DBMS > BoltDB vs. HBase vs. HEAVY.AI vs. RocksDB vs. SAP HANA

System Properties Comparison BoltDB vs. HBase vs. HEAVY.AI vs. RocksDB vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

github.com/boltdb/bolt

hbase.apache.org

github.com/heavyai/heavydb
www.heavy.ai

rocksdb.org

www.sap.com/products/hana.html

Technical documentation

hbase.apache.org/book.html

docs.heavy.ai

github.com/facebook/rocksdb/wiki

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

HEAVY.AI, Inc.

Facebook, Inc.

SAP

Initial release

2013

2008

2016

2013

2010

Current release

2.3.4, January 2021

5.10, January 2022

8.11.4, April 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++ and CUDA

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux

Appliance or cloud-service

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

SQL

Support of SQL

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift
Vega

C++ API
Java API

JDBC
ODBC

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Single row ACID (across millions of columns)

yes

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.

yes

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

yes

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