DBMS > Amazon Redshift vs. Google Cloud Datastore vs. LokiJS vs. Quasardb vs. TimescaleDB

System Properties Comparison Amazon Redshift vs. Google Cloud Datastore vs. LokiJS vs. Quasardb vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

In-memory JavaScript DBMS

Distributed, high-performance timeseries database

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/redshift

cloud.google.com/datastore

github.com/techfort/LokiJS

quasar.ai

www.timescale.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

techfort.github.io/LokiJS

doc.quasar.ai/master

docs.timescale.com

Developer

Amazon (based on PostgreSQL)

Google

quasardb

Timescale

Initial release

2012

2008

2014

2009

2017

Current release

3.14.1, January 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

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

JavaScript

C++

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

integer and binary

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

yes

via views

yes

with tags

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (GQL)

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

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

JavaScript API

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC

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

JavaScript

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

using Google App Engine

View functions in JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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.

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

atomic operations within a single collection possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

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)

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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