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Kristoffer Dyrkorn – Beating the traffic jam using NoSQL - NoSQL matters Barcelona 2014

NoSQLmatters
NoSQLmatters
NoSQLmattersNoSQL matters

Kristoffer Dyrkorn – Beating the traffic jam using NoSQL Most people have experienced the boredom of being stuck in traffic. Up-to-date and credible information about congestions and detours could save us time and frustration in our everyday lives.The Norwegian Public Roads Administration is now building a new infrastructure for road traffic measurements, and the system will provide high-quality, near-realtime information as publicly available open data. The project relies heavily on NoSQL technology (Elasticsearch) for high-performance data gathering and statistical analysis.This talk will give a walkthrough of the project and the solution and show how NoSQL has helped in building an application that meets demanding requirements. Several use cases that illustrate the value of the system, both for the general public and for private companies and public institutions, will be given.

Kristoffer Dyrkorn – Beating the traffic jam using NoSQL - NoSQL matters Barcelona 2014

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BEATING THE TRAFFIC JAM 
USING NOSQL 
NoSQL Matters 
Kristoffer Dyrkorn, BEKK 
22/11/14
BEKK Public Roads Administration 
Norwegian consulting firm 
Private and public sector enterprises 
Strategy, technology, digital services 
370 employees 
Responsible for state and county roads 
Planning, construction, operation 
7500 employees 
Spending: € 7 Billion (2013) 
BACKGROUND
Population 
(mill) 
Area 
(km2) 
Roads 
(1000 km) 
Germany 81 357 644 
Spain 46 500 683 
Norway 5 385 95 
CONTEXT
Kristoffer Dyrkorn – Beating the traffic jam using NoSQL - NoSQL matters Barcelona 2014
E18 Vestfold – Undrumsdal. Photo: Hans A. Rosbach
Atlanterhavsveien, www.nasjonaleturistveger.no. Photo: Harald Mowinckel

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Kristoffer Dyrkorn – Beating the traffic jam using NoSQL - NoSQL matters Barcelona 2014

  • 1. BEATING THE TRAFFIC JAM USING NOSQL NoSQL Matters Kristoffer Dyrkorn, BEKK 22/11/14
  • 2. BEKK Public Roads Administration Norwegian consulting firm Private and public sector enterprises Strategy, technology, digital services 370 employees Responsible for state and county roads Planning, construction, operation 7500 employees Spending: € 7 Billion (2013) BACKGROUND
  • 3. Population (mill) Area (km2) Roads (1000 km) Germany 81 357 644 Spain 46 500 683 Norway 5 385 95 CONTEXT
  • 5. E18 Vestfold – Undrumsdal. Photo: Hans A. Rosbach
  • 9. ROADS ARE INFRASTRUCTURE BUILDING AND MAINTAINING ROADS IS EXPENSIVE PROPER PLANNING DEPENDS ON TRAFFIC ANALYSIS VEHICLE COUNTS & WEIGHTS DECIDE PRECISE REPORTS ARE NEEDED
  • 10. Butunellen. Photo: Knut Opeide, Statens Vegvesen
  • 13. TIME IS ESSENTIAL ROAD TRAFFIC IS DYNAMIC UPDATED TRAFFIC INFORMATION HELPS: • SAFETY • FLOW CONTROL • ROUTE PLANNING REAL TIME DATA IS NEEDED
  • 14. Intelligent Transport Systems: Measure & adjust road traffic
  • 16. SYSTEM GOALS & REQUIREMENTS EASE OF INSTALLATION AND VERIFICATION OF ROADSIDE EQUIPMENT INCREASED DATA QUALITY INCREASED DATA AVAILABILITY ALL EVENTS MUST BE KEPT (NO PRE-AGGREGATION) MINIMAL LATENCY AD-HOC REPORTING SCALABILITY ROBUSTNESS
  • 20. A TRAFFIC EVENT Voltage signature OPC-UA event Bulked data Sensor Data logger Application Storage
  • 21. Sensors System status SenSsenosrosr s System Other backends GUI Vehicle info Reports DATA FLOW
  • 22. SOLUTION ARCHITECTURE HTML5 GUI (HTTP, JSON) Application logic Support libraries Java VM OS Traffic events Reports (CSV, SOAP) Elasticsearch Java VM OS Request/ response Data logger N data loggers M application servers K storage servers
  • 23. "measure_point_number": 1601436, "county_id": 16, "region_id": 2, "server_local_timestamp": "2014-­‐10-­‐01T01:58:44.330+02:00", "server_utc_timestamp": "2014-­‐09-­‐30T23:58:44.330Z", "client_utc_timestamp": "2014-­‐09-­‐30T23:58:45.229Z", "event_number": 2319762, "vehicle_type": 3, "vehicle_type_raw": "9", "vehicle_type_quality": 22228, "vehicle_number": 2319762, "speed": 80.9, "length": 16.46, "lane": 1, "gap": 10
  • 24. HOW WE USE ELASTICSEARCH BULK INDEXING, JAVA API DATA IS INDEXED, STORED, NOT ANALYZED TEMPORAL SHARDING SPATIAL SHARDING DATA CENTER-AWARE REPLICATION NO SPECIAL OPTIMIZATIONS! RAM, CPU, DISK
  • 25. REPORTING FOR A GIVEN TIME INTERVAL, PROVIDE: • TOTAL VEHICLE COUNT AND AVERAGE SPEED, • THE 85 AND 95 PERCENTILE SPEEDS, • IN EACH OF 5 LENGTH CATEGORIES: THE VEHICLE COUNT AND AVERAGE SPEED, • IN EACH OF 12 SPEED CATEGORIES: THE VEHICLE COUNT, ...AND ALL OF THIS FOR • EACH TRAFFIC LANE AT A MEASURE POINT, • EACH MEASURE POINT IN A REGION
  • 26. VEHICLE FLOW (10 MIN), NOV 4TH 2014
  • 27. VEHICLE SPEED (1 MIN), NOV 4TH 2014
  • 28. SYSTEM VALUE REPORTING: • MORE COST-EFFICIENT ROAD MAINTENANCE REAL TIME: • ROUTING OF EMERGENCY VEHICLES • GENERAL TRAFFIC INFORMATION TO THE PUBLIC • ROUTE PLANNING ON HOLIDAYS • ROUTE PLANNING FOR PARCEL SERVICES
  • 29. EXPERIENCES USING ELASTICSEARCH ENSURE FITNESS-TO-PURPOSE UPGRADE CONTINUOUSLY REVISE THE RUN-TIME ENVIRONMENT CONTINUOUSLY THE AGGREGATIONS MODULE IS FANTASTIC USE TOOLING (WE LIKE KOPF) MONITOR THE RESOURCE UTILIZATION THE JAVA API IS SOMETIMES COMPLEX WE ARE HAPPY!
  • 30. ?
  • 31. THANK YOU! Kristoffer Dyrkorn BEKK kristoffer.dyrkorn@bekk.no