Real-time OLAP Big Data Use Cases

Real-time “OLAP” for Big Data (+ use cases)
Cosmin Lehene | Adobe
#bigdataro - 30 January 2013

© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.

What we needed … and built

 OLAP Semantics
 Low Latency Ingestion
 High Throughput
 Real-time Query API

© 2012 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 2

“Physical” Building Blocks


Logical Building Blocks

 Dimensions, Metrics
 Aggregations
 Roll-up, drill-down, slicing and dicing, sorting


OLAP 101 – Queries example

Date Country City OS Browser Sale

2012-05-21 USA NY Windows FF 0.0

2012-05-21 USA NY Windows FF 10.0

2012-05-22 USA SF OSX Chrome 25.0

2012-05-22 Canada Ontario Linux Chrome 0.0

2012-05-23 USA Chicago OSX Safari 15.0

5 visits, 2 4 cities: 3 OS-es 3 browsers 50.0
3 days countries NY: 2 Win: 2 FF: 2 3 sales
USA: 4 SF: 1 OSX: 2 Chrome:2
Canada: 1


OLAP 101 – Queries example

 Rolling up to country level: Country visits sales
SELECT COUNT(visits), SUM(sales)
USA 4 $50
GROUP BY country
Canada 1 0

 “Slice” by browser Country visits sales
SELECT COUNT(visits), SUM(sales) USA 2 $10
GROUP BY country
Canada 0 0
HAVING browser = “FF”

Browser sales visits
 Top browsers by sales
SELECT SUM(sales), COUNT(visits) Chrome $25 2

GROUP BY browser Safari $15 1
ORDER BY sales FF $10 2


OLAP – Runtime Aggregation vs. Pre-aggregation

 Aggregate at runtime  Pre-aggregate
 Most flexible  Fast
 Fast – scatter gather  Efficient – O(1)
 Space efficient  High throughput
 But  But
 I/O, CPU intensive  More effort to process (latency)
 slow for larger data  Combinatorial explosion (space)
 low throughput  No flexibility


SaasBase Map


SaasBase Domain Model Mapping


SaasBase - Domain Model Mapping


SaasBase - Ingestion, Processing, Indexing, Querying


Ingestion


Ingestion(ETL) throughput vs. latency

 Historical data (large batches)
 Optimize for throughput
 Increments (latest data, smaller)
 Optimize for latency


Processing


Processing

 Processing involves reading the Input (files, tables, events), pre-
aggregating it (reducing cardinality) and generating cubes that can be
queried in real-time

 “Super Processor” code running in Storm, Map-Reduce, HBase


Processing for OLAP semantics

 GROUP BY (process, query)
 COUNT, SUM, AVG, etc. (process, query)
 SORT (process, query)
 HAVING (mostly query, can define pre-process constraints)


SaasBase vs. SQL Views Comparison


Query Engine

 Always reads indexed, compact data
 Query parsing
 Scan strategy
 Single vs. multiple scans
 Start/stop rows (prefixes, index positions, etc.)
 Index selection (volatile indexes with incremental processing)
 Deserialization
 Post-aggregation, sorting, fuzzy-sorting etc.
 Paging
 Custom dimension/metric class loading


Adobe Business Catalyst

 Online business presence: e-commerce, marketing, web analytics etc.
 Use case: Web Analytics (visitors, channels, content, e-
commerce, campaigns, etc.)


BC - Workflow


Adobe Business Catalyst - Stats

 3 active datacenters
 Raw data ~6TB (from ~1TB 18 months ago)
 Visits table: ~1TB each(compressed)
 OLAP cubes (stats): 49GB – 64GB (compressed)

 ~30 minutes latency (from actual pageview/sale to chart in UI)
 10s – 100s of milliseconds latency for queries
 ~3000/s max concurrent OLAP queries (actual traffic is much lower)


Adobe Pass for TV Everywhere

 Authentication & Authorization
 Single sign-on to Programmer content (e.g.
Turner, NBC, Hulu, MTV, etc) with Cable operator credentials (e.g.
Comcast, Dish, etc.)


Adobe Pass – Use Case

 Analytics use case: Operational metrics (users, devices, latencies, etc.)
 Real-time ingestion in HBase
 High Frequency Map Reduce jobs (every 2 minutes)


Adobe Pass - Stats (London Olympics 2012)

 67M streams ~ 5.3M hours
 1.5M concurrent streams
 > 7M unique users

 1 Technical & Engineering Emmy Award ;)


Adobe Primetime – Real-time Video Analytics

 Unified video platform (acquisition, transcoding, broadcast, ads,
analytics)


Adobe Primetime – Use Case

 Use Cases:
 Audience metrics – minutes latency ok
 Ads metrics – seconds to minutes ok
 Streaming QoS metrics – seconds must

 Requirements:
 Massive throughput (millions of streams, multiple
heartbeats every 10 seconds)
 Low latency (end-to-end)


Conclusions

 OLAP semantics on a simple data model
 Data as first class citizen
 Domain Specific “Language” for Dimensions, Metrics, Aggregations
 Framework for vertical analytics systems
 Tunable performance, resource allocation


Thank you!
Cosmin Lehene @clehene

http://hstack.org


Related

http://www.hbasecon.com/sessions/low-latency-olap-with-hbase/
http://www.slideshare.net/clehene/low-latency-olap-with-hbase-hbasecon-2012


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