DBMS > Quasardb vs. SAP HANA vs. TimescaleDB vs. VictoriaMetrics vs. XTDB

System Properties Comparison Quasardb vs. SAP HANA vs. TimescaleDB vs. VictoriaMetrics vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Distributed, high-performance timeseries database

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Score	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

quasar.ai

www.sap.com/products/hana.html

www.timescale.com

victoriametrics.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

doc.quasar.ai/master

help.sap.com/hana

docs.timescale.com

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

www.xtdb.com/docs

Developer

quasardb

SAP

Timescale

VictoriaMetrics

Juxt Ltd.

Initial release

2009

2010

2017

2018

2019

Current release

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

2.15.0, May 2024

v1.91, May 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

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

C++

Clojure

Server operating systems

BSD
Linux
OS X
Windows

Appliance or cloud-service

Linux
OS X
Windows

FreeBSD
Linux
macOS
OpenBSD

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

yes, extensible-data-notation format

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

limited SQL, making use of Apache Calcite

APIs and other access methods

HTTP API

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

HTTP REST
JDBC

Supported programming languages

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

SQLScript, R

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

yes

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

yes

Source-replica replication with hot standby and reads on replicas

Synchronous replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

by using LevelDB

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

yes

fine grained access rights according to SQL-standard

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