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

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