DBMS > Cachelot.io vs. Google BigQuery vs. Prometheus vs. Sphinx vs. TimescaleDB

System Properties Comparison Cachelot.io vs. Google BigQuery vs. Prometheus vs. Sphinx vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Large scale data warehouse service with append-only tables

Open-source Time Series DBMS and monitoring system

Open source search engine for searching in data from different sources, e.g. relational databases

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

Primary database model

Secondary database models

Relational DBMS

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

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

cachelot.io

cloud.google.com/bigquery

prometheus.io

sphinxsearch.com

www.timescale.com

Technical documentation

cloud.google.com/bigquery/docs

prometheus.io/docs

sphinxsearch.com/docs

docs.timescale.com

Developer

Google

Sphinx Technologies Inc.

Timescale

Initial release

2015

2010

2015

2001

2017

Current release

3.5.1, February 2023

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence 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

C++

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

full PostgreSQL SQL syntax

APIs and other access methods

Memcached protocol

RESTful HTTP/JSON API

Proprietary protocol

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

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.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

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by Federation

none

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

none

Immediate Consistency

none

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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)

fine grained access rights according to SQL-standard

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