DBMS > Apache Impala vs. DataFS vs. EsgynDB vs. KairosDB

System Properties Comparison Apache Impala vs. DataFS vs. EsgynDB vs. KairosDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Secondary database models

Document store

Graph DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Website

impala.apache.org

newdatabase.com

www.esgyn.cn

github.com/kairosdb/kairosdb

Technical documentation

impala.apache.org/impala-docs.html

dev.mobiland.com/Overview.xsp

kairosdb.github.io

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Mobiland AG

Esgyn

Initial release

2013

2018

2015

2013

Current release

4.1.0, June 2022

1.1.263, October 2022

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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++

C++, Java

Java

Server operating systems

Linux

Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/ADO.Net

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Windows-Profile

fine grained access rights according to SQL-standard

simple password-based access control

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

provided by Google News

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