DBMS > CockroachDB vs. Spark SQL vs. Transwarp KunDB vs. XTDB

System Properties Comparison CockroachDB vs. Spark SQL vs. Transwarp KunDB vs. XTDB

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Editorial information provided by DB-Engines

Name

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.

Spark SQL is a component on top of 'Spark Core' for structured data processing

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#344	Overall
	#151	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.cockroachlabs.com

spark.apache.org/sql

www.transwarp.cn/en/product/kundb

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.cockroachlabs.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

www.xtdb.com/docs

Developer

Cockroach Labs

Apache Software Foundation

Transwarp

Juxt Ltd.

Initial release

2015

2014

2019

Current release

23.1.1, May 2023

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

Open Source

Apache 2.0

commercial

Open Source

MIT License

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

Scala

Clojure

Server operating systems

Linux
macOS
Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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, wire compatible with PostgreSQL

SQL-like DML and DDL statements

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

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

Java
Python
R
Scala

Clojure
Java

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

yes, utilizing Spark Core

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

Role-based access control

yes

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