SlideShare a Scribd company logo
1 of 8
Download to read offline
FIS GT.M™ – A Gentle Introduction
K.S. Bhaskar, FIS
Agenda

• What is GT.M? Why should I care?
• Technology Overview
• Where to go for more information
What is GT.M? Why should I care?

• NoSQL database + embedded procedural scripting language
   – Layered mappings for “Universal NoSQL” and SQL
• System of record for the two largest real time core banking systems
  in the world that we know of
   – Production database sizes of a few TB
   – Serving around 10,000 concurrent online users + ATMs, voice response
     units, web & mobile access...
   – 1000s of online banking transactions/second with full ACID properties
• Increasingly used in health care for electronic health records
• Operating database for at least one multi-sourced “big data” project
• Mature code base
   – First live production use in 1986; actively developed and supported
   – Free / open source software (AGPL v3) on x86 Linux (proprietary license
     on other platforms, including proprietary UNIX systems)
   – Free community based support on active forums
   – Commercial support with assured service levels
Technology Overview – Database Engine

• Hierarchical key-value (multi-dimensional array) data store, e.g.:
     – Set ^Capital("United States",1774,1776)="Philadelphia"
• Software Transaction Memory model
    Tstart
        …
    TCommit
•   Map key-value pairs to SQL tables with JDBC access – FIS PIP
•   Universal NoSQL: Map to other NoSQL uses cases with layered FOSS
    – e.g., M/DB SimpleDB clone, M/DB:X native XML database, M/Wire
    (modelled on Redis protocol)
•   Logical database consists of unlimited number of database files; each
    database file is 224M blocks (1024M blocks next release)
•   Keys up to 255 bytes long (1023 bytes next release); values up to
    65,008 bytes long (1MB next release)
Technology Overview – CAP Theorem

• Eventual Consistency requirement
   – Financial application requirement is that all nodes must eventually have
     the same path through state space, not just the same state, with
     Consistency at each point
• Business (application) logic runs on one originating primary instance
   – Updates streamed in real time to up to 16 replicating secondary
     instances, 256 tertiary instances, etc. without limit
   – Other instances available for querying / read-only access
• Any downstream instance can be switched to primary role
   – Roll-back / roll-forward to restore Consistency requires cooperation
     between database and application logic
   – Support for rolling upgrades even when schema change involved
• 12,450 mile distance limit
   – Longest known: Manchester, England to San Diego, CA (5,300 miles)
   – Longest known high volume: Delaware to Minnesota (1,000 miles)
Technology Overview – Scripting Language

• Official name is M – ISO/IEC standard 11756:1999
• Popular name is MUMPS – Massachusetts General Hospital Utility
  Multi-Programming System
   – De facto standard in healthcare, used by virtually all major VARs – Epic,
     IDX (now part of GE), McKesson, Eclipsys... – and by major institutions,
     e.g, Mayo, Kaiser, Cleveland Clinic, Partners, Quest, Lab Corp
   – Largest user is US Government – Dept. of Veterans Affairs, Dept. of
     Defense, Indian Health Service
   – Used in diverse industries including banking, retail, manufacturing
• Use it to create
   – Applications directly (largest applications are ERP systems with tens of
     thousands of modules)
   – An API to call from C (or anything compatible with C)
   – A server for an RPC protocol layered on TCP
Technology Overview – Engineering

•   No database daemon – processes cooperate to manage database
•   Optimistic concurrency control
•   Processes run with normal user / group ids
•   Simple security model written in plain English
•   Written mostly in C (some bits in assembly language)
•   Compiler generates dynamically linked threaded code
For More Information

• FIS GT.M home page – http://fis-gtm.com
     – User documentation – User documentation tab on home page
     – Download from http://sf.net/projects/fis-gtm (working its way into
       Debian repositories)
• FIS PIP home page – http://fis-pip.com
     – Download from http://sf.net/projects/pip
• M/DB, M/DB:X/ M/Wire, EWD (rich application platform):
    http://mgateway.com
•   Universal NoSQL -
    http://www.mgateway.com/docs/universalNoSQL.pdf
•   fosm (public big-data project) – http://fosm.org
•   NoSQL benchmark – http://ksbhaskar@blogspot.com
•   K.S. Bhaskar / ks.bhaskar@fisglobal.com / +1 (610) 578-4265

