DBMS > Hive vs. YugabyteDB

System Properties Comparison Hive vs. YugabyteDB

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Our visitors often compare Hive and YugabyteDB with Cassandra, PostgreSQL and MongoDB.

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Primary database model

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	78.50
Rank	#14	Overall
	#9	Relational DBMS

Score	0.92
Rank	#179	Overall
	#90	Relational DBMS

Website

hive.apache.org

www.yugabyte.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

Developer

Apache Software Foundation

initially developed by Facebook

Yugabyte Inc.

Initial release

2012

2017

Current release

3.1.2, August 2019

2.4, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2

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.

Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM

Linux
OS X

Data scheme

yes

depending on used data model

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, PostgreSQL compatible

APIs and other access methods

JDBC
ODBC
Thrift

YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language

Supported programming languages

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Hash and Range Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual 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

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

In-memory capabilities

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

User concepts

Access control

Access rights for users, groups and roles

yes

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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