DBMS > Hive vs. OrientDB vs. Snowflake vs. Solr

System Properties Comparison Hive vs. OrientDB vs. Snowflake vs. Solr

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Editorial information provided by DB-Engines

Name

Description

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

Multi-model DBMS (Document, Graph, Key/Value)

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

Technical documentation

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

www.orientdb.com/docs/last/index.html

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Apache Software Foundation

initially developed by Facebook

OrientDB LTD; CallidusCloud; SAP

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2010

2014

2006

Current release

3.1.3, April 2022

3.2.29, March 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

commercial

Open Source

Apache Version 2

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

Server operating systems

All OS with a Java VM

All OS with a Java JDK (>= JDK 6)

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language, no joins

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
Thrift

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
PHP
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java, Javascript

user defined functions

Java plugins

Triggers

Hooks

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

could be achieved with distributed queries

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

optimistic locking

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

Access rights for users, groups and roles

Access rights for users and roles; record level security configurable

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

yes

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