Scaling Big Data with Hadoop And Mesos

Bernardo Gomez Palacio Software Engineer at Guavus Inc

Beyond Buzz Words

Mesos and Data Analysis Yes, you don't need Hadoop to start using Mesos and Spark.

Now, If You... 4 Need to store large files? by default each block is 128MB. 4 Data is written mainly as new files or by appending into existing ones?

Convinced you want to jump into the Hadoop bandwagon? Read Sammer, Eric. "Hadoop Operations." Sebastopol, CA: O'Reilly, 2012. Print.

Welcome to the Jungle

Version Hell

Distributions Apache Bigtop, CDH, HDP, MapR

Hadoop HDFS MRV1 MRV2

Assuming You Already Have Mesos 4 Mesosphere Packages 4 https://mesosphere.io/downloads/ 4 From Source. 4 https://github.com/apache/mesos

Hadoop MRV1 in Meso https://github.com/mesos/hadoop

Hadoop MRV1 in Mesos 4 Requires Hadoop MRV1 4 Officially works with CDH5 MRV1 4 Apache Hadoop 0.22, 0.23 and 1+ 4 Apache Hadoop 2+ doesn't come with MRV1!

Hadoop MRV1 in Mesos 4 Requires a JobTracker. 4 By default uses the org.apache.hadoop.mapred.JobQueueTaskScheduler 4 You can change it .e.g ...mapred.FairScheduler

Hadoop MRV1 in Mesos 4 Requires TaskTracker. 4 That is org.apache.hadoop.mapreduce.server.jobtracker. TaskTracker. 4 And not org.apache.hadoop.mapred.TaskTracker.java.

How Hadoop MRV1 Runs In Mesos?

How Hadoop MRV1 in Mesos works? 1. Framework Mesos Scheduler creates the Job Tracker as part of the driver. 2. The Job Trakcer will use org.apache.hadoop.mapred.MesosScheduler to lunch tasks.

Mesos Hadoop Task Scheduling 4 mapred.mesos.slot.cpus (1) 4 mapred.mesos.slot.disk (1024MB) 4 mapred.mesos.slot.mem (1024MB)

Additional Mesos parameters 4 mapred.mesos.checkpoint (false) 4 mapred.mesos.role (*)

Thoughts What about Hadoop 2.4? Namenode HA? MRV2 and YARN?

Personal Preference 4 Use Hadoop 2.4.0 or above. 4 Name Node HA through the Quorum Journal Manager. 4 Move to Spark if Possible.

Example of a Mesos Data Analysis Stack 1. HDFS stores files. 2. Use the Spark CLI to test ideas. 3. Use Spark Submit for jobs. 4. Use Chronos or Oozie to schedule workflows.

Spark On Mesos

Spark On Mesos https://spark.apache.org/docs/latest/img/cluster-overview.png

Know that Each Spark Application 1. Has its own driving process. 2. Has its own RDDs 3. Has its own cache.

Spark Schedulers on Mesos Fine Grained Coarse Grained

Spark Fine Grained Scheduling 4 Enabled by default. 4 Each Spark task runs as a separate Mesos task. 4 Has an overhead in launching each task.

Spark Coarse Grained Scheduling 4 Uses only one long-running Spark task on each Mesos slave. 4 Dynamically schedules its own mini-tasks, using Akka. 4 Lower startup overhead. 4 Reserving the cluster resources for the complete duration of the application.

Be ware of... 4 Greedy Scheduling (Coarse Grain) 4 Over committing and deadlocks (Fine Grained)

Using Spark Understand Parametrization and Usage 4 spark.app.name 4 spark.executor.memory 4 spark.serializer 4 spark.local.dir 4 ....

Use Spark Submit Avoid parametrizing the Spark Context in your code as much as possible. Leverage the spark-submit arguments, properties files as well as environment variables to configure your application.

Using Spark Accept That Tunning is a Science & an Art

Understand and Tune Your Applications 4 Know your Working Set. 4 Understand Spark Partitioning and Block management. 4 Define your Spark workflow and where to cache/ persist. 4 If you cache you will serialize, use Kryo.

Example Spark API PairRDDFunctions def combineByKey[C]( createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, numPartitions: Int): RDD[(K, C)]

PairRDDFunctions.combineByKey 4 Combines the elements for key using a custom set of aggregations. 4 RDD[(K, V)] to RDD[(K, C)]

PairRDDFunctions.combineByKey 4 createCombiner: Turns a V into a C 4 mergeValue: merge a V into a C 4 mergeCombiners: to combine two C's into a single one. partitioner defaults to HashPartitioner.

Example Spark API PairRDDFunctions self: RDD[(K, V)] def aggregateByKey[U: ClassTag](zeroValue: U)( seqOp: (U, V) => U, combOp: (U, U) => U ): RDD[(K, U)] Uses the default partitioner.

Understand your Data

Tune your Data 4 Per Data Source understand its optimal block size 4 Leverage Avro as the serialization format. 4 Leverage Parquet as the storage format. 4 Try to keep your Avro & Parquet schemas flat.

Suggestions

Each Application 4 Instrument the Code. 4 Measure Input size in number of records and byte size. 4 Measure Output size in the same way.

Standardize 4 JDK & JRE version across your cluster. 4 The Spark version across your cluster. 4 The libraries that will be added to the JVM classpath by default. 4 A packaging strategy for your application, uber jar.

About YARN and Spark

Some Differences with YARN 4 Execution Cluster vs Client modes. 4 Isolation process vs cgroups 4 Docker support? LXC Templates? 4 Deployment complexity?

Wrapping Up

Some Ideas..

References 1. "Hadoop - Apache Hadoop 2.4.0." Apache Hadoop 2.4.0. Apache Software Foundation, 31 Mar. 2014. Web. 24 July 2014. link. 2. "Hadoop Distributed File System-2.4.0 - HDFS High Availability Using the Quorum Journal Manager." Apache Hadoop 2.4.0. Apache Software Foundation, 31 Mar. 2014. Web. 23 July 2014. link.

References 1. Sammer, Eric. Hadoop Operations. Sebastopol, CA: O'Reilly, 2012. Print. 2. "Spark Configuration." Spark 1.0.1 Documentation. Apache Software Foundation, n.d. Web. 24 July 2014. link. 3. "Tuning Spark." Spark 1.0.1 Documentation. Apache Software Foundation, n.d. Web. 24 July 2014. link.

References 1. Ryza, Sandy. "Managing Multiple Resources in Hadoop 2 with YARN." Cloudera Developer Blog. Cloudera, 2 Dec. 2013. Web. 24 July 2014. link.

Thank you!