CouchDB vs Hadoop

Overview

CouchDB

Stacks529

Followers584

Votes139

GitHub Stars6.7K

Forks1.1K

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

CouchDB vs Hadoop: What are the differences?

Storage Model: One key difference between CouchDB and Hadoop is their storage model. CouchDB uses a document-oriented storage model where data is stored in JSON documents, making it easy to retrieve and query data. On the other hand, Hadoop uses a distributed file system (HDFS) to store data in a more structured manner, making it highly scalable for big data processing.
Query Language: Another difference is in the query language each platform uses. CouchDB utilizes a MapReduce query language for data retrieval and manipulation, allowing for flexible querying and indexing of documents. In contrast, Hadoop relies on distributed computing frameworks like Apache Spark or Hive to query and analyze large datasets, offering more advanced analytics capabilities.
Consistency Model: CouchDB and Hadoop also differ in their consistency models. CouchDB offers eventual consistency by default, allowing for faster writes and updates to documents but potentially leading to some inconsistencies. Hadoop, on the other hand, ensures strong consistency by default, guaranteeing data accuracy and reliability at the cost of slower performance in some cases.
Scalability: When it comes to scalability, Hadoop is designed to scale horizontally by adding more nodes to its cluster to handle increasing data volumes and processing demands efficiently. In contrast, while CouchDB can also be scaled horizontally to some extent, it is more limited compared to the scalability capabilities of Hadoop.
Use Cases: CouchDB is typically used for real-time applications that require quick access to data and flexible querying capabilities, making it ideal for web and mobile applications. On the other hand, Hadoop is more suited for batch processing and analyzing large datasets, making it a popular choice for big data analytics and offline processing tasks.
Fault Tolerance: In terms of fault tolerance, Hadoop provides robust fault tolerance mechanisms through data replication and job recovery mechanisms, ensuring data reliability and integrity even in the event of node failures. While CouchDB also offers some level of fault tolerance, it may not be as comprehensive as Hadoop's fault tolerance features.

In Summary, CouchDB and Hadoop differ in their storage models, query languages, consistency models, scalability, use cases, and fault tolerance mechanisms, catering to different needs in data storage and processing.

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Advice on CouchDB, Hadoop

Gabriel

CEO at Naologic

Jan 2, 2020

Decidedon

CouchDB

Couchbase

Memcached

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

Feb 24, 2022

Decided

I’m newbie I was developing a pouchdb and couchdb app cause if the sync. Lots of learning very little code available. I dropped the project cause it consumed my life. Yeats later I’m back into it. I researched other db and came across rethinkdb and mongo for the subscription features. With socketio I should be able to create and similar sync feature. Attempted to use mongo. I attempted to use rethink. Rethink for the win. Super clear l. I had it running in minutes on my local machine and I believe it’s supposed to scale easy. Mongo wasn’t as easy and there free online db is so slow what’s the point. Very easy to find mongo code examples and use rethink code in its place. I wish I went this route years ago. All that corporate google Amazon crap get bent. The reason they have so much power in the world is cause you guys are giving it to them.

79.7k views79.7k

Comments

Karan

Senior Software Developer at Shyplite

Jan 13, 2022

Decided

So, we started using foundationDB for an OLAP system although the inbuilt tools for some core things like aggregation and filtering were negligible, with the high through put of the DB, we were able to handle it on the application. The system has been running pretty well for the past 6 months, although the data load isn’t very high yet, the performance is fairly promising

40.9k views40.9k

Comments

Detailed Comparison

CouchDB	Hadoop
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.	The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync	-
Statistics
GitHub Stars 6.7K	GitHub Stars 15.3K
GitHub Forks 1.1K	GitHub Forks 9.1K
Stacks 529	Stacks 2.7K
Followers 584	Followers 2.3K
Votes 139	Votes 56
Pros & Cons
Pros 43 JSON 30 Open source 18 Highly available 12 Partition tolerant 11 Eventual consistency	Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws

What are some alternatives to CouchDB, Hadoop?

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

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

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

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 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

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 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

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.

Related Comparisons

CouchDB vs Hadoop: What are the differences?

Storage Model: One key difference between CouchDB and Hadoop is their storage model. CouchDB uses a document-oriented storage model where data is stored in JSON documents, making it easy to retrieve and query data. On the other hand, Hadoop uses a distributed file system (HDFS) to store data in a more structured manner, making it highly scalable for big data processing.
Query Language: Another difference is in the query language each platform uses. CouchDB utilizes a MapReduce query language for data retrieval and manipulation, allowing for flexible querying and indexing of documents. In contrast, Hadoop relies on distributed computing frameworks like Apache Spark or Hive to query and analyze large datasets, offering more advanced analytics capabilities.
Consistency Model: CouchDB and Hadoop also differ in their consistency models. CouchDB offers eventual consistency by default, allowing for faster writes and updates to documents but potentially leading to some inconsistencies. Hadoop, on the other hand, ensures strong consistency by default, guaranteeing data accuracy and reliability at the cost of slower performance in some cases.
Scalability: When it comes to scalability, Hadoop is designed to scale horizontally by adding more nodes to its cluster to handle increasing data volumes and processing demands efficiently. In contrast, while CouchDB can also be scaled horizontally to some extent, it is more limited compared to the scalability capabilities of Hadoop.
Use Cases: CouchDB is typically used for real-time applications that require quick access to data and flexible querying capabilities, making it ideal for web and mobile applications. On the other hand, Hadoop is more suited for batch processing and analyzing large datasets, making it a popular choice for big data analytics and offline processing tasks.
Fault Tolerance: In terms of fault tolerance, Hadoop provides robust fault tolerance mechanisms through data replication and job recovery mechanisms, ensuring data reliability and integrity even in the event of node failures. While CouchDB also offers some level of fault tolerance, it may not be as comprehensive as Hadoop's fault tolerance features.

CouchDB vs Hadoop

Overview