DBMS > Hypertable vs. RocksDB vs. SAP SQL Anywhere vs. Solr vs. VoltDB

System Properties Comparison Hypertable vs. RocksDB vs. SAP SQL Anywhere vs. Solr vs. VoltDB

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

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A widely used distributed, scalable search engine based on Apache Lucene

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

Secondary database models

Spatial DBMS

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

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

rocksdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

solr.apache.org

www.voltdb.com

Technical documentation

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

docs.voltdb.com

Developer

Hypertable Inc.

Facebook, Inc.

SAP

formerly Sybase

Apache Software Foundation

VoltDB Inc.

Initial release

2009

2013

1992

2006

2010

Current release

0.9.8.11, March 2016

8.11.4, April 2024

17, July 2015

9.6.0, April 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

Open Source

BSD

commercial

Open Source

Apache Version 2

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

Java, C++

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X

for development

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

restricted

only exact value or prefix value scans

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

only a subset of SQL 99

APIs and other access methods

C++ API
Thrift

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes, in C/C++, Java, .Net or Perl

Java plugins

Java

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes

Source-replica replication

Database mirroring

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

yes

ACID

optimistic locking

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

Users and roles with access to stored procedures

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