DBMS > Databricks vs. Riak TS vs. RocksDB vs. Solr vs. Tkrzw

System Properties Comparison Databricks vs. Riak TS vs. RocksDB vs. Solr vs. Tkrzw

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.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

A widely used distributed, scalable search engine based on Apache Lucene

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store
Relational DBMS

Secondary database models

Spatial DBMS

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

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

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

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

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

Technical documentation

docs.databricks.com

www.tiot.jp/riak-docs/riak/ts/latest

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Databricks

Open Source, formerly Basho Technologies

Facebook, Inc.

Apache Software Foundation

Mikio Hirabayashi

Initial release

2013

2015

2013

2006

2020

Current release

3.0.0, September 2022

9.2.1, May 2024

9.6.1, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

Java

C++

Server operating systems

hosted

Linux
OS X

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
macOS

Data scheme

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

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

with Databricks SQL

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

Erlang

Java plugins

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

ACID

yes

optimistic locking

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

using specific database classes

User concepts

Access control

yes

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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