Aquery: A DATABASE SYSTEM FOR ORDER

1. Aquery: A DATABASE SYSTEM FOR ORDER Dennis Shasha, joint work with Alberto Lerner {lerner,shasha}@cs.nyu.edu

2. MotivationThe need for ordered data • Queries in finance, signal processing etc. depend on order. • SQL 99 has extensions but they are clumsy.

3. Moving Averages is algorithmically linear but… Sales(month, total) SELECT t1.month+1 AS forecastMonth, (t1.total+ t2.total + t3.total)/3 AS 3MonthMovingAverageFROM Sales AS t1, Sales AS t2, Sales AS t3WHERE t1.month = t2.month - 1 AND t1.month = t3.month – 2 Can optimizer make a 3-way (in general, n-way) join linear time? Ref: Data Mining and Statistical Analysis Using SQL Trueblood and LovettApress, 2001

4. Moving Averages in SQL99 is awkward Sales(month, total) Alberto, please put in SQL 99

5. Choose query from Jennifer Alberto, I suggest query 2 because that may be hard in sql 99.

6. MotivationProblems Extending SQL with Order • Queries are hard to read • Cost of execution is often non-linear (would not pass basic algorithms course) • Few operators preserve order, so optimization hard.

7. Idea • Whatever can be done on a table can be done on an ordered table (arrable). Not vice versa. • Aquery – query language on arrables; operations on columns. • Upward compatible from SQL 92. • Optimization ideas are new, but natural.

8. SQL + OrderMoving Averages Sales(month, total) SELECT month, avgs(8, total)FROM Sales ASSUMING ORDER month avgs: vector-to-vector function, order-dependant and size-preserving order to be used on vector-to-vector functions • Execution (Sales is an arrable): • FROM clause – enforces the order in ASSUMING clause • SELECT clause – for each month yields the moving average (window size 8) ending at that month.

9. SQL + OrderTop N Employee(ID, salary) SELECT first(N, salary) FROM Employee ASSUMING ORDER Salary first: vector-to-vector function, order-dependant and non size-preserving • Execution: • FROM clause – orders arrable by Salary • SELECT clause – applies first() to the ‘salary’ vector, yielding first N values of that vector given the order. Could get the top earning IDs by saying first(N, ID).

10. SQL + OrderVector-to-Vector Functions size-preserving non size-preserving prev, next, $, [] avgs(*), prds(*), sums(*), deltas(*), ratios(*), reverse, … drop, first, last order-dependant rank, tile min, max, avg, count non order-dependant

11. Given a table with securities’ price ticks, ticks(ID, date, price, timestamp), find the best possible profit if one bought and sold security ‘S’ in a given day Note that max – min doesn’t work, because must buy before you sell (no selling short). Challenge

12. Complex queries: Best spread In a given day, what would be the maximum difference between a buying and selling point of each security? Ticks(ID, price, tradeDate, timestamp, …) SELECT ID, max(price – mins(price))FROM Ticks ASSUMING ORDER timestampWHERE tradeDate = ‘99/99/99’GROUP BY ID max bestspread running min min • Execution: • For each security, compute the running minimum vector for price and then subtract from the price vector itself; result is a vector of spreads. • Note that max – min would overstate spread.

13. Best-profit query SELECT max(price – mins(price)) FROM ticks ASSUMING ORDER timestamp WHERE day = ‘12/09/2002’ AND ID = ‘S’ price 15 19 13 7 4 5 4 7 12 2 mins 15 15 13 7 4 4 4 4 4 4 - 0 4 0 0 0 1 0 3 8 2

14. [AQuery] SELECT max(price–mins(price)) FROM ticks ASSUMING timestamp WHERE ID=“x” AND tradeDate='99/99/99' [SQL:1999] SELECT max(rdif) FROM (SELECT ID,tradeDate, price - min(price) OVER (PARTITION BY ID ORDER BY timestamp ROWS UNBOUNDED PRECEDING) AS rdif FROM Ticks ) AS t1 WHERE ID=“x” AND tradeDate='99/99/99' Best-profit query comparison

15. Complex queries: Crossing averages part I When does the 21-day average cross the 5-month average? Market(ID, closePrice, tradeDate, …)TradedStocks(ID, Exchange,…) INSERT INTO temp FROMSELECT ID, tradeDate, avgs(21 days, closePrice) AS a21, avgs(5 months, closePrice) AS a5, prev(avgs(21 days, closePrice)) AS pa21, prev(avgs(5 months, closePrice)) AS pa5FROM TradedStocks NATURAL JOIN Market ASSUMING ORDER tradeDateGROUP BY ID

