DBMS > Apache Hive vs. EsgynDB vs. SvectorDB

System Properties Comparison Apache Hive vs. EsgynDB vs. SvectorDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Serverless cloud-native vector database

Primary database model

Relational DBMS

Vector DBMS

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

Score	70.08
Rank	#18	Overall
	#12	Relational DBMS

Score	0.19
Rank	#317	Overall
	#140	Relational DBMS

Score	0.02
Rank	#374	Overall
	#20	Vector DBMS

Website

hive.apache.org

www.esgyn.cn

svectordb.com

Technical documentation

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

www.svectordb.com/docs

Developer

Apache Software Foundation

initially developed by Facebook

Esgyn

SvectorDB

Initial release

2012

2015

2023

Current release

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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

C++, Java

Server operating systems

All OS with a Java VM

Linux

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

yes

APIs and other access methods

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

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

Supported programming languages

C++
Java
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

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

Multi-source replication between multi datacenters

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

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

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

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

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

Share this page