DBMS > KeyDB vs. Kinetica vs. Snowflake vs. Spark SQL vs. SQream DB

System Properties Comparison KeyDB vs. Kinetica vs. Snowflake vs. Spark SQL vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Cloud-based data warehousing service for structured and semi-structured data

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

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

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.73
Rank	#228	Overall
	#105	Relational DBMS

Website

github.com/Snapchat/KeyDB
keydb.dev

Technical documentation

docs.keydb.dev

docs.kinetica.com

docs.snowflake.net/manuals/index.html

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

docs.sqream.com

Developer

EQ Alpha Technology Ltd.

Kinetica

Snowflake Computing Inc.

Apache Software Foundation

SQream Technologies

Initial release

2019

2012

2014

2017

Current release

7.1, August 2021

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

BSD-3

commercial

Open Source

Apache 2.0

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

C++

C, C++

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

hosted

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes, ANSI Standard SQL 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

by using the Redis Search module

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

.Net
JDBC
ODBC

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++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

user defined functions

user defined functions in Python

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency or Eventual Consistency depending on configuration

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

GPU vRAM or System RAM

User concepts

Access control

simple password-based access control and ACL

Access rights for users and roles on table level

Users with fine-grained authorization concept, user roles and pluggable authentication

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