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Elasticsearch

Open Source, Distributed, RESTful Search Engine
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What is Elasticsearch?

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Elasticsearch is a tool in the Search as a Service category of a tech stack.
Elasticsearch is an open source tool with 52.4K GitHub stars and 18.3K GitHub forks. Here鈥檚 a link to Elasticsearch's open source repository on GitHub

Who uses Elasticsearch?

Companies
3152 companies reportedly use Elasticsearch in their tech stacks, including Uber, Udemy, and Shopify.

Developers
16564 developers on StackShare have stated that they use Elasticsearch.

Elasticsearch Integrations

Kibana, Logstash, Datadog, Contentful, and Couchbase are some of the popular tools that integrate with Elasticsearch. Here's a list of all 60 tools that integrate with Elasticsearch.
Public Decisions about Elasticsearch

Here are some stack decisions, common use cases and reviews by companies and developers who chose Elasticsearch in their tech stack.

I would like to assess search functionality along with some analytical use cases like aggregating, faceting etc.,. I would like to know which is the best database to go with among Elasticsearch, MongoDB and FaunaDB.

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Hi, I need advice on which Database tool to use in the following scenario:

I work with Cesium, and I need to save and load CZML snapshot and update objects for a recording program that saves files containing several entities (along with the time of the snapshot or update). I need to be able to easily load the files according to the corresponding timeline point (for example, if the update was recorded at 13:15, I should be able to easily load the update file when I click on the 13:15 point on the timeline). I should also be able to make geo-queries relatively easily.

I am currently thinking about Elasticsearch or PostgreSQL, but I am open to suggestions. I tried looking into Time Series Databases like TimescaleDB but found that it is unnecessarily powerful than my needs since the update time is a simple variable.

Thanks for your advice in advance!

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Nilesh Akhade
Technical Architect at Self Employed | 5 upvotes 路 103.3K views
Shared insights
on
Kafka
Elasticsearch

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Shared insights
on
Elasticsearch
at

Elasticsearch Used for powering full-text search applications.

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Sunil Chaudhari

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Rana Usman Shahid
Chief Technology Officer at TechAvanza | 5 upvotes 路 69.5K views

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Blog Posts

Elasticsearch's Features

  • Distributed and Highly Available Search Engine
  • Multi Tenant with Multi Types
  • Various set of APIs including RESTful
  • Clients available in many languages including Java, Python, .NET, C#, Groovy, and more
  • Document oriented
  • Reliable, Asynchronous Write Behind for long term persistency
  • (Near) Real Time Search
  • Built on top of Apache Lucene
  • Per operation consistency
  • Inverted indices with finite state transducers for full-text querying
  • BKD trees for storing numeric and geo data
  • Column store for analytics
  • Compatible with Hadoop using the ES-Hadoop connector
  • Open Source under Apache 2 and Elastic License

Elasticsearch Alternatives & Comparisons

What are some alternatives to Elasticsearch?
Datadog
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
Solr
Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
Lucene
Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Algolia
Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
See all alternatives

Elasticsearch's Followers
14470 developers follow Elasticsearch to keep up with related blogs and decisions.