DBMS > Apache Druid vs. Google BigQuery vs. GridGain vs. Realm vs. StarRocks

System Properties Comparison Apache Druid vs. Google BigQuery vs. GridGain vs. Realm vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

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

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Document store

Relational DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

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

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

druid.apache.org

cloud.google.com/bigquery

www.gridgain.com

realm.io

www.starrocks.io

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

realm.io/docs

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Apache Software Foundation and contributors

Google

GridGain Systems, Inc.

Realm, acquired by MongoDB in May 2019

The Linux Foundation

since Feb 2023

Initial release

2012

2010

2007

2014

2020

Current release

29.0.1, April 2024

GridGain 8.5.1

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

Java, C++, .Net

C++, Java

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X
Solaris
Windows

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

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

SQL for querying

yes

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

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

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

JDBC
MySQL protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

runs within the applications so server-side scripts are unnecessary

user defined functions

Triggers

yes (cache interceptors and events)

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes (replicated cache)

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

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

In-Memory realm

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

yes

Role based access control and fine grained access rights

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