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Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.https://dudodiprj2sv7.cloudfront.net/product-logos/vL/Vh/UHT9H0QQVEZZ.pngElasticsearch 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,Find 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 IDE
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Elasticsearch
87 Ratings
Score 8.9 out of 101
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Elasticsearch Reviews

Elasticsearch
87 Ratings
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Score 8.9 out of 101
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Anatoly Geyfman profile photo
October 09, 2018

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

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!"

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
Josh Kramer profile photo
October 08, 2018

"Elasticsearch Review"

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"

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.
Read this authenticated review
Brett Knighton profile photo
April 13, 2018

User Review: "Find more faster with Elasticsearch"

Score 8 out of 10
Vetted Review
Verified User
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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"

Score 10 out of 10
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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!"

Score 9 out of 10
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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"

Score 10 out of 10
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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.
Read this authenticated review
Colby Shores profile photo
August 31, 2017

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

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"

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"

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"

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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June 13, 2017

Elasticsearch Review: "Powerful and resilient database"

Score 10 out of 10
Vetted Review
Verified User
Review Source
We have used Elasticsearch for indexing both large and small documents for rapid searching and retrieval. Our other services analyze the documents we index in Elasticsearch to look for interesting information that can help us and our customers make informed decisions.

We also enjoy leveraging the built-in data replication features to keep our data as available and easily retrievable as possible.
  • Indexing. Elasticsearch can index thousands of documents per second.
  • Searching. Elasticsearch provides plenty of options for querying your data to get just the right information back.
  • Scalability. Elasticsearch has built-in features for replicating data and distributing load, so you don't have to invest a ton of time and effort into third-party or customized clustering and/or sharding solutions.
  • Backup. Elasticsearch has built-in options for backing up your data. If you're dealing with a large cluster, backing things up can get rather interesting from a storage perspective, but Elasticsearch has worked very well for us thus far.
  • Recovery. If part of your cluster goes offline, Elasticsearch generally does a decent job of staying online and recovering from the outage. Occasionally you'll lose nodes that house all copies of a given set of shards (which isn't fun), but Elasticsearch still handles that situation as well as can be expected.
  • Elasticsearch can struggle if you're trying to create too many new indexes at the same time.
As the name implies, when you need to search thousands, millions, or billions text-based documents for keywords, Elasticsearch is great. The way it indexes and internally analyzes the content of your documents is very powerful. Assuming you have enough servers in your cluster with fast enough storage, querying those documents becomes a breeze.
Read this authenticated review
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November 10, 2017

Review: "ElasticSearch : a powerful and complete tool"

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"

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"

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"

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
Read Rowan Hughes's full review
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April 05, 2017

Review: "Elasticsearch - Great tool for search applications"

Score 8 out of 10
Vetted Review
Verified User
Review Source
We use ElasticSearch for the search functionality in our application. We have a lot of data to search from and ElasticSearch makes it ridiculously fast by tokenizing the content. It enables us to do free text search in a large blob of audio transcripts that we have.
  • Easy to scale - It's designed to be used across distributed environments. Indexes can be divided into shards, with each shard able to have any number of replicas.
  • Search queries can be structured as JSON objects (in addition to text strings) that enables complex and robust searches.
  • If your application needs an effective solution for dynamic searching, I think ElasticSearch is the way to go.
  • If you want to store or retrieve data outside of searching, you may want to try a different solution since ElasticSearch's capabilities are limited.
  • If you want to do large or complex computations with the data, ElasticSearch isn't really good at that.
  • ElasticSearch shouldn't be the primary source of data because data backups and durability are not high priority.
It does the thing that it was designed for (quickly searching through bulk load of data) very very well. Also, it's scalable and flexible. Don't try it for other cases. It's not a NoSQL data store where you will want to store and retrieve data. Also, don't try any complex computations. That will make the retrieval slow and the benefits will be lost.
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Aaron Gussman profile photo
June 10, 2016

Review: "Stretch Your Ambitions With ElasticSearch"

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!"

