DBMS > Google Cloud Spanner vs. LokiJS vs. NCache vs. Sphinx vs. SQLite

System Properties Comparison Google Cloud Spanner vs. LokiJS vs. NCache 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.

In-memory JavaScript DBMS

Open-Source and Enterprise in-memory Key-Value Store

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

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

cloud.google.com/spanner

github.com/techfort/LokiJS

www.alachisoft.com/ncache

sphinxsearch.com

www.sqlite.org

Technical documentation

cloud.google.com/spanner/docs

techfort.github.io/LokiJS

www.alachisoft.com/resources/docs

sphinxsearch.com/docs

www.sqlite.org/docs.html

Developer

Google

Alachisoft

Sphinx Technologies Inc.

Dwayne Richard Hipp

Initial release

2017

2014

2005

2001

2000

Current release

5.3.3, April 2024

3.5.1, February 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Enterprise Edition available

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

JavaScript

C#, .NET, .NET Core, Java

C++

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

via views

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

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

JavaScript API

IDistributedCache
JCache
LINQ
Proprietary native API

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

JavaScript

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

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

View functions in JavaScript

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

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

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

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

atomic operations within a single collection possible

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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)

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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