DBMS > HarperDB vs. Ignite vs. Machbase Neo vs. Microsoft Azure Cosmos DB

System Properties Comparison HarperDB vs. Ignite vs. Machbase Neo vs. Microsoft Azure Cosmos DB

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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.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

Key-value store
Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.harperdb.io

ignite.apache.org

machbase.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.harperdb.io/docs

apacheignite.readme.io/docs

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

Developer

HarperDB

Apache Software Foundation

Machbase

Microsoft

Initial release

2017

2015

2013

2014

Current release

3.1, August 2021

Apache Ignite 2.6

V8.0, August 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

free test version available

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

Node.js

C++, Java, .Net

Server operating systems

Linux
OS X

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

hosted

Data scheme

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like data manipulation statements

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table 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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes (compute grid and cache interceptors can be used instead)

JavaScript

Triggers

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes (replicated cache)

selectable replication factor

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

yes

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

Access rights for users and roles

Security Hooks for custom implementations

simple password-based access control

Access rights can be defined down to the item level

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