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Amazon ElastiCache vs Elasticsearch: What are the differences?

Amazon ElastiCache and Elasticsearch are two popular services provided by AWS. Both services have their own unique features and use cases. It is important to understand the key differences between them in order to choose the appropriate service for specific requirements.

  1. Data Storage Model: Amazon ElastiCache is primarily used as an in-memory data store for structured and unstructured data. It is well-suited for caching and session management. On the other hand, Elasticsearch is a distributed search and analytics engine that allows for full-text search and real-time analytics. It is designed to handle and analyze large volumes of data.

  2. Querying Capabilities: ElastiCache supports key-value-based querying using operations like GET and SET. It is not designed for complex querying. Elasticsearch, on the other hand, offers powerful search capabilities with support for full-text search, fuzzy search, and complex querying using a query DSL (Domain-Specific Language) based on JSON. It allows for advanced text analysis and relevance scoring.

  3. Scalability and High Availability: ElastiCache allows for horizontal scalability by adding or removing cache nodes. It offers high availability through automatic replication and failover. Elasticsearch, on the other hand, is designed to handle large-scale data processing and analytics. It supports horizontal scalability by adding or removing data nodes. It also provides automatic data replication and high availability through sharding and replica management.

  4. Data Persistence: ElastiCache is an in-memory caching service and does not persist data to disk by default. It is primarily used for temporary data storage. Elasticsearch, on the other hand, provides the option to persist data to disk, allowing for long-term storage and analysis. It supports various data storage options, including SSDs and cloud-based storage.

  5. Support for Structured and Unstructured Data: ElastiCache is used for storing and retrieving structured and unstructured data in key-value format. It does not provide advanced indexing or analysis capabilities for unstructured data. Elasticsearch, on the other hand, excels in handling unstructured data by providing advanced indexing and analysis features like analyzers, tokenizers, and aggregations. It supports JSON-based document storage and indexing.

  6. Integration with Other AWS Services: ElastiCache integrates well with other AWS services like Amazon RDS and Amazon EC2. It can be used to offload read operations from a relational database or to store session data for web applications. Elasticsearch also integrates with other AWS services and provides plugins for data ingestion from various sources like S3, Kinesis, and CloudWatch. It can be used for log analytics, real-time monitoring, and business intelligence applications.

In summary, Amazon ElastiCache is primarily used as an in-memory caching service for structured and unstructured data, while Elasticsearch is a distributed search and analytics engine that excels in handling large volumes of unstructured data with advanced indexing and analysis capabilities. ElastiCache focuses on high-performance caching and session management, while Elasticsearch offers powerful search capabilities and real-time analytics.

Advice on Amazon ElastiCache and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza | 6 upvotes 路 376.9K views
Needs advice

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 路 281K views

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
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 Amazon ElastiCache
Pros of Elasticsearch
  • 58
  • 32
  • 26
    Backed by amazon
  • 21
  • 14
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
  • 200
    Near real-time search
  • 98
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
  • 26
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
  • 2
  • 2
    Easy setup
  • 1
  • 1
    Easy to get hot data
  • 1
  • 1
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
  • 1
    Not stable
  • 1
  • 0

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Cons of Amazon ElastiCache
Cons of Elasticsearch
    Be the first to leave a con
    • 7
      Resource hungry
    • 6
      Diffecult to get started
    • 5
    • 4
      Hard to keep stable at large scale

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    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).

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    What tools integrate with Amazon ElastiCache?
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    Blog Posts

    May 21 2019 at 12:20AM


    What are some alternatives to Amazon ElastiCache and Elasticsearch?
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    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