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Amazon DynamoDB vs Elasticsearch: What are the differences?

Introduction:

Amazon DynamoDB and Elasticsearch are both popular database technologies used in the industry. Despite being used for similar purposes, they have key differences in terms of their architecture and functionalities.

  1. Data Model: Amazon DynamoDB is a NoSQL database with a flexible schema where each item can have different attributes, while Elasticsearch is schema-free and document-oriented, allowing for complex nested data structures.

  2. Query Language: DynamoDB uses a simple query language with limited complex querying capabilities, whereas Elasticsearch provides a powerful search DSL (Domain Specific Language) allowing for advanced queries like full-text search, aggregations, and filtering.

  3. Scalability: Amazon DynamoDB is fully managed and scales automatically by adjusting read and write capacity units, while Elasticsearch requires manual scaling by adding more nodes to the cluster to handle increased workloads.

  4. Use Cases: DynamoDB is optimized for fast and predictable performance on small to large-scale applications with high availability requirements, while Elasticsearch is designed for full-text search and analytics use cases, making it a go-to solution for log analysis and real-time data monitoring.

  5. Data Replication: In DynamoDB, data replication is handled by the service itself across multiple Availability Zones for high durability, while Elasticsearch provides the ability to set up data replication and sharding configurations for improved performance and fault tolerance.

  6. Indexing Capabilities: DynamoDB does not provide indexing capabilities beyond primary and secondary indexes, whereas Elasticsearch has a sophisticated indexing mechanism that includes inverted indexes, allowing for fast and efficient search operations on large data sets.

In Summary, Amazon DynamoDB and Elasticsearch differ in their data model flexibility, query capabilities, scalability options, use case suitability, data replication mechanisms, and indexing capabilities.

Advice on Amazon DynamoDB and Elasticsearch
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 394.9K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

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 · 296.6K views
Recommends
on
AlgoliaAlgolia

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
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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 DynamoDB
Pros of Elasticsearch
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 329
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Awesome, great tool
  • 4
    Great docs
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Nosql DB
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Reliable
  • 2
    Potato
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great piece of software
  • 1
    Open
  • 1
    Scalability
  • 1
    Not stable
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 0
    Community

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Cons of Amazon DynamoDB
Cons of Elasticsearch
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale

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What companies use Amazon DynamoDB?
What companies use Elasticsearch?
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What are some alternatives to Amazon DynamoDB and Elasticsearch?
Google Cloud Datastore
Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
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.
Amazon SimpleDB
Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
Amazon S3
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
See all alternatives