DBMS > EventStoreDB vs. Google BigQuery vs. PlanetScale vs. searchxml

System Properties Comparison EventStoreDB vs. Google BigQuery vs. PlanetScale vs. searchxml

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Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Large scale data warehouse service with append-only tables

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

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Event Store

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

www.eventstore.com

cloud.google.com/bigquery

planetscale.com

www.searchxml.net/category/products

Technical documentation

developers.eventstore.com

cloud.google.com/bigquery/docs

planetscale.com/docs

www.searchxml.net/support/handouts

Developer

Event Store Limited

Google

PlanetScale

informationpartners gmbh

Initial release

2012

2010

2020

2015

Current release

21.2, February 2021

1.0

License

Commercial or Open Source

Open Source

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++

Server operating systems

Linux
Windows

hosted

Docker
Linux
macOS

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
MySQL protocol
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

sychronisation to multiple collections

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

Immediate Consistency

Foreign keys

Referential integrity

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 at shard level

multiple readers, single writer

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

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)

Users with fine-grained authorization concept

no user groups or roles

Domain, group and role-based access control at the document level and for application services

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