logs Elasticsearch should have a phone/sms alert feature as well as an event trigger Learning guides could be more detailed,9,Improved decision making Better log investigations have helped early action on issues Helped in digitizing the organization value chain for maintaining the applications,Splunk Enterprise,Skype for Business (formerly Lync), xMatters, Azure DevOps (formerly VSTS)ElasticSearch handles a large number of requests quickly and easilyElasticSearch is used to store all searchable data indices from our product. We use ElasticSearch because it is extremely fast, highly available, and able to meet the demand of our product. We were using a different index-based search technology before, and it failed terribly. We migrated to ElasticSearch and have been very happy with the results.,Easy to install Easy to use/lots of documentation Easy to scale up as demand increases,The price point for the X-Pack plugins (ie. Security, Alerting, etc.) is a bit high, especially if you only want to do something small and simple and you don't need to leverage the full power of the plugin Configuring the right hardware and capacity planning (when at scale) can get really tricky. In order to get the best performance, a lot of tweaking is needed, and not all of the secret tricks are documented Getting used to ElasticSearch's query language was a bit of an adjustment. You really have to delve into defining analyzers and tokenizers in order to get application-specific results,9,ElasticSearch was able to meet the high demands of our product when it mattered most. Implementation of ElasticSearch was easy and quick, saving on the cost of implementation. Managing ElasticSearch is very easy. With the right monitoring tools in place, it really is "set it and forget it".,Riak, Apache Solr, Redis, MongoDB, MySQL and Amazon Relational Database Service,Riak, Redis, MySQL, Amazon Relational Database Service, Amazon Elastic Compute Cloud (EC2), AWS Elastic Beanstalk, AWS Lambda, Docker, Bitbucket, GitHub, Gitlab, Amazon AuroraElasticsearch: A Great Lab / Development Platform for Data Architects and DevOpsElasticsearch (ES) is being used to measure the performance metrics of our web crawlers for our web metrics department. They employ a series of crawlers: setting up data feeds to an ELK stack to measure and monitor organic messages related to our marketing campaigns. It primarily allows us to bring advanced analytics in-house.,Free of SQL: ES does not have the overhead of relying on SQL. In fact, you can use most (if not all) DBMs out there. Java: Normally, this is not a strength: Java is slow and cumbersome. I believe in this case, it's truly a feature: by utilizing a language with universal support, it makes ES VERY DevOps friendly, simply by being able to focus on Problem-oriented vs Solutions-based thinking. Although ES has been known to consume RAM, it's very flexible, and I have implemented on a number of distinct hardware configuration with success. Linux: It's not locked down to an OS (which is the way of the future), and as a result-running it on Linux means you get the power of Linux, in a data science package.,Elastic Search IS a resource hog: most of the time, I will run ES on a dedicated VM (often a dedicated blade, too!) and allow the other components of the stack to run on separate blades/VMs. Works great for small projects, but is NOT industrial strength: When you are performing a data architecture project, where you are capturing and mining datasets, ES is fine, until you start getting into much denser data sources (orders to TBs), such that ES will violate Data integrity. It only supports JSON output: Which is very friendly to a lot of DevOps/Data Architecture projects but may become a hassle when your endpoints require CVS, XML, etc.,7,(Negative) Expense: Just Time. Early on, I had issues getting it installed in an exotic distribution, so labor/hours invested. (Positive) Configuration and Modularity: You don't *have* to implement a full ELK stack. In fact, you could just run one, two or three components. However, marrying up ES with something like Syslog-ng, you combine two very powerful, feature-rich software packages in their own right, into an amazingly powerful data collection and gathering tool. (Positive) Shallow learning curve: if you can write your own Unix configuration files, you will be able to maintain and develop on Elasticsearch.,Logstash, Redis, Jenkins, Ansible, Puppet Enterprise (formerly Puppet Data Center Automation), Chef and Loggly,Logstash, Loggly, Jenkins, AnsibleElasticsearch is the rare unicorn product—search and analytics in oneWe use Elasticsearch for our online (realtime) search engine. We've indexed over 2 billion documents, including every physician, hospital, and clinic in the United States. We started using ES from the beginning since I had a bunch of great experiences with the technology from my last job. We load data into Elasticsearch from multiple locations, including Postgres and BigQuery. On top of Elasticsearch, we've built a number of analytics tools that let us not only search but also deliver analytics for our stored data -- like top physicians performing a specific service and geography-based analyses. Overall we're super happy with Elasticsearch.,Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range. Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.,Elasticsearch is highly distributed, but it takes time to tune so you get the right performance out of your cluster. The query language is not SQL, so it's not a straightforward conversion from an RDBMS to Elasticsearch for searching through data. There are lots of ways to insert data into Elasticsearch, and some are better than others (batch vs. single insert). Need to experiment with your own data and environment.,10,We have had great luck with implementing Elasticsearch for our search and analytics use cases. While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled. We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.,Apache Solr, PostgreSQL, MySQL, MongoDB and Cassandra,Google BigQuery, PostgreSQL, Google Cloud StorageNeed a text search to your data, Elasticsearch is the answer to it!Elasticsearch is being used for multiple purposes at Iconscout. Starting from a search engine to viewing detailed analytics. We're even using it for logging of the server. It helps us to query through the millions of data easily and efficiently.,Fast Search through millions of data Uses a very limited storage to store the data - high compression Easy to get started & configure,Their documentation needs a lot of imporvement Difficult to understand query language New updates are difficult to adopt,10,Improved the speed of our website Improved the user experience by providing highly efficient text search We're using it for logging, which makes it easy to query the errors and solve them It takes time to understand the advanced queries in Elasticsearch,,InVisionElasticsearch ReviewIt is used in our custom software application for advanced searching and filtering capabilities for our users.,It allows extremely fast search and filtering on large datasets It has a very powerful aggregation engine that can allow for tons of customizable analytics and reports.,The documentation could be a bit more detailed and have more examples, especially for advanced functionality. The ability to update/change existing live field mappings would be nice. The ingest pipeline structure is a bit more complicated and confusing than previous implementations for using things like attachment plug-ins.,10,It has allowed fast searching on large datasets which allow our customers to conduct business in a timely and simple manner.,Apache Solr and Amazon Redshift,Redis, PostgreSQL, MySQLThe gold standard for text-based searchWe use Elasticsearch to power a web search engine that allows users of our web platform to search for products, content, and more. With Elasticsearch we were able to quickly and effectively develop and deploy a search solution that is fast, scalable, and was a breeze for our developers to implement.,Lightning fast Easily scalable Powerful feature set,Additional complexities when in need of frequent & rapid updates to the Elasticsearch data set New syntax can be confusing, particularly with advanced features and more powerful queries,10,Quick to implement a powerful, effective search product that makes our user happy Cheap, fast, and scalable - an ideal component of our stack,The Best AvailableIt provides a distributed, multitenant-capable, full-text search engine with an HTTP web interface and schema-free JSON documents. We use this in our IT department, but also resell it as part of a predictive AIOps platform that offers automation for many of the tedious tasks that data center staff struggle with every day.,Search Correlation Analysis Big data,Pagination Presentation Mapping,9,Time saving Employee time Innovation,Ansible and Splunk Enterprise,Jive, SOLIDWORKS, AutoCADFind more faster with ElasticsearchWe use Elasticsearch to efficiently search large pools of data. Elasticsearch gives us the ability