Druid vs Elasticsearch

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Druid vs Elasticsearch: What are the differences?


Druid and Elasticsearch are both open-source distributed data stores used for real-time analytics and search purposes. While they share some similarities, they also have key differences that set them apart in terms of functionality and use cases. Below are the key differences between Druid and Elasticsearch.

  1. Data Model and Querying: Druid is designed specifically for time-series and event-driven data, making it ideal for analyzing real-time streaming data. It excels at performing fast aggregations and time-based queries, offering sub-second query response times. On the other hand, Elasticsearch is a document-oriented search engine that is optimized for full-text search and complex queries on structured and unstructured data. It provides powerful search functionality, including features like fuzzy matching and relevance scoring.

  2. Scalability: Both Druid and Elasticsearch offer horizontal scalability, allowing them to handle large amounts of data. However, Druid is designed to scale for high ingestion rates and supports real-time data streaming, making it well-suited for deployments that require fast, continuous data updates. Elasticsearch, on the other hand, can handle massive amounts of indexed data and is commonly used for log analysis, monitoring, and search use cases.

  3. Storage and Indexing: Druid uses a columnar storage format that optimizes data processing and query performance. It compresses and indexes data in memory for faster access, enabling efficient aggregations and filtering. Elasticsearch, on the other hand, leverages a distributed inverted index for indexing and searching documents. It is highly flexible in terms of data schema and allows for real-time indexing and search updates.

  4. Aggregation Capabilities: Druid is known for its powerful and efficient aggregations, making it a preferred choice for analyzing high-dimensional and time-based data. It can perform complex roll-up, slicing-and-dicing, and grouping operations on large datasets, providing quick insights into time-series data. Elasticsearch also supports aggregations but may face performance limitations when dealing with large datasets or complex aggregations.

  5. Real-Time Analytics vs. Real-Time Search: While both Druid and Elasticsearch provide real-time capabilities, they focus on different aspects of real-time data processing. Druid is optimized for real-time analytics and exploratory data analysis, offering fast query response times and support for complex analytical queries. Elasticsearch, on the other hand, excels in real-time search scenarios, allowing users to perform fast and accurate full-text searches on large, constantly changing datasets.

  6. Use Cases: Due to their differences in data model and capabilities, Druid and Elasticsearch cater to different use cases. Druid is commonly used for operational analytics, time-series analysis, and real-time monitoring, making it well-suited for applications in the IoT, ad tech, and log analytics domains. Elasticsearch, on the other hand, finds applications in search and recommendation engines, log analysis, e-commerce search, and content management systems.

In Summary, Druid and Elasticsearch differ in their data model and querying capabilities, scalability, storage and indexing approach, aggregation capabilities, focus on real-time analytics versus real-time search, and their use cases.

Advice on Druid and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 379.9K views
Needs advice

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 · 283.6K views

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
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 Druid
Pros of Elasticsearch
  • 15
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 5
  • 3
  • 2
    Combining stream and historical analytics
  • 1
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
  • 200
    Near real-time search
  • 98
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
  • 26
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
  • 2
  • 2
    Easy setup
  • 1
  • 1
    Easy to get hot data
  • 1
  • 1
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
  • 1
    Not stable
  • 1
  • 0

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Cons of Druid
Cons of Elasticsearch
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
  • 4
    Hard to keep stable at large scale

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What is Druid?

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

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).

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What are some alternatives to Druid and Elasticsearch?
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
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.
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
See all alternatives