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

Elasticsearch

34.6K
26.9K
+ 1
1.6K
Manticore Search

10
21
+ 1
22
Add tool

Elasticsearch vs Manticore Search: What are the differences?

Elasticsearch and Manticore Search are both powerful search engines that can be used to index and analyze large amounts of data, but they have some key differences.

  1. Data Structures and Indexing: Elasticsearch uses an inverted index, which allows for quick searching and retrieval of data. Manticore Search, on the other hand, uses a hybrid data structure known as a inverted index with a disk-based hash table. This provides a more efficient indexing process and allows for faster data retrieval.

  2. Full-text Search Features: Elasticsearch offers a wide range of full-text search features, including tokenization, stemming, and relevance scoring. Manticore Search, however, takes full-text search to another level with support for advanced features like faceted search, infix search, and indexing of custom data types.

  3. Scalability: Both Elasticsearch and Manticore Search are designed to be scalable, but Elasticsearch has a more mature and robust clustering mechanism. It provides built-in features for horizontal scaling and automatic load balancing, making it better suited for larger deployments and high-volume search applications.

  4. Query Language: Elasticsearch uses the Query DSL (Domain Specific Language) for querying, which is a powerful and flexible way to construct complex queries. Manticore Search, on the other hand, uses a simplified version of the SQL language, making it easier for developers familiar with SQL to get started.

  5. Faceting and Aggregation: Elasticsearch has extensive support for faceting and aggregation, allowing users to extract valuable insights from their data. Manticore Search also supports faceting and aggregation, but the functionality is not as comprehensive as Elasticsearch's.

  6. Logging and Monitoring: Elasticsearch provides a comprehensive logging and monitoring system, which includes built-in tools like the Elasticsearch Monitoring API and the Elastic Stack. Manticore Search, while it does have some logging and monitoring capabilities, does not have the same level of built-in tools and integration options as Elasticsearch.

In Summary, Elasticsearch and Manticore Search differ in terms of data structures and indexing, full-text search features, scalability, query language, faceting and aggregation capabilities, and logging and monitoring options.

Advice on Elasticsearch and Manticore Search
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 391.4K 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!

See more
Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 293.8K 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.

See more
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.

See more
Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of Elasticsearch
Pros of Manticore Search
  • 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
  • 2
    Easy to scale
  • 2
    Free
  • 2
    Distributed
  • 2
    Easy to get started
  • 2
    Real-time inserts
  • 2
    Lightweight
  • 2
    Open source
  • 2
    Low RAM consumption
  • 2
    JSON over HTTP
  • 2
    MySQL/PostgreSQL/ODBC/xml/csv sync out of the box
  • 2
    SQL syntax

Sign up to add or upvote prosMake informed product decisions

Cons of Elasticsearch
Cons of Manticore Search
  • 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

    - 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 Manticore Search?

    It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

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

    Jobs that mention Elasticsearch and Manticore Search as a desired skillset
    What companies use Elasticsearch?
    What companies use Manticore Search?
    Manage your open source components, licenses, and vulnerabilities
    Learn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Elasticsearch?
    What tools integrate with Manticore Search?
      No integrations found

      Sign up to get full access to all the tool integrationsMake informed product decisions

      Blog Posts

      May 21 2019 at 12:20AM

      Elastic

      ElasticsearchKibanaLogstash+4
      12
      5302
      GitHubPythonReact+42
      49
      40937
      GitHubPythonNode.js+47
      55
      72816
      What are some alternatives to Elasticsearch and Manticore Search?
      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