DBMS > BoltDB vs. Databricks vs. Oracle Rdb vs. RocksDB vs. XTDB

System Properties Comparison BoltDB vs. Databricks vs. Oracle Rdb vs. RocksDB vs. XTDB

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)

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Document store
Relational DBMS

Relational DBMS

Key-value store

Document store

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	1.14
Rank	#178	Overall
	#80	Relational DBMS

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

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/boltdb/bolt

www.databricks.com

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

rocksdb.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.databricks.com

www.oracle.com/database/technologies/related/rdb-doc.html

github.com/facebook/rocksdb/wiki

www.xtdb.com/docs

Developer

Databricks

Oracle, originally developed by Digital Equipment Corporation (DEC)

Facebook, Inc.

Juxt Ltd.

Initial release

2013

1984

2013

2019

Current release

7.4.1.1, 2021

9.2.1, May 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

BSD

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

HP Open VMS

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

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

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

HTTP REST
JDBC

Supported programming languages

Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

yes, on a single node

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

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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