DBMS > Apache Druid vs. Hive vs. Oracle Berkeley DB vs. QuestDB vs. SAP HANA

System Properties Comparison Apache Druid vs. Hive vs. Oracle Berkeley DB vs. QuestDB vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Widely used in-process key-value store

A high performance open source SQL database for time series data

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Relational DBMS

Secondary database models

Relational 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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

druid.apache.org

hive.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

questdb.io

www.sap.com/products/hana.html

Technical documentation

druid.apache.org/docs/latest/design

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

docs.oracle.com/cd/E17076_05/html/index.html

questdb.io/docs

help.sap.com/hana

Developer

Apache Software Foundation and contributors

Apache Software Foundation

initially developed by Facebook

Oracle

originally developed by Sleepycat, which was acquired by Oracle

QuestDB Technology Inc

SAP

Initial release

2012

1994

2014

2010

Current release

29.0.1, April 2024

3.1.3, April 2022

18.1.40, May 2020

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

commercial license available

Open Source

Apache 2.0

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, Java, C++ (depending on the Berkeley DB edition)

Java (Zero-GC), C++, Rust

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Appliance or cloud-service

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

SQL with time-series extensions

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Java
PHP
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

none

horizontal partitioning (by timestamps)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

Source-replica replication

Source-replica replication with eventual consistency

yes

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID for single-table writes

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

through memory mapped files

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles

yes

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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