DBMS > GigaSpaces vs. Google Cloud Datastore vs. PieCloudDB vs. RocksDB vs. Splunk

System Properties Comparison GigaSpaces vs. Google Cloud Datastore vs. PieCloudDB vs. RocksDB vs. Splunk

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

A cloud-native analytic database platform with new technologoy for elastic MPP

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

Analytics Platform for Big Data

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Graph DBMS
Search engine

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.32
Rank	#289	Overall
	#133	Relational DBMS

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

Score	89.10
Rank	#14	Overall
	#2	Search engines

Website

www.gigaspaces.com

cloud.google.com/datastore

www.openpie.com

rocksdb.org

www.splunk.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/datastore/docs

github.com/facebook/rocksdb/wiki

docs.splunk.com/Documentation/Splunk

Developer

Gigaspaces Technologies

Google

OpenPie

Facebook, Inc.

Splunk Inc.

Initial release

2000

2008

2013

2003

Current release

15.5, September 2020

2.1, January 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

BSD

commercial

Limited free edition and free developer edition available

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++, .Net

C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language (GQL)

yes

Splunk Search Processing Language for search commands

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

CLI Client
JDBC
ODBC

C++ API
Java API

HTTP REST

Supported programming languages

.Net
C++
Java
Python
Scala

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

Java
PL/SQL
Python
R

C
C++
Go
Java
Perl
Python
Ruby

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

using Google App Engine

user defined functions

yes

Triggers

yes, event driven architecture

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication using Paxos

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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.

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

User concepts

Access control

Role-based access control

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

User Roles and pluggable authentication with full SQL Standard

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

PieCloudDB is ideal for Data mining applications that require extreme scalability...
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Key customers

Sail-Cloud China Shipbuilding Group Haizhou System Soochow Securities etc.,
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Licensing and pricing models

PieCloudDB Community Edition: Community License, Free Download, Self-Hosted Deployment；...
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