DBMS > Apache Druid vs. GigaSpaces vs. HugeGraph vs. Snowflake

System Properties Comparison Apache Druid vs. GigaSpaces vs. HugeGraph vs. Snowflake

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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

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

A fast-speed and highly-scalable Graph DBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS
Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Graph DBMS

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

druid.apache.org

www.gigaspaces.com

github.com/hugegraph
hugegraph.apache.org

www.snowflake.com

Technical documentation

druid.apache.org/docs/latest/design

docs.gigaspaces.com/latest/landing.html

hugegraph.apache.org/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

Gigaspaces Technologies

Baidu

Snowflake Computing Inc.

Initial release

2012

2000

2018

2014

Current release

29.0.1, April 2024

15.5, September 2020

0.9

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2.0

commercial

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

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Unix

Linux
macOS
Solaris
Windows

Linux
macOS
Unix

hosted

Data scheme

yes

schema-less columns are supported

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

SQL for querying

SQL-99 for query and DML statements

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Java API
RESTful HTTP API
TinkerPop Gremlin

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C++
Java
Python
Scala

Groovy
Java
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

user defined functions

Triggers

yes, event driven architecture

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of 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

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

Role-based access control

Users, roles and permissions

Users with fine-grained authorization concept, user roles and pluggable authentication

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