DBMS > Datomic vs. RocksDB vs. SAP SQL Anywhere vs. Solr vs. UniData,UniVerse

System Properties Comparison Datomic vs. RocksDB vs. SAP SQL Anywhere vs. Solr vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.datomic.com

rocksdb.org

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

solr.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

docs.datomic.com

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Cognitect

Facebook, Inc.

SAP

formerly Sybase

Apache Software Foundation

Rocket Software

Initial release

2012

2013

1992

2006

1985

Current release

1.0.6735, June 2023

8.11.4, April 2024

17, July 2015

9.6.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

BSD

commercial

Open Source

Apache Version 2

commercial

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

Java, Clojure

C++

Java

Server operating systems

All OS with a Java VM

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)

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

optional

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

RESTful HTTP API

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

Clojure
Java

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

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

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

Java plugins

yes

Triggers

By using transaction functions

yes

User configurable commands triggered on index changes

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Source-replica replication

Database mirroring

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Transaction concepts

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

ACID

yes

ACID

optimistic locking

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Access rights according to SQL-standard and operating system based

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