Elasticsearch vs Lucene

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Elasticsearch

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Elasticsearch vs Lucene: What are the differences?

Introduction

Elasticsearch and Lucene are both open-source search engines widely used in information retrieval applications. While they share similarities, they also have key differences that set them apart.

  1. Data Model: Elasticsearch is a document-oriented search engine, while Lucene is a low-level library that provides access to inverted index structures. In Elasticsearch, data is stored as JSON documents, allowing for flexible and schema-less indexing. On the other hand, Lucene operates at a lower level, providing APIs to index and search individual fields within a document.

  2. Scalability and Distribution: Elasticsearch is designed to be highly scalable and distributed from the ground up. It allows for horizontal scaling by dividing the data across multiple nodes in a cluster, enabling efficient retrieval and processing even as the amount of data grows. Lucene, on the other hand, is a Java library that focuses on providing powerful indexing and search capabilities within a single machine.

  3. Query Language: Elasticsearch offers a RESTful API with its own query language called Query DSL. This language allows users to perform complex searches, aggregations, and statistical calculations on their data. In contrast, Lucene provides a programmatic API to perform searches, which requires writing code to construct queries and process search results.

  4. Full-Text Search vs. Indexing: Elasticsearch provides full-text search capabilities out-of-the-box, allowing users to perform efficient search operations on large volumes of text. Lucene primarily focuses on indexing and retrieval tasks and can be used as a building block for implementing search functionality. While Lucene can be utilized for full-text search, additional code and configurations are required.

  5. Real-Time Search: Elasticsearch offers real-time search capabilities, meaning that documents are indexed and made available for search almost immediately after they are added or modified. Lucene, being a lower-level library, does not provide this real-time functionality by default and requires additional effort to achieve similar capabilities.

  6. Community and Ecosystem: Elasticsearch has a large and active community, providing a wide range of plugins and integrations with other tools and frameworks. It has gained popularity as a versatile and scalable search and analytics platform. Lucene, being the underlying library for Elasticsearch, also has an active community but is more focused on providing low-level indexing and search capabilities.

In summary, Elasticsearch provides a distributed, scalable, and document-oriented search engine with its own query language, while Lucene is a powerful Java library that offers low-level indexing and search capabilities within a single machine.

Advice on Elasticsearch and Lucene
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 377.8K 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 · 281.8K 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 Lucene
  • 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
  • 1
    Fast
  • 1
    Small

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Cons of Elasticsearch
Cons of Lucene
  • 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

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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).

    What is 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.

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    What tools integrate with Elasticsearch?
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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

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    GitHubPythonReact+42
    49
    40785
    GitHubPythonNode.js+47
    55
    72429
    What are some alternatives to Elasticsearch and Lucene?
    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.
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    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
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