DBMS > Apache Hive vs. Heroic

System Properties Comparison Apache Hive vs. Heroic

Please select another system to include it in the comparison.

Our visitors often compare Apache Hive and Heroic with Trino, Apache Solr and Apache Pinot.

Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Relational DBMS

Time Series DBMS

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

Score	70.08
Rank	#18	Overall
	#12	Relational DBMS

Score	0.02
Rank	#373	Overall
	#40	Time Series DBMS

Website

hive.apache.org

github.com/spotify/heroic

Technical documentation

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

spotify.github.io/heroic

Developer

Apache Software Foundation

initially developed by Facebook

Spotify

Initial release

2012

2014

Current release

3.1.3, April 2022

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

Server operating systems

All OS with a Java VM

Data scheme

yes

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.

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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.

User concepts

Access control

Access rights for users, groups and roles

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Design patterns for implementing Hive Metastore for Amazon EMR on EKS
28 February 2025, AWS Blog

Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster
15 March 2025, Towards Data Science

What Is Apache Iceberg?
18 December 2024, IBM

Pinot for Low-Latency Offline Table Analytics
29 August 2024, Uber

Presto SQL + S3 Data + Superset = Data Lake
25 October 2024, hackernoon.com

provided by Google News

How Code Wizards load tested Heroic Labs’ Nakama to two million concurrent players with AWS
1 October 2024, AWS Blog

provided by Google News

Share this page