Elasticsearch vs Splunk

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Elasticsearch

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Elasticsearch vs Splunk: What are the differences?

Introduction

Elasticsearch and Splunk are both popular platforms used for managing and analyzing large volumes of data. However, there are key differences between the two.

  1. Querying and Search Capability: Elasticsearch is a search engine that is optimized for searching, querying, and analyzing structured and unstructured data. It uses inverted indices for fast retrieval of information and supports full-text search. On the other hand, Splunk is a log management and analysis tool that excels at parsing and indexing machine-generated data, making it easier to search and analyze log files and event data.

  2. Data Collection and Indexing: Elasticsearch can index and search data in real-time as it is ingested, making it suitable for use cases that require real-time data analysis. It supports a wide range of data sources and provides flexible indexing capabilities. Splunk, on the other hand, requires data to be indexed before it can be searched and analyzed. It uses an indexing pipeline to parse, extract, and transform data into searchable events.

  3. Scalability and Distributed Architecture: Elasticsearch is designed to be distributed and horizontally scalable, allowing it to handle large volumes of data and high query loads. It can be easily scaled by adding more nodes to the cluster. Splunk, on the other hand, does not have a distributed architecture by default and relies on a single-instance deployment. It does offer distributed search capabilities but requires additional configuration and setup.

  4. Data Visualization and User Interface: Splunk provides a rich set of visualization tools and a user-friendly interface for analyzing and visualizing data. It offers pre-built dashboards, charts, and reports that make it easy to explore and understand data. Elasticsearch, on the other hand, focuses more on providing the underlying search and analytics capabilities. It offers APIs and integrations with other visualization tools like Kibana for data visualization.

  5. Pricing and Licensing: Elasticsearch is open-source and free to use, but it also offers commercial licenses and subscription plans for additional features and support. Splunk, on the other hand, is a commercial product and requires a paid license for enterprise use. Its pricing is typically based on the volume of data ingested and indexed.

  6. Community and Ecosystem: Elasticsearch has a vibrant and active open-source community. It has a wide range of community-contributed plugins and integrations, making it easier to extend and integrate with other systems. Splunk also has a strong community and ecosystem, but it is more focused on its core product offerings.

In summary, Elasticsearch is a powerful search engine optimized for querying and analyzing structured and unstructured data in real-time, while Splunk is a log management and analysis tool that excels at parsing and indexing machine-generated data for easy log file search and analysis. Elasticsearch provides better scalability and distributed architecture, while Splunk offers a more user-friendly interface and visualization capabilities.

Advice on Elasticsearch and Splunk
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 366K 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 Splunk
  • 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
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Dashboarding on any log contents
  • 2
    Custom log parsing as well as automatic parsing
  • 2
    Ability to style search results into reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Splunk language supports string, date manip, math, etc
  • 2
    Rich GUI for searching live logs
  • 1
    Query any log as key-value pairs
  • 1
    Granular scheduling and time window support

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Cons of Elasticsearch
Cons of Splunk
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
  • 1
    Splunk query language rich so lots to learn

Sign up to add or upvote consMake informed product decisions

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 Splunk?

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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What are some alternatives to Elasticsearch and Splunk?
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