DBMS > Drizzle vs. Faircom EDGE vs. Google Cloud Spanner vs. Hawkular Metrics vs. SAP IQ

System Properties Comparison Drizzle vs. Faircom EDGE vs. Google Cloud Spanner vs. Hawkular Metrics vs. SAP IQ

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

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Columnar RDBMS optimized for Big Data analytics

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

column-oriented

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Website

www.faircom.com/products/faircom-edge

cloud.google.com/spanner

www.hawkular.org

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/spanner/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

help.sap.com/docs/SAP_IQ

Developer

Drizzle project, originally started by Brian Aker

FairCom Corporation

Google

Community supported by Red Hat

SAP, formerly Sybase

Initial release

2008

1979

2017

2014

1994

Current release

7.2.4, September 2012

V3, October 2020

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Restricted, free version available

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++

ANSI C, C++

Java

Server operating systems

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

yes

with proprietary extensions

yes

ANSI SQL queries

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

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

HTTP REST

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Go
Java
JavaScript (Node.js)
Python

Go
Java
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

hooks for callbacks inside the server can be used.

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

based on Cassandra

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication with 3 replicas for regional instances.

selectable replication factor

based on Cassandra

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

fine grained access rights according to SQL-standard

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More resources

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