Salesforce is introducing a new product called BigObjects that allows building apps on the Force.com platform to store and query extremely large datasets using Apache Phoenix and Apache HBase. BigObjects provides a familiar Force.com-like development model to scale to hundreds of billions of records. Key challenges addressed include enabling stable query performance independent of data size, resilient querying with full SOQL power, high throughput data loading, and ensuring efficient resource usage of the Apache Phoenix client in a multitenant environment through techniques like connection pooling and performance testing.

1. Salesforce’s Trusted Enterprise
Platform and Apache Phoenix
Jan Fernando
Principal Member of Technical Staff
jfernando@salesforce.com
@janfern25

2. Forward-Looking Statements
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3. Background: Salesforce App Cloud
Fast Development for Everyone
Build apps with clicks AND code with modern tools
Connected Experience Across Apps
Unified user experience across every device
Trusted Enterprise Cloud
Get unparalleled security in the cloud
Proven Success and Scale
5.5M apps, 2.5M developers, 4B daily transactions,
AppExchange
The fastest way to build apps for customers, employees, and partners
AppExchange Trailhead
Shared Data, Networks, and Identity Across Services
FORCE
HEROKU
ENTERPRISE
THUNDERLIGHTNING

4. Background on the Multitenant Force.com database
- SObjects make up each tenant’s model
• Metadata defined fields and relationships
• Fully custom models supported
- Various access patterns
• Query support via SOQL
• Programmatic access via Apex
• SOAP/REST API automatically exposed
- Many features auto-enabled
• UI, Triggers, Search, Workflows, Reports,
Dashboards, etc.
A 100% metadata defined data model

5. Introducing BigObjects
High Volume Storage for Force.com

6. BigObjects
● Built on Apache Phoenix and Apache HBase
● Familiar, object-based development model
● Familiar platform semantics and features
Scale to 100s of billions of records on Force.com

7. Why Apache Phoenix?

8. Apache Phoenix a great fit for
Force.com relational-like
semantics
SOQL SQL

9. Key Apache Phoenix features used
● SQL and JDBC semantics
● Native Multi-tenancy
● Views
● Secondary Indexes
● Integrations with Pig, Spark etc.
● Operational Features:
• Integrated Metrics
• Pherf
Phoenix features that make BigObjects possible on Force.com

10. Force.com Developer
Expectations:
● Easy to use even for non-professional developers
● Predictable behavior and performance
● Reliable and Resilient

11. Meeting Developer
Expectations

12. Requirement 1:
Stable SOQL query times
independent of data size

13. Solution: Synchronous SOQL Constraints
● Support a subset of SOQL grammar
● Prevent table scans and unbounded query
runtimes
● Query along rowkey axis only
● All other queries blocked e.g no aggregates
● Use Apache Phoenix Query Plan to evaluate
queries at runtime

14. Requirement 2:
Resiliently Query BigObjects
with full power of SOQL

15. Solution: AsyncSOQL
● Asynchronous processing directly on Apache Hadoop/HBase
clusters
● Built on Apache Phoenix - Apache Pig integration
● Query, filter, aggregate data using SOQL without restrictions
● Fault-tolerant query engine for BigObject data
● New API for BigObject analytics and processing
Batch Analytics for BigObjects
+

16. Requirement 3:
Load data at scale into
BigObjects

17. Solution: Integrated Map/Reduce Based Data Ingest
● M/R behind Force.com Bulk API semantics
● Use Apache Phoenix/ Apache Pig integration
● Support for metadata data validation rules and
security model
● Robust error handling
● Idempotent writes to allow retries at any level in
the stack

18. Operational Requirements
Ensuring Apache Phoenix client is a good citizen in a multitenant environment

19. Requirement 1:
Fast Recovery

20. Solution: Short timeouts and connection management
● Connection Pool like semantics for managing
resource consumption
• Counting checked out connections
● Apache HBase and Apache Phoenix Timeout
configurations for fast recovery
• Avoid thread backups in the event pieces of
higher latencies

21. Requirement 2:
Visibility

22. Solution: Apache Phoenix Metrics, Logging and Graphing
● Visibility into production phoenix query performance and
resource utilization
● PHOENIX-1452 and PHOENIX-1819 were mission critical
contributions for us
● Metrics Examples:
• TASK_QUEUE_WAIT_TIME
• SCAN_BYTES
https://phoenix.apache.org/metrics.html

23. Requirement 3:
Manage Apache Phoenix
client resource utilization

24. Solution: Set Apache Phoenix Client Configs Appropriately
● Configure small Apache Phoenix thread pool size
• phoenix.query.threadPoolSize
• phoenix.query.queueSize
● Configure Apache Phoenix memory consumption
conservatively
• phoenix.query.maxGlobalMemoryPercentage
● Avoid queries with heavy client-side processing
https://phoenix.apache.org/tuning.html

25. Requirement 4:
Able to identify inefficiencies
during development

26. Solution: Pherf and automated Performance Testing
● Performance testing critical to identify resource expensive
queries
● Pherf an integral part of Perf strategy
• Pherf is a standalone tool that can perform performance
and functional testing.
• Great for automated regression testing - daily runs.
• Insights into performance of queries patterns early on
during design.
https://phoenix.apache.org/pherf.html

27. Round-up
● Apache Phoenix underpins BigObjects on Force.com
Platform
● Unique challenges in extending our platform to Big Data
● How we addressed challenges and used Phoenix to
provide a scalable and predictable way for developers to
work with Big Data on Force.com

28. Questions?

29. thank y u