DBMS > Datomic vs. HEAVY.AI vs. RocksDB vs. SAP SQL Anywhere vs. Spark SQL

System Properties Comparison Datomic vs. HEAVY.AI vs. RocksDB vs. SAP SQL Anywhere vs. Spark SQL

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

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

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	4.57
Rank	#78	Overall
	#43	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.datomic.com

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

rocksdb.org

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

spark.apache.org/sql

Technical documentation

docs.datomic.com

docs.heavy.ai

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cognitect

HEAVY.AI, Inc.

Facebook, Inc.

SAP

formerly Sybase

Apache Software Foundation

Initial release

2012

2016

2013

1992

2014

Current release

1.0.6735, June 2023

5.10, January 2022

8.11.4, April 2024

17, July 2015

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

commercial

Open Source

Apache 2.0

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++ and CUDA

C++

Scala

Server operating systems

All OS with a Java VM

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

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

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Round robin

horizontal partitioning

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication

yes

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

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

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