DBMS > Google BigQuery vs. OrigoDB vs. PlanetScale vs. Prometheus

System Properties Comparison Google BigQuery vs. OrigoDB vs. PlanetScale vs. Prometheus

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A fully ACID in-memory object graph database

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

cloud.google.com/bigquery

origodb.com

planetscale.com

prometheus.io

Technical documentation

cloud.google.com/bigquery/docs

origodb.com/docs

planetscale.com/docs

prometheus.io/docs

Developer

Google

Robert Friberg et al

PlanetScale

Initial release

2010

2009

under the name LiveDB

2020

2015

License

Commercial or Open Source

commercial

Open Source

commercial

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

Server operating systems

hosted

Linux
Windows

Docker
Linux
macOS

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

Numeric data only

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 be achieved using .NET

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
MySQL protocol
ODBC

RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

none

Foreign keys

Referential integrity

depending on model

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

In-memory capabilities

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

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role based authorization

Users with fine-grained authorization concept

no user groups or roles

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