DBMS > Google BigQuery vs. IBM Db2 Event Store vs. Percona Server for MongoDB vs. PlanetScale

System Properties Comparison Google BigQuery vs. IBM Db2 Event Store vs. Percona Server for MongoDB vs. PlanetScale

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

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

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	0.50
Rank	#250	Overall
	#39	Document stores

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2-event-store

www.percona.com/mongodb/software/percona-server-for-mongodb

planetscale.com

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2-event-store

docs.percona.com/percona-distribution-for-mongodb

planetscale.com/docs

Developer

Google

IBM

Percona

PlanetScale

Initial release

2010

2017

2015

2020

Current release

2.0

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL Version 2

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 and C++

C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

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

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

via In-Memory Engine

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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