Sum and Average Aggregation using DataFlow - google-cloud-dataflow

Question

I have following type of sample data.
s.n., time, user, time_span, user_level
1, 2016-01-04T1:26:13, Hari, 8, admin
2, 2016-01-04T11:6:13, Gita, 2, admin
3, 2016-01-04T11:26:13, Gita, 0, user
Now I need to find average_time_span/user, average_time_span/user_level and total_time_span/user.
I'm able to find each of above mention value but couldn't able to find all of those at once. As I'm new to DataFlow, please suggest me appropriate method to do so.
static class ExtractUserAndUserLevelFn extends DoFn<String, KV<String, Long>> {
#Override
public void processElement(ProcessContext c) {
String[] words = c.element().split(",");
if (words.length == 5) {
Instant timestamp = Instant.parse(words[1].trim());
KV<String, Long> userTime = KV.of(words[2].trim(), Long.valueOf(words[3].trim()));
KV<String, Long> userLevelTime = KV.of(words[4].trim(), Long.valueOf(words[3].trim()));
c.outputWithTimestamp(userTime, timestamp);
c.outputWithTimestamp(userLevelTime, timestamp);
}
}
}
public static void main(String[] args) {
TestOptions options = PipelineOptionsFactory.fromArgs(args).withValidation()
.as(TestOptions.class);
Pipeline p = Pipeline.create(options);
p.apply(TextIO.Read.named("ReadLines").from(options.getInputFile()))
.apply(ParDo.of(new ExtractUserAndUserLevelFn()))
.apply(Window.<KV<String, Long>>into(
FixedWindows.of(Duration.standardSeconds(options.getMyWindowSize()))))
.apply(GroupByKey.<String, Long>create())
.apply(ParDo.of(new DoFn<KV<String, Iterable<Long>>, KV<String, Long>>() {
public void processElement(ProcessContext c) {
String key = c.element().getKey();
Iterable<Long> docsWithThatUrl = c.element().getValue();
Long sum = 0L;
for (Long item : docsWithThatUrl)
sum += item;
KV<String, Long> userTime = KV.of(key, sum);
c.output(userTime);
}
}))
.apply(MapElements.via(new FormatAsTextFn()))
.apply(TextIO.Write.named("WriteCounts").to(options.getOutput()).
withNumShards(options.getShardsNumber()));
p.run();
}

Answer 1

One approach would be to first parse the lines into one PCollection that contains a record per line, and the from that collection create two PCollection of key-value pairs. Let's say you define a record representing a line like this:
static class Record implements Serializable {
final String user;
final String role;
final long duration;
// need a constructor here
}
Now, create a LineToRecordFn that create Records from the input lines, so that you can do:
PCollection<Record> records = p.apply(TextIO.Read.named("ReadLines")
.from(options.getInputFile()))
.apply(ParDo.of(new LineToRecordFn()));
You can window here, if you want. Whether you window or not, you can then create your keyed-by-role and keyed-by-user PCollections:
PCollection<KV<String,Long>> role_duration = records.apply(MapElements.via(
new SimpleFunction<Record,KV<String,Long>>() {
#Override
public KV<String,Long> apply(Record r) {
return KV.of(r.role,r.duration);
}
}));
PCollection<KV<String,Long>> user_duration = records.apply(MapElements.via(
new SimpleFunction<Record,KV<String,Long>>() {
#Override
public KV<String,Long> apply(Record r) {
return KV.of(r.user, r.duration);
}
}));
Now, you can get the means and sum in just a few lines:
PCollection<KV<String,Double>> mean_by_user = user_duration.apply(
Mean.<String,Long>perKey());
PCollection<KV<String,Double>> mean_by_role = role_duration.apply(
Mean.<String,Long>perKey());
PCollection<KV<String,Long>> sum_by_role = role_duration.apply(
Sum.<String>longsPerKey());
Note that dataflow does some optimization before running your job. So, while it might look like you're doing two passes over the records PCollection, that may not be true.

Answer 2

The Mean and Sum transforms look like they would work well for this use case. Basic usage looks like this:
PCollection<KV<String, Double>> meanPerKey =
input.apply(Mean.<String, Integer>perKey());
PCollection<KV<String, Integer>> sumPerKey = input
.apply(Sum.<String>integersPerKey());