DBMS > Google BigQuery vs. IBM Db2 Event Store vs. NebulaGraph vs. Qdrant vs. Titan

System Properties Comparison Google BigQuery vs. IBM Db2 Event Store vs. NebulaGraph vs. Qdrant vs. Titan

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

A distributed, linear scalable, high perfomant Graph DBMS

A high-performance vector database with neural network or semantic-based matching

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Graph DBMS

Vector DBMS

Graph DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.23
Rank	#116	Overall
	#10	Graph DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Website

cloud.google.com/bigquery

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

github.com/vesoft-inc/nebula
www.nebula-graph.io

github.com/qdrant/qdrant
qdrant.tech

github.com/thinkaurelius/titan

Technical documentation

cloud.google.com/bigquery/docs

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

docs.nebula-graph.io

qdrant.tech/documentation

github.com/thinkaurelius/titan/wiki

Developer

Google

IBM

Vesoft Inc.

Qdrant

Aurelius, owned by DataStax

Initial release

2010

2017

2019

2021

2012

Current release

2.0

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache Version 2.0 + Common Clause 1.0

Open Source

Apache Version 2.0

Open Source

Apache license, version 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++

C++

Rust

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

Docker
Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

Strong typed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, 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

Nebula Graph internally uses the Key-Value store RocksDB for persistency. The vertices, edges, and their properties are stored as Key while their values are stored as Value. The primary indexes are per Key and secondary indexes are per Value.

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Browser interface
console (shell)
Cypher Query Language
GO Object Graph Mapper
Java Object Graph Mapper
NGBatis

ORM framework for NebulaGraph and Spring-Boot
Proprietary native API
Python Object Graph Mapper
Query language nGQL

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

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

.Net
C++
Go
Java
PHP
Python

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Causal Clustering using Raft protocol

Collection-level replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency, tunable consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in graph

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

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

using RocksDB

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

fine grained access rights according to SQL-standard

Role-based access control

Key-based authentication

User authentification and security via Rexster Graph Server

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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