DBMS > Google Cloud Datastore vs. Heroic vs. IBM Db2 vs. Oracle vs. Spark SQL

System Properties Comparison Google Cloud Datastore vs. Heroic vs. IBM Db2 vs. Oracle vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Common in IBM host environments, 2 different versions for host and Windows/Linux

Widely used RDBMS

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

Primary database model

Document store

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	125.90
Rank	#9	Overall
	#6	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

cloud.google.com/datastore

github.com/spotify/heroic

www.ibm.com/products/db2

www.oracle.com/database

spark.apache.org/sql

Technical documentation

cloud.google.com/datastore/docs

spotify.github.io/heroic

www.ibm.com/docs/en/db2

docs.oracle.com/en/database

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

Developer

Google

Spotify

IBM

Oracle

Apache Software Foundation

Initial release

2008

2014

1983

host version

1980

2014

Current release

12.1, October 2016

23c, September 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free version is available

commercial

restricted free version is available

Open Source

Apache 2.0

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 and C++

Scala

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes, details here

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

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

using Google App Engine

yes

PL/SQL

also stored procedures in Java possible

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

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

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