16. Complex queries: Crossing averages part uses non 1NF • Execution: • FROM clause – order-preserving join • GROUP BY clause – groups are defined based on the value of the Id column • SELECT clause – functions are applied; non-grouped columns become vector fields so that target cardinality is met. Violates first normal form Vectorfield groups in ID and non-grouped column grouped ID and non-grouped column two columns withthe same cardinality

17. Complex queries: Crossing averages part II Get the result from the resulting non first normal form relation temp SELECT ID, tradeDateFROM flatten(temp)WHERE a21 > a5 AND pa21 <= pa5 • Execution: • FROM clause – flatten transforms temp into a first normal form relation (for row r, every vector field in r MUST have the same cardinality). Could have been placed at end of previous query. • Standard query processing after that.

18. SQL + OrderRelated Work: Research • SEQUIN – Seshadri et al. • Sequences are first-class objects • Difficult to mix tables and sequences. • SRQL – Ramakrishnan et al. • Elegant algebra and language • No work on transformations. • SQL-TS – Sadri et al. • Language for finding patterns in sequence • But: Not everything is a pattern!

19. SQL + OrderRelated Works: Products • RISQL – Red Brick • Some vector-to-vector, order-dependent, size-preserving functions • Low-hanging fruit approach to language design. • Analysis Functions – Oracle 9i • Quite complete set of vector-to-vector functions • But: Can only be used in the select clause; poor optimization (our preliminary study) • KSQL – Kx Systems • Arrable extension to SQL but syntactically incompatible. • No cost-based optimization.

20. Order-related optimization techniques • Starburst’s “glue” (Lohman, 88) and Exodus/Volcano “Enforcers” (Graefe and McKeena, 93) • DB2 Order optimization (Simmen et al., 96) • Top-k query optimization (Carey and Kossman, 97) • Hash-based order-preserving join (Claussen et al., 01) • Temporal query optimization addressing order and duplicates (Slivinskas et al., 01)

21. Interchange sorting + order preserving operators SELECT ts.ID, ts.Exchange, avgs(10, hq.ClosePrice)FROM TradedStocks AS ts NATURAL JOIN HistoricQuotes AS hq ASSUMING ORDER hq.TradeDateGROUP BY Id avgs avgs avgs g-by sort g-by op avgs op sort g-by g-by op op sort op (1) Sort then joinpreserving order (2) Preserve existingorder (3) Join then sortbefore grouping (4) Join then sortafter grouping

22. TransformationsEarly sorting + order preserving operators

23. UDFs evaluation order Gene(geneId, seq)SELECT t1.geneId, t2.geneId, dist(t1.seq, t2.seq)FROM Gene AS t1, Gene AS tWHERE dist(t1.seq, t2.seq) < 5 AND posA(t1.seq, t2.seq) posA asks whether sequences have Nucleo A in same position. Dist gives edit distance between two Sequences. posA dist Switch dynamicallybetween (1) and (2) depending on the execution history dist posA (1) (2) (3)

24. TransformationsUDFs Evaluation Order

25. Transformations Building Blocks • Order optimization • Simmens et al. `96 – push-down sorts over joins, and combining and avoiding sorts • Order preserving operators • KSQL – joins on vector • Claussen et al. `00 – OP hash-based join • Push-down aggregating functions • Chaudhuri and Shim `94, Yan and Larson `94 – evaluate aggregation before joins • UDF evaluation • Hellerstein and Stonebraker ’93 – evaluate UDF according to its ((output/input) – 1)/cost per tuple • Porto et al. `00 – take correlation into account

26. SELECT last(price)FROM ticks t,base b ASSUMING ORDER name, timestampWHERE t.ID=b.ID AND name=“x” Original Query  last(price)  Name=“x” sort name,timesamp ID ticks base

27. The sort on name can be eliminated because there will be only one name Then, push sort sortA(r1 r2) A sortA(r1) lop r2 sortA(r) A r Last price for a name query  last(price) sort name,timesamp ID ticks  Name=“x” base

28. The projection is carrying an implicit selection: last(price) = price[n], where n is the last index of the price array f(r.col[i])(r) order(r)f(r.col)(pos()=i(r)) Last price for a name query  price  last(price)  pos()=last lop ID ticks  Name=“x” base

29. But why join the entire relation if we are only using the last tuple? Can we somehow push the last selection down the join? Last price for a name query  price  pos()=last lop ID ticks  Name=“x” base

30. We can take the last position of each ID on ticks to reduce cardinality, but we need to group by ticks.ID first But trading a join for a group by is usually a good deal?! One more step: make this an “edge by” Last price for a name query  price  safety lop ID   pos()=last Name=“x” Gby base ID ticks

31. The “edgeby” operator • The pattern edge condition(Gby(r)) can be efficiently implemented without grouping the whole arrable • An edgeby is a kind of scan that take as parameters the grouping column(s), the direction to scan, a position, and if that position is the last (scan up to) or the first one the group (scan from this on). • In the previous example, to find the last rows for each ID in ticks one would use, respectively, `ID,' `backwards,' ` 1,' and 'up to.'