to have blazing fast searches even when doing partial text matches on multiple fields.,The best solution we've found for blazing fast searches, especially text-based. Easy to add nodes for data redundancy. Good documentation makes getting up and running easy.,I found the learning curve fairly difficult having a SQL background.,8,Full-text searches on certain tables have dropped by up to 98%. Searches that used to take upwards of 45 seconds to complete now take a fraction of a second. From a users perspective. Taking the computational load off our servers has allowed us to decrease the number of Oracle cores we have saving us a lot of money in license fees.,MongoDB, Visual Studio IDEEnthusiastic for ElasticsearchWe decided to start looking into Elasticsearch after we had good success with using lucene (the full-text search indexer that Elastic uses). We had some queries in Oracle that were running EXTREMELY slow and knew we had to do something for the customer to make their experience better. We had a few thoughts on what we could use and Elasticsearch fit what we really wanted.,Searching, it does it well and searches are fast...real fast. Ease of use, we were able to get an Elasticsearch cluster up and running in a half hour and doing basic searches after that was very easy with simple requests Redundancy built in and stability. We haven't had any of our Elastic clusters go down intentionally, but testing out redundancy by removing nodes Elasticsearch has gone flawlessly. Only breaking changes between versions when they are absolutely necessary. Works well with .Net libraries that are supported and coded by Elastic.,A bit more of a learning curve for complex searches, indexing more complex things. Some of our updates between versions haven't gone as smoothly as we would like, but in more recent versions Elastic has done a much better job at trying to allow for full uptime upgrades. Configuration needs to be set up to do larger searches, or more complex searches and at times while starting it wasn't obvious what configuration needed to be changed.,10,The first and highest reason for switching to Elasticsearch was to speed up the queries that we had that were running slowly(full text search over millions of records). We had some Oracle searches that were taking upwards to 45 seconds. After switching to Elasticsearch those same exact queries were running under half a second. It was obvious to us what the return on the investment was there. The first thing(unexpected but made sense) that we noticed when switching to elasticsearch was our database servers didn't need as many cores. As you pay for Oracle licensing by cores, this was a huge benefit. We dropped about 6 cores in our Oracle licensing as soon as we could after switching to ES. Since we had such great success with searching one table we decided to include more tables into our searching to help with our database.,MongoDB, Cassandra and Apache Lucene,Progress Test Studio, Crucible, ServiceNow, Concur Expense, PeopleFluent Mirror Suite for Talent ManagementNeed fast querying of text data? Go with Elasticsearch!We use Elasticsearch to store data for quick querying of our various data sets via our APIs. It has allowed us to write APIs that perform much faster compared to their older versions that had complex relational queries. We also use Elasticsearch to store log data for fast querying via Kibana.,Very fast querying of data, especially text based searches. Nice clustering of nodes built in, to ensure a stable, redundant environment. Great integration with Kibana for visualizing and exploring data.,Query syntax can be hard for developers to pick up, especially if they are used to SQL. Tooling leaves a lot to be desired, especially compared to the RDMS tooling that is out there. Updates to Elastic search data aren't the fastest, especially compared to some other nosql solutions like MongoDB,9,Elasticsearch has allowed us to shave off many (> 100 is some cases) milliseconds from our API response times. Elasticsearch coupled with Kibana has allowed us a whole new level of visibility into our log data.,MongoDB, Couchbase Data Platform and MySQL,MySQL, MongoDB, Redis, Apache SolrElasticsearch for Log ManagementWe utilize Elasticsearch (with Kibana and Logstash) to provide log management services internally and as an offering to our IT clients. This helps clients meet compliance regulations requiring log review and SIEM implementation without paying the premium at other high-end products. In essence, Elasticsearch allows us and our clients on the platform to gain greater visibility into their applications and endpoints.,Elasticsearch has a great ecosystem and user base. Elasticsearch is easy to use and set up (once you have the basic training). The document/searching focused feature of the database is perfect for log management (or any searching) application.,I wish many of the features in the X-Pack were native.,10,We have built out an entire service line using Elasticsearch. Clients see their compliance audits eased by the Elasticsearch based products.,AlienVault and MongoDB,LogstashElasticSearch is a simple straightforward search engine that literally anyone can get started with!We use Elasticsearch as the storage/search component of our logging infrastructure (ElasticStack). Once we have broken apart the individual variable components of each log as their own variable type using Logstash, we store those records in to Elasticsearch. Kibana queries Elasticsearch to display the resulting data. We also utilize Elasticsearch to display the cluster status for each of our markets across our entire web cluster using an internal reporting tool we wrote.,Effortless to set up. Literally set the memory thresholds for Java and start throwing JSON formatted records in to the database, it "Just Works". Even clustering is automated as the cluster finds other ElasticSearch servers on the network and assigns each a name. Very simple to use interface either through it's RESTFUL API (ala Curl) or via its speedy protocol on port 9300. Once records are added, the very easy to use Apache Lucene syntax is supported to extract data. It's search capabilities are fast on huge datasets, even on very modest hardware. Our organization operates in the hundreds of servers taking thousands of requests a second, each with it's own log w/ a 2 week retention. The ElasticSearch server we recently decommissioned was Pentium 4 Netburst class Xeon, it rarely skipped a beat.,Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated. The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that. This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.,10,When we where initially exploring logging solutions, Splunk was the only vendor in town and they where extremely expensive ($60,000). We haven't revisited them since as ElasticSearch has accomplished all of our needs. We haven't spent anything but Admin hours to maintain our ElasticSearch cluster. Right now we haven't incurred any cost of ownership as I have been maintaining the cluster myself. We have a huge project to grow a new part of our business, but I am not sure if I can spend the time to really update cluster to support the new Logstash features & any syntax changes so I am reluctant to do so. Time is increasingly becoming scarce, so catering to the latest and greatest features that offer little to our organization isn't something we are interested in pursuing though we are going to need to update the ElasticStack eventually. Since all of our metrics are in ElasticSearch, we have had nice trove of data to build our apps around, apps that require specific metrics. Prior to ElasticSearch, we had to build our own tools that handled that metric collection. The cost savings here is that we maintain a simple script that reports back information in our reporting interface vs rolling our own database metric solution that must be modified for every app we develop. That has equated to a huge saving in developer hours in our organization.,Apache Solr,Apache Solr, VMware ESXi, Apache Web ServerExcelent choice for data analytics and search engineElasticsearch helped us to provide comprehensive reports, and frequent queries on our data (millions of rows), provided us a performance that we could not achieve before (though we have only 40 concurrent users at most) We also consolidate data from many sources within our company, and elasticsearch made it easy for us to do data analyzing, to have many useful insights of our data; things that we could never do (so easily) in the past.,Comprehensive reports and queries Data analytics,A better way to provide custom functions. I struggled with implementing the PercentileExc (exlusive) funtion, the one that Excel provided, because the business users requested it. Better IntelliSense in development console, when the query is complex, I often lost the IntelliSense feature. The “exists” query is not supported by IntelliSense.,10,Positive impact. We could, for the first time, implement a really useful system to support our business users in working with our big data repositories.,Apache