DBMS > Google Cloud Datastore vs. Oracle NoSQL vs. Sqrrl

System Properties Comparison Google Cloud Datastore vs. Oracle NoSQL vs. Sqrrl

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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

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store

Document store
Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

cloud.google.com/datastore

www.oracle.com/database/nosql/technologies/nosql

sqrrl.com

Technical documentation

cloud.google.com/datastore/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Google

Oracle

Amazon

originally Sqrrl Data, Inc.

Initial release

2008

2011

2012

Current release

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Linux
Solaris SPARC/x86

Linux

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes, details here

optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like DML and DDL statements

APIs and other access methods

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

RESTful HTTP API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

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

C
C#
Go
Java
JavaScript (Node.js)
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

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

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

Foreign keys

Referential integrity

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

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

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

Access rights for users and roles

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

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