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.
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June 10, 2016

Elasticsearch Review: "Highly recommended Search provider software for e-commerce platform"

Score 9 out of 10
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We use Elasticsearch as a search provider for our ecommerce software. Our search, category and navigation pages are rendered from ElasticSearch.
  • More relevant search results. There are lot of in build algorithms that are part of Elasticsearch. Using these algorithms improved search results.
  • Decrease in the page load time since read operation is very fast.
  • Easy to implement when compared to other software.
  • Installation and configuration of Elasticsearch on windows server is not straight forward.
  • Completion suggester algorithm in Elasticsearch (v 2.0) saves information in memory. So any deletes/updates are not reflected immediately unless a flush command is executed. Execution of flush command is not advised by Elasticsearch team.
  • Elasticsearch Nest API code is not updated to match with Elasticsearch release version. So we have to write our own implementation.
The only scenario where I see Elasticsearch is less appropriate is when there are transactions involved. If data is corrupted there is no rollback.
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May 24, 2016

Review: "Elasticsearch is a great open source alternative to Splunk while stacking up to products like Cassandra and Solr"

Score 10 out of 10
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The oil and gas web application is heavily used for monitoring active wells. We need app-specific analytics based on user behavior and error context. Elasticsearch is used to collect arbitrary information during production. Kibana is used to view these messages in an effort to "fix" the app before the user is able to submit a ticket (proactive feature and defect resolution).
  • It indexes anything. Just use structured logging to begin sending messages to it.
  • Kibana, the UI for it, allows you to easily build dashboards with real-time widgets.
  • The REST API for Elasticsearch is well-written, should you choose to incorporate the data on your own custom application.
Web app analytics is a great example of use for it because logging messages isn’t necessarily structured. Elasticsearch does a great job of indexing structured or unstructured data. Think of Elasticsearch and Kibana being an open source "Splunk" replacement. It may not be appropriate to use Elasticsearch for true real-time data. It is not a time series database although it may be used as one. Perhaps a better solution for time series data would be InfluxDB or Graphite, whereas Elasticsearch is more of a search engine.
Read Ivan Portugal's full review
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May 10, 2016

User Review: "How Elasticsearch changed our culture"

Score 10 out of 10
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We use Elasticsearch in tandem with Logstash and Kibana, in order to graph trends through log line analysis. The tool has become invaluable as we can peer into data on a deeper level, and set up alerts if there is a high frequency of errors. This becomes useful to study how changes positively or negatively impact production.
  • Consolidate data
  • Searchable
  • Graphable
  • Kibana GUI could use some work, better than Logstash though
  • URL shortening was just released
  • Graph coloring was just released
Elasticsearch is good for any production stack for data analysis, and error monitoring and alerting. The only thing you need is an engineer who's willing to dig through log lines, write queries, and build graphs which accurately track the health of your production systems. I equate this tool to something like New Relic, where if used the right way can provide a lot of insight. If used incorrectly, it doesn't do a whole lot out of the box. It needs to be set up by someone who knows the system and cares to monitor it.
Read Shannon Donohue's full review
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June 10, 2016

Elasticsearch Review: "Elastic - ruler in the index world"

Score 10 out of 10
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We use Elasticsearch in our Web-Payment Fraud and Security Solution. We index every Http Request Response message of our customers' eBanking applications to analyze for fraud/malware/security threats. We then provide flexible and robust analytics on their data including free text search, reporting and real time data insights.
  • Free text search. Query String Query is totally awesome and allows complex search in real time.
  • Very scalable and highly configurable, there is no scalability problem we couldn't solve.
  • Aggregations are great for analytics and we utilize them in our proprietary reporting tool.
  • Aggregations scalability - elastic search doesn't do a very good job in protecting its cluster from bad queries. Circuit breakers are good, but to completely protect ourselves we had to implement our own mechanisms.
Great for storing big data, very scalable. Many great features.
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June 07, 2016

User Review: "Elasticsearch - go for it!"

Score 9 out of 10
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We get a lot of scientific journals in pdf format. Windows only allows title search. Some scientists use Mendeley but there is a licensing cost involved. We implemented Elasticsearch to help the scientists to search by author or look for keywords in the title or in the content. And we have provided options to look for an exact match as well as partial match.
  • The snippet that we get back before and after the search words is very helpful for the scientists to get the right content.
  • At my previous job with a simple 3 node cluster, Elasticsearch did not do a good job in electing a new master, when the master node went down. Many times, I had to stop and restart all the nodes to make it function again. I have heard implementation models where customers had dedicated multiple nodes just for master.
At my previous job as well as the current one, the use cases suit the usage of Elasticsearch very well.
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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