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CS 542 Database Management Systems Query Optimization J Singh March 28, 2011
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan
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Desired Endpoint x=1 AND y=2 AND z<5 (R) R ⋈ S ⋈ U Example Physical Query Plans two-pass hash-join 101 buffers Filter(x=1 AND z<5) materialize IndexScan(R,y=2) two-pass hash-join 101 buffers TableScan(U) TableScan(R) TableScan(S)
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Physical Plan Selection The particular operation being performed Size of intermediate results, as derived last week (sec 16.4 of book) Physical Operator Implementation used, e.g., one- or two-pass Operation ordering, esp. Join ordering Operation output: materialized or pipelined. Governed by disk I/O, which in turn is governed by
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Index-based physical plans (p1) Selection example. What is the cost of a=v(R) assuming B(R) = 2000 T(R) = 100,000 V(R, a) = 20 Table scan (assuming R is clustered): B(R) = 2,000 I/Os Index based selection: If index is clustering: B(R) / V(R,a) = 100 I/Os If index is unclustered: T(R) / V(R,a) = 5,000 I/Os For small V(R, a), table scan can be faster than an unclustered index Heuristics that pick indexed over not-indexed can lead you astray Determine the cost of both methods and let the algorithm decide 5
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Index-based physical plans (p2) Example: Join if S has an index on the join attribute For each tuplein R, fetch corresponding tuple(s) from S Assume R is clustered. Cost: If index on S is clustering: B(R) + T(R) B(S) / V(S,a) If index on S is unclustered: B(R) + T(R) T(S) / V(S,a) Another case: when R is output of another Iterator. Cost: B(R) is accounted for in the iterator If index on S is clustering: T(R) B(S) / V(S,a) If index on S is unclustered: T(R) T(S) / V(S,a) If S is not indexed but fits in memory: B(S) A number of other cases
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Index-based physical plans (p3) Index Based Join ifboth R and S have a sorted index (B+ tree) on the join attribute Then perform a merge join called zig-zag join Cost: B(R) + B(S)
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Grand Summary of Physical Plans (p1) Scans and Selects Index: N = None, C = Clustering, NC = Non-clustered
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Grand Summary of Physical Plans (p2) Joins Index: N = None, C = Clustering, NC = Non-clustered Relation fits in memory: F = Yes, NF = No
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Physical plans at non-leaf Operators (p1) What if the input of the operator is from another operator? For Select, cost= 0. Cost of pipelining is assumed to be zero The number of tuples emitted is reduced For Join, when R is from an operator and S from a table: B(R) is accounted for in the iterator If index on S is clustering: T(R) B(S) / V(S,a) If index on S is unclustered: T(R) T(S) / V(S,a) If S is not indexed but fits in memory: B(S) If S is not indexed and doesn’t fit: k*B(S) for k chunks If S is not indexed and doesn’t fit: 3*B(S) for sort- or hash-join
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Physical plans at non-leaf Operators (p2) For Join, when R and S are both from operators, cost depends on whether the result are sorted by the Join attribute(s) If yes, we use the zig-zag algorithm and the cost is zero. Why? If either relation will fit in memory, the cost is zero. Why? At most, the cost is 2*(B(R) + B(S)). Why?
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Example (787) Product(pname, maker), Company(cname, city) Select Product.pname From Product, Company Where Product.maker=Company.cname and Company.city = “Seattle” How do we execute this query ?
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Example (787) Product(pname, maker), Company(cname, city) Select Product.pname From Product, Company Where Product.maker=Company.cname and Company.city = “Seattle” Logical Plan Clustering Indices: Product.pname Company.cname Unclustered Indices: Product.maker Company.city maker=cname scity=“Seattle” Product(pname,maker) Company(cname,city)
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Example (787) Physical Plans Physical Plan 1 Physical Plans 2a and 2b Merge-join Index-basedjoin Index-basedselection maker=cname scity=“Seattle” cname=maker scity=“Seattle” Product(pname,maker) Company(cname,city) Product(pname,maker) Company(cname,city) Index-scan Scan and sort (2a)index scan (2b)
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Final Evaluation Plan Costs: Plan 1: T(Company) / V(Company, city) T(Product)/V(Product, maker) Plan 2a: B(Company) + 3B(Product) Plan 2b: B(Company) + T(Product) Which is better? It depends on the data
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Example (787) Evaluation Results Common assumptions: T(Company) = 5,000 B(Company) = 500 M = 100 T(Product) = 100,000 B(Product) = 1,000 Assume V(Product, maker) T(Company) Case 2: V(Company, city) << T(Company) V(Company, city) = 20 Plan 1: 250 20 = 5,000 Plan 2a: 3,500 Plan 2b: 100,500 Case 1: V(Company, city) T(Company) V(Company, city) = 5,000 Plan 1: 1 20 = 20 Plan 2a: 3,500 Plan 2b: 100,500 Reference from previous page:
Plan 1: T(Company)/V(Company,city) T(Product)/V(Product,maker)
Lessons Need to consider several physical plans even for one, simple logical plan No magic “best” plan: depends on the data In order to make the right choice need to have statistics over the data the B’s, the T’s, the V’s
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Query Optimzation Have a SQL query Q Create a plan P Find equivalent plans P = P’ = P’’ = … Choose the “cheapest”. HOW ??
