DBMS > HarperDB vs. InfinityDB vs. LevelDB vs. MonetDB vs. MySQL

System Properties Comparison HarperDB vs. InfinityDB vs. LevelDB vs. MonetDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

A Java embedded Key-Value Store which extends the Java Map interface

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

A relational database management system that stores data in columns

Widely used open source RDBMS

Primary database model

Document store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.harperdb.io

boilerbay.com

github.com/google/leveldb

www.monetdb.org

www.mysql.com

Technical documentation

docs.harperdb.io/docs

boilerbay.com/infinitydb/manual

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

www.monetdb.org/Documentation

dev.mysql.com/doc

Developer

HarperDB

Boiler Bay Inc.

Google

MonetDB BV

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2017

2002

2011

2004

1995

Current release

3.1, August 2021

4.0

1.23, February 2021

Dec2023 (11.49), December 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

BSD

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

Node.js

Java

C++

C and C++

Server operating systems

Linux
OS X

All OS with a Java VM

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

dynamic schema

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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.

yes

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like data manipulation statements

yes

SQL 2003 with some extensions

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Java

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes, in SQL, C, R

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

none

Sharding via remote tables

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic execution of specific operations

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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