DBMS > IBM Db2 warehouse vs. RavenDB vs. SQLite vs. TerarkDB vs. Vitess

System Properties Comparison IBM Db2 warehouse vs. RavenDB vs. SQLite vs. TerarkDB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Cloud-based data warehousing service

Open Source Operational and Transactional Enterprise NoSQL Document Database

Widely used embeddable, in-process RDBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

www.ibm.com/products/db2/warehouse

ravendb.net

www.sqlite.org

github.com/bytedance/terarkdb

vitess.io

Technical documentation

ravendb.net/docs

www.sqlite.org/docs.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

vitess.io/docs

Developer

IBM

Hibernating Rhinos

Dwayne Richard Hipp

ByteDance, originally Terark

The Linux Foundation, PlanetScale

Initial release

2014

2010

2000

2016

2013

Current release

5.4, July 2022

3.46.0 (23 May 2024), May 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

Open Source

Public Domain

commercial

restricted open source version available

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

hosted

Linux
macOS
Raspberry Pi
Windows

server-less

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

dynamic column types

schema-free

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

yes

SQL-92 is not fully supported

yes

with proprietary extensions

APIs and other access methods

.NET Client API
JDBC
ODBC
OLE DB

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

C++ API
Java API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C++
Java

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

Server-side scripts

Stored procedures

PL/SQL, SQL PL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

table locks or row locks depending on storage engine

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

Authorization levels configured per client per database

Users with fine-grained authorization concept

no user groups or roles

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