DBMS > Axibase vs. Oracle Berkeley DB vs. Spark SQL vs. TimesTen

System Properties Comparison Axibase vs. Oracle Berkeley DB vs. Spark SQL vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Widely used in-process key-value store

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

In-Memory RDBMS compatible to Oracle

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	0.32
Rank	#288	Overall
	#25	Time Series DBMS

Score	2.52
Rank	#114	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.oracle.com/database/technologies/related/berkeleydb.html

spark.apache.org/sql

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

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

docs.oracle.com/database/timesten-18.1

Developer

Axibase Corporation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

1994

2014

1998

Current release

15585

18.1.40, May 2020

3.5.0 ( 2.13), September 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

commercial license available

Open Source

Apache 2.0

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

C, Java, C++ (depending on the Berkeley DB edition)

Scala

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Java
Python
R
Scala

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

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 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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