Elasticsearch vs Vespa

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Elasticsearch

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Elasticsearch vs Vespa: What are the differences?

Introduction

Elasticsearch and Vespa are both popular open-source search engines used for data storage and retrieval. While they have some similarities, there are key differences that set them apart.

  1. Scalability and Performance: Elasticsearch is designed to be horizontally scalable, meaning it can easily handle a large volume of data and provides high-performance search capabilities. Vespa, on the other hand, is designed for extreme scalability, enabling it to handle billions of documents and petabytes of data with low latency.

  2. Data Model and Schema: Elasticsearch uses a flexible schema-less data model, where documents can have varying fields and structures. It allows for dynamic changes to the schema, making it easy to adapt to evolving needs. Vespa, however, uses a structured data model with a predefined schema. This ensures data consistency and allows for more efficient indexing and querying.

  3. Query Language and Features: Elasticsearch uses a JSON-based query language with a wide range of search features, including full-text search, filtering, sorting, and aggregations. Vespa, on the other hand, uses a specialized query language called Vespa Query Language (VQL) that provides advanced search capabilities, such as ranking and grouping, along with support for machine learning-based ranking models.

  4. Multi-tenancy and Security: Elasticsearch provides multi-tenancy support, allowing multiple users or applications to share the same cluster while maintaining data separation and access control. It also offers features for securing data and communication, including authentication and role-based access control. Vespa also provides multi-tenancy support and implements strict security measures, including encryption at rest and in transit, fine-grained access control, and auditing.

  5. Distribution and Fault Tolerance: Elasticsearch uses a distributed architecture with sharding and replica mechanisms to ensure high availability and fault tolerance. It automatically distributes data across multiple nodes and maintains replicas for fault tolerance. Vespa also uses a distributed architecture but employs a combination of content distribution and partitioning strategies to optimize data distribution and provide fault tolerance.

  6. Use Cases and Ecosystem: Elasticsearch is widely used for full-text search, log analysis, and data analytics in various domains, including e-commerce, content management, and cybersecurity. It has a large and active community, extensive documentation, and a rich ecosystem of plugins and integrations. Vespa, on the other hand, is primarily used for search and recommendation systems in large-scale applications, such as news portals, e-commerce platforms, and social media. It has a smaller but growing community and offers features specifically designed for high-performance content serving.

In summary, Elasticsearch and Vespa differ in terms of scalability and performance, data model and schema, query language and features, multi-tenancy and security, distribution and fault tolerance, as well as their use cases and ecosystem. These differences make them suitable for different scenarios and requirements in the field of search and data retrieval.

Advice on Elasticsearch and Vespa
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 365.9K 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 · 271.2K 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 Vespa
  • 326
    Powerful api
  • 315
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 97
    Free
  • 84
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Highly Available
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 3
    Easy to scale
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Potato
  • 2
    Nosql DB
  • 2
    Document Store
  • 1
    Not stable
  • 1
    Scalability
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Easy to get hot data
  • 0
    Community
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    Cons of Elasticsearch
    Cons of Vespa
    • 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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      - 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 Vespa?

      Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

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      Blog Posts

      May 21 2019 at 12:20AM

      Elastic

      ElasticsearchKibanaLogstash+4
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      GitHubPythonReact+42
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      GitHubPythonNode.js+47
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      What are some alternatives to Elasticsearch and Vespa?
      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.
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