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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.
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.
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.
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.
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.
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.
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.
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 Amazon ElastiCache
- Redis58
- High-performance32
- Backed by amazon26
- Memcached21
- Elastic14
Pros of Elasticsearch
- Powerful api328
- Great search engine315
- Open source231
- Restful214
- Near real-time search200
- Free98
- Search everything85
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Great docs4
- Awesome, great tool4
- Highly Available3
- Easy to scale3
- Potato2
- Document Store2
- Great customer support2
- Intuitive API2
- Nosql DB2
- Great piece of software2
- Reliable2
- Fast2
- Easy setup2
- Open1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Not stable1
- Scalability1
- Community0
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Cons of Amazon ElastiCache
Cons of Elasticsearch
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
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