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Logical Query Plan SELECT P.buyer FROM Purchase P, Person Q WHERE P.buyer=Q.name AND Q.city=‘seattle’ AND Q.phone > ‘5430000’ Plan buyer City=‘seattle’ phone>’5430000’ Buyer=name In class: find a “better” plan P’ Person Purchase
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CS 542 Database Management Systems Query Optimization – Choosing the Order of Operations J Singh March 28, 2011
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan
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Join Trees Recall that the following are equivalent:
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But they are not equivalent from an execution viewpoint.
Considerable research has gone into picking the best order for Joins
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Join Trees R1 ⋈R2 ⋈ …⋈Rn Join tree: Definitions A plan = a join tree A partial plan = a subtree of a join tree R3 R1 R2 R4 24
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Left & Right Join Arguments The argument relations in joins determine the cost of the join In Physical Query Plans, the left argument of the join is Called the build relation Assumed to be smaller Stored in main-memory
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Left & Right Join Arguments The right argument of the join is Called the probe relation Read a block at a time Its tuples are matched with those of build relation The join algorithms which distinguish between the arguments are: One-pass join Nested-loop join Index join
Left deep: Bushy R3 R4 R1 R2 R5 R3 R2 R4 R5 R2 R4 R3 R1 Many different orders, very important to pick the right one R5 R1
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Optimization Algorithms Heuristic based Cost based Dynamic programming: System R Rule-based optimizations: DB2, SQL-Server
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Dynamic Programming Given: a query R1 ⋈R2 ⋈… ⋈Rn Assume we have a function cost() that gives us the cost of a join tree Find the best join tree for the query
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Dynamic Programming Problem Statement Given: a query R1 ⋈ R2 ⋈… ⋈Rn Assume we have a function cost() that gives us the cost of a join tree Find the best join tree for the query Idea: for each subset of {R1, …, Rn}, compute the best plan for that subset Algorithm: In increasing order of set cardinality, compute the cost for Step 1: for {R1}, {R2}, …, {Rn} Step 2: for {R1,R2}, {R1,R3}, …, {Rn-1, Rn} … Step n: for {R1, …, Rn} It is a bottom-up strategy Skipping further details of the algorithm Read from book if interested Will not be on the exam
Reducing the Search Space Left-deep trees vsBushy trees Combinatoric explosion of the number of possible trees Computing the cost of all possible trees is not feasible For a 6-way Join, we can have More than 30,000 bushy trees 6!, or 720 left-deep trees Left-deep trees leave their result in memory, making it possible to pipeline efficiently Trees without Cartesian product Example: R(A,B) ⋈S(B,C) ⋈ T(C,D) Plan: (R(A,B) ⋈T(C,D)) ⋈S(B,C) has a Cartesian product Most query optimizers will not consider it
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan Three topics Choosing the physical implementations (e.g., select and join methods) Decisions regarding materialized vs pipelined Notation for physical query plans
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Choosing a Selection Method Algorithm for each selection operator 1. Can we use an created index on an attribute? If yes, index-scan. (Otherwise table-scan) 2. After retrieving all condition-satisfied tuples in (1), filter them with the remaining selection conditions In other words, When computing c1 c2 … cn(R), we index-scan on ci, then filter the result on all other ci, where j i. The next 2 pages show an example where we examine several options and pick the best one
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Selection Method Example (p1) Selection: x=1 y=2 z < 5 (R) Where parameters of R are: T(R) = 5,000 B(R) = 200 V(R, x) = 100 V(R, y) = 500 Relation R is clustered x and y have non-clustering indices z is a clustering index
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Selection Method Example (p2) Selection options: Table-scan filter x, y, z. Cost isB(R) = 200since R is clustered. Use index on x =1 filter on y, z. Cost is 50 sinceT(R) / V(R, x) is (5000/100) = 50 tuples, x is not clustering. Use index on y =2 filter on x, z. Cost is 10 sinceT(R) / V(R, y) is (5000/500) = 10 tuples, y is not clustering. Index-scan on clustering index w/ z < 5 filter x ,y. Cost is about B(R)/3 = 67 Therefore: First retrieve all tuples with y = 2 (option 3) Then filter for x and z
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan Three topics Choosing the physical implementations (e.g., select and join methods) Decisions regarding materialized vs pipelined Notation for physical query plans
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Pipelining Versus Materialization Materialization store (intermediate) result of each operations on disk Pipelining Interleave the execution of several operations, the tuples produced by one operation are passed directly to the operations that used it store (intermediate) result of each operations on buffer, which is implemented on main memory Prefer Pipelining where possible Sometimes not possible, as the following example shows Next few pages, a fully worked-out example
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R⋈S⋈U Example (p1) Consider physical query plan for the expression (R(w, x) ⋈ S(x, y)) ⋈ U(y, z) Assumption R occupies 5,000 blocks, S and U each 10,000 blocks. The intermediate result R ⋈ S occupies k blocks for some k. Both joins will be implemented as hash-joins, either one-pass or two-pass depending on k There are 101 buffers available.
