Elasticsearchย vsย Solr

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Elasticsearch

23.8K
17.8K
+ 1
1.6K
Solr

640
503
+ 1
124
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Elasticsearch vs Solr: What are the differences?

Developers describe Elasticsearch as "Open Source, Distributed, RESTful Search Engine". 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). On the other hand, Solr is detailed as "An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc". 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.

Elasticsearch and Solr are primarily classified as "Search as a Service" and "Search Engines" tools respectively.

Some of the features offered by Elasticsearch are:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

On the other hand, Solr provides the following key features:

  • Advanced Full-Text Search Capabilities
  • Optimized for High Volume Web Traffic
  • Standards Based Open Interfaces - XML, JSON and HTTP

"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "Powerful" was stated as the key factor in picking Solr.

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

Uber Technologies, Instacart, and Slack are some of the popular companies that use Elasticsearch, whereas Solr is used by Slack, Coursera, and Zalando. Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to Solr, which is listed in 140 company stacks and 42 developer stacks.

Advice on Elasticsearch and Solr
Rana Usman Shahid
Chief Technology Officer at TechAvanza ยท | 5 upvotes ยท 120.6K views
Needs advice
on
Firebase
Elasticsearch
and
Algolia

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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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit ยท | 5 upvotes ยท 90K views
Recommends
Algolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
Cloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Elasticsearch
Pros of Solr
  • 321
    Powerful api
  • 311
    Great search engine
  • 231
    Open source
  • 213
    Restful
  • 200
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Great docs
  • 3
    Awesome, great tool
  • 3
    Easy to scale
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Fast
  • 2
    Nosql DB
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Document Store
  • 2
    Great customer support
  • 1
    Reliable
  • 1
    Not stable
  • 1
    Potato
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Scalability
  • 0
    Easy to get hot data
  • 0
    Community
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Restful
  • 5
    Apache Software Foundation
  • 3
    Great Search engine
  • 2
    Security built-in

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Cons of Elasticsearch
Cons of Solr
  • 6
    Diffecult to get started
  • 5
    Resource hungry
  • 4
    Expensive
  • 3
    Hard to keep stable at large scale
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    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).

    What is 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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Elasticsearch?
    What companies use Solr?
    See which teams inside your own company are using Elasticsearch or Solr.
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    What tools integrate with Elasticsearch?
    What tools integrate with Solr?

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

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    What are some alternatives to Elasticsearch and Solr?
    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!
    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.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    See all alternatives
    How developers use Elasticsearch and Solr
    imgur uses
    Elasticsearch

    Elasticsearch is the engine that powers search on the site. From a high level perspective, itโ€™s a Lucene wrapper that exposes Luceneโ€™s features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

    Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

    Instacart uses
    Elasticsearch

    The very first version of the search was just a Postgres database query. It wasnโ€™t terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesnโ€™t come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

    AngeloR uses
    Elasticsearch

    We use ElasticSearch for

    • Session Logs
    • Analytics
    • Leaderboards

    We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

    The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

    Brandon Adams uses
    Elasticsearch

    Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

    Ana Phi Sancho uses
    Elasticsearch

    Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

    Kang Hyeon Ku uses
    Solr

    elastic search ์™€ ํ•จ๊ป˜ ์œ ๋ช…ํ•œ ๊ฒ€์ƒ‰ ์—”์ง„ ์˜คํ”ˆ ์†Œ์Šค ์ค‘ ํ•˜๋‚˜ ์ด๋‹ค. ์ฒ˜์Œ ์„ค์ •ํ•  ๊ฒƒ์ด ๋งŽ์€๋ฐ, ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์ดํ•ด๊ฐ€ ์—†๋‹ค๋ฉด ์žฆ์€ ์ˆ˜์ •์ด ํ•„์š”ํ•˜๋‹ค. Solr Client ๋กœ ์ œ์–ด ํ•  ์ˆ˜ ์—†๊ณ  Server ์—์„œ ์„ค์ •ํ•ด ์ค˜์•ผํ•˜๋Š” ๊ฒƒ๋“ค์ด ์žˆ์–ด ์„œ๋ฒ„ ์„ค์ •ํ•˜๋Š” ๋ถ€๋ถ„์ด ์ค‘์š”ํ•˜๋‹ค. ์„œ๋ฒ„ ์„ค์ •๋งŒ ์ž˜ ๋˜์žˆ๋‹ค๋ฉด, Client ์ชฝ ์†Œ์Šค๋Š” ๋ณ„๊ฒŒ ์—†๋‹ค.

    ์ค‘์š”ํ•œ ๊ฑด ํ˜•ํƒœ์†Œ ๋ถ„์„๊ธฐ....

    The Independent uses
    Solr

    Full text search is provided by a SOLR cluster. This is done on Master/Slave replication with Varnish as a cache.

    Blue Kangaroo uses
    Solr

    Personalized search engine (for content-based filtering)

    Satoru Ishikawa uses
    Solr

    ๆŸใƒ—ใƒญใƒ—ใƒฉใ‚คใ‚จใ‚ฟใƒชใชWebใ‚ขใƒ—ใƒชใ‚’ๅ‹•ใ‹ใ™ใฎใซๅฟ…่ฆใ ใฃใŸ(ใ‚คใƒณใ‚นใƒˆใƒผใƒซใจๆง‹็ฏ‰ใฎใฟ)

    SAP Hybris uses
    Solr

    standard hybris-commerce implementation