DBMS > Apache Phoenix vs. Bangdb vs. Sadas Engine vs. YugabyteDB

System Properties Comparison Apache Phoenix vs. Bangdb vs. Sadas Engine vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

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

Converged and high performance database for device data, events, time series, document and graph

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Wide column store

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

Technical documentation

phoenix.apache.org

docs.bangdb.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Apache Software Foundation

Sachin Sinha, BangDB

SADAS s.r.l.

Yugabyte Inc.

Initial release

2014

2012

2006

2017

Current release

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

BangDB 2.0, October 2021

8.0

2.19, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

commercial

free trial version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

Java

C, C++

C++

C and C++

Server operating systems

Linux
Unix
Windows

Linux

AIX
Linux
Windows

Linux
OS X

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

depending on used data model

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

SQL like support with command line tool

yes

yes, PostgreSQL compatible

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

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

C
C#
C++
Java
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

user defined functions

yes

sql, plpgsql, C

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

none

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

based on RocksDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

managed by 'Learn by Usage'

User concepts

Access control

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

yes (enterprise version only)

Access rights for users, groups and roles according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

YugabyteDB is an open source distributed SQL database for cloud native transactional...
» more

Competitive advantages

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
» more

Typical application scenarios

Systems of record and engagement for cloud native applications that require resilience,...
» more

Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
» more

Licensing and pricing models

Apache 2.0 license for the database
» more

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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