Creating Modern Metadata Systems with New Relic, Dow Jones [FutureStack16]

C r e a t i n g M o d e r n M e t a d a t a S y s t e m w i t h N e w R e l i c

CREATING MODERN METADATA SYSTEMSBill Sammons, Head of Content Enrichment, DowJones

C r e a t i n g M o d e r n M e t a d a t a S y s t e m w i t h N e w R e l i c

Bill SammonsHead o f Conten t Enr i chment

November 17, 2016

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C r e a t i n g M o d e r n M e t a d a t a S y s t e m s w i t h N e w R e l i c

CREATING MODERN METADATA SYSTEMSTechnology Stack Transformation

New Relic Insights for Classification Engine Transition

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Content Pipeline Rebuild

• Ingestion & Enrichment Pipeline• Legacy Architecture– Mainly Centralized Functionality– Monolithic– Hard Impossible to scale efficiently

• Goals– Easy to scale – Expectations for Significant Growth– Reduce Data Center Footprint– Update technologies that had gone stale for a long period

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Legacy Technology Stack

• Coding Language – C++• Software Development – manual

build, test; CVS• System Resource Monitoring –

Cacti• Interface – http• Infrastructure – Physical Servers

& Load Balancers in Corporate Data Centers

• Server Acquisition – Purchase• OS – RHEL 4!

• Server Deployment – Sys Admins• Log Collection – Splunk• Escalations – Operations staff

monitoring Splunk output• Content Classification – SAP SDX• Communications –

emails/meetings• Project Management – MS

Project/Project Manager

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Impossible Tasks now Possible!

• New Annotators – How do we apply new Metadata to an Archive of 1.5B Articles?

• Refresh annually – Even more challenging

• Reusability of full Content Pipeline for Consumer Business Purposes

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Early Days with New Relic• APM on Legacy Systems – modest value – C++ code base

• Alerts Integration with OpsGenie

• Built Plug-in to extract custom data from legacy code

• APM on Rearchitected Systems – increased value – Java code base

• Insights for Technology Purposes primarily

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APM & OpsGenie

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PRODUCTION RELEASE PERFORMANCE YTD

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WEEKLY CHANGE IN PERFORMANCE - CLASSIFIER

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Classification Engine Update• Classification of documents– 3 Taxonomies – News Subjects, Industries, Regions– 1000’s of Nodes– 7 Languages

• Key Component to Discovery and Organization in Products

• Very Different Technologies – Different Results Expected

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Insights to the Rescue• Business Partner ask:– Scores of spreadsheets– Static data– Compare old vs new

• New Relic Insights– A few dashboards– Dynamic Data– Drill through capabilities

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Code Simple!// Declare map.NewRelicInsightsParams = new ConcurrentHashMap<String, Object>();

// Populate it.     long mdp_queue_time =         start.getTimeInMillis()             - auditTrail.get(auditTrail.size() - 1).getAuditEntryCreatedTime()                 .toGregorianCalendar().getTimeInMillis();     long time_since_creation =         start.getTimeInMillis()             - auditTrail.get(0).getAuditEntryCreatedTime().toGregorianCalendar()                 .getTimeInMillis();     NewRelicInsightsParams.put("queue_time", mdp_queue_time);     NewRelicInsightsParams.put("time_since_creation", time_since_creation);…

// Record custom event.NewRelic.getAgent().getInsights().recordCustomEvent("MetadataPipelineComponent", NewRelicInsightsParams);

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What Makes it Magic?• Simple to code as we have seen – just Name/Value pairs in Map &

Send

• Iterations of dashboards/NRQL incredibly fast

• NRQL – “SQL for Managers”

• Refresh rates on large datasets during drill downs very fast even on complex NRQL

• Ready to answer questions not yet asked

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NRQL• Looks a bit complex but tools and prediction make it easy

– SELECT filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect!='None') AS '# Doc', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect!='None') AS '% Doc', uniquecount(mpc_doc_hash) AS 'Total Doc', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Search') AS '# Add Search', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Search') AS '% Add Search', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Nav') AS '# Add Nav', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Nav') AS '% Add Nav', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Nav & Search') AS '# Add N&S', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Added to Nav & Search') AS '% Add N&S', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Search') AS '# Loss Search', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Search') AS '% Loss Search', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Nav') AS '# Loss Nav', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Nav') AS '% Loss Nav', filter(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Nav & Search') AS '# Loss N&S', percentage(uniquecount(mpc_doc_hash),WHERE essex_product_effect='Lost from Nav & Search') AS '% Loss N&S' from MetadataRegionCodes FACET code where environment='INT' and nr_ver=1 since 1 week ago limit 1000 where language in ('en', 'fr', 'de', 'ru', 'es', 'pt', 'it')

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Business Partner Feedback• “New Relic Insights gives us the big picture – in near real-time!”

• “Instant Statistics! We’ve moved from a few static analyses of 100s of stories to 10s of thousands of stories every day with drill down capability”

• “We can now prioritize our work and it has become integral to our daily workflow”

• “New Relic Insights gives us vision into code competition that would have been nearly impossible in the past”

• “Insights gives us high confidence that we are delivering a quality solution to our customer in a highly complex problem space”

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WHAT’S ON DECK?

• NewRelic Insights Alerts

• NewRelic Insights Dashboards w/Time Picker

• NewRelic Infrastructure

• Docker

• Bringing NewRelic to our Product Platform

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Thank You!

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