DBMS > HarperDB vs. MySQL vs. OpenQM vs. Spark SQL vs. TimescaleDB

System Properties Comparison HarperDB vs. MySQL vs. OpenQM vs. Spark SQL vs. TimescaleDB

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.

Widely used open source RDBMS

QpenQM is a high-performance, self-tuning, multi-value DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store

Relational DBMS

Key/Value like access via memcached API

Multivalue DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Relational DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.harperdb.io

www.mysql.com

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

spark.apache.org/sql

www.timescale.com

Technical documentation

docs.harperdb.io/docs

dev.mysql.com/doc

spark.apache.org/docs/latest/sql-programming-guide.html

docs.timescale.com

Developer

HarperDB

Oracle

since 2010, originally MySQL AB, then Sun

Rocket Software, originally Martin Phillips

Apache Software Foundation

Timescale

Initial release

2017

1995

1993

2014

2017

Current release

3.1, August 2021

8.4.0, April 2024

3.4-12

3.5.0 ( 2.13), September 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPLv2, extended commercial license available

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

C and C++

Scala

Server operating systems

Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Windows

Data scheme

dynamic schema

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

SQL

Support of SQL

SQL-like data manipulation statements

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

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

.Net
Basic
C
Java
Objective C
PHP
Python

Java
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

proprietary syntax

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

yes, utilizing Spark Core

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Multi-source replication
Source-replica replication

yes

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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