More Related Content

What's hot

CDC patterns in Apache Kafka®
CDC patterns in Apache Kafka®CDC patterns in Apache Kafka®
CDC patterns in Apache Kafka®confluent
 
Chicago Data Summit: Apache HBase: An Introduction
Chicago Data Summit: Apache HBase: An IntroductionChicago Data Summit: Apache HBase: An Introduction
Chicago Data Summit: Apache HBase: An IntroductionCloudera, Inc.
 
MariaDB 10: The Complete Tutorial
MariaDB 10: The Complete TutorialMariaDB 10: The Complete Tutorial
MariaDB 10: The Complete TutorialColin Charles
 
Data and AI summit: data pipelines observability with open lineage
Data and AI summit: data pipelines observability with open lineageData and AI summit: data pipelines observability with open lineage
Data and AI summit: data pipelines observability with open lineageJulien Le Dem
 
Wars of MySQL Cluster ( InnoDB Cluster VS Galera )
Wars of MySQL Cluster ( InnoDB Cluster VS Galera ) Wars of MySQL Cluster ( InnoDB Cluster VS Galera )
Wars of MySQL Cluster ( InnoDB Cluster VS Galera ) Mydbops
 
Sizing Your Scylla Cluster
Sizing Your Scylla ClusterSizing Your Scylla Cluster
Sizing Your Scylla ClusterScyllaDB
 
Spark Summit East 2015 Advanced Devops Student Slides
Spark Summit East 2015 Advanced Devops Student SlidesSpark Summit East 2015 Advanced Devops Student Slides
Spark Summit East 2015 Advanced Devops Student SlidesDatabricks
 
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
 
Introduction to redis
Introduction to redisIntroduction to redis
Introduction to redisTanu Siwag
 
Advanced backup methods (Postgres@CERN)
Advanced backup methods (Postgres@CERN)Advanced backup methods (Postgres@CERN)
Advanced backup methods (Postgres@CERN)Anastasia Lubennikova
 
Spark and S3 with Ryan Blue
Spark and S3 with Ryan BlueSpark and S3 with Ryan Blue
Spark and S3 with Ryan BlueDatabricks
 
ELK: Moose-ively scaling your log system
ELK: Moose-ively scaling your log systemELK: Moose-ively scaling your log system
ELK: Moose-ively scaling your log systemAvleen Vig
 
Big Data Analytics with MariaDB ColumnStore
Big Data Analytics with MariaDB ColumnStoreBig Data Analytics with MariaDB ColumnStore
Big Data Analytics with MariaDB ColumnStoreMariaDB plc
 
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...DataStax
 
Cassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthCassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthdaveconnors
 
SQL-on-Hadoop Tutorial
SQL-on-Hadoop TutorialSQL-on-Hadoop Tutorial
SQL-on-Hadoop TutorialDaniel Abadi
 
MySQL Performance Schema in Action
MySQL Performance Schema in ActionMySQL Performance Schema in Action
MySQL Performance Schema in ActionSveta Smirnova
 
Survey of High Performance NoSQL Systems
Survey of High Performance NoSQL SystemsSurvey of High Performance NoSQL Systems
Survey of High Performance NoSQL SystemsScyllaDB
 
From distributed caches to in-memory data grids
From distributed caches to in-memory data gridsFrom distributed caches to in-memory data grids
From distributed caches to in-memory data gridsMax Alexejev
 

What's hot (20)

CDC patterns in Apache Kafka®
CDC patterns in Apache Kafka®CDC patterns in Apache Kafka®
CDC patterns in Apache Kafka®
 
Chicago Data Summit: Apache HBase: An Introduction
Chicago Data Summit: Apache HBase: An IntroductionChicago Data Summit: Apache HBase: An Introduction
Chicago Data Summit: Apache HBase: An Introduction
 
