add
Add single key-value pair to KeyValueStore
Syntax
Description
Examples
Add Key/Value Pairs to KeyValueStore
Use add
in map and reduce functions to pass data into the intermediate and final KeyValueStore
. This example uses identity map and reduce functions that pass the inputs straight through to the output. The map and reduce functions are listed at the end of the example as local functions.
inds = tabularTextDatastore('airlinesmall.csv','SelectedVariableNames','ArrDelay','TreatAsMissing','NA'); preview(inds)
ans=8×1 table
ArrDelay
________
8
8
21
13
4
59
3
11
outds = mapreduce(inds,@myMapper,@myReducer,mapreducer(0));
******************************** * MAPREDUCE PROGRESS * ******************************** Map 0% Reduce 0% Map 16% Reduce 0% Map 32% Reduce 0% Map 48% Reduce 0% Map 65% Reduce 0% Map 81% Reduce 0% Map 97% Reduce 0% Map 100% Reduce 0% Map 100% Reduce 100%
readall(outds)
ans=1×2 table
Key Value
____________ _________________
{'ArrDelay'} {123523x1 double}
Local Functions
function myMapper(data,info,intermKV) add(intermKV, 'ArrDelay',data.ArrDelay); end function myReducer(key,intermValIter,outKV) data = getnext(intermValIter); while hasnext(intermValIter) data = [data; getnext(intermValIter)]; end add(outKV,key,data); end
Input Arguments
KVStore
— Key-value pair storage object
KeyValueStore
object
Key-value pair storage object, specified as a KeyValueStore
object.
The mapreduce
function automatically creates
the KeyValueStore
object during execution:
In the map function, the name of the intermediate
KeyValueStore
object is the third input argument to the map function,myMapper(data, info, intermKVStore)
. Use that same variable name to add intermediate key-value pairs withadd
oraddmulti
in the map function.In the reduce function, the name of the final
KeyValueStore
object is the third input argument to the reduce function,myReducer(intermKey, intermValIter, outKVStore)
. Use that same variable name to add final key-value pairs withadd
oraddmulti
in the reduce function.
For more information, see KeyValueStore
.
key
— Key
numeric scalar | character vector | string
Key, specified as a numeric scalar, character vector, or string.
All of the keys added by the map function must have the same class. The keys added by the reduce function also must have the same class, but that class can differ from the class of the keys added by the map function.
Numeric keys cannot be NaN
, complex, logical,
or sparse.
Example: add(intermKVStore,'Sum',sum(X))
adds
a key-value pair to an intermediate KeyValueStore
object
(named intermKVStore
) in a map function.
Example: add(outKVStore,'Stats',[mean(X) max(X) min(X)
var(X) std(X)])
adds a key-value pair to a final KeyValueStore
object
(named outKVStore
) in a reduce function.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| char
| string
value
— Value
any MATLAB® object
Value, specified as any MATLAB object. This includes all valid MATLAB data types.
The OutputType
argument of mapreduce
affects
the type of values that the reduce function can add:
If the
OutputType
is'Binary'
(the default), then a value added by the reduce function can be any MATLAB object.If the
OutputType
is'TabularText'
, then a value added by the reduce function can be a numeric scalar, character vector, or string scalar when using theadd
function. Additionally, you can use theaddmulti
function to add multiple values with a numeric vector, cell vector of character vectors, cell vector of numeric scalars, or string array. In each case, the numeric values cannot beNaN
, complex, logical, or sparse.
Note
The above key-value pair requirements may differ when using other products with mapreduce. See the documentation for the appropriate product to get product-specific key-value pair requirements.
Example: add(intermKVStore,'Sum',sum(X))
specifies
a single scalar value to pair with a key.
Example: add(outKVStore,'Stats',[mean(X)
max(X) min(X) var(X) std(X)])
specifies a numeric array
as the value to pair with a key.
Tips
Avoid using
add
in a loop, as it can negatively affectmapreduce
execution time. Instead, use cell arrays to collect multiple values (using vectorized operations if possible) and use a single call toaddmulti
.
Version History
Introduced in R2014b
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