DBMS > Badger vs. Hawkular Metrics vs. LeanXcale vs. Teradata Aster vs. TimescaleDB

System Properties Comparison Badger vs. Hawkular Metrics vs. LeanXcale vs. Teradata Aster vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Platform for big data analytics on multistructured data sources and types

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

Primary database model

Key-value store

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

github.com/dgraph-io/badger

www.hawkular.org

www.leanxcale.com

www.timescale.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

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

docs.timescale.com

Developer

DGraph Labs

Community supported by Red Hat

LeanXcale

Teradata

Timescale

Initial release

2017

2014

2015

2005

2017

Current release

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

yes

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

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

in Aster File Store

yes

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

yes

full PostgreSQL SQL syntax

APIs and other access methods

HTTP REST

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
ODBC
OLE DB

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

Supported programming languages

Go
Java
Python
Ruby

C
Java
Scala

C
C#
C++
Java
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

R packages

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

none

Sharding

based on Cassandra

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate 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

User concepts

Access control

fine grained access rights according to SQL-standard

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