DBMS > Bangdb vs. Google BigQuery vs. Quasardb vs. RocksDB vs. TimesTen

System Properties Comparison Bangdb vs. Google BigQuery vs. Quasardb vs. RocksDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Large scale data warehouse service with append-only tables

Distributed, high-performance timeseries database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

In-Memory RDBMS compatible to Oracle

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

bangdb.com

cloud.google.com/bigquery

quasar.ai

rocksdb.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.bangdb.com

cloud.google.com/bigquery/docs

doc.quasar.ai/master

github.com/facebook/rocksdb/wiki

docs.oracle.com/database/timesten-18.1

Developer

Sachin Sinha, BangDB

Google

quasardb

Facebook, Inc.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2012

2010

2009

2013

1998

Current release

BangDB 2.0, October 2021

3.14.1, January 2024

9.2.1, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

BSD 3

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

BSD

commercial

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, C++

C++

Server operating systems

Linux

hosted

BSD
Linux
OS X
Windows

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

integer and binary

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

secondary, composite, nested, reverse, geospatial

yes

with tags

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

HTTP API

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

consistent hashing

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication with selectable replication factor

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

by using LevelDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes, run db with in-memory only mode

yes

Transient mode

yes

User concepts

Access control

yes (enterprise version only)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.