DBMS > Google BigQuery vs. GridGain vs. HyperSQL vs. InfluxDB vs. Realm

System Properties Comparison Google BigQuery vs. GridGain vs. HyperSQL vs. InfluxDB vs. Realm

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

DBMS for storing time series, events and metrics

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS

with GEO package

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	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Website

cloud.google.com/bigquery

www.gridgain.com

hsqldb.org

www.influxdata.com/products/influxdb-overview

realm.io

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

hsqldb.org/web/hsqlDocsFrame.html

docs.influxdata.com/influxdb

realm.io/docs

Developer

Google

GridGain Systems, Inc.

Realm, acquired by MongoDB in May 2019

Initial release

2010

2007

2001

2013

2014

Current release

GridGain 8.5.1

2.7.2, June 2023

2.7.6, April 2024

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

MIT-License; commercial enterprise version available

Open Source

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

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X

through Homebrew

Android
Backend: server-less
iOS
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

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

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API
JSON over UDP

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC
Java

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

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Java, SQL

runs within the applications so server-side scripts are unnecessary

Triggers

yes (cache interceptors and events)

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

in enterprise version only

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

selectable replication factor

in enterprise version only

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Depending on used storage engine

yes

In-Memory realm

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

fine grained access rights according to SQL-standard

simple rights management via user accounts

yes

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