DBMS > CockroachDB vs. GridGain vs. Microsoft SQL Server vs. TimescaleDB

System Properties Comparison CockroachDB vs. GridGain vs. Microsoft SQL Server vs. TimescaleDB

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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.

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	4.74
Rank	#67	Overall
	#36	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

www.cockroachlabs.com

www.gridgain.com

www.microsoft.com/en-us/sql-server

www.timescale.com

Technical documentation

www.cockroachlabs.com/docs

www.gridgain.com/docs/index.html

learn.microsoft.com/en-US/sql/sql-server

docs.timescale.com

Developer

Cockroach Labs

GridGain Systems, Inc.

Microsoft

Timescale

Initial release

2015

2007

1989

2017

Current release

24.1.0, May 2024

GridGain 8.5.1

SQL Server 2022, November 2022

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial, open source

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net, Python, REST, SQL

C++

Server operating systems

Linux
macOS
Windows

Linux
OS X
Solaris
Windows
z/OS

Linux
Windows

Linux
OS X
Windows

Data scheme

dynamic schema

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

yes, wire compatible with PostgreSQL

ANSI-99 for query and DML statements, subset of DDL

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes (cache interceptors and events)

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

yes (replicated cache)

yes, but depending on the SQL-Server Edition

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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