How Lynx Analytics uses the Druid analytical database.

14. timestamp gender age membership hi_score
2019-04-09 male 35 to 44 Gold 4.0
2019-04-09 female 45 to 54 Platinum 5.8
2019-04-09 male 35 to 44 Gold 6.0

19. timestamp gender age membership hi_score
2019-04-09 male 35 to 44 Gold 4.0
2019-04-09 female 45 to 54 Platinum 5.8
2019-04-09 male 35 to 44 Gold 6.0

20. timestamp gender age membership hi_score
2019-04-09 male 35 to 44 Gold 4.0
2019-04-09 female 45 to 54 Platinum 5.8
2019-04-09 male 35 to 44 Gold 6.0

21. timestamp gender age membership hi_score count
2019-04-09 male 35 to 44 Gold 4.0 1
2019-04-09 female 45 to 54 Platinum 5.8 1
2019-04-09 male 35 to 44 Gold 6.0 1

22. timestamp gender age membership hi_score sum count
2019-04-09 male 35 to 44 Gold 10.0 2
2019-04-09 female 45 to 54 Platinum 5.8 1

23. timestamp gender age membership hi_score sum count
2019-04-09 male 35 to 44 Gold 10.0 2
2019-04-09 female 45 to 54 Platinum 5.8 1

24. timestamp gender age membership hi_score sum count
2019-04-09 male 35 to 44 Gold 10.0 2
2019-04-09 female 45 to 54 Platinum 5.8 1
2019-04-09 female 35 to 44 Silver 4.5 1
2019-04-09 male 25 to 34 Silver 7.9 2
2019-04-09 male 35 to 44 Platinum 5.3 1

25. age
35 to 44
45 to 54
35 to 44
25 to 34
35 to 44

26. 25 to 34 ⇒ 0
35 to 44 ⇒ 1
45 to 54 ⇒ 2
age
35 to 44
45 to 54
35 to 44
25 to 34
35 to 44

27. 25 to 34 ⇒ 0
35 to 44 ⇒ 1
45 to 54 ⇒ 2
age
1
2
1
0
1

28. 25 to 34 ⇒ 0
35 to 44 ⇒ 1
45 to 54 ⇒ 2
age
1
2
1
0
1
age=0 age=1 age=2
1
1
1
1
1

29. SELECT AVG(hi_score) FROM table
WHERE age = "35 to 44"
AND membership != "Silver"

30. table.filter(`
$age == "35 to 44"
and $membership != "Silver"
`).average("$hi_score")

31. age=1
1
1
1
age = "35 to 44" membership != "Silver"
membership=2
1
1

32. age=1
1
1
1
age = "35 to 44" membership != "Silver" age=1 - membership=2
membership=2
1
1
match
1
1

33. hi_score sum
10.0
5.8
4.5
7.9
5.3
age=1 - membership=2
count
2
1
1
2
1
match
1
1

34. hi_score sum
10.0
5.8
4.5
7.9
5.3
age=1 - membership=2
count
2
1
1
2
1
match
1
1

35. hi_score sum
10.0
5.8
4.5
7.9
5.3
count
2
1
1
2
1
sum(hi_score sum) = 15.3 sum(count) = 3

36. hi_score sum
10.0
5.8
4.5
7.9
5.3
avg(hi_score) = 5.1
count
2
1
1
2
1
sum(hi_score sum) = 15.3 sum(count) = 3

40. ●
●
●
●
●
●
●
●

41. ●
●
●
●
●
●
●
●
PATCH: Error immediately
instead of waiting a minute
for Hadoop to time out.
PATCH: Run shell script to
preprocess incoming SQL.
(unlucky ⇒ lucky)
Add utility columns like
“ONE” so we can always
group by something.
Try half dozen different
builds of the closed source
Hortonworks JDBC driver.
python -m http.server
To get files to Windows
machine.

42. ●
●
●
●

43. ~/logs

47. from pyroaring import BitMap
bm1 = BitMap()
bm1.add(3)
bm1.add(18)
bm2 = BitMap([3, 27, 42])
print(bm1 & bm2)
print(bm1 | bm2)

48. popcnt cnt