DBMS > Blueflood vs. HyperSQL vs. Snowflake vs. Splice Machine

System Properties Comparison Blueflood vs. HyperSQL vs. Snowflake vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Cloud-based data warehousing service for structured and semi-structured data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

Technical documentation

github.com/rax-maas/blueflood/wiki

hsqldb.org/web/hsqlDocsFrame.html

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Rackspace

Snowflake Computing Inc.

Splice Machine

Initial release

2013

2001

2014

Current release

2.7.2, June 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

based on BSD license

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

Linux
OS X

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

Linux
OS X
Solaris
Windows

Data scheme

predefined scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

APIs and other access methods

HTTP REST

HTTP API

JDBC via HTTP
JDBC
ODBC

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC
Java

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Java, SQL

user defined functions

yes

Java

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users, groups and roles according to SQL-standard

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