DBMS > Dgraph vs. Google BigQuery vs. IBM Db2 Event Store vs. NuoDB vs. Oracle Coherence

System Properties Comparison Dgraph vs. Google BigQuery vs. IBM Db2 Event Store vs. NuoDB vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Oracles in-memory data grid solution

Primary database model

Graph DBMS

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Key-value store

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

dgraph.io

cloud.google.com/bigquery

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

www.3ds.com/nuodb-distributed-sql-database

www.oracle.com/java/coherence

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

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

doc.nuodb.com

docs.oracle.com/en/middleware/standalone/coherence

Developer

Dgraph Labs, Inc.

Google

IBM

Dassault Systèmes

originally NuoDB, Inc.

Oracle

Initial release

2016

2010

2017

2013

2007

Current release

2.0

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

commercial

limited edition free

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C++

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux

Linux, macOS, Windows for the developer addition

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

All OS with a Java VM

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JCache
JPA
RESTful HTTP API

Supported programming languages

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

.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

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

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Java, SQL

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Active-active shard replication

yes

Managed transparently by NuoDB

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

tunable commit protocol

configurable

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

MVCC

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

optionally

In-memory capabilities

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

yes

Temporary table

yes

User concepts

Access control

Planned for future releases

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

Standard SQL roles/ privileges, Administrative Users

authentification to access the cache via certificates or http basic authentication

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