DBMS > HEAVY.AI vs. Hive vs. KeyDB vs. Microsoft SQL Server vs. SiteWhere

System Properties Comparison HEAVY.AI vs. Hive vs. KeyDB vs. Microsoft SQL Server vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Microsofts flagship relational DBMS

M2M integration platform for persisting/querying time series data

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

hive.apache.org

github.com/Snapchat/KeyDB
keydb.dev

www.microsoft.com/en-us/sql-server

github.com/sitewhere/sitewhere

Technical documentation

docs.heavy.ai

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

docs.keydb.dev

learn.microsoft.com/en-US/sql/sql-server

sitewhere1.sitewhere.io/index.html

Developer

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

EQ Alpha Technology Ltd.

Microsoft

SiteWhere

Initial release

2016

2012

2019

1989

2010

Current release

5.10, January 2022

3.1.3, April 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

Open Source

BSD-3

commercial

restricted free version is available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++ and CUDA

Java

C++

Java

Server operating systems

Linux

All OS with a Java VM

Linux

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

predefined scheme

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

selectable replication factor

based on HBase

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

Immediate Consistency

Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

simple password-based access control and ACL

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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