Acunu Analytics

•

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Acunu

Technology Business

Making big data useful
How do we turn this ...
time page session id duration

... ... ... ...

14:58:03.234 /index.html 248.180.3.40 898

14:58:03.234 /csi/csi/council/freedom.html 248.180.3.40 1234

14:58:03.234 /docs/access/chapter8.txt 99.1.10.178 52

... ... ... ...

x billions

Tuesday, 27 March 2012

MY
Introduction

into this...

Tuesday, 27 March 2012

• SQL + materialised views

Tuesday, 27 March 2012

• SQL + materialised views
... would be nice if it scaled

Tuesday, 27 March 2012

• Hadoop/Map-Reduce can do anything

Tuesday, 27 March 2012

• Hadoop/Map-Reduce can do anything
Not real-time

Inefﬁcient re-computation

Tuesday, 27 March 2012

• Hadoop/Map-Reduce can do anything
Not real-time

Inefﬁcient re-computation

(100TB on a 100 node cluster is > 3 hours)

Tuesday, 27 March 2012

• Cassandra counters are pretty cool

Tuesday, 27 March 2012

• Cassandra counters are pretty cool
but the query semantics is spartan

=> DIY solutions

Tuesday, 27 March 2012

Acunu Analytics
• Simple, real-time, incremental analytics
• push processing into ingest phase
AA
event Cassandra
counter
updates

Tuesday, 27 March 2012

Acunu Analytics
• Event template, e.g.,
select : ["COUNT", "AVG(loadTime)"],
type : {
time : [TIME(HOUR; MIN; SEC), ?, 0],
page : PATH(/),
loadTime : [LONG, 0, 0]
}

• speciﬁes “blow-up” strategy according to
supported queries

Tuesday, 27 March 2012

Acunu Analytics
type : {
time : TIME(HOUR; MIN),
category : STRING,
user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ...
}
22:00 all→3221 :00→22 :00→19 :02→104 ...

... ...

click all→228 user01→1 user14→12 user99→7 ...

open all→354 user01→4 user04→8 user56→17 ...

...

click, 22:00 all→1904 ...

∅ all→87314 click→238 open→354 ...

Tuesday, 27 March 2012

Acunu Analytics
type : {
time : TIME(HOUR; MIN),
category : STRING,
user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ...
}
22:00 all→3221 :00→22 :00→19 :02→104 ...

... ...

(22:02, “click”, user01) click all→228 user01→1 user14→12 user99→7 ...

open all→354 user01→4 user04→8 user56→17 ...

...

click, 22:00 all→1904 ...

∅ all→87314 click→238 open→354 ...

Tuesday, 27 March 2012

Acunu Analytics
type : {
time : TIME(HOUR; MIN),
category : STRING,
user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ...
}
22:00 all→3222 :00→22 :00→19 :02→105 ...

... ...

(22:02, “click”, user01) click all→229 user01→2 user14→12 user99→7 ...

open all→354 user01→4 user04→8 user56→17 ...

...

click, 22:00 all→1905 ...

∅ all→87315 click→239 open→355 ...

Tuesday, 27 March 2012

Acunu Analytics
Pre-assembled queries, e.g. ...
21:00 all→1345 :00→45 :01→62 :02→87 ...

for 22:00-23:00, 22:00 all→3222 :00→22 :00→19 :02→105 ...

group by minute ... ...

click all→229 user01→2 user14→12 user99→7 ...
group all by user, open all→354 user01→4 user04→8 user56→17 ...
where category=click ...

count all click, 22:00 all→1905 ...

∅ all→87315 click→239 open→355 ...

group all by category
Tuesday, 27 March 2012

Summary
• Simple, real-time, incremental analytics
• work done on ingest
• sum, count, distinct, avg, stddev, min-max etc
• time + hierarchy bucketing
• efﬁcient ‘group’ semantics
• works with Apache Cassandra
Tuesday, 27 March 2012

Early Access Program

analytics@acunu.com

Tuesday, 27 March 2012

count
distinct
(session)
count

Tuesday, 27 March 2012

count
distinct
(session)
count

avg(duration)

Tuesday, 27 March 2012

count
grouped by ...
day
count
distinct
(session)
count

avg(duration)

Tuesday, 27 March 2012

count
grouped by ...
day
count
distinct
(session)
count ... geography

avg(duration)

Tuesday, 27 March 2012

count
grouped by ...
day
count
distinct
(session)
count ... geography

avg(duration)
... browser

Tuesday, 27 March 2012

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1. Acunu Analytics Simple, powerful, real-time Andrew Byde Principal Scientist Tuesday, 27 March 2012

