DBMS > Apache Impala vs. ArcadeDB vs. Atos Standard Common Repository vs. H2GIS

System Properties Comparison Apache Impala vs. ArcadeDB vs. Atos Standard Common Repository vs. H2GIS

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Spatial extension of H2

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store
Key-value store

Spatial DBMS

Secondary database models

Document store

Relational DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.02
Rank	#374	Overall
	#52	Document stores
	#38	Graph DBMS
	#56	Key-value stores
	#38	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#7	Spatial DBMS

Website

impala.apache.org

arcadedb.com

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.h2gis.org

Technical documentation

impala.apache.org/impala-docs.html

docs.arcadedb.com

www.h2gis.org/docs/home

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Arcade Data

Atos Convergence Creators

CNRS

Initial release

2013

2021

2016

2013

Current release

4.1.0, June 2022

September 2021

1703

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

LGPL 3.0

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

Server operating systems

Linux

All OS with a Java VM

Linux

Data scheme

yes

schema-free

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
ODBC

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

LDAP

Supported programming languages

All languages supporting JDBC/ODBC

Java

All languages with LDAP bindings

Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

based on H2

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

based on H2

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

LDAP bind authentication

yes

based on H2

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More resources

Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

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

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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

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