## VarOpt Sampling Sketch Pig UDFs

### Instructions

• get jars
• save the following script as varopt_example.pig
• adjust jar versions and paths as necessary
• save the below data into a file called data.txt
• copy data to hdfs: “hadoop fs -copyFromLocal data.txt”
• run pig script: “pig varopt_example.pig”

### varopt_example.pig script

register datasketches-memory-1.2.0-incubating.jar;

-- very small sketch just for the purpose of this tiny example

raw_data = LOAD 'data.txt' USING PigStorage('\t') AS
(weight: double, id: chararray);

-- make a few independent sketches from the input data
bytes = FOREACH
(GROUP raw_data ALL)
GENERATE
DataToSketch(raw_data) AS sketch0,
DataToSketch(raw_data) AS sketch1
;

sketchBag = FOREACH
bytes
GENERATE
TOBAG(sketch0,
sketch1)) AS sketches
;

unioned = FOREACH
sketchBag
GENERATE
VarOptUnion(sketchBag.sketches) AS binSketch
;

result = FOREACH
unioned
GENERATE
FLATTEN(GetVarOptSamples(binSketch)) AS (vo_weight, record:(id, weight))
;

DUMP result;
DESCRIBE result;


The test data has 2 fields: weight and id. The first step of the query creates several varopt sketches from the input data. We merge the sketches into a bag in the next step, followed by unioning the set of independent sketches. Finally, the last step gets the final set of results.

Results:

From ‘DUMP result’:

(30.0,(30.0,h))
(30.0,(30.0,h))
(28.0,(4.0,d))
(28.0,(7.0,g))


By running this script repeatedly, we can obesrve that the heavy items (h) will always be included, but that the remaining 2 items will differ across runs, appearing in proportion to their weights. We can also see that the output varopt weight on the non-heavy samples represents an adjusetd weight, although by keeping the entire input tuple the original weight value is also stored.

From ‘DESCRIBE result’:

result: {vo_weight: double,record: (id: bytearray,weight: bytearray)}


### data.txt (tab separated)

1.0	a
2.0	b
3.0	c
4.0	d
5.0	e
6.0	f
7.0	g
30.0	h