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

System Properties Comparison Google Cloud Spanner vs. Graph Engine vs. HarperDB vs. HyperSQL 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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Document store

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	0.55
Rank	#248	Overall
	#38	Document stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

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

docs.harperdb.io/docs

hsqldb.org/web/hsqlDocsFrame.html

www.sqlite.org/docs.html

Developer

Google

Microsoft

HarperDB

Dwayne Richard Hipp

Initial release

2017

2010

2017

2001

2000

Current release

3.1, August 2021

2.7.2, June 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

free community edition available

Open Source

based on BSD license

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

Node.js

Java

Server operating systems

hosted

.NET

Linux
OS X

All OS with a Java VM

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

server-less

Data scheme

yes

dynamic schema

yes

dynamic column types

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like data manipulation statements

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C#
C++
F#
Visual Basic

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

All languages supporting JDBC/ODBC
Java

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

Custom Functions

since release 3.1

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

A table resides as a whole on one (or more) nodes in a cluster

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

yes

the nodes on which a table resides can be defined

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

Atomic execution of specific operations

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, using LMDB

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)

Access rights for users and roles

fine grained access rights according to SQL-standard

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