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Elasticsearch vs Redis: What are the differences?
Developers describe Elasticsearch as "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). On the other hand, Redis is detailed as "An in-memory database that persists on disk". Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
Elasticsearch can be classified as a tool in the "Search as a Service" category, while Redis is grouped under "In-Memory Databases".
"Powerful api", "Great search engine" and "Open source" are the key factors why developers consider Elasticsearch; whereas "Performance", "Super fast" and "Ease of use " are the primary reasons why Redis is favored.
Elasticsearch and Redis are both open source tools. It seems that Elasticsearch with 42.4K GitHub stars and 14.2K forks on GitHub has more adoption than Redis with 37.4K GitHub stars and 14.4K GitHub forks.
Airbnb, Uber Technologies, and Instagram are some of the popular companies that use Redis, whereas Elasticsearch is used by Uber Technologies, Instacart, and Slack. Redis has a broader approval, being mentioned in 3265 company stacks & 1788 developers stacks; compared to Elasticsearch, which is listed in 2003 company stacks and 979 developer stacks.
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!
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.
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.
Pros of Elasticsearch
- Powerful api326
- Great search engine315
- Open source230
- Restful214
- Near real-time search199
- Free97
- Search everything84
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Highly Available3
- Awesome, great tool3
- Great docs3
- Easy to scale3
- Fast2
- Easy setup2
- Great customer support2
- Intuitive API2
- Great piece of software2
- Reliable2
- Potato2
- Nosql DB2
- Document Store2
- Not stable1
- Scalability1
- Open1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Easy to get hot data1
- Community0
Pros of Redis
- Performance884
- Super fast541
- Ease of use512
- In-memory cache443
- Advanced key-value cache323
- Open source193
- Easy to deploy182
- Stable164
- Free155
- Fast121
- High-Performance42
- High Availability40
- Data Structures34
- Very Scalable32
- Replication24
- Pub/Sub22
- Great community22
- "NoSQL" key-value data store19
- Hashes15
- Sets13
- Sorted Sets11
- Lists10
- BSD licensed9
- NoSQL9
- Integrates super easy with Sidekiq for Rails background8
- Async replication8
- Bitmaps8
- Open Source7
- Keys with a limited time-to-live7
- Lua scripting6
- Strings6
- Awesomeness for Free5
- Hyperloglogs5
- Written in ANSI C4
- LRU eviction of keys4
- Networked4
- Outstanding performance4
- Runs server side LUA4
- Transactions4
- Feature Rich4
- Performance & ease of use3
- Data structure server3
- Object [key/value] size each 500 MB2
- Simple2
- Scalable2
- Temporarily kept on disk2
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Existing Laravel Integration2
- Channels concept2
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Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
Cons of Redis
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1