The document discusses Workday's Prism Analytics platform, which provides lightning-fast analytics for Workday transactional data. It summarizes the architecture of Prism Analytics, including its use of Apache Spark for data preparation, lens building, and query processing. It also describes some notable observations around memory allocation strategies and implementing row-level security in Spark to optimize query performance for large customer datasets.

1. Pavel Hardak, Dir Product (Workday)
Ned Borisov (Ph.D), Sr Eng Mgr (Workday)
Lightning-Fast Analytics for
Workday Transactional Data
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2. Agenda
• Workday (Pavel H)
– Introduction to Workday
– Business challenges
– Platform for Transactional Apps
• Prism Analytics (Ned B)
– High Level Architecture
– Functional Modules
– Problems encountered
• Wrap-up (Pavel H)
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3. Workday
• Pure SaaS company (founded in 2005)
• Enterprise cloud apps – HCM and Finances
– Named as “Leader” in Gartner Magic Quadrants
• 2200+ customers, 175+ of Fortune 500
– Revenue: $2.1B, 36% YoY
• 8600+ employees worldwide
– #7 in FORTUNE "100 Best Companies to Work For”
– Pleasanton (HQ), San Mateo, San Francisco
– Boulder (CO), Dublin (Ireland), Victoria (BC), …
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4. Workday Confidential
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5. Continuous Innovation in Cloud
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7. Enterprise SaaS Challenges
• Concurrency
– From small to huge companies - every ‘worker’ is Workday user
• Reliability
– All users add and change data, generating many transactions
• Security
– Customers trust us with very confidential and private information
• Scalability
– Import several years from the previous system(s) and keep growing
• Speed
– Everybody wants fast response time J
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8. Business Process
Framework
Object
Data Model
Reporting and
Analytics
Security Integration
Cloud
One Source for Data | One Security Model | One Experience | One Community
Machine
Learning
One Platform
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9. Object
Data Model
One Source for Data | One Security Model | One Experience | One Community
One Platform
Object Data Model
MetadataExtensibleDurable
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10. Reporting and
Analytics
One Source for Data | One Security Model | One Experience | One Community
One Platform
Reporting and Analytics
Dashboards CollaborationDistribution

11. But we want more…
• Import 3rd party data from external sources
– Unknown schema, need validations and cleansing
• Blend external data with Workday data
– Self Service Data Preparation
– Publish custom report sources
– Leverage the same security paradigms
• Data Discovery and Reporting
– Visualize, slice and dice by any dimension
– Perform faster than ever before
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13. Just add some …
• Water (?)
• Coffee (?)
• Energy drink (?)
• Apache Spark (!)
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14. Why Apache Spark
• Wanted to standardize on ONE data processing
technology which keeps evolving
• Needed extensibility to handle diverse use cases
• Scalability for on-disk views and in-memory
processing
• SQL processing is a HUGE plus
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15. High Level Prism Architecture
Report Queries Web UI Requests
Data Prep:
Interactive Transforms
HDFS
Workday Data
External Data
Samples
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Prism Server

16. Data Preparation
• A dataset may import
other datasets to
transform them (think
SQL View)
• Transforms include:
Filter, Join, Union,
Group By, etc.
• Example data are shown
to help verify the
transformation
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17. High Level Prism Architecture
Report Queries Web UI Requests
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Data
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Prism Server

18. Lens Build
Lens
• Materializing all
transforms
• Columnar format with
further split into small
blocks
Spark
Jobs
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19. High Level Prism Architecture
Report Queries Web UI Requests
Query Engine:
Interactive BI Queries
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Lens
Data
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Prism Server

20. Query Engine
• Analyst-driven Analysis
• Drag & drop chart creation
• Analyst defined computed fields
• Quick measurement aggregates
• Execution
• Query Engine executes the queries
• Interactive response is required
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21. High Level Prism Architecture
Report Queries Web UI Requests
Query Engine:
Interactive BI Queries
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Lens
Data
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Prism Server

22. Spark in Prism Architecture
Prism Analytics launches and maintains lifecycle of three types
of Spark Applications
• Data Prep: a single (smaller) always-on Spark Application
– executes dataset transformations over small samples of data
• Lens Build: on-demand batch Application
– one per Lens Build process
– executes dataset transformations over full datasets
• Query Engine: a single (larger) always-on Application
– executes reporting queries over Lens data
– caches columns of Lenses in memory
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23. Query Engine & Spark
Query Engine
Prism
Spark
Server
Spark
Driver
Prism Server
Data Prep
. . .
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
Spark
Executor
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24. Notable Observations
• Memory Allocation Strategy
• Row Level Security
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25. Memory Allocation Strategy
• Executors
• Driver
Column Data
Cache
30%
Execution
60% 10%
Buffer
Accumulators
20%
Streaming
60% 20%
Buffer
Executor JVM
Driver JVM
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à 20% faster queries

26. Row-Level Security
• Implemented as a dimension predicate. For example:
• In-List for supervisory_org could be very large
• More than one In-List
• Complex list values (e.g. nested conjunctions)
SELECT employee, SUM(quantity)
FROM Employee_Stock_Grants
WHERE supervisory_org IN (org1, org33, org_508)
GROUP BY employee;
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27. Scenario Details
• Customer Use Case
– Predicates with 10+ In-Lists
– Values between 6K and 12K
– Additional mix of conjunctions and disjunctions
• The Same Query
With Security = 100X Without Security
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28. Analysis
• Finding 1
– Parsing, planning and optimizing was taking ~27 seconds
– We did it 4 times
• Finding 2
– Major cause is the number of times the Catalyst
expressions (In and InSet) and their arguments were
being traversed and copied during plan analysis and
optimization.
– Minor cause is the amount of time spent in serializing
Scala’s TrieSet when shipping the plan to executors
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29. Solution
• Custom InSet-Like expressions (case classes)
– Hide the large literals sets through a curried-argument
– Resulted in queries going from 27 sec to 4 sec.
• Further Optimizations
– Our InSet-Like expression did not materialize the target
in-sets until after the plan was de-serialized on the
executors
– Resulted in improvement from 4 sec to 2 sec.
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30. Future Plans
• Better query latency for big datasets
• Deeper integration with reports and apps
• Integration with Kubernetes and AWS
• Improved scalability and concurrency
• Achieve ‘Zero DownTime’
…and much more I can not share here J
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31. Questions?
• IF ( you are looking for …
Great work culture &&
Technology challenges &&
Lots of fun and perks )
• THEN
Come to work with us!!!
workday.com/jobs
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32. More Info
• Building a modern data discovery and BI platform using
Apache Spark and Catalyst by Kevin Beyer
• Data Preparation in Workday Prism Analytics: Solving
Complex Problems the Workday Way by Jianneng Li
• Exploring Workday’s Architecture by James Pasley
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