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R⋈S⋈U Example (p2) When joining R ⋈ S, neither relation fits in buffers Need two-pass hash-join to partition R How many hash buckets for R? 100 at most The 2nd pass hash-join uses 51 buffers, leaving 50 buffers for joining result of R ⋈ S with U. Why 51?
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R⋈S⋈U Example (p3) Case 1: Suppose k 49, the result of R ⋈ S occupies at most 49 blocks. Steps Pipeline in R ⋈ S into 49 buffers Organize them for lookup as a hash table Use one buffer left to read each block of U in turn Execute the second join as one-pass join. The total number of I/O’s is 55,000 45,000 for two-pass hash join of R and S 10,000 to read U for one-pass hash join of (R⋈ S) ⋈U.
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R⋈S⋈U Example (p4) Case 2: suppose k > 49 but < 5,000, we can still pipeline, but need another strategy where intermediate results join with U in a 50-bucket, two-pass hash-join. Steps are: Before start on R ⋈ S, we hash U into 50 buckets of 200 blocks each. Perform two-pass hash join of R and U using 51 buffers as case 1, and placing results in 50 remaining buffers to form 50 buckets for the join of R ⋈ S with U. Finally, join R ⋈ S with U bucket by bucket. The number of disk I/O’s is: 20,000 to read U and write its tuples into buckets 45,000 for two-pass hash-join R ⋈ S k to write out the buckets of R ⋈ S k+10,000 to read the buckets of R ⋈ S and U in the final join The total cost is 75,000+2k.
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R⋈S⋈U Example (p5) Case 3: k > 5,000, we cannot perform two-pass join in 50 buffers available if result of R ⋈ S is pipelined. We are forced to materialize the relation R ⋈ S. The number of disk I/O’s is: 45,000 for two-pass hash-join R and S k to store R ⋈ S on disk 30,000 + 3k for two-pass join of U in R ⋈ S The total cost is 75,000+4k.
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R⋈S⋈U Example (p6) In summary, costs of physical plan as function of R ⋈ S size. Pause and Reflect It’s all about the expected size of the intermediate result R ⋈ S What would have happened if We guessed 45 but had 55? Guessed 55 but only had 45? Guessed 4,500 but had 5,500? Guessed 5,500 but only had 4,500?
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan Three topics Choosing the physical implementations (e.g., select and join methods) Decisions regarding materialized vs pipelined Notation for physical query plans
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Notation for Physical Query Plans Several types of operators: Operators for leaves (Physical) operators for Selection (Physical) Sorts Operators Other Relational-Algebra Operations In practice, each DBMS uses its own internal notation for physical query plans
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PQP Notation Leaves:Replace a leaf in an LQP by TableScan(R): Read all blocks SortScan(R, L): Read in order according to L IndexScan(R, C): Scan R using index attribute A by condition AC IndexScan(R, A): Scan R using index attribute A Selects: Replace a Select in an LQP by one of the leaf operators plus: Filter(D) for condition D Sorts: Replace a leaf-level sort as shown above. For other operation, Sort(L): Sort a relation that is not stored Other Operators: Operation- and algorithm-specific (e.g., Hash-Join) Also need to specify # passes, buffer sizes, etc.
