DBMS > BoltDB vs. Databricks vs. RocksDB vs. Splunk vs. Ultipa

System Properties Comparison BoltDB vs. Databricks vs. RocksDB vs. Splunk vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Analytics Platform for Big Data

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Key-value store

Document store
Relational DBMS

Key-value store

Search engine

Graph DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

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

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

github.com/boltdb/bolt

Technical documentation

docs.databricks.com

github.com/facebook/rocksdb/wiki

docs.splunk.com/Documentation/Splunk

www.ultipa.com/document

Developer

Databricks

Facebook, Inc.

Splunk Inc.

Ultipa

Initial release

2013

2003

2019

Current release

9.2.1, May 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

BSD

commercial

Limited free edition and free developer edition available

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux

Linux
OS X
Solaris
Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

Typing

predefined data types such as float or date

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

with Databricks SQL

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

HTTP REST

RESTful HTTP API

Supported programming languages

Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

C#
Java
JavaScript
PHP
Python
Ruby

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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 and roles

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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