DBMS > GBase vs. Google Cloud Bigtable vs. LeanXcale vs. QuestDB vs. Vertica

System Properties Comparison GBase vs. Google Cloud Bigtable vs. LeanXcale vs. QuestDB vs. Vertica

Editorial information provided by DB-Engines

Name

Description

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.

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

A high performance open source SQL database for time series data

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

www.gbase.cn

cloud.google.com/bigtable

www.leanxcale.com

questdb.io

www.vertica.com

Technical documentation

cloud.google.com/bigtable/docs

questdb.io/docs

vertica.com/documentation

Developer

General Data Technology Co., Ltd.

Google

LeanXcale

QuestDB Technology Inc

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2004

2015

2014

2005

Current release

GBase 8a, GBase 8s, GBase 8c

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, Java, Python

Java (Zero-GC), C++, Rust

C++

Server operating systems

Linux

hosted

Linux
macOS
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Standard with numerous extensions

yes

through Apache Derby

SQL with time-series extensions

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

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

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

C
Java
Scala

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning (by timestamps)

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication with eventual consistency

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

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

ACID for single-table writes

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

through memory mapped files

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; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

News

Combine Java and Rust Code Coverage in a Polyglot Project
10 September 2024

Weather data visualization and forecasting with QuestDB, Kafka and Grafana
4 September 2024

Building a new vector based storage model
22 August 2024

Calibrating VWAP executions with QuestDB and Grafana
16 August 2024

Write Time: a call for community writers
13 August 2024

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