JOIN ALGORITHMS USING MAPREDUCE

Haiping Wang

ctqlwhp1022@163.com

OUTLINE

MapReduce Framework MapReduce implementation on Hadoop Join algorithms using MapReduce

MAPREDUCE: SIMPLIFIED DATA PROCESSING ON LARGE CLUSTERS. IN OSDI, 2004

MAPREDUCE WORDCOUNT DIAGRAM

ah ah er ah if or or uh or ah if

ah:1,1,1,1

ah:1 if:1 or:1 or:1 uh:1 or:1 ah:1 if:1

er:1 if:1,1or:1,1,1uh:1

ah:1 ah:1 er:1

4 1 2 3 1

file1 file2 file3 file4 file5 file6 file7

(ah) (er) (if) (or) (uh)

reduce(String outputkey, Iterator intermediate_alues):

map(String inputkey, String inputvalue):

JobTracker

TaskTracker

Record Reader

Record Writer

MapperPartitioner Sorter

Reducer

Copy

InputFormatOutputFormat

MAPREDUCE IMPLEMENTATION ON HADOOP

MAPREDUCE IMPLEMENTATION ON HADOOP

HADOOP MAPREDUCE FRAMEWORK ARCHITECTURE

JOIN ALGORITHMS USING MAPREDUCE

Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters sigmod07

Semi-join Computation on Distributed File Systems Using Map-Reduce-Merge Model Sac10

Optimizing joins in a map-reduce environment VLDB09,EDBT2010

A Comparison of Join Algorithms for Log Processing in MapReduce sigmod10

MAP-REDUCE-MERGE: SIMPLIFIED RELATIONAL DATA PROCESSING ON LARGE CLUSTERS SIGMOD07

MAP-REDUCE-MERGE IMPLEMENTATIONS OFRELATIONAL JOIN ALGORITHMS

Sort-merger join

Map range partitioner , ordered bucket s, each bucket a reducer

Reduce Read the designed buckets from all mappers and merged them into a sorted set

Merge Read sorted buckets from two data sets and do sort-merge join

Hash join Map Hash partitioner, hashed buckets, each bucket a reducer

Reduce Read the designed buckets from all mappers , use a hash table to group and aggregate these records(the same hash function as the mapper ), does not need a sorter

Merge In memory hash join

Block Nested loop join

Map The same as the hash join

Reduce The same as the hash join

Merge Nested loop join

EXAMPLE: HASH JOIN

split split split split

mapper mapper mapper

reducer reducer reducer

merger merger merger

split split split split

mapper mapper mapper

reducer reducer reducer

Use a hash partitioner

Read from every mapper for one designated partition

• Read from two sets of reducer outputs that share the same hashing buckets

• One is used as a build set and the other probe

ANALYSIS AND CONCLUSION

Connections A(ma, ra ), B(mb , rb ), r mergers suppose ra=rb=r Map->Reduce connections=

ra*ma+rb*mb=r*(ma+mb) Reduce->Merge in one-to-one case, connections=2r matcher: compare tuples to see id they should be

merged or not Conclusion

Use multiple map-reduce job Partitioner may cause data skew problem The number of ma, ra, mb, rb, r ra=rb? –>

connections

SEMI-JOIN COMPUTATION STEPS AND WORKFLOW

Equal join reduce communication costs disk I/O costs Insensitive to data skew ?

A COMPARISON OF JOIN ALGORITHMS FOR LOG PROCESSING IN MAPREDUCE SIGMOD10

Equi-join between a log table L and a reference table R on a single column.

L,R and the Join Result is stored in DFS. Scans are used to access L and R. Each map or reduce task can optionally implement

two additional functions: init() and close() . These functions can be called before or after each

map or reduce task.

