DBMS > Apache Impala vs. OrigoDB vs. SAP IQ vs. Transwarp Hippo

System Properties Comparison Apache Impala vs. OrigoDB vs. SAP IQ vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A fully ACID in-memory object graph database

Columnar RDBMS optimized for Big Data analytics

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

column-oriented

Vector DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

impala.apache.org

origodb.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.transwarp.cn/en/subproduct/hippo

Technical documentation

impala.apache.org/impala-docs.html

origodb.com/docs

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Robert Friberg et al

SAP, formerly Sybase

Initial release

2013

2009

under the name LiveDB

1994

2023

Current release

4.1.0, June 2022

16.1 SPS04, April 2019

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

C++

Server operating systems

Linux

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

Vector, Numeric and String

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net

C
C#
C++
Java
Perl
PHP
Python
Ruby

C++
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Write ahead log

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role based authorization

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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More resources

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