DBMS > Brytlyt vs. Cubrid vs. Google BigQuery vs. Hypertable vs. NSDb

System Properties Comparison Brytlyt vs. Cubrid vs. Google BigQuery vs. Hypertable vs. NSDb

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Primary database model

Relational DBMS

Wide column store

Time Series DBMS

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

brytlyt.io

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

nsdb.io

Technical documentation

docs.brytlyt.io

cubrid.org/manuals

cloud.google.com/bigquery/docs

nsdb.io/Architecture

Developer

Brytlyt

CUBRID Corporation, CUBRID Foundation

Google

Hypertable Inc.

Initial release

2016

2008

2010

2009

2017

Current release

5.0, August 2023

11.0, January 2021

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

GNU version 3. Commercial license available

Open Source

Apache Version 2.0

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++ and CUDA

C, C++, Java

C++

Java, Scala

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

restricted

only exact value or prefix value scans

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

C++ API
Thrift

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

Java Stored Procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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