This document discusses how a company modernized their metadata systems with New Relic. Their legacy architecture was monolithic and difficult to scale. With New Relic, they were able to rebuild their content pipeline using microservices that are easy to scale. New Relic APM provided monitoring of their new systems. New Relic Insights allowed them to analyze performance and compare their new and legacy classification engines, providing business partners with dynamic dashboards instead of static spreadsheets. Insights also gave near real-time visibility into system performance.

1. 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
Head of Content Enrichm ent
August 9, 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
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CREATING MODERN METADATA SYSTEMS
Technology Stack Transformation
New Relic Insights for Classification Engine Transition

3. 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

4. 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

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6. 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

7. 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 s w i t h N e w
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8. 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

9. 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

10. 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

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12. 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

13. 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

14. 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

15. 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 s w i t h N e w
R e l i c

16. 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

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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
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18. 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);

19. 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

20. 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')

21. 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”

22. 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 c
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