Elasticsearch聽vs聽Locust

Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Elasticsearch
Elasticsearch

9K
6.1K
+ 1
1.6K
Locust
Locust

55
59
+ 1
4
Add tool

Elasticsearch vs Locust: What are the differences?

What is Elasticsearch? Open Source, Distributed, RESTful Search Engine. 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? Define user behaviour with Python code, and swarm your system with millions of simultaneous users. 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.

Elasticsearch can be classified as a tool in the "Search as a Service" category, while Locust is grouped under "Load and Performance Testing".

Some of the features offered by Elasticsearch are:

  • Distributed and Highly Available Search Engine.
  • Multi Tenant with Multi Types.
  • Various set of APIs including RESTful

On the other hand, Locust provides the following key features:

  • Define user behaviour in code
  • Distributed & scalable
  • Proven & battle tested

Elasticsearch and Locust are both open source tools. Elasticsearch with 41.9K GitHub stars and 14K forks on GitHub appears to be more popular than Locust with 10.3K GitHub stars and 1.48K GitHub forks.

According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 1976 company stacks & 936 developers stacks; compared to Locust, which is listed in 10 company stacks and 5 developer stacks.

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.
Get Advice Icon

Need advice about which tool to choose?Ask the StackShare community!

Why do developers choose Elasticsearch?
Why do developers choose Locust?

Sign up to add, upvote and see more prosMake informed product decisions

What companies use Elasticsearch?
What companies use Locust?

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Elasticsearch?
What tools integrate with Locust?

Sign up to get full access to all the tool integrationsMake informed product decisions

What are some alternatives to Elasticsearch and Locust?
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.
Splunk
Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
See all alternatives
Decisions about Elasticsearch and Locust
Tim Specht
Tim Specht
鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 69K views
atDubsmashDubsmash
Memcached
Memcached
Algolia
Algolia
Elasticsearch
Elasticsearch
#SearchAsAService

Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

#SearchAsAService

See more
Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 16 upvotes 366.4K views
atSmartZipSmartZip
Amazon DynamoDB
Amazon DynamoDB
Ruby
Ruby
Node.js
Node.js
AWS Lambda
AWS Lambda
New Relic
New Relic
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Elasticsearch
Elasticsearch
Superset
Superset
Amazon Quicksight
Amazon Quicksight
Amazon Redshift
Amazon Redshift
Zapier
Zapier
Segment
Segment
Amazon CloudFront
Amazon CloudFront
Memcached
Memcached
Amazon ElastiCache
Amazon ElastiCache
Amazon RDS for Aurora
Amazon RDS for Aurora
MySQL
MySQL
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Docker
Docker
Capistrano
Capistrano
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Rails API
Rails API
Rails
Rails
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

See more
Interest over time
Reviews of Elasticsearch and Locust
Avatar of ashokkn
Staff Software Engineer at Intuit
Review ofLocustLocust

This is the best open source tool i have ever come across which does load testing at its best.

Python config code is really simple to write and good part is its extendable and there are many hooks available ... what else you need ..

Lastly, the web UI to monitor your swarming activity is too good and very helpful for identify bottlenecks and spikes real-time.

How developers use Elasticsearch and Locust
Avatar of imgur
imgur uses ElasticsearchElasticsearch

Elasticsearch is the engine that powers search on the site. From a high level perspective, it鈥檚 a Lucene wrapper that exposes Lucene鈥檚 features via a RESTful API. It handles the distribution of data and simplifies scaling, among other things.

Given that we are on AWS, we use an AWS cloud plugin for Elasticsearch that makes it easy to work in the cloud. It allows us to add nodes without much hassle. It will take care of figuring out if a new node has joined the cluster, and, if so, Elasticsearch will proceed to move data to that new node. It works the same way when a node goes down. It will remove that node based on the AWS cluster configuration.

Avatar of Instacart
Instacart uses ElasticsearchElasticsearch

The very first version of the search was just a Postgres database query. It wasn鈥檛 terribly efficient, and then at some point, we moved over to ElasticSearch, and then since then, Andrew just did a lot of work with it, so ElasticSearch is amazing, but out of the box, it doesn鈥檛 come configured with all the nice things that are there, but you spend a lot of time figuring out how to put it all together to add stemming, auto suggestions, all kinds of different things, like even spelling adjustments and tomato/tomatoes, that would return different results, so Andrew did a ton of work to make it really, really nice and build a very simple Ruby gem called SearchKick.

Avatar of AngeloR
AngeloR uses ElasticsearchElasticsearch

We use ElasticSearch for

  • Session Logs
  • Analytics
  • Leaderboards

We originally self managed the ElasticSearch clusters, but due to our small ops team size we opt to move things to managed AWS services where possible.

The managed servers, however, do not allow us to manage our own backups and a restore actually requires us to open a support ticket with them. We ended up setting up our own nightly backup since we do per day indexes for the logs/analytics.

Avatar of Brandon Adams
Brandon Adams uses ElasticsearchElasticsearch

Elasticsearch has good tooling and supports a large api that makes it ideal for denormalizing data. It has a simple to use aggregations api that tends to encompass most of what I need a BI tool to do, especially in the early going (when paired with Kibana). It's also handy when you just want to search some text.

Avatar of Ana Phi Sancho
Ana Phi Sancho uses ElasticsearchElasticsearch

Self taught : acquired knowledge or skill on one's own initiative. Open Source Search & Analytics. -time search and analytics engine. Search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.

How much does Elasticsearch cost?
How much does Locust cost?
Pricing unavailable