How AdMobius uses Cascading in AdTech Stack

Jyotirmoy Sundi Sr Data Engineer in Lotame

(Acquired by LOTAME on March, 2014)

What does AdMobius do

AdMobius is a Mobile Audience Management Platform (MAMP). It helps advertiser identify mobile audiences by demographics and interest through standard, custom, private segments and reach them at scale.

Target effectively across all platforms in multiple devices

Laptop

Mobile

Ipod

Ipad

Wearables

Topics

Device graph building and scoring device links Cascading Taps for Hive, MySQL, HBase Modularized Testing Optimal Config Setups Running in YARN Conclusion

AdMobius Stack

Cascading | Hive | Hbase | GiraphCascading | Hive | Hbase | Giraph

Hadoop | (Experimental Spark)Hadoop | (Experimental Spark)

RackspaceRackspace

YARN | MR1YARN | MR1

Custom WorkflowsCustom Workflows

Why Cascading Easy custom aggregators.

• In the existing MR framework it was very difficult to write a series of complex aggregated logic and run them in scale before making sure of its correctness. You can do that in hive by UDFs or UDAFs but we found it much easier in Cascading.

Easy for Java Developers to understand• visualize and write complicated workflows though

the concept of pipes, taps, tuples.

Workflow for audience profile scoring

Driven

https://driven.cascading.io/index.html#/apps/D818DDDA9D9DC182E3228EDF8B9B05C2

Audience Profiling Cascading is used to do

complex aggregations create the device multi-dimensional vectors device pair scoring based on the vectors rule engine based filters

Size Total number of mobile devices ~ 2.7B ~500M devices in Giraph computation.

Example: Parallel aggregation of values across multiple fields.

Aggregations

No need to know group modes like in UDAF Buffer

use for more complex grouping operations

output multiple tuples per group Aggregator (simple aggregations, prebuilt

aggregators like SumBy, CountBy)

public class MinGraphScoring extends BaseOperation implements Buffer{

@Override

public void operate(FlowProcess flowProcess, BufferCall bufferCall) {

Iterator<TupleEntry> arguments = bufferCall.getArgumentsIterator();

Graph g = new Graph();

while( arguments.hasNext() )

{

TupleEntry tpe = arguments.next();

ByteBuffer b = ByteBuffer.wrap((byte[])tpe.getObject("field1"););//use kyro serialization

g.put(b)

}

Node[] nodes = g.nodes;

//For each pair of nodes : i,j {

double minmaxscore = scoring(g,i,j)

Tuple t1 = new Tuple(nodes[i].id ,nodes[j].id ,minmaxscore);

bufferCall.getOutputCollector().add(t1);

}

}

public class PotentialMatchAggregator extends BaseOperation<PotentialMatchAggregator.IDList> implements Aggregator<PotentialMatchAggregator.IDList> {

start(FlowProcess flowProcess, AggregatorCall<IDList> aggregatorCall) {

IDList idList = new IDList();

aggregatorCall.setContext(idList);

}

aggregate(FlowProcess flowProcess, AggregatorCall<IDList> aggregatorCall) {

TupleEntry arguments = aggregatorCall.getArguments();

IDList idList = aggregatorCall.getContext();

idList.updateDev(amid, match);

}

complete(FlowProcess flowProcess, AggregatorCall<IDList> aggregatorCall) {

IDList idList = aggregatorCall.getContext();

…...

}

Joins

CoGroup: two pipes cant fit into memory

HashJoin when one of the pipes fit into memory

Pipe jointermsPipe = new HashJoin(termsPipe, new Fields("term_token"),dictionary, new Fields("word"), new Fields("app","term_token","score","d_count","index","word"), new InnerJoin());

CustomJoins and BloomJoin

Custom Src/Sink Taps

Cascading has good support to read/write to/from different form of data sources. Slight tuning or change might be required but most of code already exists.

Hive (with different file formats), HBase, MySQL

http://www.cascading.org/extensions/

Set proper Config parameters while reading from source tap, example while reading from Hbase Tap,

String tableName = "device_ids";

String[] familyNames = new String[] { "id:type1", "id:type2", “id:type3”,...”id:typen” };

Scan scan = new Scan();

scan.setCacheBlocks(false);

scan.setCaching(10000);

scan.setBatch(10000);

Hive Src TapsExampleWorkflow.java

Tap dmTap = new HiveTableTap(HiveTableTap.SchemeType.SEQUENCE_FILE, admoFPbase, admoFPBasePartitions, dmFullFilter);

HiveTableTap.java

public class HiveTableTap extends GlobHfs {

static Scheme getScheme(SchemeType st) {

if(st.equals(SchemeType.SEQUENCE_FILE))

return new AdmobiusWritableSequenceFile(new Fields("value"), BytesWritable.class);

else if(st.equals(SchemeType.TEXT_TSV))

return new TextDelimited();

else

return null;

}

…..

}

Hive Sink TapsExampleWorkflow.java

Tap srcDstIdsSinkTap = new Hfs(new AdmobiusWritableSequenceFile(new Fields("value"), (Class<? extends Writable>) Text.class),"/tmp/srcDstIdsSinkTap" , SinkMode.REPLACE);

HiveTableTap.java

public class HiveTableTap extends GlobHfs {

static Scheme getScheme(SchemeType st) {

if(st.equals(SchemeType.SEQUENCE_FILE))

return new AdmobiusWritableSequenceFile(new Fields("value"), BytesWritable.class);

else if(st.equals(SchemeType.TEXT_TSV))

return new TextDelimited();

else

return null;

}

…..

}

conf.setOutputFormat( SequenceFileOutputFormat.class );

valueValue = (Writable) (new Text(tupleEntry.getObject( 0 ).toString().getBytes()));

Hive table

CREATE TABLE CASCADING_HIVE_INTER

(

admo_id string,

segments string

)

PARTITIONED BY ( batch_id STRING )

ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'

STORED AS SEQUENCEFILE

Good Practices Use Checkpointing optimally Use subassemblies instead of rewriting logic.

For further control pass additional parameters to subassemblies.

Use Compression and SequenceFile() in sink taps to chain multiple cascading workflows.

Use Failure Traps to filter faulty records. Avoid creating too small or too long workflows.

Chain them in Oozie or similar workflow management engines Example: workflows with 10-20 MR jobs are good

Some Properties for Optimal Performance

Problems with improper configuration

1. Set compression parameters : Jobs would run slow and may take sometime double the time. Set the correct compression Type based on cluster configs

2. mapred.reduce.tasks : Its required to be set manually depending on the size of your job. Keeping it too low would slow down reducer jobs.

3. small file issue : The input split files read by mappers would be too small eventually bringing up more mappers then required.

4. Any custom configuration parameters : You should set it here and use getProperty to access them anywhere in the data workflow

properties.setProperty("min_cutoff_score", "0.7");

FlowConnector flowConnector = new HadoopFlowConnector(properties);

Running in Yarn

Yarn deployment is smooth with cascading 2.5 Make sure the config properties are set as per

YARN as they are different from MR1. While running in in workflow engines like oozie ,

make sure properties are set for • mapred.job.classpath.files and mapred.cache.file

are set with all dependency files in colon separated formatted

Cascading DSLs in other languages

Scalding (Scala)

PyCascading (Python)

cascading.jruby (Jruby)

Cascalog (Closure)

Thank you for your time Q & A