Solr,Visual Studio IDE, Eset Smart Security, Microsoft Azure, Windows ServerElasticsearch reviewElasticsearch is used across the whole org. It's used mainly for storing and searching application logs. We have many elastic clusters set up differently. Sometimes it's one cluster per app; sometimes it's one cluster for many apps; depending upon the volume of data being generated. Elasticsearch is used mainly for debugging purposes rather than metrics, but sometimess it's used along with Kibana to visualize metrics also.,Elasticsearch search with its clustering solution provides a scalable logging solution. A number of query nodes, data node and master node can be added on demand to make the whole system very scalable making it possible to store and search terabytes of data. Elasticsearch provides logstash, file beat, and many others. It makes it really easy to ingest a log with less setup. Elasticsearch query language is based on Lucene and is very powerful. Elasticsearch is mostly free except a few features such as authentication and authorization; making it really financially economical for companies to deploy it on large scale.,Elasticsearch doesn't have a free alerting solution. It has elastalert but it's not comparable to the paid version. It's lacking authentication and authorization which makes Graylog a more enticing option. It's lacking a mechanism to protect cluster against runoff queries. Can bring down cluster to its knees.,10,Most of elasticsearch is free except few things which most of the organizations can live without or have a workaround. Not having to pay splunk whole bunch of money is a huge ROI right there. Indexing the logs and making it searchable has a huge impact on the way we operate. Developers no longer have to log in to the system to know what's happening. Especially when we have hundreds of servers, having a central place for all the logs is essential to operate the system. It's really easy to set up and maintain even in a scale. Its hot and warm cluster notion is awesome. The self-maintenance makes a huge impact on the need for system admins.,splunk, Apache Solr and Graylog,Splunk Enterprise, Graylog, Apache SolrDeveloper's Elasticsearch ReviewElasticsearch is currently in our organization for multiple use cases. With the data volume growing huge and rapidly, we push the data into an Elasticsearch cluster setup. We collect logs from multiple systems and push into E C using logstash and few other message brokers system. We collect telemetry from multiple systems and run algorithms to analyze the data.,Indexing Text analysis Time series data handling,Connector to other big data software Plugins to visualize the data other than Kibana Better query editor,7,Server cost and infrastructure management became easy. Efficiency in data handling. Less development time.,Apache Solr and MongoDB,MongoDB, Hadoop, Teradata DatabaseElasticSearch : a powerful and complete toolElastic Search is used in our organization to index Oracle Data. As there is a huge volume of data, Oracle Database is not able to respond quickly to our request. What we did is to index Oracle Data with ElasticSearch and key ElasticSearch to retrieve Data into a Web application to monitor TIBCO BW flows.,It is built on Lucene. It allows very complex and complete text searches. It is an open source product and very easy to install. It is easily scalable. It needs few configurations to do that. The solution is immediately ready on the cloud.,There's not much control over consistency of your data Complex searches queries are not obvious to all users. The syntax is very heavy Administration and monitoring of ElasticSearch are complex,8,Open source. ElasticSearch is immediately ready on the cloud Resolving some data volume limitations that a relational database can handle Quick to install do quick searches on a large volume of data,IBM API Management, Apigee, WSO2 API ManagerFind things with ElasticsearchThe first use case is log aggregation. We have a number of micro-services running, some of them in Docker, and we use the ELK to ensure we have easy access to our most recent logs. This proves invaluable for fault detection and diagnosis and is used primarily by engineers. Another use case in a customer-centric search index. Each of our customers is described by a set of data points that come from various sources and are indexed in Elasticsearch. The index is later used by marketing, customer service, and other departments to get quick insights on our customer base.,Flexible and advanced search. Ease of creating time-based indices and automatic archiving of old indices. Quick data ingestion.,Configuration. Looking forward to seeing Elasticsearch detecting hardware specs and self-adjusting its config. The lack of _changes streams. They were promised to appear in 2.0... The price of the hosted solution could be lower.,10,Sped up fault detection, diagnosis and recovery. Facilitated getting insights on customer base. Helped implement data-driven approach across the whole business.,Solr,Docker, IBM Cloudant, CloudFlareOne of the best search engines out thereElasticsearch its a critical piece of our platform. We rely on it not only for searching of our documents (that is 80% of our business goal and most used feature) but for tracking logs and analytics with Kibana. Elasticsearch allows us to build this amazing search component that gets the user the refinement they want so they can find easily and quickly the results they are looking for. Monitoring our logs is almost that important; we track incidents and code quality among others.,Search Organize data Scale,Mapping and data type auto conversion,10,Great search capabilities of our users' transform into more users on the platform Analyze the logs and extract data of code issues, allowing us to increase code quality,sphinx,MySQL, MongoDB, GitHubIncredible ROI, Easy to set upWe use Elasticsearch to index large sets of data for an extremely fast and searchable database for reporting dashboards. We use Elasticsearch for several of our client projects as well as internal projects here at Xertigo.,Searching for data across many database tables. JSON Response makes it easy to implement on different platforms. Plenty of documentation.,Searching by dates seems a bit complicated. Attributes across indexes need to be the same type. Can be very cumbersome.,10,Optimizations on traditional Relational Databases. Minimal operating costs. Open source.,Apache Solr and Apache Lucene,JIRA Service Desk, Slack, CakePHP, GitHub, BitbucketStretch Your Ambitions With ElasticSearchWe use ElasticSearch for multiple projects across our company, everything from development proof-of-concept efforts to large production systems supporting real-time data ingestion and multiple simultaneous users. ElasticSearch is our go-to data storage solution for anything requiring a responsive web interface. While it's full text search capabilities are its most often touted feature, we get more use out of its rapid search aggregations (formerly facets) and its scalability for large data sets.,Store large numbers of documents in a redundant, distributed fashion across multiple hosts. It handles sharding out of the box with a minimal amount of configuration. Extensive search capabilities, particularly full text search. It also supports aggregations/facets and geospatial searching. Native REST API is great for web applicaitons.,The online documentation is very difficult to use, both as a teaching tool and as a quick reference. The search syntax is arcane and not particularly "human friendly" and examples from the documentation are often insufficiently detailed to apply directly. ElasticSearch is touted as "schemaless" when in fact mappings (aka schemas) are required for all but the most basic use cases.,10,ElasticSearch allows us to build beautiful, responsive web applications that allow users to rapidly filter large amounts of data to find the documents they are interested in.,MongoDB,Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service), Neo4j,30,,Document Search Data Science Data Visualization,Built a race tracking website as part of a hackathon.,10Elasticsearch is the way to go!We used Elasticsearch to build and search a complex index of tv shows, actors, seasons, episodes etc... Using Elasticsearch we can derive information very quickly about what season belongs to which tv show. This allowed us to dynamically build a tree like data structure on the fly without any performance degradation.,Fuzzy query matching Indexing and Sharding data High availability and cluster managment,A better user interface Better integration with AWS,9,Cost of maintaining a cluster in the cloud (e.g. AWS memory optomized instances) is expensive. Project is open sourced, we are free to contribute if a bug is found. And its free!,Apache Solr
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Elasticsearch
106 Ratings
Score 8.7 out of 101
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Elasticsearch
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Gedson Silva profile photo
June 05, 2019

