Speaker: Michael Oltman, Chief Technology Officer, Apervita | Anant Srivastava, Sr Consulting Engineer, MongoDB

1. When a Startup Hits Growth Mode

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Harness more
diverse Data
Health IT solutions were initially built for single purposes
To get paid, improve performance, & provide better care, health enterprises must work together
Apply more
valuable Analytics
Get results to
many Places
Work across
many Organizations

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eClinical
Quality Measure
(eCQM)
eGuideline &
ePathway
eClinical
Decision Support
(eCDS) ePredictive
Analytics
eTriggers
eQuality
Reporting
ePayment
Programs
eCare
Pathways
eCase
Reporting
ePerformance
Improvement
At Apervita, we believe in…
Open, secure, industry-scale collaboration for health analytics & data

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Co-Operation Across Organizations
A multi-tenant, multi-affiliate platform to securely share & transact
• Collaborate across
organizations &
workspaces
• Publish & Subscribe
in public or private
marketplace
• Provision privately to
partners & customer
• Orchestrate &
Automate business
processes across
organizations

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ePathways & Decision Support
Empowerment via Localizable Cognitive Support
Reduce wasteful variation and consistently
deliver best practice, anywhere
• Monitor, reinforce, and measure
conformance to Best Practice Standards
• Deliver Interventions well-fit into clinician
mental models & clinical workflow
• Share best practice pathways and plans
across care settings & transitions
• Detect, discover, and deliver the next best
practices
• Requires analysis of most recent data as
well as historical data

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Apervita Architecture
High-level View of Architecture Components

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Current vs Initial State
Business Goals: Moving from Individual Hospitals to Enterprise Institutions
Supporting Enterprise Health Networks
1000s of institutions
10s of thousands of patients network wide
100s of thousands of events per day
Over 8 Billions Calculations per day
Large dataset backfills
Supporting individual institutions and enterprise
views across the network to provide clinical risk,
quality reporting, outcome management, and 360-
degree quality improvement.
3 Months to Do It
Supporting Many Individual Institutions
100s of patients per day
100s of events per day
Limited Historical Backfill
Millions of Calculations per day
Working with individual institutions to provide
individual views of clinical risk and quality reports.

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Initial vs Goal State
Technical Goals: Taking a baseline and growing by multiple factors
Original baseline
• 200 GB Data
• 3 Shards
○ Medium Instance Sizes
○ 500+ GB Storage per node
• 300 Compute Engines
• Supporting data growth of 100s of MBs
Steady state environment to support individual
institutions for Clinical Decision Support and
Clinical Risk
Target
• 20 TB
• 9+ Shards
○ Large Instance Sizes
○ 2 TB Storage per node
• 10,000 Compute Engines
• Up to 10 GB per day
Support initial load of 20 million patients data,
retrospective calculations against initial data load.

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Partnering with MongoDB
Goal: Prove that we can scale our environment to support the business and technical needs. Provide a
recipe for immediate scaling needs and how to continue to scale in the future.
Approach: Assemble a cross-organizational dedicated team to iteratively test capacity and
performance, ease of scaling, identify critical paths, apply changes, repeat.
First Steps: Apervita reached out to MongoDb sales and professional services to discuss the project
goals, timelines, possible methods of partnership.
Considerations:
• Iterative methodology for testing.
• Identifying the resource needs from each organization.
• Short-term and long-term timelines
• What is the right architecture for our goals?

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Building The Project Team, Methodology, Architecture
Team: Enterprise Architect & Manager, DevOps, Testing, Development, MongoDB Architect and
MongoDB-Application designer, MongoDB-Infrastructure Designer.
Methodology: Agile, daily standups and testing goals, define clear test iterations, clear critical paths.
Technical Environment: Dedicated, Isolated Performance Test environment.
Scale the Testing Framework: Ensure the testing team has the right tools and infrastructure to setup
and execute tests at a much larger scale.
Metrics, Metrics, Metrics: Instrument all systems involved in the solution and ensure ability to gather
and aggregate metrics to help debug and identify critical paths.
Architecture: Use technology that is flexible to scale, easy to rebuild and reconfigure, is fully
instrumented.
MongoDb Atlas on Amazon Web Services!!

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Benefits of Atlas, MongoDB Professional Services
Ease of Scaling and Configuration
Ease of test, react, scale, repeat without waiting on infrastructure.
Pause and Resume
For performance test environment, using Atlas to reduce cost
using the Pause and Resume feature during idle periods.
VPC Peering
Worked seamlessly with our existing Cloud Architecture and
Amazon Web Services deployment.
Analysis of Test Runs, Recommendations on Resolutions
Working with MongoDB Professional Services to monitor and
gain detail analysis of slow queries, index improvements,
overused and underused collections.

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First Iteration
Goal: Stand up the performance environment with 1500 compute engines and work on scaling strategy.
Task: Deploy baseline environment, generate test data, and execute baseline test cycle
Key Findings/Activities
One shard used more than others, unsharded collections on the overused shard

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Additional Findings in Early Iterations
Query Optimizations
Using Skip and Limit for Pagination
Aggregation vs Count on sharded collection
db.collection.aggregate([{$count: "nDocs"}]);
Index Optimizations
Remove unused indexes
Avoid in-memory sorts
Use covered queries
Use compound indexes to avoid redundancy

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Exploring Sharding Options to Enable Scale
Working with the Balancer Off
Pre-split your data. How would you add more shards?
Use smart shard keys with sharding zones
Original Shard Key -> Hash and Convert to Integer (0-1000) -> Append Current Date
Hash Keys Go Everywhere
Insert heavy loads will have to work hard to keep the hash index in memory

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Ongoing Iterations
Goal: Increase compute capacity and data volume, execute and repeat test cycles.
Task: Execute, analyze, recommend, implement, repeat
Key Findings/Activities
- Caching and queuing layer optimization( Not built in MongoDB)
- Code and configuration tuning
- Disk and Connection Saturation on Atlas

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Final Iteration
Goal: Hit the target and exceed it!
Task: Push to 10k compute engines, max data volume, minimize shards, multiple Atlas instances