DBMS > DuckDB vs. GBase vs. Google Cloud Bigtable vs. JSqlDb vs. LeanXcale

System Properties Comparison DuckDB vs. GBase vs. Google Cloud Bigtable vs. JSqlDb vs. LeanXcale

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

JavaScript Query Language Database, stores JavaScript objects and primitives

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Key-value store
Wide column store

Document store
Object oriented DBMS

Key-value store
Relational DBMS

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Website

duckdb.org

www.gbase.cn

cloud.google.com/bigtable

jsqldb.org (offline)

www.leanxcale.com

Technical documentation

duckdb.org/docs

cloud.google.com/bigtable/docs

Developer

General Data Technology Co., Ltd.

Google

Konrad von Backstrom

LeanXcale

Initial release

2018

2004

2015

2018

2015

Current release

1.0.0, June 2024

GBase 8a, GBase 8s, GBase 8c

0.8, December 2018

License

Commercial or Open Source

Open Source

MIT License

commercial

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++

C, Java, Python

Server operating systems

server-less

Linux

hosted

Linux
macOS
Windows

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

yes

Standard with numerous extensions

yes

through Apache Derby

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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++
Go
Java
JavaScript (Node.js)
Python

JavaScript

C
Java
Scala

Server-side scripts

Stored procedures

user defined functions

functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using RocksDB

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)

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More resources

Recent citations in the news

MotherDuck Announces General Availability; Brings Simplicity and Power of DuckDB in a Serverless Data Warehouse
11 June 2024, PR Newswire

DuckDB: The tiny but powerful analytics database
15 May 2024, InfoWorld

DuckDB promises greater stability with 1.0 release
5 June 2024, The Register

My First Billion (of Rows) in DuckDB | by João Pedro | May, 2024
1 May 2024, Towards Data Science

DuckDB: In-Process Python Analytics for Not-Quite-Big Data
31 May 2024, The New Stack

provided by Google News

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

Google Cloud makes it cheaper to run smaller workloads on Bigtable
7 April 2020, TechCrunch

Google introduces Cloud Bigtable managed NoSQL database to process data at scale
6 May 2015, VentureBeat

provided by Google News

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