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 Sammons

H e a d o f C o n t e n t En r i c h m e n t

August 9, 2016

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 cC 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 SYSTEMS

Technology Stack Transformation

New Relic Insights for Classification Engine Transition

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

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

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

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

• 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

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

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

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

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 cC 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

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

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

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

APM & OpsGenie

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

PRODUCTION RELEASE PERFORMANCE YTD

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

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

WEEKLY CHANGE IN PERFORMANCE -CLASSIFIER

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

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

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

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

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 cC 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

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

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 cC 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

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

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);

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

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

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

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')

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

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”

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

Thank You!

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 c2 2