DBMS > KeyDB vs. Netezza vs. Spark SQL vs. STSdb

System Properties Comparison KeyDB vs. Netezza vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

Data warehouse and analytics appliance part of IBM PureSystems

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Key-value store

Relational DBMS

Key-value store

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

Score	0.80
Rank	#219	Overall
	#31	Key-value stores

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

github.com/Snapchat/KeyDB
keydb.dev

www.ibm.com/products/netezza

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

docs.keydb.dev

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

Developer

EQ Alpha Technology Ltd.

IBM

Apache Software Foundation

STS Soft SC

Initial release

2019

2000

2014

2011

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD-3

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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++

Scala

Server operating systems

Linux

included in appliance

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

partial

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

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC
OLE DB

JDBC
ODBC

.NET Client API

Supported programming languages

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++
Fortran
Java
Lua
Perl
Python
R

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

Lua

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Foreign keys

Referential integrity

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

simple password-based access control and ACL

Users with fine-grained authorization concept

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