DBMS > Google Cloud Datastore vs. Heroic vs. PostGIS vs. RocksDB vs. TimesTen

System Properties Comparison Google Cloud Datastore vs. Heroic vs. PostGIS vs. RocksDB vs. TimesTen

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

Spatial extension of PostgreSQL

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Relational DBMS

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	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

cloud.google.com/datastore

github.com/spotify/heroic

postgis.net

rocksdb.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/datastore/docs

spotify.github.io/heroic

postgis.net/documentation

github.com/facebook/rocksdb/wiki

docs.oracle.com/database/timesten-18.1

Developer

Google

Spotify

Facebook, Inc.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2008

2014

2005

2013

1998

Current release

3.4.2, February 2024

9.2.1, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL v2.0

Open Source

BSD

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++

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

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

APIs and other access methods

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

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

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

PL/SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

based on PostgreSQL

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

based on PostgreSQL

yes

Multi-source replication
Source-replica replication

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 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

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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)

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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