API Snapshots: Java Core, Memory, Pig, Hive,

VarOpt Sampling Sketch Pig UDFs


  • 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;
register datasketches-java-1.2.0-incubating.jar;
register datasketches-pig-1.0.0-incubating.jar;

-- very small sketch just for the purpose of this tiny example
DEFINE DataToSketch org.apache.datasketches.pig.sampling.DataToVarOptSketch('4', '0');
DEFINE VarOptUnion org.apache.datasketches.pig.sampling.VarOptUnion('4');
DEFINE GetVarOptSamples org.apache.datasketches.pig.sampling.GetVarOptSamples();

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)
    DataToSketch(raw_data) AS sketch0,
    DataToSketch(raw_data) AS sketch1

sketchBag = FOREACH
          sketch1)) AS sketches

unioned = FOREACH
    VarOptUnion(sketchBag.sketches) AS binSketch

result = FOREACH
    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.


From ‘DUMP result’:


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