Elasticsearch Review: "An amazing search engine"

Gedson Silva
Score 9 out of 10
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Elasticsearch is being used for multiple purposes in multiple projects: centralized log management, APM, Metrics Collection as a TSDB, and as a replacement for traditional OLAP databases. It provides a high-performance indexing and search engine, which has become an invaluable tool addressing hard problems that would otherwise be very difficult to solve.
  • Ingress and indexing.
  • Searching.
  • Aggregations.
  • Aggregations on top of other aggregations.
  • Encryption at rest.
  • Has a performance penalty when using inked documents.
Elasticsearch is so versatile and so easy to set up that it's really a no-brainer including it in most projects as the indexing and search engine components, as well as for analytics and aggregations. It's not so well-suited to be used as the main database, as there's a minor risk of data loss.
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Jose Adan Ortiz profile photo
June 02, 2019

Review: "Elasticsearch, centralized logs and anomaly detection, easily deployed."

Jose Adan Ortiz
Score 10 out of 10
Vetted Review
Verified User
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Elasticsearch has been a big help for us. We used to work with Application Performance Management Tools that need another layer of visualization and data treatment, and with Elasticsearch we have delivered better insights for our customers.
We use Elasticsearch at our Technology & Services Department to address these issues for our customers:
- Customized Dashboards.
- Anomaly Detection.
- Metrics Predictability.
  • Anomaly detection. It can find patterns over a wide variety of metrics and values.
  • Behind the walls, Elasticsearch has a robust distributed architecture to support queries and data processing, and it is easy to maintain and scale.
  • Elasticsearch has a new Elastic Cloud SaaS solution which is very easy to deploy, set up, and scale with all features and more.
  • Elasticsearch has an important security layer to separate access to data and dashboards.
  • If you want to explode Elasticsearch's capabilities, you need to have a medium-high SQL and Database knowledge.
  • The user interface is heavy in Java requirements, and sometimes you can get some lag displaying heavy results for heavy queries.
  • It will be helpful if you can construct Logstash queries with a drag&drop based user interface.
Elasticsearch can be used perfectly inside a site for searching features in order to respond quickly to user queries. It can be used to act as a Centralized Log Server, where you can define events based on pattern detection for anomaly detection.
Elasticsearch has potent visualization features with Canvas and OOB Dashboards that can respond to business and technical requirements.
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Ben Williams profile photo
February 26, 2019

Elasticsearch Review: "Works well but is difficult to set up and manage"

Ben Williams
Score 6 out of 10
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Verified User
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We currently use it to log the details of our RPA processes as they run through their production and development environments. They log back checkpoints, statues and error messages back to the Kibana database we use in conjunction with Elasticsearch.
  • Powerful beats modules.
  • Later number of input/output pipelines.
  • Open documentation.
  • Documentation is often incomplete.
  • Forums are very full but misleading.
  • The programs don't work well together. They have different methodology and flavors in each.
  • Different configurations in each element make it difficult to use.
It works well for what we need. Short sharp logs of data from ongoing consistent processes.
Read Ben Williams's full review
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June 26, 2019

