DBMS > Cachelot.io vs. GBase vs. Google Cloud Datastore vs. JaguarDB vs. Sqrrl

System Properties Comparison Cachelot.io vs. GBase vs. Google Cloud Datastore vs. JaguarDB 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

In-memory caching system

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

Performant, highly scalable DBMS for AI and IoT applications

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Key-value store

Relational DBMS

Document store

Key-value store
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

cachelot.io

www.gbase.cn

cloud.google.com/datastore

www.jaguardb.com

sqrrl.com

Technical documentation

cloud.google.com/datastore/docs

www.jaguardb.com/support.html

Developer

General Data Technology Co., Ltd.

Google

DataJaguar, Inc.

Amazon

originally Sqrrl Data, Inc.

Initial release

2015

2004

2008

2015

2012

Current release

GBase 8a, GBase 8s, GBase 8c

3.3 July 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

GPL V3.0

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

C++

C, Java, Python

C++

the server part. Clients available in other languages

Java

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

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

SQL

Support of SQL

Standard with numerous extensions

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

Memcached protocol

ADO.NET
C API
JDBC
ODBC

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

JDBC
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication using Paxos

Multi-source replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

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

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.

User concepts

Access control

yes

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

rights management via user accounts

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

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