SlideShare a Scribd company logo
Datomic




                 JP Erkelens
                 @jperkelens
 Http://github.com/jperkelens
Disclaimer!!
Nearly everything that follows is cribbed from the
 datomic website: www.datomic.com
Rationale

    Database design conventions were created
    when memory and disk space was limited and
    expensive

    Place-oriented programming

    Records should not be erased when new ones
    are made
Classic DB Architecture
Datomic Architecture
Peers

    Library embedded in app

    Submits transactions to transactor

    Handles query/caching/data access
Transactors

    Process transactions serially

    Transmit changes

    Index in background
Consequences

    Reads never lock out writes

    Each peer has own cache

    Higher memory footprint/quicker responses
Data Model


    Data is immutable
    
        How? Time.

    Datom
    
        Consists of entity/attr/value/transaction
    
        We don't update records or documents
    
        We add/remove datoms

    Minimal Schema
Programming Model - Queries

    The Peer pulls data from storage as needed
    and caches

    It receives updates from the transactor

    Queries run from a merged view of the two

    After a while minimal network activity is needed

    No strings! (Maps and vectors)

    Query language – Datalog.

    Implicit joins
Programming Model – Time


    Apps always work on a consistent snapshot of
    the database

    Queries are applied to values of the database
    in time, or windows
DEEEEEMMMOOOO!!!
Additional Resources
  
      Www.datomic.com
  
      Http://github.com/limadelic/datomic
  
      https://github.com/gns24/pydatomic

More Related Content

PPT
Giga Spaces Getting Ready For The Cloud
PPTX
In memory computing
PPTX
Capacity Management of an ETL System
PPT
How To Buy Data Warehouse
PPTX
IN-MEMORY DATABASE SYSTEMS FOR BIG DATA MANAGEMENT.SAP HANA DATABASE.
PDF
Performance Considerations in Logical Data Warehouse
PPT
Raising Up Voters with Microsoft Azure Cloud
 
PPT
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING
Giga Spaces Getting Ready For The Cloud
In memory computing
Capacity Management of an ETL System
How To Buy Data Warehouse
IN-MEMORY DATABASE SYSTEMS FOR BIG DATA MANAGEMENT.SAP HANA DATABASE.
Performance Considerations in Logical Data Warehouse
Raising Up Voters with Microsoft Azure Cloud
 
DATASTAGE AND QUALITY STAGE 9.1 ONLINE TRAINING

What's hot (20)

PPTX
Big Data Business Transformation - Big Picture and Blueprints
PDF
Data Engineering Basics
PPTX
sap hana|sap hana database| Introduction to sap hana
PPTX
Demystifying data engineering
PPTX
Building an Effective Data Warehouse Architecture
PPTX
Massive parallel processing database systems mpp
PPTX
Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"
PPTX
MongoDB and In-Memory Computing
PDF
Understanding big data testing
PPTX
Melt iron heterogeneous computing - lspe v3
PPTX
Data Warehousing Trends, Best Practices, and Future Outlook
PDF
So You Want to Build a Data Lake?
PPTX
Database awareness
PPTX
Anatomy of a data driven architecture - Tamir Dresher
PPTX
Design Principles for a Modern Data Warehouse
PDF
Google take on heterogeneous data base replication
PDF
The Warranty Data Lake – After, Inc.
PPTX
Avaali Solutions - Sap archiving and document access by open text
PPTX
La creación de una capa operacional con MongoDB
PDF
SAP HANA for Beginners from a Beginner
Big Data Business Transformation - Big Picture and Blueprints
Data Engineering Basics
sap hana|sap hana database| Introduction to sap hana
Demystifying data engineering
Building an Effective Data Warehouse Architecture
Massive parallel processing database systems mpp
Eugene Polonichko "Azure Data Lake: what is it? why is it? where is it?"
MongoDB and In-Memory Computing
Understanding big data testing
Melt iron heterogeneous computing - lspe v3
Data Warehousing Trends, Best Practices, and Future Outlook
So You Want to Build a Data Lake?
Database awareness
Anatomy of a data driven architecture - Tamir Dresher
Design Principles for a Modern Data Warehouse
Google take on heterogeneous data base replication
The Warranty Data Lake – After, Inc.
Avaali Solutions - Sap archiving and document access by open text
La creación de una capa operacional con MongoDB
SAP HANA for Beginners from a Beginner
Ad

Viewers also liked (20)

PDF
PPTX
Elon Musk
PPTX
Datomic
PDF
Clojure
PDF
Simo Ahava - Tag Management Solutions – Best. Data. Ever. MKTFEST 2014
PDF
Management Consulting
PPT
Selena Gomez
PDF
French Property Market 2014
PPTX
ReactJs
PPTX
intel core i7
PPTX
Medical devices
PDF
French Property market 2015 - Cushman & Wakefield
PPT
Reverse Engineering
PDF
The big bang theory
PDF
Chess
PPTX
Manchester city
PDF
ReactJS | 서버와 클라이어트에서 동시에 사용하는
PPT
Bill Gates, Who is he?
PPTX
Workshop
PPT
Elon Musk
Datomic
Clojure
Simo Ahava - Tag Management Solutions – Best. Data. Ever. MKTFEST 2014
Management Consulting
Selena Gomez
French Property Market 2014
ReactJs
intel core i7
Medical devices
French Property market 2015 - Cushman & Wakefield
Reverse Engineering
The big bang theory
Chess
Manchester city
ReactJS | 서버와 클라이어트에서 동시에 사용하는
Bill Gates, Who is he?
Workshop
Ad

