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DBMS > Dragonfly vs. Google Cloud Bigtable vs. Oracle NoSQL vs. SQL.JS

System Properties Comparison Dragonfly vs. Google Cloud Bigtable vs. Oracle NoSQL vs. SQL.JS

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Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Port of SQLite to JavaScript

Primary database model

Key-value store

Key-value store
Wide column store

Document store
Key-value store
Relational DBMS

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

cloud.google.com/bigtable

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/bigtable/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

sql.js.org/documentation/index.html

Developer

DragonflyDB team and community contributors

Google

Oracle

Alon Zakai

enhancements implemented by others

Initial release

2023

2015

2011

2012

Current release

1.0, March 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Cloud-based only

Only available as a cloud service

no

yes

no

no

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

JavaScript

Server operating systems

Linux

hosted

Linux
Solaris SPARC/x86

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

scheme-free

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

no

optional

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

no

no

no

no

Secondary indexes

no

no

yes

SQL

Support of SQL

no

no

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

RESTful HTTP API

JavaScript API

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C#
Go
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

Lua

no

no

no

Triggers

publish/subscribe channels provide some trigger functionality

no

no

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

yes

with Hadoop integration

no

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency
Immediate Consistency

depending on configuration

none

Foreign keys

Referential integrity

no

no

no

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of command blocks and scripts

Atomic single-row operations

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

yes

Durability

Support for making data persistent

yes

yes

yes

no

except by serializing a db to a file

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

no

yes

off heap cache

yes

User concepts

Access control

Password-based authentication

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles

no

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More resources

DB-Engines blog posts

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