Review: "Elasticsearch - A catalyst for application maintenance and log management"

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch has been phenomenal in upgrading the log management capabilities at my organization.
Elasticsearch, along with Kibana, has provided a wide range of capabilities for our IT teams to investigate issues and create live monitoring environments.
This is being utilized as a central tool for all of our apps organization-wide.
Before Elasticsearch, our teams were finding a hard time investigating issues, tracking the root cause, and resolving them. Elasticsearch have greatly reduced the investigation time for us.
  • It's an Open Source tool
  • Elasticsearch extends its visualization and analytics capabilities through Kibana, which is a powerful tool
  • Elasticsearch provides 3rd party integration facilities using REST API
  • Search capabilities can be further improved with a much faster response time on historical logs
  • Elasticsearch should have a phone/sms alert feature as well as an event trigger
  • Learning guides could be more detailed
Elasticsearch is well suited for environments where multiple logs are being generated and investigation needs to be done in relation to multiple log files with each other.
Elasticsearch can help to provide a better visualization of the logs and an easy (sql like) search capability.
It also provides analytics capabilities powered with machine learning tools to help make decisions based on the log data.
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February 27, 2019

Review: "ElasticSearch handles a large number of requests quickly and easily"

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
ElasticSearch is used to store all searchable data indices from our product. We use ElasticSearch because it is extremely fast, highly available, and able to meet the demand of our product. We were using a different index-based search technology before, and it failed terribly. We migrated to ElasticSearch and have been very happy with the results.
  • Easy to install
  • Easy to use/lots of documentation
  • Easy to scale up as demand increases
  • The price point for the X-Pack plugins (ie. Security, Alerting, etc.) is a bit high, especially if you only want to do something small and simple and you don't need to leverage the full power of the plugin
  • Configuring the right hardware and capacity planning (when at scale) can get really tricky. In order to get the best performance, a lot of tweaking is needed, and not all of the secret tricks are documented
  • Getting used to ElasticSearch's query language was a bit of an adjustment. You really have to delve into defining analyzers and tokenizers in order to get application-specific results
ElasticSearch is great when you need a lot of data indexed really fast, as well as when you need to retrieve a large number of documents based on a complex query. Searching is super-fast.

If you need a large data store for documents where not everything needs to be indexed, don't use JUST ElasticSearch. We use one KV database system to store all of our data and use ElasticSearch as our Index. All searches are run off of ElasticSearch, and the main data store that it pulls from is the other database.
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February 23, 2019

Review: "Elasticsearch: A Great Lab / Development Platform for Data Architects and DevOps"

Verified User
Score 7 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch (ES) is being used to measure the performance metrics of our web crawlers for our web metrics department. They employ a series of crawlers: setting up data feeds to an ELK stack to measure and monitor organic messages related to our marketing campaigns. It primarily allows us to bring advanced analytics in-house.
  • Free of SQL: ES does not have the overhead of relying on SQL. In fact, you can use most (if not all) DBMs out there.
  • Java: Normally, this is not a strength: Java is slow and cumbersome. I believe in this case, it's truly a feature: by utilizing a language with universal support, it makes ES VERY DevOps friendly, simply by being able to focus on Problem-oriented vs Solutions-based thinking.
  • Although ES has been known to consume RAM, it's very flexible, and I have implemented on a number of distinct hardware configuration with success.
  • Linux: It's not locked down to an OS (which is the way of the future), and as a result-running it on Linux means you get the power of Linux, in a data science package.
  • Elastic Search IS a resource hog: most of the time, I will run ES on a dedicated VM (often a dedicated blade, too!) and allow the other components of the stack to run on separate blades/VMs.
  • Works great for small projects, but is NOT industrial strength: When you are performing a data architecture project, where you are capturing and mining datasets, ES is fine, until you start getting into much denser data sources (orders to TBs), such that ES will violate Data integrity.
  • It only supports JSON output: Which is very friendly to a lot of DevOps/Data Architecture projects but may become a hassle when your endpoints require CVS, XML, etc.
Elasticsearch is great for development/research projects: It's fast, and *fairly* simple to set up. Project ideas of the calibre of: Watching a marketing feed from Twitter, or scraping sites. But for High availability in (say) a SCADA environment, probably not helpful. Though, I would recommend it for logging system nodes: such as a data center, trouble ticketing dashboard, or health/status visualizations.
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October 09, 2018

Review: "Elasticsearch is the rare unicorn product—search and analytics in one"

Anatoly Geyfman
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch for our online (realtime) search engine. We've indexed over 2 billion documents, including every physician, hospital, and clinic in the United States. We started using ES from the beginning since I had a bunch of great experiences with the technology from my last job. We load data into Elasticsearch from multiple locations, including Postgres and BigQuery. On top of Elasticsearch, we've built a number of analytics tools that let us not only search but also deliver analytics for our stored data -- like top physicians performing a specific service and geography-based analyses. Overall we're super happy with Elasticsearch.
  • Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range.
  • Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system
  • Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.
  • Elasticsearch is highly distributed, but it takes time to tune so you get the right performance out of your cluster.
  • The query language is not SQL, so it's not a straightforward conversion from an RDBMS to Elasticsearch for searching through data.
  • There are lots of ways to insert data into Elasticsearch, and some are better than others (batch vs. single insert). Need to experiment with your own data and environment.
Elasticsearch is extremely well suited for structured (faceted) search, full-text search, and analytics workloads. Elasticsearch and the ELK stack are also a good fit for operations teams that want to be able to interrogate their logs in an online (read: fast) query tool. Elastic is amazing at creating super fast search experiences over very large datasets, where traditional RDBMS systems are either too costly or too slow.
Read Anatoly Geyfman's full review
Tarun Mangukiya profile photo
October 08, 2018

Review: "Need a text search to your data, Elasticsearch is the answer to it!"

