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  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Amazon DynamoDB vs CouchDB

Amazon DynamoDB vs CouchDB

OverviewDecisionsComparisonAlternatives

Overview

Amazon DynamoDB
Amazon DynamoDB
Stacks4.0K
Followers3.2K
Votes195
CouchDB
CouchDB
Stacks529
Followers584
Votes139
GitHub Stars6.7K
Forks1.1K

Amazon DynamoDB vs CouchDB: What are the differences?

Introduction

Amazon DynamoDB and CouchDB are both NoSQL databases that offer different features and capabilities. Understanding the key differences between these two databases can help you make an informed decision when choosing the right database for your needs.

  1. Data Model:

    • DynamoDB is a key-value store that organizes data in tables with a primary key. It allows for flexible schemas with each item having a unique identifier and various attributes.
    • CouchDB, on the other hand, is a document-based database that stores data in JSON-like documents. It uses a schema-less approach, allowing for more flexible and dynamic data structures.
  2. Ease of Scalability:

    • DynamoDB offers automatic scaling by default, allowing you to increase or decrease the capacity of your table based on demand. It provides horizontal scaling and can handle millions of requests per second.
    • CouchDB also supports scaling, but it requires manual setup and configuration. It follows a distributed architecture, where multiple instances of CouchDB can be set up and synced. However, scaling can be more complex in comparison to DynamoDB.
  3. ACID Compliance:

    • DynamoDB guarantees consistency, durability, and isolation in terms of ACID (Atomicity, Consistency, Isolation, Durability) properties for individual items or transactions.
    • CouchDB follows a more relaxed approach called eventual consistency. It allows for faster read and write operations but may result in data inconsistencies in certain scenarios.
  4. Querying and Indexing:

    • DynamoDB offers a simple key-value access pattern and allows querying based on the primary key or secondary indexes. It also provides a rich set of querying capabilities with features like global and local secondary indexes, sort keys, filtering, and conditional expressions.
    • CouchDB provides a powerful query mechanism called MapReduce. It allows complex queries using JavaScript functions, which can retrieve, filter, and transform documents based on specified criteria.
  5. Conflict Resolution:

    • DynamoDB doesn't handle conflicts implicitly and relies on the application's logic to resolve conflicts that may arise during concurrent updates to the same item.
    • CouchDB has built-in conflict resolution mechanisms and handles conflicts automatically. It uses a revision-based approach, where conflicts are tracked and can be resolved based on conflict resolution algorithms.
  6. Replication:

    • DynamoDB offers automatic data replication across multiple Availability Zones within a region to provide high availability and fault tolerance. It also supports global tables for cross-region replication and data locality.
    • CouchDB is designed to support offline replication and peer-to-peer synchronization. It allows for bidirectional replication between CouchDB instances, enabling data synchronization across multiple devices or sites.

In summary, the key differences between Amazon DynamoDB and CouchDB lie in their data models, scalability approaches, ACID compliance, querying capabilities, conflict resolution mechanisms, and replication features. These differences should be carefully considered when choosing the appropriate database for your specific requirements.

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Advice on Amazon DynamoDB, CouchDB

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k
Comments
Doru
Doru

Solution Architect

Jun 9, 2019

ReviewonAmazon DynamoDBAmazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

1.37k views1.37k
Comments
akash
akash

Aug 27, 2020

Needs adviceonCloud FirestoreCloud FirestoreFirebase Realtime DatabaseFirebase Realtime DatabaseAmazon DynamoDBAmazon DynamoDB

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

199k views199k
Comments

Detailed Comparison

Amazon DynamoDB
Amazon DynamoDB
CouchDB
CouchDB

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.

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture
Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync
Statistics
GitHub Stars
-
GitHub Stars
6.7K
GitHub Forks
-
GitHub Forks
1.1K
Stacks
4.0K
Stacks
529
Followers
3.2K
Followers
584
Votes
195
Votes
139
Pros & Cons
Pros
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
Cons
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
Pros
  • 43
    JSON
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
Integrations
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
PostgreSQL
PostgreSQL
MySQL
MySQL
SQLite
SQLite
Azure Database for MySQL
Azure Database for MySQL
No integrations available

What are some alternatives to Amazon DynamoDB, CouchDB?

MongoDB

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.

MySQL

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.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

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.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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