DBMS > Ehcache vs. Google BigQuery vs. Hawkular Metrics vs. Machbase Neo vs. TimescaleDB

System Properties Comparison Ehcache vs. Google BigQuery vs. Hawkular Metrics vs. Machbase Neo vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

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

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.ehcache.org

cloud.google.com/bigquery

www.hawkular.org

machbase.com

www.timescale.com

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

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

machbase.com/dbms

docs.timescale.com

Developer

Terracotta Inc, owned by Software AG

Google

Community supported by Red Hat

Machbase

Timescale

Initial release

2009

2010

2014

2013

2017

Current release

3.10.0, March 2022

V8.0, August 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache 2.0

commercial

free test version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Windows

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JCache

RESTful HTTP/JSON API

HTTP REST

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Cache Event Listeners

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

Sharding

based on Cassandra

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

selectable replication factor

based on Cassandra

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

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

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

simple password-based access control

fine grained access rights according to SQL-standard

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