DBMS > Google Cloud Spanner vs. Graph Engine vs. HyperSQL vs. Sphinx vs. SQLite

System Properties Comparison Google Cloud Spanner vs. Graph Engine vs. HyperSQL vs. Sphinx vs. SQLite

Editorial information provided by DB-Engines

Name

Description

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Search engine

Relational DBMS

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

cloud.google.com/spanner

Technical documentation

cloud.google.com/spanner/docs

www.graphengine.io/docs/manual

hsqldb.org/web/hsqlDocsFrame.html

sphinxsearch.com/docs

www.sqlite.org/docs.html

Developer

Google

Microsoft

Sphinx Technologies Inc.

Dwayne Richard Hipp

Initial release

2017

2010

2001

2000

Current release

2.7.2, June 2023

3.5.1, February 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

based on BSD license

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

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

.NET and C

Java

C++

Server operating systems

hosted

.NET

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

yes

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

APIs and other access methods

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C#
C++
F#
Visual Basic

All languages supporting JDBC/ODBC
Java

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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.

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.