DBMS > Geode vs. Google BigQuery vs. IBM Db2 Event Store vs. RethinkDB vs. SwayDB

System Properties Comparison Geode vs. Google BigQuery vs. IBM Db2 Event Store vs. RethinkDB vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Relational DBMS

Event Store
Time Series DBMS

Document store

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

geode.apache.org

cloud.google.com/bigquery

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

rethinkdb.com

swaydb.simer.au

Technical documentation

geode.apache.org/docs

cloud.google.com/bigquery/docs

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

rethinkdb.com/docs

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

IBM

The Linux Foundation

since July 2017

Simer Plaha

Initial release

2002

2010

2017

2009

2018

Current release

1.1, February 2017

2.0

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

free developer edition available

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

C++

Scala

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

SQL-like query language (OQL)

yes

through the embedded Spark runtime

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Cache Event Listeners

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Active-active shard replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

Since BigQuery is designed for querying data

Atomic single-document operations

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

MVCC based

yes

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

User concepts

Access control

Access rights per client and object definable

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

yes

users and table-level permissions

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