DBMS > GBase vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft SQL Server vs. Oracle Berkeley DB

System Properties Comparison GBase vs. Hazelcast vs. Microsoft Azure Data Explorer vs. Microsoft SQL Server vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

Fully managed big data interactive analytics platform

Microsofts flagship relational DBMS

Widely used in-process key-value store

Primary database model

column oriented

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Document store

JSON support with IMDG 3.12

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

Document store
Graph DBMS
Spatial DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

www.gbase.cn

hazelcast.com

azure.microsoft.com/services/data-explorer

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

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

hazelcast.org/imdg/docs

docs.microsoft.com/en-us/azure/data-explorer

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

docs.oracle.com/cd/E17076_05/html/index.html

Developer

General Data Technology Co., Ltd.

Hazelcast

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2004

2008

2019

1989

1994

Current release

GBase 8a, GBase 8s, GBase 8c

5.3.6, November 2023

cloud service with continuous releases

SQL Server 2022, November 2022

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

Open Source

commercial license 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

Java

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-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

the object must implement a serialization strategy

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

Standard with numerous extensions

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

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

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

yes

Event Listeners, Executor Services

Yes, possible languages: KQL, Python, R

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

Triggers

yes

Events

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

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

User concepts

Access control

yes

Role-based access control

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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