spark-workshop

Spark Administration and Monitoring Workshop

What You Will Learn (aka Goals)

The goal of the Spark Administration and Monitoring Workshop is to give you a practical, complete and hands-on introduction to Apache Spark and the tools to manage, monitor, troubleshoot and fine-tune Spark infrastructure.

NOTE: The workshop uses a tailor-made Docker image but it is also possible to use a commercial distribution for Spark like Cloudera’s CDH (possibly Hortonworks Data Platform (HDP) or MapR Sandbox).

The workshop uses an intense learn-by-doing approach in which the modules start with just enough knowledge to get you going and quickly move on to applying the concepts in assignments. There are a lot of practical exercises.

The workshop comes with many practical sessions that should meet (and possibly exceed) expectations of administrators, operators, devops, and other technical roles like system architects or technical leads (perhaps also software developers for whom Spark and Scala (Application Development) Workshop might be a better fit).

CAUTION: The workshop is very hands-on and practical, i.e. not for faint-hearted. Seriously! After just a couple of days your mind, eyes, and hands will all be trained to recognise the patterns how to set up and operate Spark infrastructure in your Big Data projects.

CAUTION I have already trained people who expressed their concern that there were too many exercises. Your dear drill sergeant, Jacek.

Duration

5 days

Target Audience

• Aspiring Spark administrators, operators, devops
• Perhaps system architects or technical leads

Agenda

1. Anatomy of Spark Data Processing
2. SparkContext
   • SparkConf
3. Transformations and Actions
4. Units of Physical Execution: Jobs, Stages, and Tasks
5. RDD Lineage
   • DAG View of RDDs
   • Logical Execution Plan
6. Spark Execution Engine
   • DAGScheduler
   • TaskScheduler
   • Scheduler Backends
   • Executor Backends
7. Partitions and Partitioning
8. Shuffle
   • Wide and Narrow Dependencies
9. Caching and Persistence
10. Checkpointing
11. Elements of Spark Runtime Environment
12. The Driver
13. Executors
    • TaskRunners
14. Deploy Modes
15. Spark Clusters
    • Master and Workers
16. RPC Environment (RpcEnv)
17. BlockManagers
18. Spark Tools
19. spark-shell
20. spark-submit
21. web UI
22. spark-class
23. Monitoring Spark Applications using web UI
24. The Different Tabs in web UI
25. Exercise: Monitoring using web UI
    • Executing Spark Jobs to Enable Different Statistics and Statuses
26. Spark on Hadoop YARN cluster
27. Exercise: Setting up Hadoop YARN
    • Accessing Resource Manager’s web UI
28. Exercise: Submitting Applications using spark-submit
    • --master yarn
    • yarn-site.xml
    • yarn application -list
    • yarn application -status
    • yarn application -kill
29. Runtime Properties - Meaning and Application
30. Troubleshooting
    • log files
31. YarnShuffleService – ExternalShuffleService on YARN
32. Multi-tenant YARN Cluster Setup and Spark
    • Overview of YARN Schedulers (e.g. Capacity Scheduler)
    • spark-submit --queue
33. Clustering Spark using Spark Standalone
34. Exercise: Setting up Spark Standalone
    • Using standalone Master’s web UI
35. Exercise: Submitting Applications using spark-submit
    • --master spark://...
    • --deploy-mode with client and cluster
36. Clustering Spark using Spark Standalone
37. Tuning Spark Infrastructure
38. Exercise: Configuring CPU and Memory for Master and Executors
39. Exercise: Observing Shuffling using groupByKey-like operations.
40. Scheduling Modes: FIFO and FAIR
    • Exercise: Configuring Pools in FAIR Scheduling Mode
41. Monitoring Spark using SparkListeners
42. LiveListenerBus
43. StatsReportListener
44. Event Logging using EventLoggingListener and History Server
45. Exercise: Event Logging using EventLoggingListener
46. Exercise: Developing Custom SparkListener
47. Dynamic Allocation (of Executors)
48. External Shuffle Service
49. Spark Metrics System
50. (optional) Using Spark Streaming and Kafka
51. (optional) Clustering Spark using Apache Mesos
52. Exercise: Setting up Mesos cluster
53. Exercise: Submitting Applications using spark-submit
    • --master mesos://...

Requirements

• Training classes are best for groups up to 8 participants.
• Participants have decent computers, preferably with Linux or Mac OS operating systems
  • There are issues with running Spark on Windows (mostly with Spark SQL / Hive).
• Participants have to download the following packages to their computers before the class:
  • spark-1.6.1-bin-hadoop2.6.tgz
  • Optional downloads (have them ready):
    • kafka_2.11-0.10.0.0.tgz
    • H2 Database Engine - download zip file with Version 1.4.191 for All Platforms
    • apache-cassandra-3.6-bin.tar.gz
    • Cassandra Spark Connector by executing the following command: $SPARK_HOME/bin/spark-shell --packages datastax:spark-cassandra-connector:1.6.0-M2-s_2.11
    • (optional) PostgreSQL 9.5.2 (or later) and JDBC42 Postgresql Driver 9.4-1208 (or later)
• Participants are requested to git clone this project and follow README.

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