Elasticsearch vs Solr

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Elasticsearch

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Solr

775
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Elasticsearch vs Solr: What are the differences?

Introduction

Elasticsearch and Solr are both widely used open-source search engines used for full-text search, analytics, and distributed search and analytics. While they have similar functionalities, there are key differences between the two. This article will highlight six key differences between Elasticsearch and Solr.

  1. Query Types: Elasticsearch supports a broader range of query types than Solr. Elasticsearch has a rich query language that allows for more complex queries, including nested queries, fuzzy queries, and wildcard queries, among others. Solr, on the other hand, has a more limited set of query types and is generally more focused on keyword-based search.

  2. Scalability and Distributed Search: Elasticsearch is built on a distributed architecture and is designed for scalability and distributed search out of the box. It uses automatic sharding and replication, making it easier to scale horizontally. Solr has added distributed capabilities over the years but requires manual configuration for scaling out and distributed search.

  3. Document Oriented: Elasticsearch is document-oriented, meaning it stores and indexes whole documents rather than individual fields. This makes it more suitable for scenarios where the entire document needs to be searched and retrieved. Solr, on the other hand, is traditionally field-oriented, meaning it focuses more on individual fields within a document.

  4. Data Replication and High Availability: Elasticsearch comes with built-in support for data replication and high availability. It automatically replicates data across nodes, ensuring that there are copies of the data available in case of node failures. Solr, while it has some support for replication and high availability, requires manual setup and configuration.

  5. Real-time Analytics: Elasticsearch has better support for real-time analytics compared to Solr. It offers near real-time search, meaning that documents are indexed and made searchable almost immediately. Solr, on the other hand, has a delay in indexing and can take some time before new documents are searchable.

  6. Ecosystem and Community: Elasticsearch has a larger and more active community compared to Solr. This means that there are more resources, plugins, and community support available for Elasticsearch. Additionally, Elasticsearch has a broader ecosystem of tools and integrations, making it easier to integrate with other systems.

In summary, Elasticsearch offers a more extensive query language, better scalability and distributed search capabilities, supports document-oriented indexing, provides built-in data replication and high availability mechanisms, offers near real-time search, and has a larger ecosystem and community compared to Solr.

Advice on Elasticsearch and Solr
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 377.7K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

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 · | 8 upvotes · 281.7K views
Recommends
on
AlgoliaAlgolia

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
on
Cloud FirestoreCloud 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
  • 328
    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
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Restful
  • 5
    Apache Software Foundation
  • 4
    Great Search engine
  • 2
    Security built-in
  • 1
    Easy Operating

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Cons of Elasticsearch
Cons of Solr
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    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.

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    What companies use Elasticsearch?
    What companies use 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

    ElasticsearchKibanaLogstash+4
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    GitHubPythonReact+42
    49
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    GitHubPythonNode.js+47
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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