Avoiding Sorting and Grouping In Processing Queries

Transcription

1 Avoiding Sorting and Grouping In Processing Queries

2 Outline Motivation Simple Example Order Properties Grouping followed by ordering Order Property Optimization Performance Results Conclusion

3 Motivation Previous presentation: Fundamental Techniques for Order Optimization Using FDs and selection predicates Determining order propagation from input to output Infer from ordering Current presentation: Aside from orderings, we also infer how relations are grouped (i.e., how records in relations are clustered according to value of certain attributes) Infer from grouping Infer from secondary ordering

4 Motivation(cont.) Inferred orderings Make it possible to avoid ing when preprocessing ORDER BY clauses of SQL query Inferred groupings Avoid ing or hashing prior to computing aggregates for GROUP BY clauses Reduce the cost of projection with duplicate elimination Complete projection and duplicate elimination in a single pass Reduce the cost of evaluating selection queries in the form σ A=k (R) in the absence of indexes or an ordering on A Inference of secondary ordering and grouping Avoid unnecessary ing or grouping over multiple attributes Infer new primary orderings or groupings (example follows)

5 Simple Example Benefits of inferring grouping and secondary ordering TPC-H Query SELECT c_custkey, COUNT (*) FROM Customer, Supplier WHERE c_nationkey = s_nationkey GROUPBY c_custkey How many suppliers could supply each costumer directly without having to go through customs

6 Simple Example (cont.) -merge join result is ed (and hence grouped) on c_nationkey and the output tuples are in the same group with respect to c_nationkey, they are themselves grouped on the key of outer relation (c_custkey) But one-pass aggregation requires data only to be grouped and not ed! group c_custkey, count(*) c_custkey merge-join c_nationkey = s_nationkey c_nationkey G c_custkey G =>no Postgres Plan first s the join result to on the grouping attribute c_custkey so as to be able to aggregate over groups in a single pass. TPC-H Query c_nationkey table scan customer s_nationkey table scan supplier SELECT c_custkey, COUNT (*) FROM Customer, Supplier WHERE c_nationkey = s_nationkey GROUPBY c_custkey Postgres QEP of the Query

7 Order Properties order properties have the form: each A i is an attribute, each α i either specifies an ordering (α i = O) or a grouping (α i =G) A 1 α 1 primary ordering or grouping and A2 α 2 secondary Signatures for ordering constructs

8 Order Properties (cont.) The general properties have the form: Shorthand: Also, given and Shorthand: o 1 o 2 (concatenation of OP):

9 Order Properties (cont.) Identities for any order property that holds of a physical relation, all prefixes of that order property also hold of R an ordering on any attribute implies a grouping on that attribute If X functionally determines B, and an order property that includes all attributes in X (ordered or grouped) appearing before B α, then B α is superfluous.

10 Order Properties (cont.) Identities (cont.) special case of identity #3, covering the case where X consists of a single attribute the grouping of an attribute that is functionally determined by the attribute that follows it in the order property is superfluous

11 Grouping followed by ordering Suppose that R=(A,B) consists of 10 tuples, t 1,,t 10, and its physical representation satisfies the order property, A o B G is illustrated on the next slide

12 Grouping followed by ordering (cont.) The primary ordering (A O ) says that the group of tuples with A=1 precedes the group of tuples with A=2 which precedes the group with A=3 A=1 B=1 B=2 t 1 t 2 t 3 A=3 B=3 B=2 t 7 t 6 B=1 t 5 t 4 The secondary ordering (B G ) says that within each group of tuples with like values of A, tuples are clustered together if they have the same value for B t 1 can precede t2 or t2 can precede t1 but the must be adjacent < B=1 t 8 A=2 < B=2 t 9 t 10 Two Example permutations that satisfies the order property : t 2, t 1, t 3, t 10, t 8, t 9, t 6, t 7, t 4, t 5 An illustration of A O B G t 1, t 2, t 3, t 9, t 8, t 10, t 4, t 5, t 6, t 7

13 Order Property Optimization Postgres Plan Operators Summarized The data structures for all plan nodes in postgres include the following fields: inp1, inp n : the fields contained in all input tuples to the node left: the left subtree of the node (set to Null for leaf nodes and Append) right: the right subtree of the node (set to Null for leaf nodes, unary operators and Append).

14 Order Property Optimization Postgres Plan Operators Summarized(cont.) Additional operator-specific fields provided by Postgres and used by our refinement algorithm

15 Order Property Optimization Postgres Plan Operators Summarized (cont.) Group performs two passes over its input: 1. insert Null values between pairs of consecutive tuples with different values for attributes, att 1,,att k, 2. apply functions F k+1,, F n to the collection of values of attributes att k+1,,att n respectively, for each set of tuples separated by Nulls. Hash: builds a hash table over its input using a predetermined hash function over attribute att.

16 Order Property Optimization HJoin: performs a (non-order-preserving) simple hash equijoin (att 1 = att 2 ) with the relation produced by left as the probe relation, and the relation produced by right as the build relation. Merge: performs a merge equijoin (att 1 = att 2 ) with the relation produced by left as the outer relation, and the relation produced by right as the inner relation. NOP: we have added as a dummy plan operator that we temporarily make the root of a Postgres plan prior to its refinement.