MariaDB 10: The Complete Tutorial
MariaDB 10: The Complete TutorialMariaDB 10: The Complete Tutorial
MariaDB 10: The Complete Tutorial
 
Data and AI summit: data pipelines observability with open lineage
Data and AI summit: data pipelines observability with open lineageData and AI summit: data pipelines observability with open lineage
Data and AI summit: data pipelines observability with open lineage
 
Wars of MySQL Cluster ( InnoDB Cluster VS Galera )
Wars of MySQL Cluster ( InnoDB Cluster VS Galera ) Wars of MySQL Cluster ( InnoDB Cluster VS Galera )
Wars of MySQL Cluster ( InnoDB Cluster VS Galera )
 
Sizing Your Scylla Cluster
Sizing Your Scylla ClusterSizing Your Scylla Cluster
Sizing Your Scylla Cluster
 
Spark Summit East 2015 Advanced Devops Student Slides
Spark Summit East 2015 Advanced Devops Student SlidesSpark Summit East 2015 Advanced Devops Student Slides
Spark Summit East 2015 Advanced Devops Student Slides
 
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
 
Introduction to redis
Introduction to redisIntroduction to redis
Introduction to redis
 
Intro to HBase
Intro to HBaseIntro to HBase
Intro to HBase
 
Advanced backup methods (Postgres@CERN)
Advanced backup methods (Postgres@CERN)Advanced backup methods (Postgres@CERN)
Advanced backup methods (Postgres@CERN)
 
Spark and S3 with Ryan Blue
Spark and S3 with Ryan BlueSpark and S3 with Ryan Blue
Spark and S3 with Ryan Blue
 
ELK: Moose-ively scaling your log system
ELK: Moose-ively scaling your log systemELK: Moose-ively scaling your log system
ELK: Moose-ively scaling your log system
 
Big Data Analytics with MariaDB ColumnStore
Big Data Analytics with MariaDB ColumnStoreBig Data Analytics with MariaDB ColumnStore
Big Data Analytics with MariaDB ColumnStore
 
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
Deletes Without Tombstones or TTLs (Eric Stevens, ProtectWise) | Cassandra Su...
 
Cassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per monthCassandra & puppet, scaling data at $15 per month
Cassandra & puppet, scaling data at $15 per month
 
SQL-on-Hadoop Tutorial
SQL-on-Hadoop TutorialSQL-on-Hadoop Tutorial
SQL-on-Hadoop Tutorial
 
MySQL Performance Schema in Action
MySQL Performance Schema in ActionMySQL Performance Schema in Action
MySQL Performance Schema in Action
 
Survey of High Performance NoSQL Systems
Survey of High Performance NoSQL SystemsSurvey of High Performance NoSQL Systems
Survey of High Performance NoSQL Systems
 
From distributed caches to in-memory data grids
From distributed caches to in-memory data gridsFrom distributed caches to in-memory data grids
From distributed caches to in-memory data grids
 

Viewers also liked

Core Banking System modernization for Japanese Bank
Core Banking System modernizationfor Japanese BankCore Banking System modernizationfor Japanese Bank
Core Banking System modernization for Japanese BankHirofumi Nakata
 
Preemptive Customer Service: Learning from Customer Data Silos
Preemptive Customer Service: Learning from Customer Data SilosPreemptive Customer Service: Learning from Customer Data Silos
Preemptive Customer Service: Learning from Customer Data SilosHenry Sampson
 
Big Data Banking: Customer vs. Accounting
Big Data Banking: Customer vs. AccountingBig Data Banking: Customer vs. Accounting
Big Data Banking: Customer vs. AccountingHenry Sampson
 
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」schoowebcampus
 
Evaluation conventions etc.
Evaluation   conventions etc.Evaluation   conventions etc.
Evaluation conventions etc.RoryNicholson
 
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明schoowebcampus
 
India with a new hope
India with a new hopeIndia with a new hope
India with a new hopeNeha Sharma
 