Tarun Mangukiya
Score 10 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch is being used for multiple purposes at Iconscout. Starting from a search engine to viewing detailed analytics. We're even using it for logging of the server. It helps us to query through the millions of data easily and efficiently.
  • Fast Search through millions of data
  • Uses a very limited storage to store the data - high compression
  • Easy to get started & configure
  • Their documentation needs a lot of imporvement
  • Difficult to understand query language
  • New updates are difficult to adopt
Elasticsearch has a very fast an efficient searching process. If you've searched a heavy project, you can't just be dependent on databases. Plus, they have a REST API for everything, making it easy to use with any programming language or database.
Read Tarun Mangukiya's full review
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October 08, 2018

"Elasticsearch Review"

Josh Kramer
Score 10 out of 10
Vetted Review
Verified User
Review Source
It is used in our custom software application for advanced searching and filtering capabilities for our users.
  • It allows extremely fast search and filtering on large datasets
  • It has a very powerful aggregation engine that can allow for tons of customizable analytics and reports.
  • The documentation could be a bit more detailed and have more examples, especially for advanced functionality.
  • The ability to update/change existing live field mappings would be nice.
  • The ingest pipeline structure is a bit more complicated and confusing than previous implementations for using things like attachment plug-ins.
It is well suited for anything involving large data - searching, filtering, aggregations, analytics, reporting, etc.
Read Josh Kramer's full review
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October 15, 2018

Elasticsearch Review: "The gold standard for text-based search"

Verified User
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch to power a web search engine that allows users of our web platform to search for products, content, and more. With Elasticsearch we were able to quickly and effectively develop and deploy a search solution that is fast, scalable, and was a breeze for our developers to implement.
  • Lightning fast
  • Easily scalable
  • Powerful feature set
  • Additional complexities when in need of frequent & rapid updates to the Elasticsearch data set
  • New syntax can be confusing, particularly with advanced features and more powerful queries
Elasticsearch is the gold standard for text-based search. Across large data sets it performs admirably, and we will certainly make it our first choice search solution in the future. For a use case where needs are simple and regular database queries might suffice, Elasticsearch may or may not provide any benefits.
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January 10, 2019

Elasticsearch Review: "The Best Available"

Verified User
Score 9 out of 10
Vetted Review
Reseller
Review Source
It provides a distributed, multitenant-capable, full-text search engine with an HTTP web interface and schema-free JSON
documents. We use this in our IT department, but also resell it as part of a predictive AIOps platform that offers automation for many of the tedious tasks that data center staff struggle with every day.
  • Search
  • Correlation
  • Analysis
  • Big data
  • Pagination
  • Presentation
  • Mapping
Elasticsearch is a great fit for a data lake environment that is being created to get rid of the typical siloed environment in so many data centers today. Being able to easily search, analyze, and correlate device information in easy to read JSON files is crazy valuable to our internal team.
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April 13, 2018

User Review: "Find more faster with Elasticsearch"

Brett Knighton
Score 8 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch to efficiently search large pools of data. Elasticsearch gives us the ability to have blazing fast searches even when doing partial text matches on multiple fields.
  • The best solution we've found for blazing fast searches, especially text-based.
  • Easy to add nodes for data redundancy.
  • Good documentation makes getting up and running easy.
  • I found the learning curve fairly difficult having a SQL background.
If you are in a scenario where you are constantly trying to optimize queries to get better performance from your database searches, Elasticsearch is probably a product worth trying out. With the amount of data we have, doing text searches via SQL isn't even an option. If you aren't struggling with getting reasonably fast queries getting Elasticsearch up probably isn't going to be worth the hassle.
Read Brett Knighton's full review
David Greenwell profile photo
March 01, 2018

User Review: "Enthusiastic for Elasticsearch"

David Greenwell
Score 10 out of 10
Vetted Review
Verified User
Review Source
We decided to start looking into Elasticsearch after we had good success with using lucene (the full-text search indexer that Elastic uses). We had some queries in Oracle that were running EXTREMELY slow and knew we had to do something for the customer to make their experience better. We had a few thoughts on what we could use and Elasticsearch fit what we really wanted.
  • Searching, it does it well and searches are fast...real fast.
  • Ease of use, we were able to get an Elasticsearch cluster up and running in a half hour and doing basic searches after that was very easy with simple requests
  • Redundancy built in and stability. We haven't had any of our Elastic clusters go down intentionally, but testing out redundancy by removing nodes Elasticsearch has gone flawlessly.
  • Only breaking changes between versions when they are absolutely necessary.
  • Works well with .Net libraries that are supported and coded by Elastic.
  • A bit more of a learning curve for complex searches, indexing more complex things.
  • Some of our updates between versions haven't gone as smoothly as we would like, but in more recent versions Elastic has done a much better job at trying to allow for full uptime upgrades.
  • Configuration needs to be set up to do larger searches, or more complex searches and at times while starting it wasn't obvious what configuration needed to be changed.
The best situation where we have found elasticsearch to help was when you have searches and your database just isn't doing them with the speed that you want, and even where the DB is going the speed needed Elasticsearch can take some of the processing from the database(which isn't necessarily built specifically for searching) to a system that was designed for searches.

If you are doing searching, then I would suggest going with Elasticsearch.
Read David Greenwell's full review
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January 23, 2018

Review: "Need fast querying of text data? Go with Elasticsearch!"

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch to store data for quick querying of our various data sets via our APIs. It has allowed us to write APIs that perform much faster compared to their older versions that had complex relational queries.

