DBMS > Altibase vs. Amazon Aurora vs. Apache IoTDB vs. CockroachDB vs. SQLite

System Properties Comparison Altibase vs. Amazon Aurora vs. Apache IoTDB vs. CockroachDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

MySQL and PostgreSQL compatible cloud service by Amazon

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

altibase.com

aws.amazon.com/rds/aurora

iotdb.apache.org

www.cockroachlabs.com

www.sqlite.org

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.cockroachlabs.com/docs

www.sqlite.org/docs.html

Developer

Altibase

Amazon

Apache Software Foundation

Cockroach Labs

Dwayne Richard Hipp

Initial release

1999

2015

2018

2015

2000

Current release

v7.3, 2023, August 2023

1.1.0, April 2023

24.1.0, May 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0, commercial license available

Open Source

Public Domain

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

Server operating systems

AIX
HP-UX
Linux

hosted

All OS with a Java VM (>= 1.8)

Linux
macOS
Windows

server-less

Data scheme

yes

dynamic schema

yes

dynamic column types

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

yes

SQL-like query language

yes, wire compatible with PostgreSQL

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
ODBC

JDBC
Native API

JDBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C++
Java

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

C
C#
C++
Go
Java
Python
Scala

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

stored procedures and stored functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication using RAFT

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

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

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

fine grained access rights according to SQL-standard

yes

Role-based access control

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