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)schoowebcampus
 
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔schoowebcampus
 
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔schoowebcampus
 
Indian government gaining ground
Indian government gaining groundIndian government gaining ground
Indian government gaining groundNeha Sharma
 

Viewers also liked (20)

Core Banking System modernization for Japanese Bank
Core Banking System modernizationfor Japanese BankCore Banking System modernizationfor Japanese Bank
Core Banking System modernization for Japanese Bank
 
Preemptive Customer Service: Learning from Customer Data Silos
Preemptive Customer Service: Learning from Customer Data SilosPreemptive Customer Service: Learning from Customer Data Silos
Preemptive Customer Service: Learning from Customer Data Silos
 
Big Data Banking: Customer vs. Accounting
Big Data Banking: Customer vs. AccountingBig Data Banking: Customer vs. Accounting
Big Data Banking: Customer vs. Accounting
 
Core banking
Core bankingCore banking
Core banking
 
Core banking
Core bankingCore banking
Core banking
 
Leefbaar werk en werkbaar leven
Leefbaar werk en werkbaar levenLeefbaar werk en werkbaar leven
Leefbaar werk en werkbaar leven
 
Warehousing management
Warehousing managementWarehousing management
Warehousing management
 
Psmcartabelgrado
PsmcartabelgradoPsmcartabelgrado
Psmcartabelgrado
 
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」
【再放送】料理芸人が教える!20分で2品、コンビニ食材だけで作れるオシャレレシピ「ナポリタンうどん&サバ缶でバーニャカウダ」
 
Evaluation conventions etc.
Evaluation   conventions etc.Evaluation   conventions etc.
Evaluation conventions etc.
 
Paz y democracia
Paz y democraciaPaz y democracia
Paz y democracia
 
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明
営業担当がクライアント先に行かずに売上を上げる方法 先生:菊原 智明
 
India with a new hope
India with a new hopeIndia with a new hope
India with a new hope
 
Joe paterno
Joe paternoJoe paterno
Joe paterno
 
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)
念い(おもい)が伝わる文字を書こう「書道塾 継未-TUGUMI-」(第3回 美しい字へ-楷書-編)
 
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔
面白法人カヤック社長:柳澤大輔先生に、生放送で「経営」のことを質問しよう!先生:柳澤 大輔
 
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔
「WEBサービスの利用規約」の正しい作り方~最低限おさえたい3つの注意点 先生:菅原 稔
 
140415 schoo fix_pdf
140415 schoo fix_pdf140415 schoo fix_pdf
140415 schoo fix_pdf
 
Data security
Data securityData security
Data security
 
Indian government gaining ground
Indian government gaining groundIndian government gaining ground
Indian government gaining ground
 

Similar to Intro to FIS GT.M

Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...
Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...
Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...BigDataEverywhere
 
OpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC SystemsOpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC SystemsHPCC Systems
 
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storage
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storageWebinar: OpenEBS - Still Free and now FASTEST Kubernetes storage
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storageMayaData Inc
 
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc..."An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...Maya Lumbroso
 
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc..."An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...Dataconomy Media
 
OS concepts 6 OS for various computing environments
OS concepts 6 OS for various computing environmentsOS concepts 6 OS for various computing environments
OS concepts 6 OS for various computing environmentsVaibhav Khanna
 
Manta Unleashed BigDataSG talk 2 July 2013
Manta Unleashed BigDataSG talk 2 July 2013Manta Unleashed BigDataSG talk 2 July 2013
Manta Unleashed BigDataSG talk 2 July 2013Christopher Hogue
 
What's new in informix v11.70
What's new in informix v11.70What's new in informix v11.70
What's new in informix v11.70am_prasanna
 
General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school ISSGC Summer School
 
Hpc lunch and learn
Hpc lunch and learnHpc lunch and learn
Hpc lunch and learnJohn D Almon
 
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...NETWAYS
 
Designing High performance & Scalable Middleware for HPC
Designing High performance & Scalable Middleware for HPCDesigning High performance & Scalable Middleware for HPC
Designing High performance & Scalable Middleware for HPCObject Automation
 
e-Infrastructure available for research, using the right tool for the right job
e-Infrastructure available for research, using the right tool for the right jobe-Infrastructure available for research, using the right tool for the right job
e-Infrastructure available for research, using the right tool for the right jobDavid Wallom
 
