DBMS > Google BigQuery vs. OpenTSDB vs. Oracle Berkeley DB vs. Qdrant

System Properties Comparison Google BigQuery vs. OpenTSDB vs. Oracle Berkeley DB vs. Qdrant

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Scalable Time Series DBMS based on HBase

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Vector DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Website

cloud.google.com/bigquery

opentsdb.net

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

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

cloud.google.com/bigquery/docs

opentsdb.net/docs/build/html/index.html

docs.oracle.com/cd/E17076_05/html/index.html

qdrant.tech/documentation

Developer

Google

currently maintained by Yahoo and other contributors

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Qdrant

Initial release

2010

2011

1994

2021

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

LGPL

Open Source

commercial license available

Open Source

Apache 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, Java, C++ (depending on the Berkeley DB edition)

Rust

Server operating systems

hosted

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

Numbers, Strings, Geo, Boolean

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

only with the Berkeley DB XML edition

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
Telnet API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Erlang
Go
Java
Python
R
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on HBase

Source-replica replication

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

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

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)

Key-based authentication

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.