DBMS > Databricks vs. IBM Db2 Event Store vs. RRDtool vs. SQLite vs. TimesTen

System Properties Comparison Databricks vs. IBM Db2 Event Store vs. RRDtool vs. SQLite vs. TimesTen

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.

Distributed Event Store optimized for Internet of Things use cases

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.

Widely used embeddable, in-process RDBMS

In-Memory RDBMS compatible to Oracle

Primary database model

Document store
Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

Relational 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	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.databricks.com

www.ibm.com/products/db2-event-store

oss.oetiker.ch/rrdtool

www.sqlite.org

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

Technical documentation

docs.databricks.com

www.ibm.com/docs/en/db2-event-store

oss.oetiker.ch/rrdtool/doc

www.sqlite.org/docs.html

docs.oracle.com/database/timesten-18.1

Developer

Databricks

IBM

Tobias Oetiker

Dwayne Richard Hipp

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2017

1999

2000

1998

Current release

2.0

1.8.0, 2022

3.45.3 (15 April 2024), April 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL V2 and FLOSS

Open Source

Public Domain

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 and C++

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

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

HP-UX
Linux

server-less

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

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

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

not rigid because of 'dynamic typing' concept.

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

through the embedded Spark runtime

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

in-process shared library
Pipes

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Python
R
Scala

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

by using the rrdcached daemon

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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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