Dell High-Performance Computing solutions: Enable innovations, outperform exp...
Dell High-Performance Computing solutions: Enable innovations, outperform exp...Dell High-Performance Computing solutions: Enable innovations, outperform exp...
Dell High-Performance Computing solutions: Enable innovations, outperform exp...Dell World
 
PEARC17: Live Integrated Visualization Environment: An Experiment in General...
PEARC17: Live Integrated Visualization Environment: An Experiment in General...PEARC17: Live Integrated Visualization Environment: An Experiment in General...
PEARC17: Live Integrated Visualization Environment: An Experiment in General...moneyjh
 
UWP apps development - Part 3
UWP apps development - Part 3UWP apps development - Part 3
UWP apps development - Part 3Jiri Danihelka
 
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Maya Lumbroso
 
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Dataconomy Media
 

Similar to Intro to FIS GT.M (20)

ODP Presentation LinuxCon NA 2014
ODP Presentation LinuxCon NA 2014ODP Presentation LinuxCon NA 2014
ODP Presentation LinuxCon NA 2014
 
Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...
Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...
Big Data Everywhere Chicago: High Performance Computing - Contributions Towar...
 
OpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC SystemsOpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC Systems
 
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storage
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storageWebinar: OpenEBS - Still Free and now FASTEST Kubernetes storage
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storage
 
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc..."An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
 
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc..."An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
"An introduction to Kx Technology - a Big Data solution", Kyra Coyne, Data Sc...
 
OS concepts 6 OS for various computing environments
OS concepts 6 OS for various computing environmentsOS concepts 6 OS for various computing environments
OS concepts 6 OS for various computing environments
 
Manta Unleashed BigDataSG talk 2 July 2013
Manta Unleashed BigDataSG talk 2 July 2013Manta Unleashed BigDataSG talk 2 July 2013
Manta Unleashed BigDataSG talk 2 July 2013
 
What's new in informix v11.70
What's new in informix v11.70What's new in informix v11.70
What's new in informix v11.70
 
General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school General Introduction to technologies that will be seen in the school
General Introduction to technologies that will be seen in the school
 
TechTalk: Connext DDS 5.2.
TechTalk: Connext DDS 5.2.TechTalk: Connext DDS 5.2.
TechTalk: Connext DDS 5.2.
 
Hpc lunch and learn
Hpc lunch and learnHpc lunch and learn
Hpc lunch and learn
 
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...
OSMC 2019 | Monitoring Alerts and Metrics on Large Power Systems Clusters by ...
 
Designing High performance & Scalable Middleware for HPC
Designing High performance & Scalable Middleware for HPCDesigning High performance & Scalable Middleware for HPC
Designing High performance & Scalable Middleware for HPC
 
e-Infrastructure available for research, using the right tool for the right job
e-Infrastructure available for research, using the right tool for the right jobe-Infrastructure available for research, using the right tool for the right job
e-Infrastructure available for research, using the right tool for the right job
 
Dell High-Performance Computing solutions: Enable innovations, outperform exp...
Dell High-Performance Computing solutions: Enable innovations, outperform exp...Dell High-Performance Computing solutions: Enable innovations, outperform exp...
Dell High-Performance Computing solutions: Enable innovations, outperform exp...
 
PEARC17: Live Integrated Visualization Environment: An Experiment in General...
PEARC17: Live Integrated Visualization Environment: An Experiment in General...PEARC17: Live Integrated Visualization Environment: An Experiment in General...
PEARC17: Live Integrated Visualization Environment: An Experiment in General...
 
UWP apps development - Part 3
UWP apps development - Part 3UWP apps development - Part 3
UWP apps development - Part 3
 
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
 
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...
 

