Amazon ElastiCache聽vs聽Elasticsearch

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Amazon ElastiCache

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Amazon ElastiCache vs Elasticsearch: What are the differences?

What is Amazon ElastiCache? Deploy, operate, and scale an in-memory cache in the cloud. ElastiCache improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports Memcached and Redis.

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).

Amazon ElastiCache belongs to "Managed Memcache" category of the tech stack, while Elasticsearch can be primarily classified under "Search as a Service".

Some of the features offered by Amazon ElastiCache are:

  • Support for two engines: Memcached and Redis
  • Ease of management via the AWS Management Console. With a few clicks you can configure and launch instances for the engine you wish to use.
  • Compatibility with the specific engine protocol. This means most of the client libraries will work with the respective engines they were built for - no additional changes or tweaking required.

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

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

"Redis" is the top reason why over 53 developers like Amazon ElastiCache, while over 310 developers mention "Powerful api" as the leading cause for choosing Elasticsearch.

Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.

According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to Amazon ElastiCache, which is listed in 349 company stacks and 79 developer stacks.

Advice on Amazon ElastiCache and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 5 upvotes 路 158.6K views
Needs advice
on
Firebase
Elasticsearch
and
Algolia

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 | 7 upvotes 路 120.4K views
Recommends
Algolia

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
Cloud 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 Amazon ElastiCache
Pros of Elasticsearch
  • 56
    Redis
  • 31
    High-performance
  • 25
    Backed by amazon
  • 20
    Memcached
  • 11
    Elastic
  • 321
    Powerful api
  • 311
    Great search engine
  • 231
    Open source
  • 213
    Restful
  • 200
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Great docs
  • 3
    Awesome, great tool
  • 3
    Easy to scale
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Fast
  • 2
    Nosql DB
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Document Store
  • 2
    Great customer support
  • 1
    Reliable
  • 1
    Not stable
  • 1
    Potato
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Scalability
  • 0
    Easy to get hot data
  • 0
    Community

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon ElastiCache
Cons of Elasticsearch
    Be the first to leave a con
    • 6
      Resource hungry
    • 6
      Diffecult to get started
    • 5
      Expensive
    • 3
      Hard to keep stable at large scale

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Amazon ElastiCache?

    ElastiCache improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports Memcached and Redis.

    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).

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

    What companies use Amazon ElastiCache?
    What companies use Elasticsearch?
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    What tools integrate with Amazon ElastiCache?
    What tools integrate with Elasticsearch?

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

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    What are some alternatives to Amazon ElastiCache and Elasticsearch?
    Redis
    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.
    Memcached
    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
    Azure Redis Cache
    It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon SQS
    Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
    See all alternatives