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HBase Design Patterns @ Y!
PRESENTED BY Francis Liu | toffer@apache.org⎪ May 5, 2014
Y! Grid
▪ Off-Stage Processing
▪ Hosted Service
▪ Multi-tenant
Batch Processing (with HDFS)
▪ Append-only
▪ Efficient full table scans
▪ Process entire data set (or partitions)
HBase
▪ Mutable
▪ Point Access
▪ Range scans
▪ Record-level processing
▪ 7 clusters, 1500 nodes, 6PB
Entity Store: Motivation
▪ Integrate data from multiple data sources
▪ Store historical data
▪ Share data
› Analytics
› Machine Learning
› Consume a data source
Entity Store
▪ Records as Entities
› Web pages
› Celebrities
› etc.
▪ Denormalized as a single table
Entity Store: Content Store
Ingestion Service
Sports
Enrichment
News Enrichment
xxxxxxxxx
c:content
xxxxxxxx
m:sports m:news
Serving Bulk
Ingest
xxxxxxxxx
c:message
xxxxxxxx
Feed
Entity Store: Considerations
▪ Row vs multiple rows as an entity?
› Row in most cases
▪ Blob vs Primitives as cell values?
› Blobs are more compact
› Primitives work better for granular updates
› Out of the box filters work better with primitives
› Use a compact binary format
▪ Prepare for Schema Changes
› Provide a DAO library
▪ Incremental Scan
› Batch id (via version)
› Size cache for batch
Event Processing: Motivation
▪ Process a stream of events
› Ad Targeting
› Personalization
› etc.
▪ Low average age of a record/model/etc
Event Processing
▪ Entity Store
▪ Incremental computation
› Persist incremental state
▪ Stream processing framework
› ie Storm
▪ Fit working set in Block Cache
HBase
StormData Collector Serving
Event Processing: Ad Targeting
Ad Targeting
HBase
MapReduce
Storm
HDFS
Data Collector
Index
Batch
Near realtime
ServingProcessingCollection
Event Processing - Considerations
▪ Limit large compactions
▪ Deferred log flush
▪ Avoid compaction storms
▪ Async Access
› HBase work queue
› AsyncHBase
▪ Blobs when possible
▪ Cache optimizations
Phased Event Processing: Motivation
▪ Large/Complex event pipeline
▪ Modularization
▪ Dependency between pipelines
Phased Event Processing
▪ Notifications
› Separate Table
› Separate Column Family
Topology1
Data Collector
Notifications
Topology2
Notifications
Notifications
Topology3
Serving
Phased Event Processing: Personalization
Data Collector
Notifications Enrichment Notifications Ingestion
Serving
MapReduceHDFS
HBase
ServingProcessingCollection
Fetcher
Phased Event Processing: Considerations
▪ Notifications
› Ordered
› At least once
▪ Write to multiple regions
▪ Transactions
Time Series DB: Motivation
▪ Track/Monitor changes over time
› Application Metrics
› User Analytics
› System Metrics
› etc.
▪ Alerts/Alarms
› Thresholds
› Changes over time
Time Series DB: Personalization Data Quality
HBase
StormData Collector
Web UI
Serving
Time-Series: Considerations
▪ Hot metrics
› Namespace
› Indexed tags
▪ Pre-compute aggregates if it is accessed often
▪ Consider using a block encoding scheme (PREFIX, FAST_DIFF, etc)
▪ Consider pre-computed aggregates in a separate table
▪ Consider OpenTSDB
HBaseCon 2014
Thank You!
(We’re hiring)

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HBase Design Patterns @ Yahoo!

  • 1. HBase Design Patterns @ Y! PRESENTED BY Francis Liu | toffer@apache.org⎪ May 5, 2014
  • 2. Y! Grid ▪ Off-Stage Processing ▪ Hosted Service ▪ Multi-tenant
  • 3. Batch Processing (with HDFS) ▪ Append-only ▪ Efficient full table scans ▪ Process entire data set (or partitions)
  • 4. HBase ▪ Mutable ▪ Point Access ▪ Range scans ▪ Record-level processing ▪ 7 clusters, 1500 nodes, 6PB
  • 5. Entity Store: Motivation ▪ Integrate data from multiple data sources ▪ Store historical data ▪ Share data › Analytics › Machine Learning › Consume a data source
  • 6. Entity Store ▪ Records as Entities › Web pages › Celebrities › etc. ▪ Denormalized as a single table
  • 7. Entity Store: Content Store Ingestion Service Sports Enrichment News Enrichment xxxxxxxxx c:content xxxxxxxx m:sports m:news Serving Bulk Ingest xxxxxxxxx c:message xxxxxxxx Feed
  • 8. Entity Store: Considerations ▪ Row vs multiple rows as an entity? › Row in most cases ▪ Blob vs Primitives as cell values? › Blobs are more compact › Primitives work better for granular updates › Out of the box filters work better with primitives › Use a compact binary format ▪ Prepare for Schema Changes › Provide a DAO library ▪ Incremental Scan › Batch id (via version) › Size cache for batch
  • 9. Event Processing: Motivation ▪ Process a stream of events › Ad Targeting › Personalization › etc. ▪ Low average age of a record/model/etc
  • 10. Event Processing ▪ Entity Store ▪ Incremental computation › Persist incremental state ▪ Stream processing framework › ie Storm ▪ Fit working set in Block Cache HBase StormData Collector Serving
  • 11. Event Processing: Ad Targeting Ad Targeting HBase MapReduce Storm HDFS Data Collector Index Batch Near realtime ServingProcessingCollection
  • 12. Event Processing - Considerations ▪ Limit large compactions ▪ Deferred log flush ▪ Avoid compaction storms ▪ Async Access › HBase work queue › AsyncHBase ▪ Blobs when possible ▪ Cache optimizations
  • 13. Phased Event Processing: Motivation ▪ Large/Complex event pipeline ▪ Modularization ▪ Dependency between pipelines
  • 14. Phased Event Processing ▪ Notifications › Separate Table › Separate Column Family Topology1 Data Collector Notifications Topology2 Notifications Notifications Topology3 Serving
  • 15. Phased Event Processing: Personalization Data Collector Notifications Enrichment Notifications Ingestion Serving MapReduceHDFS HBase ServingProcessingCollection Fetcher
  • 16. Phased Event Processing: Considerations ▪ Notifications › Ordered › At least once ▪ Write to multiple regions ▪ Transactions
  • 17. Time Series DB: Motivation ▪ Track/Monitor changes over time › Application Metrics › User Analytics › System Metrics › etc. ▪ Alerts/Alarms › Thresholds › Changes over time
  • 18. Time Series DB: Personalization Data Quality HBase StormData Collector Web UI Serving
  • 19. Time-Series: Considerations ▪ Hot metrics › Namespace › Indexed tags ▪ Pre-compute aggregates if it is accessed often ▪ Consider using a block encoding scheme (PREFIX, FAST_DIFF, etc) ▪ Consider pre-computed aggregates in a separate table ▪ Consider OpenTSDB