DBMS > BoltDB vs. Google Cloud Datastore vs. GridGain vs. RocksDB vs. Splunk

System Properties Comparison BoltDB vs. Google Cloud Datastore vs. GridGain vs. RocksDB vs. Splunk

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Analytics Platform for Big Data

Primary database model

Key-value store

Document store

Key-value store
Relational DBMS

Key-value store

Search engine

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

github.com/boltdb/bolt

cloud.google.com/datastore

www.gridgain.com

rocksdb.org

www.splunk.com

Technical documentation

cloud.google.com/datastore/docs

www.gridgain.com/docs/index.html

github.com/facebook/rocksdb/wiki

docs.splunk.com/Documentation/Splunk

Developer

Google

GridGain Systems, Inc.

Facebook, Inc.

Splunk Inc.

Initial release

2013

2008

2007

2013

2003

Current release

GridGain 8.5.1

8.11.4, April 2024

License

Commercial or Open Source

Open Source

MIT License

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

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Solaris
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

SQL

Support of SQL

SQL-like query language (GQL)

ANSI-99 for query and DML statements, subset of DDL

Splunk Search Processing Language for search commands

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

C++ API
Java API

HTTP REST

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
Perl
Python
Ruby

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

Callbacks using the Google Apps Engine

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes (replicated cache)

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

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

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

Security Hooks for custom implementations

Access rights for users and roles

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