DBMS > Google Cloud Spanner vs. Hazelcast vs. KeyDB vs. Newts vs. SAP Advantage Database Server

System Properties Comparison Google Cloud Spanner vs. Hazelcast vs. KeyDB vs. Newts vs. SAP Advantage Database Server

Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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 widely adopted in-memory data grid

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Time Series DBMS based on Cassandra

Low-cost RDBMS with access to ISAM and FoxPro data structures

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

cloud.google.com/spanner

hazelcast.com

github.com/Snapchat/KeyDB
keydb.dev

opennms.github.io/newts

www.sap.com/products/advantage-database-server.html

Technical documentation

cloud.google.com/spanner/docs

hazelcast.org/imdg/docs

docs.keydb.dev

github.com/OpenNMS/newts/wiki

Developer

Google

Hazelcast

EQ Alpha Technology Ltd.

OpenNMS Group

Sybase, SAP

Initial release

2017

2008

2019

2014

1993

Current release

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

BSD-3

Open Source

Apache 2.0

commercial

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

Java

C++

Java

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
OS X
Windows

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

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

JCache
JPA
Memcached protocol
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP REST
Java API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Java

Delphi
Perl
PHP

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Lua

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

yes

Replicated Map

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

one or two-phase-commit; repeatable reads; read commited

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

caching of (temporary) tables can be configured

User concepts

Access control

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

Role-based access control

simple password-based access control and ACL

fine grained access rights according to SQL-standard

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