DBMS > Amazon Redshift vs. Apache Druid vs. EsgynDB vs. Manticore Search vs. Sqrrl

System Properties Comparison Amazon Redshift vs. Apache Druid vs. EsgynDB vs. Manticore Search 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

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Multi-storage database for search, including full-text search.

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Website

aws.amazon.com/redshift

druid.apache.org

www.esgyn.cn

manticoresearch.com

sqrrl.com

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

manual.manticoresearch.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Esgyn

Manticore Software

Amazon

originally Sqrrl Data, Inc.

Initial release

2012

2015

2017

2012

Current release

29.0.1, April 2024

6.0, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Open Source

GPL version 2

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

Java

C++, Java

C++

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux

FreeBSD
Linux
macOS
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

restricted

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Actionscript
C

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

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication between multi datacenters

Synchronous replication based on Galera library

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

Immediate Consistency

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

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 Manticore index.

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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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