DBMS > Apache Impala vs. GeoSpock vs. HBase

System Properties Comparison Apache Impala vs. GeoSpock vs. HBase

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Spatial and temporal data processing engine for extreme data scale

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Relational DBMS

Wide column store

Secondary database models

Document store

Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

impala.apache.org

geospock.com

hbase.apache.org

Technical documentation

impala.apache.org/impala-docs.html

hbase.apache.org/book.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GeoSpock

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2013

2008

Current release

4.1.0, June 2022

2.0, September 2019

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, Javascript

Java

Server operating systems

Linux

hosted

Linux
Unix
Windows

using Cygwin

Data scheme

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

temporal, categorical

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI SQL for query only (using Presto)

APIs and other access methods

JDBC
ODBC

JDBC

Java API
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

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

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

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 can be defined per table

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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6 June 2023, SiliconANGLE News

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29 July 2021, iProgrammer

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28 November 2017, Datanami

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2 October 2020, AlexaBlockchain

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16 May 2018, ExchangeWire

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