We also use Elasticsearch to store log data for fast querying via Kibana.
  • Very fast querying of data, especially text based searches.
  • Nice clustering of nodes built in, to ensure a stable, redundant environment.
  • Great integration with Kibana for visualizing and exploring data.
  • Query syntax can be hard for developers to pick up, especially if they are used to SQL.
  • Tooling leaves a lot to be desired, especially compared to the RDMS tooling that is out there.
  • Updates to Elastic search data aren't the fastest, especially compared to some other nosql solutions like MongoDB
Elasticsearch is a great solution if you want lightening quick querying of data, especially text-based querying. If you are doing a lot of writing/updating to your database, this is not the best use case and you may want to evaluate other NoSQL solutions.
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January 18, 2018

User Review: "Elasticsearch for Log Management"

Verified User
Score 10 out of 10
Vetted Review
Verified User
Review Source
We utilize Elasticsearch (with Kibana and Logstash) to provide log management services internally and as an offering to our IT clients. This helps clients meet compliance regulations requiring log review and SIEM implementation without paying the premium at other high-end products. In essence, Elasticsearch allows us and our clients on the platform to gain greater visibility into their applications and endpoints.
  • Elasticsearch has a great ecosystem and user base.
  • Elasticsearch is easy to use and set up (once you have the basic training).
  • The document/searching focused feature of the database is perfect for log management (or any searching) application.
  • I wish many of the features in the X-Pack were native.
If you are building an application that requires fast retrieval, Elasticsearch would provide an excellent backend database. The distributed architecture provides high-availability and data replication natively without a large performance sacrifice. Elasticsearch also runs on minimal hardware requirements when compared to other DB solutions.
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August 31, 2017

Review: "ElasticSearch is a simple straightforward search engine that literally anyone can get started with!"

Colby Shores
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch as the storage/search component of our logging infrastructure (ElasticStack). Once we have broken apart the individual variable components of each log as their own variable type using Logstash, we store those records in to Elasticsearch. Kibana queries Elasticsearch to display the resulting data. We also utilize Elasticsearch to display the cluster status for each of our markets across our entire web cluster using an internal reporting tool we wrote.
  • Effortless to set up. Literally set the memory thresholds for Java and start throwing JSON formatted records in to the database, it "Just Works". Even clustering is automated as the cluster finds other ElasticSearch servers on the network and assigns each a name.
  • Very simple to use interface either through it's RESTFUL API (ala Curl) or via its speedy protocol on port 9300. Once records are added, the very easy to use Apache Lucene syntax is supported to extract data.
  • It's search capabilities are fast on huge datasets, even on very modest hardware. Our organization operates in the hundreds of servers taking thousands of requests a second, each with it's own log w/ a 2 week retention. The ElasticSearch server we recently decommissioned was Pentium 4 Netburst class Xeon, it rarely skipped a beat.
  • Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
  • The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
  • This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.
ElasticSearch is hands down, the absolute best solution for logging in a virtualization environment. The Kibana front end to ElasticSearch is extremely intuitive, even computer novices can be trained on how to chain together tags in the Apache Lucene syntax to extract the data they need. Once the deploy process is nailed down and system is engineered, the logging structure can remain fairly static until the next major revision. Compared to Splunk, with an administrator well versed in the ElasticSearch suite, will save an organization upwards of 10's of thousands of dollars a year even with the caveats mentioned earlier.

As a developer looking for a quick and simple search engine which has little configuration required, ElasticSearch is fast and perfect for that solution. Literally throw JSON records in to the database and push a request to get JSON out, exceptionally straightforward.
Read Colby Shores's full review
Trung Le profile photo
November 14, 2017

Elasticsearch Review: "Excelent choice for data analytics and search engine"

Trung Le
Score 10 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch helped us to provide comprehensive reports, and frequent queries on our data (millions of rows), provided us a performance that we could not achieve before (though we have only 40 concurrent users at most) We also consolidate data from many sources within our company, and elasticsearch made it easy for us to do data analyzing, to have many useful insights of our data; things that we could never do (so easily) in the past.
  • Comprehensive reports and queries
  • Data analytics
  • A better way to provide custom functions. I struggled with implementing the PercentileExc (exlusive) funtion, the one that Excel provided, because the business users requested it.
  • Better IntelliSense in development console, when the query is complex, I often lost the IntelliSense feature. The “exists” query is not supported by IntelliSense.
Read Trung Le's full review
Manish Rajkarnikar profile photo
October 04, 2017

"Elasticsearch review"

Manish Rajkarnikar
Score 10 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch is used across the whole org. It's used mainly for storing and searching application logs. We have many elastic clusters set up differently. Sometimes it's one cluster per app; sometimes it's one cluster for many apps; depending upon the volume of data being generated. Elasticsearch is used mainly for debugging purposes rather than metrics, but sometimess it's used along with Kibana to visualize metrics also.
  • Elasticsearch search with its clustering solution provides a scalable logging solution. A number of query nodes, data node and master node can be added on demand to make the whole system very scalable making it possible to store and search terabytes of data.
  • Elasticsearch provides logstash, file beat, and many others. It makes it really easy to ingest a log with less setup.
  • Elasticsearch query language is based on Lucene and is very powerful.
  • Elasticsearch is mostly free except a few features such as authentication and authorization; making it really financially economical for companies to deploy it on large scale.
  • Elasticsearch doesn't have a free alerting solution. It has elastalert but it's not comparable to the paid version.
  • It's lacking authentication and authorization which makes Graylog a more enticing option.
  • It's lacking a mechanism to protect cluster against runoff queries. Can bring down cluster to its knees.
Elk is great for app logs and search. It comes with Kibana which is great query tool. Logstash is great. It can autodetect datatype but can be tuned if needed which is awesome. It has lots of integrations such as filesystem, syslog, kafka etc., which make setting it up a breeze. It is also sometimes used for metrics. But [I] would rather use timseries db such as influx db, prometheus for metrics. Using logs for metrics tend to be expensive and inefficient.
Read Manish Rajkarnikar's full review
Devaraj Natarajan profile photo
September 15, 2017

"Developer's Elasticsearch Review"

Devaraj Natarajan
Score 7 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch is currently in our organization for multiple use cases. With the data volume growing huge and rapidly, we push the data into an Elasticsearch cluster setup. We collect logs from multiple systems and push into E C using logstash and few other message brokers system. We collect telemetry from multiple systems and run algorithms to analyze the data.
  • Indexing
  • Text analysis
  • Time series data handling
  • Connector to other big data software
  • Plugins to visualize the data other than Kibana
  • Better query editor
I have noticed Elasticsearch is good in following scenarios:
Faster Aggregation
Full-text search features
Scalable
Great performance
Stability
Complete Ecosystems of applications

