DBMS > Dgraph vs. IBM Db2 Event Store vs. Pinecone vs. Transbase

System Properties Comparison Dgraph vs. IBM Db2 Event Store vs. Pinecone vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

A managed, cloud-native vector database

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Graph DBMS

Event Store
Time Series DBMS

Vector DBMS

Relational DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

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

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

dgraph.io

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

www.pinecone.io

www.transaction.de/en/products/transbase.html

Technical documentation

dgraph.io/docs

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

docs.pinecone.io/docs/overview

www.transaction.de/en/products/transbase/features.html

Developer

Dgraph Labs, Inc.

IBM

Pinecone Systems, Inc

Transaction Software GmbH

Initial release

2016

2017

2019

1987

Current release

2.0

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

commercial

free development license

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++

Server operating systems

Linux
OS X
Windows

Linux

Linux, macOS, Windows for the developer addition

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

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

Python

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Active-active shard replication

Source-replica replication

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

Planned for future releases

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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