DBMS > Netezza vs. Percona Server for MongoDB vs. Snowflake vs. Sphinx vs. Sqrrl

System Properties Comparison Netezza vs. Percona Server for MongoDB vs. Snowflake vs. Sphinx 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

Data warehouse and analytics appliance part of IBM PureSystems

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Cloud-based data warehousing service for structured and semi-structured data

Open source search engine for searching in data from different sources, e.g. relational databases

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

www.ibm.com/products/netezza

www.percona.com/mongodb/software/percona-server-for-mongodb

www.snowflake.com

sphinxsearch.com

sqrrl.com

Technical documentation

docs.percona.com/percona-distribution-for-mongodb

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

IBM

Percona

Snowflake Computing Inc.

Sphinx Technologies Inc.

Amazon

originally Sqrrl Data, Inc.

Initial release

2000

2015

2014

2001

2012

Current release

3.4.10-2.10, November 2017

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

commercial

Open Source

GPL version 2, commercial licence available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

included in appliance

Linux

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC
OLE DB

proprietary protocol using JSON

CLI Client
JDBC
ODBC

Proprietary protocol

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

JavaScript

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

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

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

yes

via In-Memory Engine

User concepts

Access control

Users with fine-grained authorization concept

Access rights for users and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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

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