DBMS > EventStoreDB vs. Google BigQuery vs. Percona Server for MySQL vs. RavenDB vs. Warp 10

System Properties Comparison EventStoreDB vs. Google BigQuery vs. Percona Server for MySQL vs. RavenDB vs. Warp 10

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

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

Open Source Operational and Transactional Enterprise NoSQL Document Database

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series 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	2.10
Rank	#119	Overall
	#57	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

www.eventstore.com

cloud.google.com/bigquery

www.percona.com/software/mysql-database/percona-server

ravendb.net

www.warp10.io

Technical documentation

developers.eventstore.com

cloud.google.com/bigquery/docs

www.percona.com/downloads/Percona-Server-LATEST

ravendb.net/docs

www.warp10.io/content/02_Getting_started

Developer

Event Store Limited

Google

Percona

Hibernating Rhinos

SenX

Initial release

2012

2010

2008

2010

2015

Current release

21.2, February 2021

8.0.36-28, 2024

5.4, July 2022

License

Commercial or Open Source

Open Source

commercial

Open Source

GPL version 2

Open Source

AGPL version 3, commercial license available

Open Source

Apache License 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

C and C++

Java

Server operating systems

Linux
Windows

hosted

Linux

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
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

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication
XtraDB Cluster

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID, Cluster-wide transaction available

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.

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

Authorization levels configured per client per database

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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