DBMS > Amazon DynamoDB vs. H2GIS vs. HBase vs. Hive vs. SQLite

System Properties Comparison Amazon DynamoDB vs. H2GIS vs. HBase vs. Hive vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Spatial extension of H2

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Key-value store

Spatial DBMS

Wide column store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

www.h2gis.org/docs/home

hbase.apache.org/book.html

cwiki.apache.org/confluence/display/Hive/Home

www.sqlite.org/docs.html

Developer

Amazon

CNRS

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

initially developed by Facebook

Dwayne Richard Hipp

Initial release

2012

2013

2008

2012

2000

Current release

2.3.4, January 2021

3.1.3, April 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

LGPL 3.0

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

server-less

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

dynamic column types

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

based on H2

yes

Coprocessors in Java

yes

user defined functions and integration of map-reduce

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

Multi-source replication
Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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 and roles can be defined via the AWS Identity and Access Management (IAM)

yes

based on H2

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

Access rights for users, groups and roles

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