L ⊲⊳L.k=R.k R, with |L| ≫ |R|

REPARTITION JOIN(HIVE)

Drawback: all records may have to be buffered

Out of memory The key cardinality is small The data is highly skewed

L: Ratings.dat

R: movies.dat

Pairs: (key, targeted record)

shuffleinput map reduce output

1::1193::5::9783007601::661::3::9783021091::661::3::9783019681::661::4::9783002751 ::1193::5::97882429

661::James and the Glant…914::My Fair Lady..1193::One Flew Over the…2355::Bug’s Life, A…3408::Erin Brockovich…

1193, L:1::1193::5::978300760661, L :1::661::3::978302109661, L :1::661::3::978301968661, L :1::661::4::9783002751193, L :1 ::1193::5 ::97882429

661, R:661::James and the Gla…914, R: 914::My Fair Lady..1193, R: 1193::One Flew Over …2355, R: 2355::Bug’s Life, A…3408, R: 3408::Erin Brockovi…

(661, …)(661, …)(661, …)

(1193, …)(1193, …)

(661, …)(2355, …)(3048, …)(914, …)(1193, …)

(661,[L :1::661::3::97…],[R:661::James…],[L:1::661::3::978…],[L :1::661::4::97…])

(2355, [R:2355::B’…])(3408, [R:3408::Eri…])

(1,Ja..,3, …)(1,Ja..,3, …)(1,Ja..,4, …)

Group by join key

Buffers records into two sets according to the table tag

+Cross-product

{(661::James…) } X (1::661::3::97…), (1::661::3::97…), (1::661::4::97…)

Phase /Function Improvement

Map Function Output key is changed to a composite of the join key and the table tag.

Partitioning function Hashcode is computed from just the join key part of the composite key

Grouping function Records are grouped on just the join key

THE COST MEASURE FOR MR ALGORITHMS The communication cost of a process is the size

of the input to the process This paper does not count the output size for a

process The output must be input to at least one other process The final output is much smaller than its input

The total communication cost is the sum of the communication costs of all processes that constitute an algorithm

The elapsed communication cost is defined on the acyclic graph of processes Consider a path through this graph, and sum the

communication costs of the processes along that path The maximum sum, over all paths is the elapsed

communication cost

2-WAY JOIN IN MAPREDUCE R(A,B) S(B,C)

R

S

Input Reduce input

Final output

MapRe-duce

A B

a0 b0

a1 b1

a2 b2

… …

B C

b0 c0

b0 c1

b1 c2

… …

K V

b0 (a0, R)

b0 (c0, S)

b0 (c1, S)

… …K V

b1 (a1, R)

b1 (c2, S)

… …

A B C

a0 b0 c0

a0 b0 c1

a1 b1 c2

… … …

Table tuple map Partition& sort

R (a ,b ) b ->(a, R)

Hash(b) ->(a, R)

S (b , c )

b ->(c, S)

Hash(b) ->(c, S)

b->(a, c)

JOINING SEVERAL RELATIONS AT ONCE

R

S

Input Reduce input

Final output

Map Reduce

R(A,B) S(B,C) T(C,D)

T

JOINING SEVERAL RELATIONS AT ONCE

Let h be a hash function with range 1, 2, …, m S(b, c) -> (h(b),

h(c)) R(a, b) -> (h(b), all) T(c, d) -> (all, h(c))

Each Reduce process computes the join of the tuples it receives

(# of Reduce processes: 42 = 16)m=4, k=16

h(c) = 0 1 2 3

h(b) = 0

1

2

3

h(R.b) = 2

h(T.c) = 1h(S.b) = 2h(S.c) = 1

Reduce processes

R(A,B) S(B,C) T(C,D)

PROBLEM SOLVING

Problem solving using the method of Lagrange Multipliers

Take derivatives with respect to the three variables a, b, c

Multiply the three equations

SPECIAL CASES

Star Joins

Chain Joins A chain join is a join of the form

CONCLUSION

Just suitable for Equal join Use one map-reduce Does not consider the IO ( intermediate

<K,V> pairs IO ) and CPU time

Main contribution: use “Lagrangean multipliers” method