DBMS > Amazon Aurora vs. Amazon Redshift vs. Elasticsearch vs. Google Cloud Datastore vs. Sqrrl

System Properties Comparison Amazon Aurora vs. Amazon Redshift vs. Elasticsearch vs. Google Cloud Datastore 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

MySQL and PostgreSQL compatible cloud service by Amazon

Large scale data warehouse service for use with business intelligence tools

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Relational DBMS

Search engine

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Document store
Spatial DBMS
Vector DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

aws.amazon.com/rds/aurora

aws.amazon.com/redshift

www.elastic.co/elasticsearch

cloud.google.com/datastore

sqrrl.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.aws.amazon.com/redshift

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

cloud.google.com/datastore/docs

Developer

Amazon

Amazon (based on PostgreSQL)

Elastic

Google

Amazon

originally Sqrrl Data, Inc.

Initial release

2015

2012

2010

2008

2012

Current release

8.6, January 2023

License

Commercial or Open Source

commercial

Open Source

Elastic License

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

All OS with a Java VM

hosted

Linux

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

restricted

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like query language (GQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Java API
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

user defined functions

in Python

yes

using Google App Engine

Triggers

yes

by using the 'percolation' feature

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication using Paxos

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

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

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic updates per row, document, or graph entity

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

Memcached and Redis integration

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

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