DBMS > etcd vs. GridGain vs. HEAVY.AI vs. Snowflake vs. SwayDB

System Properties Comparison etcd vs. GridGain vs. HEAVY.AI vs. Snowflake vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

etcd.io
github.com/etcd-io/etcd

www.gridgain.com

github.com/heavyai/heavydb
www.heavy.ai

www.snowflake.com

swaydb.simer.au

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.gridgain.com/docs/index.html

docs.heavy.ai

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

HEAVY.AI, Inc.

Snowflake Computing Inc.

Simer Plaha

Initial release

2007

2016

2014

2018

Current release

3.4, August 2019

GridGain 8.5.1

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

GNU Affero GPL V3.0

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.

Implementation language

Java, C++, .Net

C++ and CUDA

Scala

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

schema-free

yes

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

schema-free

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

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

yes

APIs and other access methods

gRPC
JSON over HTTP

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

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

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

user defined functions

Triggers

yes, watching key changes

yes (cache interceptors and events)

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes (replicated cache)

Multi-source replication

yes

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

ACID

Atomic execution of operations

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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

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