DBMS > Citus vs. GBase vs. Hazelcast vs. Sqrrl vs. VoltDB

System Properties Comparison Citus vs. GBase vs. Hazelcast vs. Sqrrl vs. VoltDB

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

A widely adopted in-memory data grid

Adaptable, secure NoSQL built on Apache Accumulo

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

sqrrl.com

Technical documentation

docs.citusdata.com

hazelcast.org/imdg/docs

docs.voltdb.com

Developer

General Data Technology Co., Ltd.

Hazelcast

Amazon

originally Sqrrl Data, Inc.

VoltDB Inc.

Initial release

2010

2004

2008

2012

2010

Current release

8.1, December 2018

GBase 8a, GBase 8s, GBase 8c

5.3.6, November 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, Java, Python

Java

Java, C++

Server operating systems

Linux

All OS with a Java VM

Linux

Linux
OS X

for development

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

specific XML type available, but no XML query functionality

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

Standard with numerous extensions

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

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

ADO.NET
C API
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

yes

Event Listeners, Executor Services

Java

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

making use of Hadoop

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

Replicated Map

selectable replication factor

making use of Hadoop

Multi-source replication
Source-replica replication

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

Atomic updates per row, document, or graph entity

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

yes

Role-based access control

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

Users and roles with access to stored procedures

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More resources

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