It could have been slightly better in handling indexing. (Should index all the items and create index overhead)
Better load balancing
Elasticsearch aggregations are not always precise, because of how data in the shards is placed
Read Devaraj Natarajan's full review
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November 10, 2017

Review: "ElasticSearch : a powerful and complete tool"

Verified User
Score 8 out of 10
Vetted Review
Verified User
Review Source
Elastic Search is used in our organization to index Oracle Data. As there is a huge volume of data, Oracle Database is not able to respond quickly to our request. What we did is to index Oracle Data with ElasticSearch and key ElasticSearch to retrieve Data into a Web application to monitor TIBCO BW flows.
  • It is built on Lucene. It allows very complex and complete text searches.
  • It is an open source product and very easy to install.
  • It is easily scalable. It needs few configurations to do that.
  • The solution is immediately ready on the cloud.
  • There's not much control over consistency of your data
  • Complex searches queries are not obvious to all users. The syntax is very heavy
  • Administration and monitoring of ElasticSearch are complex
ElasticSearch is very well suited to index and search data but it not made to store data like a database.
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Kris Bandurski profile photo
April 03, 2017

User Review: "Find things with Elasticsearch"

Kris Bandurski
Score 10 out of 10
Vetted Review
Verified User
Review Source
The first use case is log aggregation. We have a number of micro-services running, some of them in Docker, and we use the ELK to ensure we have easy access to our most recent logs. This proves invaluable for fault detection and diagnosis and is used primarily by engineers. Another use case in a customer-centric search index. Each of our customers is described by a set of data points that come from various sources and are indexed in Elasticsearch. The index is later used by marketing, customer service, and other departments to get quick insights on our customer base.
  • Flexible and advanced search.
  • Ease of creating time-based indices and automatic archiving of old indices.
  • Quick data ingestion.
  • Configuration. Looking forward to seeing Elasticsearch detecting hardware specs and self-adjusting its config.
  • The lack of _changes streams. They were promised to appear in 2.0...
  • The price of the hosted solution could be lower.
  • Great for log aggregation and handling of time-based data in general, product search.
  • Not so great for highly "relational" data sets.
Read Kris Bandurski's full review
Yasmany Cubela Medina profile photo
April 13, 2017

Elasticsearch Review: "One of the best search engines out there"

Yasmany Cubela Medina
Score 10 out of 10
Vetted Review
Verified User
Review Source
Elasticsearch its a critical piece of our platform. We rely on it not only for searching of our documents (that is 80% of our business goal and most used feature) but for tracking logs and analytics with Kibana. Elasticsearch allows us to build this amazing search component that gets the user the refinement they want so they can find easily and quickly the results they are looking for. Monitoring our logs is almost that important; we track incidents and code quality among others.
  • Search
  • Organize data
  • Scale
  • Mapping and data type auto conversion
Elasticsearch is a great choice for search scenarios, for architectures that heavily rely on search capabilities. It's also great for analytics using Kibana. It's also great for log aggregations and again search. It can be even used as the main database layer for critical search infrastructures. But you need to [take] care with data that may change the structure and type of fields.
Read Yasmany Cubela Medina's full review
Rowan Hughes profile photo
April 05, 2017

Elasticsearch Review: "Incredible ROI, Easy to set up"

Rowan Hughes
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use Elasticsearch to index large sets of data for an extremely fast and searchable database for reporting dashboards. We use Elasticsearch for several of our client projects as well as internal projects here at Xertigo.
  • Searching for data across many database tables.
  • JSON Response makes it easy to implement on different platforms.
  • Plenty of documentation.
  • Searching by dates seems a bit complicated.
  • Attributes across indexes need to be the same type. Can be very cumbersome.
[Elasticsearch is well suited for] Data Analysis, Reporting, Searching across large datasets, Speed
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Aaron Gussman profile photo
June 10, 2016

Review: "Stretch Your Ambitions With ElasticSearch"

Aaron Gussman
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use ElasticSearch for multiple projects across our company, everything from development proof-of-concept efforts to large production systems supporting real-time data ingestion and multiple simultaneous users. ElasticSearch is our go-to data storage solution for anything requiring a responsive web interface. While it's full text search capabilities are its most often touted feature, we get more use out of its rapid search aggregations (formerly facets) and its scalability for large data sets.
  • Store large numbers of documents in a redundant, distributed fashion across multiple hosts. It handles sharding out of the box with a minimal amount of configuration.
  • Extensive search capabilities, particularly full text search. It also supports aggregations/facets and geospatial searching.
  • Native REST API is great for web applicaitons.
  • The online documentation is very difficult to use, both as a teaching tool and as a quick reference. The search syntax is arcane and not particularly "human friendly" and examples from the documentation are often insufficiently detailed to apply directly.
  • ElasticSearch is touted as "schemaless" when in fact mappings (aka schemas) are required for all but the most basic use cases.
I would say ElasticSearch is the best option on the market for web-driven document search.
Read Aaron Gussman's full review
Abdel Kamel profile photo
June 18, 2016

User Review: "Elasticsearch is the way to go!"

Abdel Kamel
Score 9 out of 10
Vetted Review
Verified User
Review Source
We used Elasticsearch to build and search a complex index of tv shows, actors, seasons, episodes etc... Using Elasticsearch we can derive information very quickly about what season belongs to which tv show. This allowed us to dynamically build a tree like data structure on the fly without any performance degradation.
  • Fuzzy query matching
  • Indexing and Sharding data
  • High availability and cluster managment
  • A better user interface
  • Better integration with AWS
Elasticsearch does one thing very well. Search and index data. Trying to go outside that realm is doable but not recommended. For example, I would not use elasticsearch as a document store. But rather treat it as a rebuildable index that can be rebuilt from a persistent database like Postgres, or MySQL.
Read Abdel Kamel's full review

Elasticsearch Scorecard Summary

Likelihood to Recommend (106)
8.7
Likelihood to Renew (1)
10.0
Usability (1)
10.0
Implementation (1)
9.0

About Elasticsearch

Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Categories:  Enterprise Search

Elasticsearch Technical Details

Operating Systems: Unspecified
Mobile Application:No