DBMS > Google BigQuery vs. Hawkular Metrics vs. TerarkDB vs. Tibero vs. Titan

System Properties Comparison Google BigQuery vs. Hawkular Metrics vs. TerarkDB vs. Tibero 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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

A secure RDBMS, designed for easy portability from Oracle

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

cloud.google.com/bigquery

www.hawkular.org

github.com/bytedance/terarkdb

us.tmaxsoft.com/products/tibero

github.com/thinkaurelius/titan

Technical documentation

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

github.com/thinkaurelius/titan/wiki

Developer

Google

Community supported by Red Hat

ByteDance, originally Terark

TmaxSoft

Aurelius, owned by DataStax

Initial release

2010

2014

2016

2003

2012

Current release

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

restricted open source version available

commercial

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

Java

C++

C and Assembler

Java

Server operating systems

hosted

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Unix
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP REST

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

C++
Java

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Persistent Stored Procedure (PSM)

yes

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

horizontal partitioning

by range, hash, list or composite

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Multi-source replication
Source-replica 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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

Durability

Support for making data persistent

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

planned for next version

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 (SQL 92, SQL 99)

User authentification and security via Rexster Graph Server

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