DBMS > Graph Engine vs. Microsoft SQL Server vs. Postgres-XL vs. Splice Machine vs. Sqrrl

System Properties Comparison Graph Engine vs. Microsoft SQL Server vs. Postgres-XL vs. Splice Machine vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Microsofts flagship relational DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.graphengine.io

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

www.postgres-xl.org

splicemachine.com

sqrrl.com

Technical documentation

www.graphengine.io/docs/manual

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

www.postgres-xl.org/documentation

splicemachine.com/how-it-works

Developer

Microsoft

Splice Machine

Amazon

originally Sqrrl Data, Inc.

Initial release

2010

1989

2014

since 2012, originally named StormDB

2014

2012

Current release

SQL Server 2022, November 2022

10 R1, October 2018

3.1, March 2021

License

Commercial or Open Source

Open Source

MIT License

commercial

restricted free version is available

Open Source

Mozilla public license

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

.NET and C

C++

Java

Server operating systems

.NET

Linux
Windows

Linux
macOS

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

APIs and other access methods

RESTful HTTP API

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

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

JDBC
Native Spark Datasource
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C#
C++
F#
Visual Basic

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

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

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

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

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights for users, groups and roles according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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