DBMS > Heroic vs. Microsoft SQL Server vs. Newts vs. SQLite vs. Sqrrl

System Properties Comparison Heroic vs. Microsoft SQL Server vs. Newts vs. SQLite 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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Microsofts flagship relational DBMS

Time Series DBMS based on Cassandra

Widely used embeddable, in-process RDBMS

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

github.com/spotify/heroic

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

opennms.github.io/newts

www.sqlite.org

sqrrl.com

Technical documentation

spotify.github.io/heroic

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

github.com/OpenNMS/newts/wiki

www.sqlite.org/docs.html

Developer

Spotify

Microsoft

OpenNMS Group

Dwayne Richard Hipp

Amazon

originally Sqrrl Data, Inc.

Initial release

2014

1989

2014

2000

2012

Current release

SQL Server 2022, November 2022

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

Public Domain

commercial

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++

Java

Server operating systems

Linux
Windows

Linux
OS X
Windows

server-less

Linux

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

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

HTTP REST
Java API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

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

Java

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

Actionscript
C

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

Server-side scripts

Stored procedures

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

based on Cassandra

none

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

selectable replication factor

based on Cassandra

none

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

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

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