DBMS > Google BigQuery vs. InfluxDB vs. LevelDB vs. RDF4J vs. Sequoiadb

System Properties Comparison Google BigQuery vs. InfluxDB vs. LevelDB vs. RDF4J vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

DBMS for storing time series, events and metrics

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	26.56
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.48
Rank	#260	Overall
	#41	Document stores
	#121	Relational DBMS

Website

cloud.google.com/bigquery

www.influxdata.com/products/influxdb-overview

github.com/google/leveldb

rdf4j.org

www.sequoiadb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.influxdata.com/influxdb

github.com/google/leveldb/blob/main/doc/index.md

rdf4j.org/documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Sequoiadb Ltd.

Initial release

2010

2013

2011

2004

2013

Current release

2.7.5, January 2024

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

BSD

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Server: AGPL; Client: Apache V2

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++

Java

C++

Server operating systems

hosted

Linux
OS X

through Homebrew

Illumos
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

oid, date, timestamp, binary, regex

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

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
JSON over UDP

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

proprietary protocol using JSON

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
PHP
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

none

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

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

Isolation support depends on the API used

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

Depending on used storage engine

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

simple password-based access control

More information provided by the system vendor

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