DBMS > OpenTSDB vs. Sadas Engine vs. ToroDB vs. Vertica vs. VictoriaMetrics

System Properties Comparison OpenTSDB vs. Sadas Engine vs. ToroDB vs. Vertica vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable Time Series DBMS based on HBase

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

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

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

opentsdb.net

www.sadasengine.com

github.com/torodb/server

www.vertica.com

victoriametrics.com

Technical documentation

opentsdb.net/docs/build/html/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

vertica.com/documentation

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

Developer

currently maintained by Yahoo and other contributors

SADAS s.r.l.

8Kdata

OpenText

previously Micro Focus and Hewlett Packard

VictoriaMetrics

Initial release

2011

2006

2016

2005

2018

Current release

8.0

12.0.3, January 2023

v1.91, May 2023

License

Commercial or Open Source

Open Source

LGPL

commercial

free trial version available

Open Source

AGPL-V3

commercial

Limited community edition free

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java

C++

Server operating systems

Linux
Windows

AIX
Linux
Windows

All OS with a Java 7 VM

Linux

FreeBSD
Linux
macOS
OpenBSD

Data scheme

schema-free

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

numeric data for metrics, strings for tags

yes

string, integer, double, boolean, date, object_id

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

HTTP API
Telnet API

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

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

Supported programming languages

Erlang
Go
Java
Python
R
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

horizontal partitioning

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on HBase

none

Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Immediate Consistency

Eventual Consistency
Immediate Consistency

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

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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

Related products and services

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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