DBMS > Apache Impala vs. EsgynDB vs. MySQL vs. Netezza vs. OceanBase

System Properties Comparison Apache Impala vs. EsgynDB vs. MySQL vs. Netezza vs. OceanBase

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Widely used open source RDBMS

Data warehouse and analytics appliance part of IBM PureSystems

A distributed, high available RDBMS compatible with Oracle and MySQL

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Document store
Wide column store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Website

impala.apache.org

www.esgyn.cn

www.mysql.com

www.ibm.com/products/netezza

en.oceanbase.com

Technical documentation

impala.apache.org/impala-docs.html

dev.mysql.com/doc

en.oceanbase.com/docs/oceanbase-database

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Esgyn

Oracle

since 2010, originally MySQL AB, then Sun

IBM

OceanBase

previously Alibaba and Ant Group

Initial release

2013

2015

1995

2000

2010

Current release

4.1.0, June 2022

8.4.0, April 2024

4.3.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C++, Java

C and C++

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux

included in appliance

Linux

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Fortran
Java
Lua
Perl
Python
R

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

yes

proprietary syntax

yes

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication between multi datacenters

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication using Paxos

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

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

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