In this session you will learn the common mistakes made when deploying a high write environment when building an analytics database in HBase, as well as tips on how to diagnose and debug performance bottlenecks, and an overview of an open source monitoring utility developed at Urban Airship for finding HBase hotspots. This session will also present a case study on how Urban Airship replaced a tag system running on a highly sharded PostgreSQL cluster to HBase, the options explored to create a high throughput Boolean tag system and how it was ultimately built on HBase.

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wh B s
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a 21
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buM
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n h Tlk
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b u Ub n irh
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h t m be f s u t e
r r u
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o m n is ks
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h
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5. B Te u br
y h N m es
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io
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t
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at im , at a ey o c s es
e e t , s o

7. M ben at c r ?
o ilIrs u t e
f r u

8. Mt e
is ks
a
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id ’t ae n H d o x eie c, is ks ee a e
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11. Mt e
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a

12. Mt e
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U in R I o vta e d k
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13. Mt e
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N t s gCo d r
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14. Mt e
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a
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f t o e a.xiees
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15. Mt e
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a
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c
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o

16. Mt e
is ks
a
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o in g int B s
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s lhwig t cesc ek n P t x t s
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B sT s Uil is ra
in it
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a h g r o cr c
c e

17. Mt e
is ks
a

18. Mt e
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a
• S hm
ce a
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19. Mt e
is ks
a
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l
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20. Mt e
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a

21. Mt e
is ks
a

22. Mt e
is ks
a

23. P r r a c M noin
ef m n e o itrg
o
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x oe Y m e m t s ru d B s c t as
r ie l
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t l
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im l t a wh o r x t aa o e
e g io in

24. P r r a c M noin
ef m n e o itrg
o

25. P r r a c M noin
ef m n e o itrg
o

26. P r r a c M noin
ef m n e o itrg
o
hts /itu .c mub n ir ips t t l
t :/ h b o /ra as / a ha e
p g h ts b

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a s yt
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e
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le
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28. T gS s m
a yt e
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ad n e tn a b ss
a a
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ag d t e r s ra u o d k
a s
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u r p n a flak o u t l cn
a l la e
• L n ra s ut te p r o te yt
o g ed h roh r at fh ss m
s e

29. T gS s m
a yt e

30. T gS s m
a yt e
• L g c ss mrn in o P s rs
e ay yt u n g n ot e
e g
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h re a st d in e sad is e u
a , d
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ed d o e g h t a l a n y ig tru h u
h w t
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u p rf ru in , n , r o
r

31. T gS s m
a yt e
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ae f e e H ae n u e e
f w
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B s wn ea s o o ea aes a d n w
io
sa bitc aat is s
cl il h rc rt
a y e ic

32. T gS s m
a yt e
• M t cn e r fl w r e
a h a rt n u r o ky
c u lo
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n o c d k h n eesr
y
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rt f e a , im l
r s e

33. T gS s m
a yt e

34. T gS s m
a yt e
• Go
od
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at o e c 2 0 / , .5 eo d t ir rsl
, e s t
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io
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cl esy
e
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a
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e p r io s a ’t e h n e
it
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n f n in h sa d o n ae
eic e

35. T a k!
h ns
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Bs
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ra A sh t / w ra as .c m
p w h
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e e irg t / b n ir .c mc m a yo s
p u h j
• @ a p ta
n t un m
e

36. Q etn?
us s
io