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Elasticsearch

Open Source, Distributed, RESTful Search Engine

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 GitHub stars and GitHub forks. Here’s a link to Elasticsearch's open source repository on GitHub

Who uses Elasticsearch?

Companies
4135 companies reportedly use Elasticsearch in their tech stacks, including Uber, Shopify, and Udemy.

Developers
29535 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 97 tools that integrate with Elasticsearch.
Pros of Elasticsearch
329
Powerful api
315
Great search engine
231
Open source
214
Restful
200
Near real-time search
98
Free
85
Search everything
54
Easy to get started
45
Analytics
26
Distributed
6
Fast search
5
More than a search engine
4
Awesome, great tool
4
Great docs
3
Highly Available
3
Easy to scale
2
Nosql DB
2
Document Store
2
Great customer support
2
Intuitive API
2
Reliable
2
Potato
2
Fast
2
Easy setup
2
Great piece of software
1
Open
1
Scalability
1
Not stable
1
Easy to get hot data
1
Github
1
Elaticsearch
1
Actively developing
1
Responsive maintainers on GitHub
1
Ecosystem
0
Community
Decisions about Elasticsearch

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

Needs advice
on
ElasticsearchElasticsearch
and
SplunkSplunk

We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.

See more

I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.

  1. They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.

  2. They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.

  3. They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.

At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.

I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.

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Needs advice
on
HBaseHBaseMilvusMilvus
and
RocksDBRocksDB

I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

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Needs advice
on
GolangGolangGrafanaGrafana
and
LogstashLogstash

Hi everyone. I'm trying to create my personal syslog monitoring.

  1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

  2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

I would like to know... Which is a cheaper and scalable solution?

Or even if there is a better way to do it.

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André Ribeiro
at Federal University of Rio de Janeiro · | 4 upvotes · 56.8K views

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

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Eric Richner
Owner at The Richner Group · | 3 upvotes · 28.2K views
Needs advice
on
AppbaseAppbaseElasticsearchElasticsearch
and
FirebaseFirebase

We are starting to work on a web-based platform aiming to connect investors/wholesalers (clients) and buyers (service providers). A third service provider, lenders, will be added in the future.

The ability to create profiles of buyers w/ their buying criteria, to create saved records of properties for sale (provided by client) to be cross-referenced against the buyers' criteria is our core functionality.

In-app, timeline-based, real-time communication between users (& storing it), file transfers, and push notifications are post MVP features we would like as well.

We are considering using React, Elasticsearch / App Search w/ their Search UI, and using Real-Time Database and functionalities of Firebase.

See more

Blog Posts

May 21 2019 at 12:20AM

Elastic

ElasticsearchKibanaLogstash+4
12
5362
GitHubPythonReact+42
49
41088
GitHubPythonNode.js+47
55
73049

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
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