DBMS > Altibase vs. Apache Impala vs. Axibase vs. Spark SQL

System Properties Comparison Altibase vs. Apache Impala vs. Axibase vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Analytic DBMS for Hadoop

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

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	1.00
Rank	#194	Overall
	#91	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	0.32
Rank	#288	Overall
	#25	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

altibase.com

impala.apache.org

axibase.com/docs/atsd/finance

spark.apache.org/sql

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

impala.apache.org/impala-docs.html

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

Developer

Altibase

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Axibase Corporation

Apache Software Foundation

Initial release

1999

2013

2014

Current release

v7.3, 2023, August 2023

4.1.0, June 2022

15585

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2

commercial

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

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

C++

Java

Scala

Server operating systems

AIX
HP-UX
Linux

Linux

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like DML and DDL statements

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C++
Java

All languages supporting JDBC/ODBC

Go
Java
PHP
Python
R
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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