1. MAP/REDUCE IN COUCHDB
<- watch the race car
Oliver Kurowski, @okurow

2. Facts about Map/Reduce
 Programming paradigm, popularized and patented by Google
 Great for parallel jobs
 No Joins between documents
 In CouchDB: Map/Reduce in JavaScript (default)
 Also Possible with other languages
Workflow
1. Map function builds a list of key/value pairs
2. Reduce function reduces the list ( to a single Value)
Oliver Kurowski, @okurow

3. Simple Map Example
 A List of Cars
Id: 1 Id: 2 Id: 3 Id: 4 Id: 5
make: Audi make: Audi make: VW make: VW make: VW
model: A3 model: A4 model: Golf model: Golf model: Polo
year: 2000 year: 2009 year: 2009 year: 2008 year: 2010
price: 5.400 price: 16.000 price: 15.000 price: 9.000 price: 12.000
 Step 1: Make a list, ordered by Price
Function(doc) {
emit (doc.price, doc.id);
}
Key Value
 Step 2: Result: Key , Value
5.400 , 1
9.000 , 4
12.000 , 5
15.000 , 3
16.000 , 2
Oliver Kurowski, @okurow

4. Querying Maps
 Original Map Key , Value
5.400 , 1
9.000 , 4
12.000 , 5
15.000 , 3
16.000 , 2
All keys
 startkey=10.000 & endkey=15.500 from 10.000
Key , Value to < 15.500
12.000 , 5
15.000 , 4
Exact
 key=10.000 Key , Value key, so no
result
 endkey=10.000 Key , Value
5.400 , 1
All
keys, less
than 10.000
Oliver Kurowski, @okurow

5. Map Function
 Has one document as input
 Can emit all JSON-Types as key and value:
- Special Values: null, true, false
- Numbers: 1e-17, 1.5, 200
- Strings : “+“, “1“, “Ab“, “Audi“
- Arrays: [1], [1,2], [1,“Audi“,true]
- Objects: {“price“:1300,“sold“:true}
 Results are ordered by key ( or revers)
(order with mixed types: see above)
 In CouchDB: Each result has also the doc._id
{"total_rows":5,"offset":0,
"rows":[
{"id":"1","key":"Audi","value":1}, {"id":"
2","key":"Audi","value":1}, {"id":"3","key":
"VW","value":1}, {"id":"4","key":"VW","va
lue":1}, {"id":"5","key":"VW","value":1} ]}
Oliver Kurowski, @okurow

6. Reduce Function
 Has arrays of keys and values as input
 Should reduce the result of a map to a single value
 Javascript (Other languages possible)
 In CouchDB: some simple built-in native erlang functions
(_sum,_count,_stats)
 Is automaticaly called after the map-function has finished
 Can be ignored with “reduce=false“
 Is needed for grouping
Oliver Kurowski, @okurow

7. Simple Map/Reduce Example
 A List of Cars
Id: 1 Id: 2 Id: 3 Id: 4 Id: 5
make: Audi make: Audi make: VW make: VW make: VW
model: A3 model: A4 model: Golf model: Golf model: Polo
year: 2000 year: 2009 year: 2009 year: 2008 year: 2010
price: 5.400 price: 16.000 price: 15.000 price: 9.000 price: 12.000
 Step 1: Make a map, ordered by make
Function(doc) {
emit (doc.make, 1);
}
Value
Key
=1
 Result: Key , Value
Audi , 1
Audi , 1
VW, 1
VW, 1
VW, 1
Oliver Kurowski, @okurow

8. Simple Map/Reduce Example
 Result: Key , Value
Audi , 1
Audi , 1
VW , 1
VW , 1
VW , 1
 Step 2: Write a “sum“-reduce
function(keys,values) {
return sum(values);
}
 Result: Key , Value
null ,5
Oliver Kurowski, @okurow

9. Simple Map/Reduce Example
 Step 3: Querying
- key=“Audi“ Key , Value
null , 2
 Step 4: Grouping by keys
- group=true Key , Value
Audi , 2
VW , 3
 Step 5: Use only the map Function
- reduce=false Key , Value Like
Audi ,1 having no
Audi ,1 reduce-
VW ,1 function
VW ,1
VW ,1
Oliver Kurowski, @okurow

