DBMS > Databricks vs. EJDB vs. Google BigQuery vs. MySQL vs. RRDtool

System Properties Comparison Databricks vs. EJDB vs. Google BigQuery vs. MySQL vs. RRDtool

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 document-store database library with JSON representation of queries (in MongoDB style)

Large scale data warehouse service with append-only tables

Widely used open source RDBMS

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Document store
Relational DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

www.databricks.com

github.com/Softmotions/ejdb

cloud.google.com/bigquery

www.mysql.com

oss.oetiker.ch/rrdtool

Technical documentation

docs.databricks.com

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigquery/docs

dev.mysql.com/doc

oss.oetiker.ch/rrdtool/doc

Developer

Databricks

Softmotions

Google

Oracle

since 2010, originally MySQL AB, then Sun

Tobias Oetiker

Initial release

2013

2012

2010

1995

1999

Current release

8.4.0, April 2024

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL V2 and FLOSS

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

hosted

server-less

hosted

FreeBSD
Linux
OS X
Solaris
Windows

HP-UX
Linux

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

Numeric data only

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

in-process shared library

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

in-process shared library
Pipes

Supported programming languages

Python
R
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

table locks or row locks depending on storage engine

yes

by using the rrdcached daemon

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

no user groups or 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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