DBMS > Google BigQuery vs. GridGain vs. LevelDB vs. RavenDB vs. RDF4J

System Properties Comparison Google BigQuery vs. GridGain vs. LevelDB vs. RavenDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Document store

RDF store

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

cloud.google.com/bigquery

www.gridgain.com

github.com/google/leveldb

ravendb.net

rdf4j.org

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

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

ravendb.net/docs

rdf4j.org/documentation

Developer

Google

GridGain Systems, Inc.

Google

Hibernating Rhinos

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

Initial release

2010

2007

2011

2010

2004

Current release

GridGain 8.5.1

1.23, February 2021

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

AGPL version 3, commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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

ACID, Cluster-wide transaction available

ACID

Isolation support depends on the API used

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

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)

Security Hooks for custom implementations

Authorization levels configured per client per database

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