DBMS > GridGain vs. HBase vs. SingleStore vs. Snowflake

System Properties Comparison GridGain vs. HBase vs. SingleStore vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

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

Primary database model

Key-value store
Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	5.91
Rank	#61	Overall
	#34	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

www.gridgain.com/docs/index.html

hbase.apache.org/book.html

docs.singlestore.com

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

SingleStore Inc.

Snowflake Computing Inc.

Initial release

2007

2008

2013

2014

Current release

GridGain 8.5.1

2.3.4, January 2021

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

free developer edition available

commercial

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java, C++, .Net

Java

C++, Go

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux

64 bit version required

hosted

Data scheme

yes

schema-free, schema definition possible

yes

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

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

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

yes

but no triggers and foreign keys

yes

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Bash
C
C#
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

yes

Coprocessors in Java

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

hash partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

Source-replica replication

stores two copies of each physical data partition on two separate nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Security Hooks for custom implementations

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Fine grained access control via users, groups and roles

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

More information provided by the system vendor

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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