DBMS > Apache Hive vs. ClickHouse vs. SvectorDB

System Properties Comparison Apache Hive vs. ClickHouse vs. SvectorDB

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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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Serverless cloud-native vector database

Primary database model

Relational DBMS

Vector DBMS

Secondary database models

Time Series DBMS

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

Score	62.49
Rank	#18	Overall
	#12	Relational DBMS

Score	18.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.00
Rank	#381	Overall
	#19	Vector DBMS

Website

hive.apache.org

clickhouse.com

svectordb.com

Technical documentation

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

clickhouse.com/docs

www.svectordb.com/docs

Developer

Apache Software Foundation

initially developed by Facebook

Clickhouse Inc.

SvectorDB

Initial release

2012

2016

2023

Current release

3.1.3, April 2022

v24.6.2.17-stable, July 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM

FreeBSD
Linux
macOS

server-less

Data scheme

yes

Typing

predefined data types such as float or date

yes

string, double, vector

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

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

JDBC
ODBC
Thrift

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

OpenAPI 3.0, RESTful HTTP API, Python SDK, JavaScript / TypeScript SDK

Supported programming languages

C++
Java
PHP
Python

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

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

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.

yes

User concepts

Access control

Access rights for users, groups and roles

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

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