32. Conclusion • Arrable-based approach to ordered databases may be scary – dependency on order, vector-to-vector functions – but it’s expressive and fast. • SQL extension that includes order is possible and reasonably simple. • Optimization possibilities are vast.

33. Streaming • Aquery has no special facilities for streaming data, but it is expressive enough. • Idea for streaming data is to split the tables into tables that are indexed with old data and a buffer table with recent data. • Optimizer works over both transparently.

34. Arrables’ Shape Properties • The cardinality of an arrable’s array-columns must be the same • An arrable’s tuple can contain single or array-valued column values. • A 1NF arrable contains only single values • A flatten-able ¬1NF arrable may contain array-valued columns, but they have the same cardinality • A non-flatten-albe ¬1NF arrable is one that is not in the previous categories

35. Arrables Primitives • Single-value indexingr[k] retrieves the tuple formed by the k-th position value of each of the arrables array-columns • Multi-value indexingr[k1 k2 … kn] is similar to forming an arrable by insertion of r[k1], r[k2], …, r[kn]

36. Array-columns Expressions • Expressions in AQuery are column-oriented, as opposed to row-oriented price xy z price xy z prev(price) - xy result T F T result - y-x z-y scalar = - = >

37. Built-in functions • prevv[i] = null, if i=0 or v[i-1], for 0<i|v|-1 • firstv,n[i] = v[i], for 0i<n • minsv[i] = v[i], if i=0 or min(v[i],mins[i-1]), for 0<i|v|-1 • indexbv[i] = i such that bv[i] is true

38. Each modifier • Built-in functions are applied over array-columns • The each keyword makes the function application occur in each tuple level

39. Group and flatten operations • An arrable can be grouped over a grouping expression that assigns a group value to each row. The net effect is to nest an 1NF arrable into a flatten-able ¬1F one • The flatten operation undoes the operation • But there are data-ordering issues!

40. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING AQuery syntax and execution

41. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING Arrables mentioned in the FROM clause are combined using cartesian product. This is operation is order-oblivious AQuery syntax and execution

42. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING The ASSUMING ORDER is enforced. From this point on any order-dependent expression can be used, regardless of the clause. AQuery syntax and execution

43. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING The WHERE expression is executed. It must generate a boolean vector that maps each row of the current arrable to true or false. The false rows are eliminated and the resulting arrable is passed along. AQuery syntax and execution

44. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING The GROUP BY expression is computed. It must map each tuple to a group. The resulting arrable has as many rows as there are groups. The columns that did not participate on the grouping expression now have array-valued values AQuery syntax and execution

45. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING The HAVING clause expression must result in a boolean vector that maps each group, i.e, row, to a boolean value. The rows that map to false are excluded. AQuery syntax and execution

46. It’s just SQL, really. But column-oriented. SELECTFROMASSUMING ORDERWHEREGROUP BYHAVING Finally, each expression on the SELECT clause is executed, generating a new array-column. The resulting arrable is achieved by “zipping” these arrays side-by-side. AQuery syntax and execution

47. SELECT ID, index(flags=‘FIN’)-index(flags=‘ACK’) FROM netmgmt ASSUMING ORDER timestamp GROUP BY ID Translating queries into Operations  each Order-preservingportion of the plan Gby sort netmgmt

48. Non- and Order-preserving Variations • Operators may or may not be required to be preserve order, depending on their location. • For instance: • a group by based on hashing is an implementation of a non-order preserving group-by • A nested-loops join is an implementation of a left-order preserving join • There are performance implications in preserving or not order

49. Early sort and order-preserving group by Early, non-order preserving group by and sort Pick the best   each each GbysessionID sorttimestamp each sorttimestamp GbysessionID netmgmt netmgmt

50. AQuery Optimization • Optimization is cost based • The main strategies are: • Define the extent of the order-preserving region of the plan, considering (correctness, obviously, and) the performance of variation of operators • Apply efficient implementations of patterns of operators • There are generic techniques yet each query category presents unique opportunities and challenges • Equivalence rules between operators can be used in the process