DBMS > GBase vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. Ignite vs. Microsoft SQL Server

System Properties Comparison GBase vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. Ignite vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed Event Store optimized for Internet of Things use cases

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Document store

Event Store
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

www.gbase.cn

cloud.google.com/datastore

www.ibm.com/products/db2-event-store

ignite.apache.org

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

Technical documentation

cloud.google.com/datastore/docs

www.ibm.com/docs/en/db2-event-store

apacheignite.readme.io/docs

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

Developer

General Data Technology Co., Ltd.

Google

IBM

Apache Software Foundation

Microsoft

Initial release

2004

2008

2017

2015

1989

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

Apache Ignite 2.6

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

C and C++

C++, Java, .Net

C++

Server operating systems

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

Standard with numerous extensions

SQL-like query language (GQL)

yes

through the embedded Spark runtime

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

using Google App Engine

yes

yes (compute grid and cache interceptors can be used instead)

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

Triggers

yes

Callbacks using the Google Apps Engine

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Active-active shard replication

yes (replicated cache)

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

yes

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

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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