DBMS > Dragonfly vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Table Storage vs. UniData,UniVerse

System Properties Comparison Dragonfly vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Table Storage vs. UniData,UniVerse

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Embeddable relational database for web apps written in pure JavaScript

A Wide Column Store for rapid development using massive semi-structured datasets

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

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

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

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

cloud.google.com/datastore

google.github.io/lovefield

azure.microsoft.com/en-us/services/storage/tables

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/datastore/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

DragonflyDB team and community contributors

Google

Microsoft

Rocket Software

Initial release

2023

2008

2014

2012

1985

Current release

1.0, March 2023

2.1.12, February 2017

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

JavaScript

Server operating systems

Linux

hosted

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

tested with Chrome, Firefox, IE, Safari

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

scheme-free

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes, details here

yes

optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based 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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

Lua

using Google App Engine

yes

Triggers

publish/subscribe channels provide some trigger functionality

Callbacks using the Google Apps Engine

Using read-only observers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

optimistic locking

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

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 based on private key authentication or shared access signatures

Access rights according to SQL-standard and operating system based

More information provided by the system vendor

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