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We have Arrived at the Desired Endpoint x=1 AND y=2 AND z<5 (R) R ⋈ S ⋈ U Example Physical Query Plans two-pass hash-join 101 buffers Filter(x=1 AND z<5) materialize IndexScan(R,y=2) two-pass hash-join 101 buffers TableScan(U) TableScan(R) TableScan(S)
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Outline Convert SQL query to a parse tree Semantic checking: attributes, relation names, types Convert to a logical query plan (relational algebra expression) deal with subqueries Improve the logical query plan use algebraic transformations group together certain operators evaluate logical plan based on estimated size of relations Convert to a physical query plan search the space of physical plans choose order of operations complete the physical query plan
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Optimization Issues and Proposals The “fuzz” in estimation of sizes Parametric Query Optimization Specify alternatives to the execution engine so it may respond to conditions at runtime Multiple-query optimization Take concurrent execution of several queries into account Combinatoric explosion of options when doing an n-way Join Becomes really expensive around n > 15 Alternatives optimizations have been proposed for special situations, but no general framework Rule-based optimizers Randomized plan generation
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CS 542 Database Management Systems Distributed Query Execution Source: Carsten Binnig, Univ of Zurich, 2006 J Singh March 28, 2011
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Motivation Algorithms based on Semi-Joins have been proposed as techniques for query optimization They shine in Distributed and Parallel Databases Good opportunity to explore them in that context Semi-join by example: Semi-join formal definition:
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Distributed / Parallel Join Processing Scenario: How to compute A ⋈B? Table A resides on Node 1 Table B resides on Node 2 Node 1 Node 2 Table A Table B
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Naïve approach (1) Idea: Use standard join and fetch table page-wise from remote node if necessary (send- and receive-operators) Example: Join is executed on node 2 using a Nested-Loop-Join Outer loop: Request page of table A from node 1 (remote) Inner loop: For each page iterate over table B and produce output => Random access of pages on node 1 (due to network delay) Node 1 Node 2 Request Table A Page A1 Table B Send
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Naïve approach (2) Idea: Ship one table completely to the other node Example: Ship complete table A from node 1 to node 2 Join table A and B locally on node 2
Avoid random page access on node 1
Node 1 Node 2 Table A Table A Table B Ship
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Naïve Approach: Implications Problems: High cost for shipping data Network cost roughly the same as I/O cost for a hard disk (or even worse because of unpredictability of network delay) Shipping A roughly equivalent to a full table scan (Trivial) Optimizations: Ship always smaller table to the other side If query contains a selection, apply selection before sending A Note: bigger table may become the smaller table (after selection)
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Semi-join Approach (p1) Idea: Before shipping a table, reduce to data that is shipped to those tuples that are only relevant for join Example: Join on A.id=B.id and table A should be shipped to node 2 Node 1 Node 2 Table A Table B
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Semi-join Approach (p2) (1) Compute projection B.id of table B on node 2 (2) Ship column B.id to node 1 Node 1 Node 2 Table A Table B Ship
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Semi-join Approach (p3) (3) Execute semi-join of B.id and table A on A.id=B.id (to select only relevant tuples of table A => table A’) (4) Send result of semi-join (table A’) to node 2 Node 1 Node 2 Table A Table B Table A’ Ship
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Semi-join Approach (p4) (5) Join the shipped table A’ locally on node 2 with table B => Optimization of this approach: If node 1 holds a join index (e.g., type 1 with A.id -> {B.RID}) we can start with step (3) Node 1 Node 2 Table A Table B Table A’ Ship
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Semi-join Approach Discussion This strategy works well if semi-join reduces size of the table that needs to be shipped Assume all rows of Table A are needed anyway => none of the rows of table A can be discarded Then this approach is more costly than shipping the entire table A in the first place! Consequence: Need to decide whether this method makes sense based on semi-join selectivity => Cost-based optimization must decide this
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Bloom-join Approach (p1) Algorithm same as semi-join approach Ship a bloom-filter instead of (foreign) key column Use bloom-filter technique to compress data Goal: only send a small bit list (to reduce network I/O) instead of all keys of column (as bit-vector) Problems: A superset of tuples that might join will be sent back (same problem as in bloom-filters for bitmap-indexes) => More tuples must be sent over network and thus net gain depends on good hash function
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Bloom-join Approach (p2) (1) Compute bloom filter BL of size n for column B.id of table B on node 2 with n << |B.id| (e.g., by B.id % n) (2) Ship bloom filter B.id’ to node 1 Node 1 Node 2 Table A Table B Ship
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Bloom-join Approach (p3) (3) Probe bloom filter B.id’ with tuples from table A to get a superset of possible join candidates (=> table A’) (4) Send result (table A’) to node 2 (table A’ might contain join candidates that do not have a partner in table B) (5) Join the shipped table A’ locally on node 2 with table B Node 1 Node 2 Table A Table B Table A’ Ship Probe
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Bloom-join Approach Discussion Communication cost much reduced But have to deal with false positives Widely used in NoSQLdatabases
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