DBMS > Google Cloud Datastore vs. Machbase Neo vs. Manticore Search vs. PostgreSQL

System Properties Comparison Google Cloud Datastore vs. Machbase Neo vs. Manticore Search vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

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

TimeSeries DBMS for AIoT and BigData

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

cloud.google.com/datastore

machbase.com

manticoresearch.com

www.postgresql.org

Technical documentation

cloud.google.com/datastore/docs

machbase.com/dbms

manual.manticoresearch.com

www.postgresql.org/docs

Developer

Google

Machbase

Manticore Software

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2008

2013

2017

1989

1989: Postgres, 1996: PostgreSQL

Current release

V8.0, August 2023

6.0, February 2023

16.3, May 2024

License

Commercial or Open Source

commercial

free test version available

Open Source

GPL version 2

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C++

Server operating systems

hosted

Linux
macOS
Windows

FreeBSD
Linux
macOS
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes, details here

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

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

standard with numerous extensions

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

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

Supported programming languages

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

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

Synchronous replication based on Galera library

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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.

Immediate Consistency

Foreign keys

Referential integrity

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

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

volatile and lookup table

User concepts

Access control

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

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

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More resources

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Monitoring PostgreSQL with Redgate Monitor
Webinar, 10-11am CT / 4-5pm BST, 22 May 2024

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