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

Frequent Items Sketch Pig UDFs


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

frequent_items.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.frequencies.DataToFrequentStringsSketch('8');
define unionSketch org.apache.datasketches.pig.frequencies.UnionFrequentStringsSketch('8');
define getEstimates org.apache.datasketches.pig.frequencies.FrequentStringsSketchToEstimates();

a = load 'data.txt' as (item:chararray, category);
b = group a by category;
c = foreach b generate flatten(group) as (category), flatten(dataToSketch(a.item)) as (sketch);
-- Sketches can be stored at this point in binary format to be used later:
-- store c into 'intermediate/$date' using BinStorage();
-- The next two lines print the results in human readable form for the purpose of this example
d = foreach c generate category, getEstimates(sketch);
dump d;

-- This can be a separate query.
-- For example, the first part can produce a daily intermediate feed and store it.
-- This part can load several instances of this daily intermediate feed and merge them
-- c = load 'intermediate/$date1,intermediate/$date2' using BinStorage() as (category, sketch); 
e = group c all;
f = foreach e generate flatten(unionSketch(c.sketch)) as (sketch);
g = foreach f generate getEstimates(sketch);
describe g;
dump g;

The example input data has 2 fields: item and category. In the first part of the query the data is grouped by category with one FrequentItemsSketch<String> per category. In the second part of the query this intermediate result is merged across categories to produce one sketch. This way the usage of all 3 UDFs is demonstrated: DataToFrequentStringsSketch, UnionFrequentStringsSketch and FrequentStringsSketchToEstimates.


From ‘dump d’ (one sketch per category):


From ‘dump g’ (merged across categories):


From ‘describe g’:

g: {bag_of_item_tuples: {item_tuple: (item: chararray,estimate: long,lower_bound: long,upper_bound: long)}}

In this example the results are exact due to small input.

data.txt (tab separated)

a	c1
a	c1
a	c1
a	c2
a	c1
b	c1
c	c2
d	c1
e	c2
a	c1
a	c2
a	c2
a	c2
d	c1
d	c2
a	c1
a	c2
a	c1
d	c2