DBMS > Apache Impala vs. EXASOL vs. HugeGraph vs. SAP HANA

System Properties Comparison Apache Impala vs. EXASOL vs. HugeGraph vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A fast-speed and highly-scalable Graph DBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

impala.apache.org

www.exasol.com

github.com/hugegraph
hugegraph.apache.org

www.sap.com/products/hana.html

Technical documentation

impala.apache.org/impala-docs.html

www.exasol.com/resources

hugegraph.apache.org/docs

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Exasol

Baidu

SAP

Initial release

2013

2000

2018

2010

Current release

4.1.0, June 2022

0.9

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

Linux
macOS
Unix

Appliance or cloud-service

Data scheme

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

asynchronous Gremlin script jobs

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Hadoop integration

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users, groups and roles according to SQL-standard

Users, roles and permissions

yes

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