How am I going to skim through these data?

Transcription

1 How am I going to skim through these data? 1

2 Trends Computers keep getting faster But data grows faster yet! Remember? BIG DATA! Queries are becoming more complex Remember? ANALYTICS! 2

3 Analytic Queries Analytic queries typically involve aggregates Simple query report the total sales of each region SELECT SUM(S.sales) FROM SALES S GROUP BY region A query involving multiple aggregates find the average supplier quantity supplied by suppliers of a particular part SELECT AVG(quantity) FROM (SELECT supp, part, SUM(quantity) as quantity FROM lineitem WHERE part = 10 GROUP BY supp, part); A CUBE operator in OLAP allows users to drill down or roll up between multiple nodes of the data cube operation SELECT SUM(S.sales) FROM SALES S GROUP BY CUBE(pid, locid, timeid) {pid, locid} {pid, locid, timeid} {pid, timeid} {locid, timeid} {pid} {locid} {timeid} { } 3

4 System perspective Aggregation queries read a large amount of data a long time to compute return a very small final result answers are or derived from summary data 4

5 User perspective Big Picture analytics Decision makers want to know something about some data quickly Precise answers typically not required; approximate results are ok Real time interaction and control over processing Visually oriented interface Time System 1 System 2 System VS 5

6 Challenge Mismatch between user needs and system functionality 6

7 Drawbacks of Current Systems Only exact answers are available A losing proposition as data volume grows Hardware improvements not sufficient HCI solution: interactive tools don t do big jobs E.g., spreadsheet programs (1 M row, 16k column limit) Systems solution: big jobs aren t interactive No user feedback or control in big DBMS queries ( back to the 60 s ) Long processing time Fundamental mismatch with preferred modes of HCI Best solutions to date precompute (store answers of queries beforehand), e.g. OLAP Don t handle ad hoc queries or data sets well 7

8 100% Desirable features for Big Picture Analytics Early (approximate) answers with guarantees! Refinement over time Interaction and ad hoc control (human in the loop) Online Traditional Time Did you see any problem with Online? 8

9 Example (Conventional) Find the average stock price from the nasdaqdb where company = abc After 1 second 30 seconds 5 minutes Conventional database 9

10 Example (Conventional) Find the average stock price from the nasdaqdb where company = abc After 30 minutes Avg Stock Price = 1000 Conventional database 10

11 Example (Online) Find the average stock price from the nasdaqdb where company = abc After 1 second Avg Stock Price $2031+/ $523 90% 5% Sampling Progress With online aggregation 11

12 Example (Online) Find the average stock price from the nasdaqdb where company = abc After 30 seconds Avg Stock Price $1890+/ $420 95% 15% Sampling Progress With online aggregation 12

13 Example (Online) Find the average stock price from the nasdaqdb where company = abc After 5 minutes Avg Stock Price $1150+/ $210 97% 40% Sampling Progress With online aggregation 13

14 Example (online) Find the average stock price from the nasdaqdb where company = abc After 30 minutes Avg Stock Price $1040+/ $70 99% 95% Sampling Progress With online aggregation 14

15 Example: Online Aggregation Additional Features: Speed up Slow down Terminate 15

16 Example: Online visualization 16

17 Example: Browsing 17

18 Key benefit: Premature termination If acceptably accurate answer reached quickly, the query can be aborted After 5 minutes Conventional database Avg Stock Price $1150+/ $210 97% 40% Sampling Progress Stop early With online aggregation 18

19 Why Stop Early?? Save human time (30 min vs 5 min) Precise vs estimate answers For exploratory applications Save machine time Save cost $$ Very important when dealing with BIG DATA in the cloud Pay for what you used (users need to justify the cost to the organization) 19

20 Analytic queries are costly! QphH = Query per Hour Performance 20

21 Solution: Online aggregation Users must get continual feedback on results of processing Observe the progress of their queries Give continually improving partial results: aggregates have running output and confidence interval Control execution on the fly Average Sales: $22,131+/ $523 $21,255+/ $286 $21,795+/ $105 $21,712+/ $47 98% 85% 90% 95% 15% 20% 35% 40% Sampling Progress For a retailer, approximate result, such as $21,712+/ $47, can provide a good estimation for its daily sale s statistics. And it is more cost effective. 21

22 Statistical estimation Users do not need to set a priori specification of stopping condition The interface is easier for users with no statistical background It requires more powerful statistical estimation techniques (Hoeffding s inequality versus Chebyshev s inequality) 22

23 Usability goals Continuous observation Control of time/precision Control of fairness/partiality 23

24 Performance goals Minimum time to accuracy produce a useful estimate of the final answer ASAP Minimum time to completion secondary goal, assume user will terminate processing long before the final answer is produced Pacing guarantee a smooth and continuous improving display 24

25 What are the tools?? 25

26 Random access to data We need to retrieve data in random order to produce meaningful statistical estimation At any time, the input to the query is a sample Input grows over time until the query is terminated prematurely or all data examined 26

27 Sampling design issues Granularity of sample Record level: high I/O cost Block level: high variability from clustering Types of sample Often simple random sample (SRS) With/without replacement?? Data structures from which to sample Files or relational tables Indexes (B + trees, etc) 27

28 Row level sampling techniques Maintain file in (pseudo) random order Sampling = scan Is file initially in random order? Statistical tests needed: e.g., Runs test, Kolmogorov Smirnov test Can start scans from random positions Best I/O behavior Must freshen ordering (online reorg) On the fly sampling Index scan via index on random column Indexed attributes are different from (and not correlated to) aggregated attributes, e.g., name is not correlated to salary Else get random page, then row within page Less efficient Problem: variable number of records on page 28

29 Sampling from Index Root * 3* 5* 7* 8* 14* 16* 22* 24* 27* 29* 33* 34* 38* 39* 29

30 2 How expensive is row level sampling? Sampling Rate (%) Pages fetched (%)

31 Group By operation Aggregate queries involve grouping, e.g., count of grades of students, average sales per month, etc How are group by queries processed? Sort on group by attributes, e.g., sort by grades, then count Hash on group by attributes Same attribute values will be hashed to the same bucket, e.g., students with the same grade will be grouped together 31

32 Non blocking GROUP BY and DISTINCT Blocking operator Cannot work on another operator while the current operator is being processed Sorting is a blocking algorithm only one group is computed at a time after sorting Hashing is non blocking, but hash table need to fit in memory to have good performance 32

33 Index striding For fair Group By: Want to update all groups Challenging for groups with small number of records Want random tuple from Group 1, random tuple from Group 2,... Idea Index gives tuples from a single group Opens many cursors in index, one per group Fetch records in round robin Can control speed by weighting the schedule Gives fairness/partiality, info/speed match! 33

34 Conclusion Big data analytics is becoming increasingly important Online aggregation (OLA) is a promising direction OLA mechanisms Random sampling Non blocking schemes Index striding Online Hadoop is a variant of Hadoop that offers a flavor of online aggregation 34