DBMS > Blueflood vs. HEAVY.AI vs. Sequoiadb vs. TDSQL for MySQL

System Properties Comparison Blueflood vs. HEAVY.AI vs. Sequoiadb vs. TDSQL for MySQL

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

NewSQL database with distributed OLTP and SQL

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Time Series DBMS

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Website

blueflood.io

github.com/heavyai/heavydb
www.heavy.ai

www.sequoiadb.com

www.tencentcloud.com/products/dcdb

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.heavy.ai

www.sequoiadb.com/en/index.php?m=Files&a=index

www.tencentcloud.com/document/product/1042

Developer

Rackspace

HEAVY.AI, Inc.

Sequoiadb Ltd.

Tencent

Initial release

2013

2016

2013

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Server: AGPL; Client: Apache V2

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

Java

C++ and CUDA

C++

Server operating systems

Linux
OS X

Linux

hosted

Data scheme

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift
Vega

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C++
Java
PHP
Python

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

Sharding

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Document is locked during a transaction

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

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

Users with fine-grained authorization concept

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