DBMS > Amazon Redshift vs. Drizzle vs. Google Cloud Spanner vs. HEAVY.AI vs. Kinetica

System Properties Comparison Amazon Redshift vs. Drizzle vs. Google Cloud Spanner vs. HEAVY.AI vs. Kinetica

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

aws.amazon.com/redshift

cloud.google.com/spanner

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

www.kinetica.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/spanner/docs

docs.heavy.ai

docs.kinetica.com

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

Google

HEAVY.AI, Inc.

Kinetica

Initial release

2012

2008

2017

2016

2012

Current release

7.2.4, September 2012

5.10, January 2022

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

Apache Version 2; enterprise edition 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

C++

C++ and CUDA

C, C++

Server operating systems

hosted

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

yes

Query statements complying to ANSI 2011

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

Go
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication with 3 replicas for regional instances.

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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