Elasticsearch vs Locust

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Elasticsearch

34K
26.5K
+ 1
1.6K
Locust

171
314
+ 1
51
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Elasticsearch vs Locust: What are the differences?

  1. Data Storage and Querying: Elasticsearch is a distributed, real-time search and analytics engine designed for storing, searching, and analyzing large volumes of data quickly. It allows for complex queries, full-text search, and real-time data retrieval. On the other hand, Locust is an open-source load testing tool that allows users to define user behavior in Python code, simulate concurrent user activity, and monitor system performance under load. It focuses on load testing web applications and services to check their performance, scalability, and reliability.

  2. Primary Use Case: Elasticsearch is commonly used for log and event data analysis, full-text search, real-time monitoring, metrics analysis, and in application search scenarios. Its versatility makes it suitable for a wide range of use cases including website search, log analysis, security information and event management (SIEM), and more. Locust, on the other hand, is specifically designed for load testing web applications and services. Its primary use case is to simulate a large number of users accessing a system concurrently to identify potential bottlenecks, performance issues, and system weaknesses.

  3. Scaling Capabilities: Elasticsearch is built for scalability and can easily scale horizontally by adding more nodes to a cluster, allowing it to handle large amounts of data and traffic efficiently. It can be deployed across multiple nodes to create a distributed system that can handle high loads and big data scenarios. Locust, on the other hand, is more focused on simulating user behavior under load and does not have native scaling capabilities like Elasticsearch. It mainly runs on a single machine or distributed across multiple machines for heavier load testing scenarios.

  4. Query Language: Elasticsearch uses a powerful query language called Elasticsearch Query DSL, which allows users to construct complex queries using JSON-based syntax. Users can perform searches based on various parameters, filter criteria, aggregations, and sorting options. Locust, on the other hand, uses Python code to define user behavior and simulate user interactions with the system. Users need to define the behavior of virtual users in Python code, specifying tasks, behaviors, and scenarios to be executed during the load testing process.

  5. Monitoring and Analytics: Elasticsearch comes with built-in monitoring and analytics tools that allow users to track cluster health, performance metrics, indexing rates, and other key indicators. It provides insights into system activity, resource usage, query performance, and data distribution across nodes in a cluster. Locust, on the other hand, offers basic monitoring and reporting capabilities to track the number of requests sent, response times, and success/failure rates during load testing. Users can monitor system performance in real-time and generate reports to analyze test results.

In Summary, Elasticsearch and Locust differ in terms of data storage and querying capabilities, primary use cases, scaling capabilities, query languages, and monitoring tools provided.

Advice on Elasticsearch and Locust
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 369.5K 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 · 274.5K 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 Locust
  • 327
    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
  • 15
    Hackable
  • 11
    Supports distributed
  • 7
    Open source
  • 6
    Easy to use
  • 6
    Easy to setup
  • 4
    Fast
  • 2
    Test Anything

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Cons of Elasticsearch
Cons of Locust
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
  • 1
    Bad design

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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 Locust?

Locust is an easy-to-use, distributed, user load testing tool. Intended for load testing web sites (or other systems) and figuring out how many concurrent users a system can handle.

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May 21 2019 at 12:20AM

Elastic

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What are some alternatives to Elasticsearch and Locust?
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