DBMS > Datomic vs. HarperDB vs. RocksDB vs. SAP SQL Anywhere vs. Teradata Aster

System Properties Comparison Datomic vs. HarperDB vs. RocksDB vs. SAP SQL Anywhere vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Platform for big data analytics on multistructured data sources and types

Primary database model

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	0.55
Rank	#252	Overall
	#38	Document stores

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

Score	4.57
Rank	#78	Overall
	#43	Relational DBMS

Website

www.datomic.com

www.harperdb.io

rocksdb.org

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

Technical documentation

docs.datomic.com

docs.harperdb.io/docs

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Cognitect

HarperDB

Facebook, Inc.

SAP

formerly Sybase

Teradata

Initial release

2012

2017

2013

1992

2005

Current release

1.0.6735, June 2023

3.1, August 2021

8.11.4, April 2024

17, July 2015

License

Commercial or Open Source

commercial

limited edition free

commercial

free community edition available

Open Source

BSD

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

Node.js

C++

Server operating systems

All OS with a Java VM

Linux
OS X

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

dynamic schema

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

JSON data types

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like data manipulation statements

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Clojure
Java

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Go
Java
Perl
Python
Ruby

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

Custom Functions

since release 3.1

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

R packages

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

A table resides as a whole on one (or more) nodes in a cluster

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

the nodes on which a table resides can be defined

yes

Source-replica replication

Database mirroring

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

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, using LMDB

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

Access rights for users and roles

fine grained access rights according to SQL-standard

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