DBMS > Amazon Redshift vs. CockroachDB vs. etcd vs. HugeGraph vs. SAP SQL Anywhere

System Properties Comparison Amazon Redshift vs. CockroachDB vs. etcd vs. HugeGraph vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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.

A distributed reliable key-value store

A fast-speed and highly-scalable Graph DBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

aws.amazon.com/redshift

www.cockroachlabs.com

etcd.io
github.com/etcd-io/etcd

github.com/hugegraph
hugegraph.apache.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.aws.amazon.com/redshift

www.cockroachlabs.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

hugegraph.apache.org/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Amazon (based on PostgreSQL)

Cockroach Labs

Baidu

SAP

formerly Sybase

Initial release

2012

2015

2018

1992

Current release

23.1.1, May 2023

3.4, August 2019

0.9

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0, commercial license available

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

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

Java

Server operating systems

hosted

Linux
macOS
Windows

FreeBSD
Linux
Windows

experimental

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

restricted

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes, wire compatible with PostgreSQL

yes

APIs and other access methods

JDBC
ODBC

JDBC

gRPC
JSON over HTTP

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Groovy
Java
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

asynchronous Gremlin script jobs

yes, in C/C++, Java, .Net or Perl

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

depending on used storage backend, e.g. Cassandra and HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using RAFT

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

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

fine grained access rights according to SQL-standard

Role-based access control

Users, roles and permissions

fine grained access rights according to SQL-standard

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