DBMS > Dragonfly vs. Hazelcast vs. Hive vs. LevelDB vs. SQLite

System Properties Comparison Dragonfly vs. Hazelcast vs. Hive vs. LevelDB vs. SQLite

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

A widely adopted in-memory data grid

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

hazelcast.com

hive.apache.org

github.com/google/leveldb

www.sqlite.org

Technical documentation

www.dragonflydb.io/docs

hazelcast.org/imdg/docs

cwiki.apache.org/confluence/display/Hive/Home

github.com/google/leveldb/blob/main/doc/index.md

www.sqlite.org/docs.html

Developer

DragonflyDB team and community contributors

Hazelcast

Apache Software Foundation

initially developed by Facebook

Google

Dwayne Richard Hipp

Initial release

2023

2008

2012

2011

2000

Current release

1.0, March 2023

5.3.6, November 2023

3.1.3, April 2022

1.23, February 2021

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

BSL 1.1

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2

Open Source

BSD

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

Illumos
Linux
OS X
Windows

server-less

Data scheme

scheme-free

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

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

yes

not rigid because of 'dynamic typing' concept.

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

C++
Java
PHP
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Lua

yes

Event Listeners, Executor Services

yes

user defined functions and integration of map-reduce

Triggers

publish/subscribe channels provide some trigger functionality

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

via file-system locks

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

User concepts

Access control

Password-based authentication

Role-based access control

Access rights for users, groups and roles

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