DBMS > Google BigQuery vs. Memcached vs. TimescaleDB vs. UniData,UniVerse

System Properties Comparison Google BigQuery vs. Memcached vs. TimescaleDB vs. UniData,UniVerse

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

In-memory key-value store, originally intended for caching

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

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Secondary database models

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

cloud.google.com/bigquery

www.memcached.org

www.timescale.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

cloud.google.com/bigquery/docs

github.com/memcached/memcached/wiki

docs.timescale.com

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Timescale

Rocket Software

Initial release

2010

2003

2017

1985

Current release

1.6.25, March 2024

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

Apache 2.0

commercial

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

Server operating systems

hosted

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

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

optional

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

yes

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

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

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

.Net
Basic

U2 Basic
C
Java

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

yes

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

none

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Repcached, a Memcached patch, provides this functionallity

Source-replica replication with hot standby and reads on replicas

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

configurable

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.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

Access rights according to SQL-standard and operating system based

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