Recently uploaded

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKSpring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKJago de Vreede
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...apidays
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024The Digital Insurer
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 

Recently uploaded (20)

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKSpring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 

Intro to FIS GT.M

  • 1. FIS GT.M™ – A Gentle Introduction K.S. Bhaskar, FIS
  • 2. Agenda • What is GT.M? Why should I care? • Technology Overview • Where to go for more information
  • 3. What is GT.M? Why should I care? • NoSQL database + embedded procedural scripting language – Layered mappings for “Universal NoSQL” and SQL • System of record for the two largest real time core banking systems in the world that we know of – Production database sizes of a few TB – Serving around 10,000 concurrent online users + ATMs, voice response units, web & mobile access... – 1000s of online banking transactions/second with full ACID properties • Increasingly used in health care for electronic health records • Operating database for at least one multi-sourced “big data” project • Mature code base – First live production use in 1986; actively developed and supported – Free / open source software (AGPL v3) on x86 Linux (proprietary license on other platforms, including proprietary UNIX systems) – Free community based support on active forums – Commercial support with assured service levels
  • 4. Technology Overview – Database Engine • Hierarchical key-value (multi-dimensional array) data store, e.g.: – Set ^Capital("United States",1774,1776)="Philadelphia" • Software Transaction Memory model Tstart … TCommit • Map key-value pairs to SQL tables with JDBC access – FIS PIP • Universal NoSQL: Map to other NoSQL uses cases with layered FOSS – e.g., M/DB SimpleDB clone, M/DB:X native XML database, M/Wire (modelled on Redis protocol) • Logical database consists of unlimited number of database files; each database file is 224M blocks (1024M blocks next release) • Keys up to 255 bytes long (1023 bytes next release); values up to 65,008 bytes long (1MB next release)
  • 5. Technology Overview – CAP Theorem • Eventual Consistency requirement – Financial application requirement is that all nodes must eventually have the same path through state space, not just the same state, with Consistency at each point • Business (application) logic runs on one originating primary instance – Updates streamed in real time to up to 16 replicating secondary instances, 256 tertiary instances, etc. without limit – Other instances available for querying / read-only access • Any downstream instance can be switched to primary role – Roll-back / roll-forward to restore Consistency requires cooperation between database and application logic – Support for rolling upgrades even when schema change involved • 12,450 mile distance limit – Longest known: Manchester, England to San Diego, CA (5,300 miles) – Longest known high volume: Delaware to Minnesota (1,000 miles)
  • 6. Technology Overview – Scripting Language • Official name is M – ISO/IEC standard 11756:1999 • Popular name is MUMPS – Massachusetts General Hospital Utility Multi-Programming System – De facto standard in healthcare, used by virtually all major VARs – Epic, IDX (now part of GE), McKesson, Eclipsys... – and by major institutions, e.g, Mayo, Kaiser, Cleveland Clinic, Partners, Quest, Lab Corp – Largest user is US Government – Dept. of Veterans Affairs, Dept. of Defense, Indian Health Service – Used in diverse industries including banking, retail, manufacturing • Use it to create – Applications directly (largest applications are ERP systems with tens of thousands of modules) – An API to call from C (or anything compatible with C) – A server for an RPC protocol layered on TCP
  • 7. Technology Overview – Engineering • No database daemon – processes cooperate to manage database • Optimistic concurrency control • Processes run with normal user / group ids • Simple security model written in plain English • Written mostly in C (some bits in assembly language) • Compiler generates dynamically linked threaded code
  • 8. For More Information • FIS GT.M home page – http://fis-gtm.com – User documentation – User documentation tab on home page – Download from http://sf.net/projects/fis-gtm (working its way into Debian repositories) • FIS PIP home page – http://fis-pip.com – Download from http://sf.net/projects/pip • M/DB, M/DB:X/ M/Wire, EWD (rich application platform): http://mgateway.com • Universal NoSQL - http://www.mgateway.com/docs/universalNoSQL.pdf • fosm (public big-data project) – http://fosm.org • NoSQL benchmark – http://ksbhaskar@blogspot.com • K.S. Bhaskar / ks.bhaskar@fisglobal.com / +1 (610) 578-4265