DBMS > Google BigQuery vs. Machbase Neo vs. Spark SQL vs. Sphinx

System Properties Comparison Google BigQuery vs. Machbase Neo vs. Spark SQL vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

TimeSeries DBMS for AIoT and BigData

Spark SQL is a component on top of 'Spark Core' for structured data processing

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	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.22
Rank	#324	Overall
	#29	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

cloud.google.com/bigquery

machbase.com

spark.apache.org/sql

sphinxsearch.com

Technical documentation

cloud.google.com/bigquery/docs

machbase.com/dbms

spark.apache.org/docs/latest/sql-programming-guide.html

sphinxsearch.com/docs

Developer

Google

Machbase

Apache Software Foundation

Sphinx Technologies Inc.

Initial release

2010

2013

2014

2001

Current release

V8.0, August 2023

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

free test version available

Open Source

Apache 2.0

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

Scala

C++

Server operating systems

hosted

Linux
macOS
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

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

JDBC
ODBC

Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
Python
R
Scala

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

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

Transaction concepts

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

Since BigQuery is designed for querying data

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.

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

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