This document summarizes Cosmin Lehene's presentation on Big Data with HBase and Hadoop at Adobe. The presentation discusses how Adobe uses Hadoop and HBase to analyze large amounts of data from sources like video logs, Flash usage logs, and image metadata. It provides examples of how Adobe uses this analysis to improve products like the Adobe Media Player and Photoshop and gain business intelligence. The presentation also covers topics like HBase data modeling, MapReduce workflows, and scaling challenges encountered by Adobe.

1. Big Data with
HBase and
Hadoop at Adobe
Cosmin Lehene
Programatica, November, 2010
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2. Who am I
Cosmin Lehene
Adobe Services and Infrastructure Team = SaaS services
HBase and Hadoop contributor
clehene@adobe.com
@clehene
h p://hstack.org
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3. Why I am here today
§ Riding the elephant since 2008
§ Analytics, BI, Machine Learning
§ Images, Videos, Flash, Web, etc.
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4. Opaque Data (logs, archives)
§ Web traffic
§ Business events
§ User interactions
§ Infrastructure data
§ Database logs, web server logs, etc.
§ Etc.
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5. h p://commons.wikimedia.org/wiki/File:AWI-core-archive_hg.jpg ®
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6. h p://www.google.com/images?q=data+visualization 6
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7. Can I
§ JOIN everything?
§ Increase user engagement?
§ Increase conversion rate?
§ Make $$$? J
§ Fast and cheap?
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8. Understand data and extract meaning
Real-time access to meaningful data
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9. Agenda
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10. noSQL 101
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11. Scaling RDBMS
§ Scale up
§ More memory
§ More CPU
§ Faster disks, SAN, etc.
§ Problems
§ Expensive
§ ere’s a limit
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12. Scaling RDBMS
§ Scale horizontally
§ Replication (reads)
§ Sharding/ Horizontal Partitioning (writes)
§ Server 1: a-m, Server 2: m-z
§ Denormalization
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13. Replication
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14. Sharding
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15. Sharding
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16. Sharding & Replication
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17. Scaling RDBMS problems
§ Hard to repartition/reshard
§ Pre allocate shards 2, 3, 100
§ Query each shard
§ High operational costs
§ Eventual consistency
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18. Enter noSQL – the beginning
§ Google: BigTable
§ Amazon: Dynamo
§ Memcached
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19. Data Models
§ Key-value
§ Columnar/Tabular
§ Document oriented
§ Graph
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20. Architectures
§ Distributed hash tables
§ Consistent Hashing
§ Gossip
§ Vector clocks
§ Locality groups
§ Partitioning, replication
§ etc.
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21. Properties
§ Scalability
§ Failover
§ Durability
§ Consistency
§ Availability
§ Partition Tolerance
§ Etc.
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22. Cartesian Product
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23. What do all these have in common
§ Different data models
noSQL
§ Different architectures
§ Different properties
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24. Hadoop
h p://hadoop.apache.org
§ HDFS (distributed fs)
§ Map-reduce (distributed processing)
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25. Adobe Media Player
Increase video
consumption
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26. AMP
§ Recommendations
§ Related content
§ Related users
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27. Video logs
§ X watched movie A (comedy)
§ Y watched movie B (drama)
§ Z watched movie C (thriller)
§ Z watched movie A (comedy)
§ X watched movie D (technology)
§ Y watched movie C (thriller)
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28. Which users are alike?
§ Compare every 2 users?
§ 5M vectors
§ 120 dimensions
§ Distance is not enough – needed groups
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29. How?
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30. Custer projections
§ 1 month
§ 6GB
§ 700k Users
§ 114 genres
§ 7 nodes
§ 5 hours
§ 27 clusters
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31. Game Constellations
§ Processing Shockwave logs
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32. Lessons learned
Need:
§ Fine grain access
§ Incremental updates
§ Deal with changes in the original dataset
§ Real-time data serving
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33. ®
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34. h p://hbase.apache.org
§ Sparse, distributed, persistent multidimensional
sorted map
§ Column oriented store
§ Autosharding
§ Data locality
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35. Data Model
table: row: family: column: value: version
domain.com/x.swf
swf:
sfw:size = 1876 bytes | 1876 bytes
swf:fps = 30
swf:avm = 3
html:
embed = dynamic
status:
last_crawl = 2010/11/26 | last_crawl = 2010/11/25
domain.com/y.swf
domain.com/z.swf ®
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36. API
§ Get
§ Put
§ Delete
§ Scan
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37. Flash
How is ash used
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38. How is ash used in the “wild”?
