DBMS > Hawkular Metrics vs. TimescaleDB vs. Tkrzw vs. XTDB vs. YottaDB

System Properties Comparison Hawkular Metrics vs. TimescaleDB vs. Tkrzw vs. XTDB vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

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

A fast and solid embedded Key-value store

Primary database model

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

www.hawkular.org

www.timescale.com

dbmx.net/tkrzw

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

yottadb.com

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.timescale.com

www.xtdb.com/docs

yottadb.com/resources/documentation

Developer

Community supported by Red Hat

Timescale

Mikio Hirabayashi

Juxt Ltd.

YottaDB, LLC

Initial release

2014

2017

2020

2019

2001

Current release

2.15.0, May 2024

0.9.3, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

AGPL 3.0

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

C++

Clojure

Server operating systems

Linux
OS X
Windows

Linux
macOS

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

Docker
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

limited SQL, making use of Apache Calcite

by using the Octo plugin

APIs and other access methods

HTTP REST

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

HTTP REST
JDBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Go
Java
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C++
Java
Python
Ruby

Clojure
Java

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

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

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication with hot standby and reads on replicas

none

yes, each node contains all data

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

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

yes

using specific database classes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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