Similar to Datomic (20)

PPTX
Handling Data in Mega Scale Systems
PPTX
Dynamic DDL: Adding structure to streaming IoT data on the fly
PDF
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
PPTX
Timesten Architecture
PPT
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
PPT
Hadoop and Voldemort @ LinkedIn
PDF
DM Radio Webinar: Adopting a Streaming-Enabled Architecture
PPTX
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
PDF
Building Super Fast Cloud-Native Data Platforms - Yaron Haviv, KubeCon 2017 EU
PPTX
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
PDF
Scylla Summit 2022: Reinventing Data Management on the Cloud for Modern Telec...
PPT
Apache hadoop and hive
PDF
OrientDB the database for the web 1.1
PPTX
Black Friday and Cyber Monday- Best Practices for Your E-Commerce Database
PDF
[Case Study] - Nuclear Power, DITA and FrameMaker: The How's and Why's
PDF
First in Class: Optimizing the Data Lake for Tighter Integration
PDF
Hpc lunch and learn
PDF
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
PPTX
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
PDF
Fundamentals Big Data and AI Architecture
Handling Data in Mega Scale Systems
Dynamic DDL: Adding structure to streaming IoT data on the fly
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Timesten Architecture
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Hadoop and Voldemort @ LinkedIn
DM Radio Webinar: Adopting a Streaming-Enabled Architecture
Webinar: Enterprise Data Management in the Era of MongoDB and Data Lakes
Building Super Fast Cloud-Native Data Platforms - Yaron Haviv, KubeCon 2017 EU
Serhiy Kalinets "Embracing architectural challenges in the modern .NET world"
Scylla Summit 2022: Reinventing Data Management on the Cloud for Modern Telec...
Apache hadoop and hive
OrientDB the database for the web 1.1
Black Friday and Cyber Monday- Best Practices for Your E-Commerce Database
[Case Study] - Nuclear Power, DITA and FrameMaker: The How's and Why's
First in Class: Optimizing the Data Lake for Tighter Integration
Hpc lunch and learn
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
Fundamentals Big Data and AI Architecture

Recently uploaded (20)

PDF
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
PDF
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
PDF
Approach and Philosophy of On baking technology
PDF
Building Integrated photovoltaic BIPV_UPV.pdf
PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
PDF
Network Security Unit 5.pdf for BCA BBA.
PDF
CIFDAQ's Market Insight: SEC Turns Pro Crypto
PPTX
Understanding_Digital_Forensics_Presentation.pptx
PDF
Dropbox Q2 2025 Financial Results & Investor Presentation
PDF
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PPTX
Big Data Technologies - Introduction.pptx
PPTX
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
PDF
Review of recent advances in non-invasive hemoglobin estimation
PDF
Spectral efficient network and resource selection model in 5G networks
PDF
Machine learning based COVID-19 study performance prediction
PPTX
Cloud computing and distributed systems.
PPTX
MYSQL Presentation for SQL database connectivity
PDF
Electronic commerce courselecture one. Pdf
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
Approach and Philosophy of On baking technology
Building Integrated photovoltaic BIPV_UPV.pdf
The Rise and Fall of 3GPP – Time for a Sabbatical?
Network Security Unit 5.pdf for BCA BBA.
CIFDAQ's Market Insight: SEC Turns Pro Crypto
Understanding_Digital_Forensics_Presentation.pptx
Dropbox Q2 2025 Financial Results & Investor Presentation
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
Digital-Transformation-Roadmap-for-Companies.pptx
Reach Out and Touch Someone: Haptics and Empathic Computing
Big Data Technologies - Introduction.pptx
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
Review of recent advances in non-invasive hemoglobin estimation
Spectral efficient network and resource selection model in 5G networks
Machine learning based COVID-19 study performance prediction
Cloud computing and distributed systems.
MYSQL Presentation for SQL database connectivity
Electronic commerce courselecture one. Pdf

Datomic

  • 1. Datomic JP Erkelens @jperkelens Http://github.com/jperkelens
  • 2. Disclaimer!! Nearly everything that follows is cribbed from the datomic website: www.datomic.com
  • 3. Rationale  Database design conventions were created when memory and disk space was limited and expensive  Place-oriented programming  Records should not be erased when new ones are made
  • 6. Peers  Library embedded in app  Submits transactions to transactor  Handles query/caching/data access
  • 7. Transactors  Process transactions serially  Transmit changes  Index in background
  • 8. Consequences  Reads never lock out writes  Each peer has own cache  Higher memory footprint/quicker responses
  • 9. Data Model  Data is immutable  How? Time.  Datom  Consists of entity/attr/value/transaction  We don't update records or documents  We add/remove datoms  Minimal Schema
  • 10. Programming Model - Queries  The Peer pulls data from storage as needed and caches  It receives updates from the transactor  Queries run from a merged view of the two  After a while minimal network activity is needed  No strings! (Maps and vectors)  Query language – Datalog.  Implicit joins
  • 11. Programming Model – Time  Apps always work on a consistent snapshot of the database  Queries are applied to values of the database in time, or windows
  • 12. DEEEEEMMMOOOO!!! Additional Resources  Www.datomic.com  Http://github.com/limadelic/datomic  https://github.com/gns24/pydatomic