DBMS > GBase vs. IBM Db2 Event Store vs. LMDB vs. Microsoft Azure Synapse Analytics

System Properties Comparison GBase vs. IBM Db2 Event Store vs. LMDB vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

A high performant, light-weight, embedded key-value database library

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Key-value store

Relational DBMS

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

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

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

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

www.gbase.cn

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

www.symas.com/symas-embedded-database-lmdb

azure.microsoft.com/services/synapse-analytics

Technical documentation

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

www.lmdb.tech/doc

docs.microsoft.com/azure/synapse-analytics

Developer

General Data Technology Co., Ltd.

IBM

Symas

Microsoft

Initial release

2004

2017

2011

2016

Current release

GBase 8a, GBase 8s, GBase 8c

2.0

0.9.32, January 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

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, Java, Python

C and C++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
Unix
Windows

hosted

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

through the embedded Spark runtime

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

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

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

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

fine grained access rights according to SQL-standard

yes

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