17 Order Property Optimization A Plan Refinement Algorithm Input: query plan tree generated by Postgres Output: an equivalent plan tree with unnecessary Sort operators (used either to order or group) removed Requires: 4 new attributes associated with every node in a query plan tree

18 Order Property Optimization A Plan Refinement Algorithm(cont.) New Attributes keys: a set of attribute sets that are guaranteed to be keys of inputs to n fds: a set of functional dependencies (attribute sets attribute) that are guaranteed to hold of inputs to n req: a single order property that is required to hold of inputs either to n or some ancestor node of n for that node to execute sat: a set of order properties that are guaranteed to be satisfied by outputs of n

19 Order Property Optimization A Plan Refinement Algorithm (cont.) Idea: decorate the input plan with the attributes above remove any Sort operator n whose child node produces a result that is guaranteed to satisfy an order property required by its parent node Accomplished in 3 passes

20 Order Property Optimization A Plan Refinement Algorithm (cont.) Refinement of the query plan NOP group c_custkey, count(*) c_custkey merge-join c_nationkey = s_nationkey c_nationkey table scan customer s_nationkey table scan supplier

21 Order Property Optimization A Plan Refinement Algorithm (cont.) Pass 1:Functional Dependencies and Keys A bottom-up pass, FDs and keys are propagated upwards when inferred to hold intermediate query result Pass 2:Required Order Properties Top-down pass requires order properties (req) which are propagated downwards from the root of the tree Operations pseudocode captured in SetReq New required operators are generated by: NOP: Node Order Property (called on the root of the plan to trigger the top-down pass) Group and Unique Join operator All other nodes pass the required order properties they inherit from parent nodes to their child nodes, except for Hash and Append which propagate the empty order property to their child nodes

22 Order Property Optimization

23 Order Property Optimization A Plan Refinement Algorithm (cont.) Pass 3:Sort Elimination A bottom-up pass of the query plan tree that determines what order properties are guaranteed to be satisfied by outputs of each node (sat), and that concurrently removes any Sort operator, n for which n.left.sat Є n.req Algorithm: InferSat!

24 Order Property Optimization A Plan Refinement Algorithm (cont.) InferSat

25 Order Property Optimization A Plan Refinement Algorithm (cont.) InferSat (cont.)

26 Example:TPC-D(now TPC-H) Query 3 TPC-D Query 3 select l_orderkey, sum (l _extendedprice*( 1- l_discount)) as rev, o_orderdate, o_shippriority from customer, order, lineitem where o_orderkey = l_orderkey and c_custkey = o_custkey and c_mktsegment = building and o_orderdate < date( ) and l_shipdate > date( ) group by l_orderkey, o_orderdate, o_shippriority order by rev desc, o_orderdate

27 Example:TPC-D(now TPC-H) Query 3 Previous presentation: We showed that the optimized plan outperformed the original plan by a factor of 2 Now: Improve using our approach to plan refinement reasons about groupings and secondary orderings

28 Example:TPC-D(now TPC-H) Query 3 NLJ R=> O o_orderkey G(U) Identitiy#5 => O o_orderkey G(T) Identitiy#4 =>O o_custkey G O o_orderkey G(T) rev, o_orderdate group by o_orderkey MJ Rule =>O c_custkey G c_custkey G o_custkey G o_orderkey G(T) and c_custkey = o_custkey => O o_custkey G o_custkey G o_custkey G o_orderkey G(T) merge-join c_custkey = o_custkey nested-loops o_orderkey = l_orderkey o_orderkey Index scan lineitem Identitiy#5 => O c_custkey G o_orderkey G(S) O c_custkey o(r)=> O c_custkey G(R) c_custkey o_custkey O o_custkey o(s)=> O o_custkey G(S) table scan customer table scan order

29 Performance Results TPC-D (now TPC-H) Results Database: PC: Customer table: 150,000 rows Supplier table: 10,000 rows Order table: 1,500,000 rows LineItem table: 6,000,000 rows 1 GHz Pentium III Linux, with 512 MB RAM, 120 GB HDD

30 Performance Results Experiment #1 our example Postgres Plan sec Refined sec Ratio group c_custkey, count(*) c_custkey merge-join c_nationkey = s_nationkey c_nationkey s_nationkey table scan customer table scan supplier

31 Performance Results Experiment #2 TPC-H Query 3 rev, o_orderdate Postgres Plan Refined Ratio group by o_orderkey sec Same value of o_orderkey were consecutive tuples thereby increased likelihood of finding joining tuples from lineitem in the cache sec 0.05 merge-join c_custkey = o_custkey nested-loops o_orderkey = l_orderkey o_orderkey Index scan lineitem c_custkey o_custkey table scan customer table scan order

32 Performance Results Experiment #2 TPC-H Query 3 With table scan on lineitem Postgres Plan Refined Ratio rev, o_orderdate group by o_orderkey sec sec 1.07 nested-loops o_orderkey = l_orderkey o_orderkey Table scan lineitem merge-join c_custkey = o_custkey c_custkey o_custkey table scan customer table scan order

33 Conclusion we present a formal approach to order optimization that integrates both orderings and groupings within the same comprehensive framework We also consider secondary orderings and groupings By inferring secondary orderings and groupings, it is possible to avoid unnecessary ing or grouping over multiple attributes we can use secondary orderings known of an operator's input to infer primary orderings of its output

34 Any Questions?