10. Array-Key Map/Reduce Example
 A List of cars (again)
Id: 1 Id: 2 Id: 3 Id: 4 Id: 5
make: Audi make: Audi make: VW make: VW make: VW
model: A3 model: A4 model: Golf model: Golf model: Polo
year: 2000 year: 2009 year: 2009 year: 2008 year: 2010
price: 5.400 price: 16.000 price: 15.000 price: 9.000 price: 12.000
 Step 1: Make a map, with array as key
Function(doc) {
emit ([doc.make,doc.model,doc.year], 1);
}
 Result (with group=true):
Key , Value
[Audi, A3, 2000] , 1
[Audi, A4, 2009] , 1
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
[VW, Polo, 2010] , 1
Oliver Kurowski, @okurow

11. Array-Key Map/Reduce Querying
 startkey=[“Audi“] Key , Value
[Audi, A3, 2000] , 1
( &group=true) [Audi, A4, 2009] , 1
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
[VW, Polo, 2010] , 1
 startkey=[“VW“] Key , Value
[Audi, A3, 2000] , 1
( &group=true) [Audi, A4, 2009] , 1
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
[VW, Polo, 2010] , 1
Key , Value
 endkey=[“VW“] [Audi, A3, 2000] , 1
Remember:
Endkey is
(&group=true) [Audi, A4, 2009] , 1
not in
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1 resultlist
[VW, Polo, 2010] , 1
Oliver Kurowski, @okurow

12. Array-Key Map/Reduce Ranges
 Step 4: Range queries: Key , Value
- startkey=[“VW“,“Golf“] [Audi, A3, 2000] , 1
[Audi, A4, 2009] , 1
- endkey= [“VW“,“Polo“] [VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
- (&group=true) [VW, Polo, 2010] , 1
 What, if we do not know the next model after Golf ?
- startkey=[“VW“,“Golf“] Key , Value
[Audi, A3, 2000] , 1
- endkey=[“VW“,“Golf“,99999] [Audi, A4, 2009] , 1
- (&group=true) [VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
[VW, Polo, 2010] , 1
- better: endkey=[“VW“,“Golf“,{}]
Oliver Kurowski, @okurow

13. Grouping with group_level
 group=true Key , Value
[Audi, A3, 2000] , 1
(aka group_level=exact) [Audi, A4, 2009] , 1
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
[VW, Polo, 2010] , 1
 group_level=1 Key , Value
(no group=true needed) [Audi] , 2
[VW] , 3
 group_level=2 Key , Value
[Audi, A3] , 1
(no group=true needed) [Audi, A4] , 1
[VW, Golf] , 2
[VW, Polo] , 1
 group_level=3 -> group_level=exact -> group=true
Oliver Kurowski, @okurow

14. Examples:
 Get all car makes: Key , Value
[Audi] , 2
- group_level=1 [VW] , 3
 Get all models from VW:
- startkey=[“VW“]&endkey=[“VW“,{}]&group_level=2
Key , Value
[VW, Golf] , 2
[VW, Polo] , 1
 Get all years of VW Golf:
- startkey=[“VW“,“Golf“]&endkey=[“VW“,“Golf“,{}]&group_level=3
Key , Value
[VW, Golf, 2008] , 1
[VW, Golf, 2009] , 1
Oliver Kurowski, @okurow

15. Reduce / Rereduce:
 A rule to use reduce-functions:
The input of a reduce function does not only accept the
result of a map, but also the result of itself
Function(doc) { Key , Value function(keys,values) {
Key , Value
emit (doc.make,1); Audi , 2 return sum(values);
null , 5
} VW , 3 }
 Why ?
 A reduce function can be used more than just once
If the map is too large, then it will be split and each part runs
through the reduce function, finally all the results run through
the same reduce function again.
Oliver Kurowski, @okurow

