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Elasticsearch vs Hadoop: What are the differences?

Introduction

In this article, we will explore the key differences between Elasticsearch and Hadoop, two popular technologies used for big data processing and analytics.

  1. Scalability and Flexibility: Elasticsearch is a distributed search and analytics engine, designed for horizontal scalability and real-time querying across large amounts of data. It provides near-instantaneous search results, making it suitable for applications that require low latency. On the other hand, Hadoop is a batch processing system that is optimized for handling large volumes of data but may not provide real-time results. It is highly scalable and can handle massive data sets efficiently, making it suitable for batch processing jobs.

  2. Data Storage and Processing: Elasticsearch is built on top of Apache Lucene and uses a distributed document-oriented storage system. It stores and indexes data in a JSON-like document format, allowing for flexible schema design and easy querying. It provides powerful search capabilities, including full-text search and complex aggregations. Hadoop, on the other hand, uses a distributed file system called Hadoop Distributed File System (HDFS) to store data, and processes it using the MapReduce programming model. It is well-suited for batch processing tasks that require reading and processing large files.

  3. Real-time Analytics: Elasticsearch excels at real-time analytics, making it suitable for applications that require instant insights into data. It supports various types of queries, including aggregations, filters, and geo-spatial queries. With its distributed architecture and near real-time indexing capabilities, it allows users to perform complex queries and aggregations in real-time. Hadoop, on the other hand, is not designed for real-time analytics. It processes data in batches and may require additional tools like Apache Spark for real-time processing.

  4. Data Processing Paradigm: Elasticsearch follows a distributed search and retrieval model, where data is indexed and stored for quick retrieval. It supports real-time updates and provides efficient search capabilities. Hadoop, on the other hand, follows a batch processing model, where data is processed in parallel by dividing it into smaller tasks and executing them across a cluster of machines. Hadoop can handle large volumes of data efficiently but may not provide real-time results.

  5. Ease of Use: Elasticsearch provides a RESTful API for interacting with the data, making it easy to integrate with existing applications. It also has a simple query syntax and provides a rich set of features for searching and analyzing data. Hadoop, on the other hand, has a steeper learning curve and requires developers to write MapReduce jobs in Java or other programming languages. It provides more low-level control over the data processing pipeline but may require additional tools like Apache Hive or Apache Pig for higher-level abstractions.

  6. Use Cases: Elasticsearch is commonly used for log analysis, real-time monitoring, and search applications. It is widely adopted in industries like e-commerce, social media, and cybersecurity, where real-time data insights are crucial. Hadoop, on the other hand, is used for large-scale data processing, such as data warehousing, ETL (extract, transform, load) jobs, and batch analytics. It is used in industries like finance, healthcare, and telecommunications, where handling big data sets efficiently is essential.

In summary, Elasticsearch is a real-time distributed search and analytics engine, designed for quick search and retrieval of data, while Hadoop is a batch processing system, optimized for handling large volumes of data efficiently. Elasticsearch excels at real-time analytics and provides high scalability and flexibility, while Hadoop is well-suited for batch processing tasks and has a steeper learning curve.

Advice on Elasticsearch and Hadoop
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 388.5K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
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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 · 291.3K views
Recommends
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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
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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 Hadoop
  • 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
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax

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Cons of Elasticsearch
Cons of Hadoop
  • 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 Hadoop?

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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    What are some alternatives to Elasticsearch and Hadoop?
    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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