§ AVM popularity
§ Frame rates
§ Video formats
§ SWF size
§ Flex data structures
§ …
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39. How
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40. How
max 1000
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41. e hard way
§ Hadoop
§ Nutch
§ HBase
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42. Work ow
§ Crawl:
§ Nutch (seed: top-1m.csv Alexa)
§ Detect ash embed, javascript
§ Browse:
§ Hadoop + FF + FP (chromeless)
§ Dump stack traces, memory, swf bytes, etc.
§ Process:
§ Parse stack traces, rank, etc.
§ Export:
§ Hbase: swf table
§ Md5, swf bytecode, memory, load time, etc. ®
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43. ®
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44. ®
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45. ®
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46. Bene ts
§ Security xes
§ Optimization
§ Prioritize based on real usage
§ Testing
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47. SaasBase – Hbase++ as a service
§ Data storage (HBase + HDFS)
§ Domains, tables,
§ API: create, put, get, scan
§ Analytics (HBase + Hadoop + query engine)
§ Reports, dimensions, metrics
§ API: query
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48. photoshop.com
Image analytics
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49. ®
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50. photoshop.com
§ 1B assets (images, videos, other)
§ 120M with EXIF metadata
§ 1.5 petabytes
§ Home grown distributed storage
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51. Intelligence
§ Targeting users:
§ Professionals or Amateurs?
§ Where are pictures taken?
§ Targeting partners:
§ Popular cameras
§ Tracking campaigns
§ New accounts
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52. 5
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53. ®
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54. ®
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55. ®
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56. Stats
§ 7 Machines (16 cores, 24 x 10K RPM SATA, 32GB
RAM, 1Gbps)
§ Map 700M records
§ 2hrs, 41mins
§ Map output: 1.9B records (~80GB)
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57. Lessons
§ SUM, COUNT, AVG, MIN, MAX, GROUP BY,
HAVING, etc.
§ Rollup, drilldown, segmentation
-----------------------------------------------------------
It’s all about Dimensions & Metrics
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58. Recap
§ Hadoop + Mahout + PIG (User clusters)
§ HBase + Hadoop + Nutch+ MySQL (Flash analytics)
§ HBase + Hadoop (EXIF Explorer, image analytics)
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59. Business Catalyst
Analytics
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60. BC
§ End to end platform for online businesses
§ E-commerce, Blogging, CRM, email marketing
§ Analytics: web traffic, affiliates, sales, etc.
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61. ®
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62. ®
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63. Successtrophe
§ Analytics is troublesome
§ SQL database was slow for analytics
§ Over 50 different reports
§ Over 100,000 websites
§ Billions of page views
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64. Requirements
§ Fast incremental processing
§ Custom reporting
§ Filtering, segmentation, rollups, drilldowns
§ Variable time ranges
§ Fast
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65. Solution
§ Continuous processing (every 10 minutes)
§ Reports de nition: dimensions, metrics
§ Real-time queries: directly from HBase
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66. Work ow
§ Import Logs ->HBase
§ Incrementally process/index last 24 hours
§ Serve from HBase
§ Index scans
§ Runtime aggregation
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67. Stats
§ 1 datacenter, 10 months = 1 hour, 24 minutes
§ > 3 Billion report items generated
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68. Lessons
§ UNIQUE is harder
§ E.g :Unique visitors, Visitor loyalty
§ Space vs. time
§ Sorting magic
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69. Not just web analytics
X Analytics
§ Feed in any le format (w3c, apache, tsv, etc.)
§ Tag the dimensions and metrics
§ Process (incremental)
§ Query in real-time
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70. Nothing but the hstack
§ structured data storage: HBase
§ le storage HDFS
§ data processing: Hadoop
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71. Conclusions
§ Keep data
§ Understand data
§ Explore data
§ Extract meaning
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72. h p://hstack.org
h p://hbase.apache.org
h p://hadoop.apache.org
h p://mahout.apache.org
h p://nutch.apache.org
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