2. Making big data useful How do we turn this ... time page session id duration ... ... ... ... 14:58:03.234 /index.html 248.180.3.40 898 14:58:03.234 /csi/csi/council/freedom.html 248.180.3.40 1234 14:58:03.234 /docs/access/chapter8.txt 99.1.10.178 52 ... ... ... ... x billions Tuesday, 27 March 2012

3. MY Introduction into this... Tuesday, 27 March 2012

4. or this... Tuesday, 27 March 2012

5. or this... Tuesday, 27 March 2012

6. • SQL + materialised views Tuesday, 27 March 2012

7. • SQL + materialised views ... would be nice if it scaled Tuesday, 27 March 2012

8. • Hadoop/Map-Reduce can do anything Tuesday, 27 March 2012

9. • Hadoop/Map-Reduce can do anything Not real-time Inefﬁcient re-computation Tuesday, 27 March 2012

10. • Hadoop/Map-Reduce can do anything Not real-time Inefﬁcient re-computation (100TB on a 100 node cluster is > 3 hours) Tuesday, 27 March 2012

11. • Cassandra counters are pretty cool Tuesday, 27 March 2012

12. • Cassandra counters are pretty cool but the query semantics is spartan => DIY solutions Tuesday, 27 March 2012

13. Acunu Analytics • Simple, real-time, incremental analytics • push processing into ingest phase AA event Cassandra counter updates Tuesday, 27 March 2012

14. Acunu Analytics • Event template, e.g., select : ["COUNT", "AVG(loadTime)"], type : { time : [TIME(HOUR; MIN; SEC), ?, 0], page : PATH(/), loadTime : [LONG, 0, 0] } • speciﬁes “blow-up” strategy according to supported queries Tuesday, 27 March 2012

15. Acunu Analytics type : { time : TIME(HOUR; MIN), category : STRING, user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ... } 22:00 all→3221 :00→22 :00→19 :02→104 ... ... ... click all→228 user01→1 user14→12 user99→7 ... open all→354 user01→4 user04→8 user56→17 ... ... click, 22:00 all→1904 ... ∅ all→87314 click→238 open→354 ... Tuesday, 27 March 2012

16. Acunu Analytics type : { time : TIME(HOUR; MIN), category : STRING, user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ... } 22:00 all→3221 :00→22 :00→19 :02→104 ... ... ... (22:02, “click”, user01) click all→228 user01→1 user14→12 user99→7 ... open all→354 user01→4 user04→8 user56→17 ... ... click, 22:00 all→1904 ... ∅ all→87314 click→238 open→354 ... Tuesday, 27 March 2012

17. Acunu Analytics type : { time : TIME(HOUR; MIN), category : STRING, user : STRING 21:00 all→1345 :00→45 :01→62 :02→87 ... } 22:00 all→3222 :00→22 :00→19 :02→105 ... ... ... (22:02, “click”, user01) click all→229 user01→2 user14→12 user99→7 ... open all→354 user01→4 user04→8 user56→17 ... ... click, 22:00 all→1905 ... ∅ all→87315 click→239 open→355 ... Tuesday, 27 March 2012

18. Acunu Analytics Pre-assembled queries, e.g. ... 21:00 all→1345 :00→45 :01→62 :02→87 ... for 22:00-23:00, 22:00 all→3222 :00→22 :00→19 :02→105 ... group by minute ... ... click all→229 user01→2 user14→12 user99→7 ... group all by user, open all→354 user01→4 user04→8 user56→17 ... where category=click ... count all click, 22:00 all→1905 ... ∅ all→87315 click→239 open→355 ... group all by category Tuesday, 27 March 2012

19. Summary • Simple, real-time, incremental analytics • work done on ingest • sum, count, distinct, avg, stddev, min-max etc • time + hierarchy bucketing • efﬁcient ‘group’ semantics • works with Apache Cassandra Tuesday, 27 March 2012

20. Early Access Program analytics@acunu.com Tuesday, 27 March 2012

21. Tuesday, 27 March 2012

22. count Tuesday, 27 March 2012

23. count distinct (session) count Tuesday, 27 March 2012

24. count distinct (session) count avg(duration) Tuesday, 27 March 2012

25. count grouped by ... day count distinct (session) count avg(duration) Tuesday, 27 March 2012

26. count grouped by ... day count distinct (session) count ... geography avg(duration) Tuesday, 27 March 2012

27. count grouped by ... day count distinct (session) count ... geography avg(duration) ... browser Tuesday, 27 March 2012

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