DBMS > CockroachDB vs. Prometheus vs. Quasardb vs. SAP Advantage Database Server vs. SQLite

System Properties Comparison CockroachDB vs. Prometheus vs. Quasardb vs. SAP Advantage Database Server vs. SQLite

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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.

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

Low-cost RDBMS with access to ISAM and FoxPro data structures

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.cockroachlabs.com

prometheus.io

quasar.ai

www.sap.com/products/advantage-database-server.html

www.sqlite.org

Technical documentation

www.cockroachlabs.com/docs

prometheus.io/docs

doc.quasar.ai/master

www.sqlite.org/docs.html

Developer

Cockroach Labs

quasardb

Sybase, SAP

Dwayne Richard Hipp

Initial release

2015

2009

1993

2000

Current release

23.1.1, May 2023

3.14.1, January 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Public Domain

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++

Server operating systems

Linux
macOS
Windows

Linux
Windows

BSD
Linux
OS X
Windows

Linux
Windows

server-less

Data scheme

dynamic schema

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Numeric data only

yes

integer and binary

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.

Import of XML data possible

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

RESTful HTTP/JSON API

HTTP API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Delphi
Perl
PHP

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

yes

by Federation

Source-replica replication with selectable replication factor

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

caching of (temporary) tables can be configured

yes

User concepts

Access control

Role-based access control

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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