DBMS > Apache Phoenix vs. Geode vs. KeyDB vs. Snowflake

System Properties Comparison Apache Phoenix vs. Geode vs. KeyDB vs. Snowflake

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

phoenix.apache.org

geode.apache.org

github.com/Snapchat/KeyDB
keydb.dev

www.snowflake.com

Technical documentation

phoenix.apache.org

geode.apache.org/docs

docs.keydb.dev

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

EQ Alpha Technology Ltd.

Snowflake Computing Inc.

Initial release

2014

2002

2019

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

1.1, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

BSD-3

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.

Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

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

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

yes

SQL-like query language (OQL)

yes

APIs and other access methods

JDBC

Java Client API
Memcached protocol
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Lua

user defined functions

Triggers

yes

Cache Event Listeners

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights per client and object definable

simple password-based access control and ACL

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.