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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Badger vs CouchDB

Badger vs CouchDB

OverviewDecisionsComparisonAlternatives

Overview

CouchDB
CouchDB
Stacks529
Followers584
Votes139
GitHub Stars6.7K
Forks1.1K
Badger
Badger
Stacks6
Followers19
Votes0
GitHub Stars15.1K
Forks1.3K

Badger vs CouchDB: What are the differences?

# Introduction
In this comparison, we will explore the key differences between Badger and CouchDB, two popular database systems.

1. **Data Model**: Badger utilizes an LSM tree data structure to store key-value pairs, while CouchDB is a document-oriented database that uses JSON documents to store data. 
2. **Query Language**: Badger does not support a query language and is mainly used as a key-value store, whereas CouchDB uses MapReduce views and Mango queries for flexible querying capabilities. 
3. **Consistency**: Badger offers strong consistency with ACID transactions, ensuring data integrity, while CouchDB provides eventual consistency by allowing conflicting updates to be resolved during replication. 
4. **Storage**: Badger is optimized for SSD storage due to its write-optimized design, resulting in high write throughput and low latency, while CouchDB is more suitable for scenarios requiring frequent read operations. 
5. **Scalability**: Badger is more suitable for single-node deployments or where the data fits entirely in memory, while CouchDB offers better horizontal scalability through built-in clustering support. 
6. **Community and Ecosystem**: CouchDB has a larger and more established community with extensive documentation, plugins, and integrations, while Badger, being a newer project, has a smaller ecosystem and fewer third-party tools available. 

In Summary, Badger and CouchDB differ in their data model, query language, consistency model, storage optimization, scalability options, and community support.

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Advice on CouchDB, Badger

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
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Detailed Comparison

CouchDB
CouchDB
Badger
Badger

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.

Badger is written out of frustration with existing KV stores which are either natively written in Go and slow, or fast but require usage of Cgo. Badger aims to provide an equal or better speed compared to industry leading KV stores (like RocksDB), while maintaining the entire code base in Go natively.

Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync
-
Statistics
GitHub Stars
6.7K
GitHub Stars
15.1K
GitHub Forks
1.1K
GitHub Forks
1.3K
Stacks
529
Stacks
6
Followers
584
Followers
19
Votes
139
Votes
0
Pros & Cons
Pros
  • 43
    JSON
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
No community feedback yet

What are some alternatives to CouchDB, Badger ?

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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