DBMS > HBase vs. Kdb vs. QuestDB vs. RocksDB vs. STSdb

System Properties Comparison HBase vs. Kdb vs. QuestDB vs. RocksDB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

High performance Time Series DBMS

A high performance open source SQL database for time series data

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Wide column store

Time Series DBMS
Vector DBMS

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

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

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

github.com/STSSoft/STSdb4

Technical documentation

hbase.apache.org/book.html

code.kx.com

questdb.io/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kx Systems, a division of First Derivatives plc

QuestDB Technology Inc

Facebook, Inc.

STS Soft SC

Initial release

2008

2000

kdb was released 2000, kdb+ in 2003

2014

2013

2011

Current release

2.3.4, January 2021

3.6, May 2018

8.11.4, April 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free 32-bit version

Open Source

Apache 2.0

Open Source

BSD

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

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

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

Windows

Data scheme

schema-free, schema definition possible

yes

schema-free via InfluxDB Line Protocol

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

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

Secondary indexes

yes

table attribute 'grouped'

SQL

Support of SQL

SQL-like query language (q)

SQL with time-series extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

C++ API
Java API

.NET Client API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

over HTTP

C
C++
Go
Java
Perl
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

horizontal partitioning (by timestamps)

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Source-replica replication with eventual consistency

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

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

Single row ACID (across millions of columns)

ACID for single-table writes

yes

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

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

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Relational model with native time series support Column-based storage and time partitioned...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
» more

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.