DBMS > Hive vs. LeanXcale vs. NSDb vs. SAP HANA vs. TinkerGraph

System Properties Comparison Hive vs. LeanXcale vs. NSDb vs. SAP HANA vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

In-memory, column based data store. Available as appliance or cloud service

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Website

hive.apache.org

www.leanxcale.com

nsdb.io

www.sap.com/products/hana.html

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

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

nsdb.io/Architecture

help.sap.com/hana

Developer

Apache Software Foundation

initially developed by Facebook

LeanXcale

SAP

Initial release

2012

2015

2017

2010

2009

Current release

3.1.3, April 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Scala

Java

Server operating systems

All OS with a Java VM

Linux
macOS

Appliance or cloud-service

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through Apache Derby

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
WebSocket

JDBC
ODBC

TinkerPop 3

Supported programming languages

C++
Java
PHP
Python

C
Java
Scala

Java
Scala

Groovy
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

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

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Using Apache Lucene

yes

optional

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

yes

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