16. WTF ?
Oliver Kurowski, @okurow

17. Reduce / Rereduce:
 Example for counting values( Will produce wrong result !)
function(keys,values) {
return count(values);
}
Key , Value
1 , 1 function(keys,values) {
Key , Value
2 , 10 return count(values);
} null , 333
…
Key , Value 333 , 23
1 , 1
2 , 10 Key , Value
3 , 4 function(keys,values) { function(keys,values) { Key , Value
334 , 15 Key , Value
… return count(values); return count(values);
335 , 99 null , 333 null ,3
} }
999 , 7 …
1000 , 12 666 , 82
Key , Value
667 , 18 function(keys,values) { Boom !
return count(values); Key , Value
668 , 149
null , 333
3 != 1000
… }
1000 , 12
Split
Oliver Kurowski, @okurow

18. Reduce / Rereduce:
 Solution: The rereduce-Flag (not mentioned yet)
- indicates, wether the function is called first or not. Set by CouchDB
function(keys ,values, rereduce) {
if(rereduce==false) {
return count(values);
}else{
return sum(values);
}
Key , Value
1 , 1 … Key , Value
2 , 10 if(rereduce==false) { null , 333
… return count(values);
Key , Value 333 , 23
1 , 1
2 , 10 Key , Value …
3 , 4 334 , 15 …
Key , Value else{ Key , Value
… 335 , 99 if(rereduce==false) {
null , 333 return sum(values) null , 1000
999 , 7 … return count(values);
}
1000 , 12 666 , 82
Key , Value
667 , 18 … Correct
Key , Value
668 , 149 if(rereduce==false) {
null , 334
… return count(values);
1000 , 12
Split rereduce=false rereduce=true
Oliver Kurowski, @okurow

19. Input of a reduce function:
 The map: Doc._id , Key , Value
4 , “Audi“ , 12.000
2 , “BMW“ , 20.000
1 , “Citroen“ , 9.000
3 , “Dacia“ , 6.500
 The function: function(keys ,values, rereduce) {
return sum(values);
}
 Input Values 1 (rereduce=false):
- keys: [ [“Audi“,4],[“BMW“,2],[“Citroen“,1],[“Dacia“,3] ]
- values: [ 12.000,20.000,9.000,6.500]
- rereduce: false
 Input Values 2 (rereduce=true):
- keys: null
- values: [47.500]
- rereduce: true
Oliver Kurowski, @okurow

20. Where does Map/Reduce live ?
 Map/Reduce functions are stored in a design document
in the “views“ key:
{
“_id“:“_design/example“,
“views“: {
“simplereduce“: {
“map“: “function(doc) { emit(doc.make,1); }“,
“reduce“: “function (keys, values) { return sum (values); }“
}
}
}
 Map/reduce functions start when a view is called:
http://localhost:5984/mapreduce/_design/example/_view/simplereduce
http://localhost:5984/mapreduce/_design/example/_view/simplereduce?key=“Audi“
http://localhost:5984/mapreduce/_design/example/_view/simplereduce?key=“VW“&group=true
Oliver Kurowski, @okurow

21. View calling
 All documents in the database are called by a view once
 After the first call: Only new and changed docs are called by the function
when calling the view again
 The results are stored in CouchDB internal B+tree
 The result, that you receive is the stored B+tree result
That means: If a view is called first, it could take a little time to build the tree
before you get the results.
If there are no changes to docs, the next time you call, the result is presented
instantly
 Key queries like startkey and endkey are performed on the B+tree result, no
rebuild needed
 There are serveral parameters for calling a view:
limit, skip, include_docs=true, key, startkey, endkey, descending, stale(ok,upd
ate_after),group, group_level, reduce (=false)
Oliver Kurowski, @okurow

22. View calling parameters
 limit: limits the output
 skip: skips a number of documents
 include_docs=true: when no reduce, docs are sent with the map-list
 key, startkey,endkey: should be known now
 startkey_docid=x: only docs with id>=x
 endkey_docid=x: only docs with id<x
 descending=true: reverse order. When using start/endkey, they must be
changed
 Stale=ok: do not start indexing, just deliver the stored result
 Stale=update_after: deliver old results, start indexing after that
 Group, group_level,reduce=false: should be known
Oliver Kurowski, @okurow

23. You‘ve made it !
Oliver Kurowski, @okurow