DBMS > DuckDB vs. Faircom EDGE vs. GBase vs. NSDb vs. XTDB

System Properties Comparison DuckDB vs. Faircom EDGE vs. GBase vs. NSDb vs. XTDB

Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

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

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Document store

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

duckdb.org

www.faircom.com/products/faircom-edge

www.gbase.cn

nsdb.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

duckdb.org/docs

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

nsdb.io/Architecture

www.xtdb.com/docs

Developer

FairCom Corporation

General Data Technology Co., Ltd.

Juxt Ltd.

Initial release

2018

1979

2004

2017

2019

Current release

1.0.0, June 2024

V3, October 2020

GBase 8a, GBase 8s, GBase 8c

1.19, September 2021

License

Commercial or Open Source

Open Source

MIT License

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

ANSI C, C++

C, Java, Python

Java, Scala

Clojure

Server operating systems

server-less

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
macOS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

yes: int, bigint, decimal, string

yes, extensible-data-notation format

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

ANSI SQL queries

Standard with numerous extensions

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

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

ADO.NET
C API
JDBC
ODBC

gRPC
HTTP REST
WebSocket

HTTP REST
JDBC

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

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

Java
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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

yes

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