DBMS > Google BigQuery vs. GreptimeDB vs. Memcached vs. Oracle Rdb vs. Sphinx

System Properties Comparison Google BigQuery vs. GreptimeDB vs. Memcached vs. Oracle Rdb vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

An open source Time Series DBMS built for increased scalability, high performance and efficiency

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

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

Primary database model

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

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

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

cloud.google.com/bigquery

greptime.com

www.memcached.org

www.oracle.com/database/technologies/related/rdb.html

sphinxsearch.com

Technical documentation

cloud.google.com/bigquery/docs

docs.greptime.com

github.com/memcached/memcached/wiki

www.oracle.com/database/technologies/related/rdb-doc.html

sphinxsearch.com/docs

Developer

Google

Greptime Inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Oracle, originally developed by Digital Equipment Corporation (DEC)

Sphinx Technologies Inc.

Initial release

2010

2022

2003

1984

2001

Current release

1.6.25, March 2024

7.4.1.1, 2021

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD license

commercial

Open Source

GPL version 2, commercial licence available

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

Rust

C++

Server operating systems

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Unix
Windows

HP Open VMS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free, schema definition possible

schema-free

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

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

gRPC
HTTP API
JDBC

Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Erlang
Go
Java
JavaScript

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Repcached, a Memcached patch, provides this functionallity

none

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

yes, on a single node

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.

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)

Simple rights management via user accounts

yes

using SASL (Simple Authentication and Security Layer) protocol

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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