Apache Spark: Only the simple answer


In this post, I'm just gonna discuss some fundational things I learned about big data with Apache Spark. Personally, I'm just a bit interested in this topic, and do not aim to really become a big data professional (not yet~). It does take tremendous effort to learn Spark well, not to mention the entire big data ecosystem. I'll update this post if I try out some new projects that really apply Spark and its APIs in a deep manner, but for now, let's just talk about some basics of Spark.

Apache Spark vs Hadoop MapReduce

  • MapReduce is a programming model, Spark is a processing framework
Apache Spark MapReduce
Processing Type Process in batches and in real-time Process in batches only
Speed nearly 100x faster slower due to large scale data processing
Storage store data in RAM i.e. in-memeory (easier to retrieve) Store in HDFS, longer time to retrieve
Memory dependence caching (for RDD) and in-memory data storage disk-dependent

Important Components of Spark Ecosystem

  • Core components[MOST IMPORTANT]:
    • Spark Core (Caching, RDD, DataFrames, Datasets || Transformations and Actions)
      1. Memory management
      2. Fault recovery
      3. Task dispatching
      4. Scheduling and monitoring jobs
    • Spark SQL (Data Query)
      • Used for structured and semi-structured data processing
      • Usually used for in-memory processing
      • Top level: DataFrame DSL(domain specific language) ; Spark SQl and HQL(Hive)
      • Level 2: DataFrame API
      • Level 3: Data Source API
      • Base Level: CSV + JSON + JDBC(Java Database connectivity) + etc storage/query
    • Spark Streaming (Stream data)
    • Spark MLlib (Machine Learnig toolkits)
    • GraphX (Graph Processing models)
  • Langauge Support
    • Java
    • Python
    • Scala
    • R
  • Spark Cluster Managers
    • Standalone mode: Default choice, run in FIFO order, and each application will try to use all available nodes
    • Apache YARN (Hadoop Integration): This is the resource manager of Hadoop, use this will help spark to connect to hdfs better
    • Kubernetes: For deployment, scaling and management of containerized applications


  • Resilient Distributed Datasets
  • RDDs are immutable, fault-tolerant distributed collections of objects that can be operated in parallel (split into partition and executed on different nodes of a cluster)
  • 2 major types of operations:
    • Transformation: map, filter, join, union, etc. Yield a new RDD containing the result
    • Action: reduce, count, first, etc. Return a value after running a computation on RDD
  • Works in a similar style as java Stream

How Spark runs applications with the help of its architecture


  • In driver program
    • Spark applications runs as independent processes (i.e. split tasks) running across different machines
    • Spark sessions/context as the entry point of the application
    • Driver: Record the flow of the application
  • Resource manager/Cluster manager (DAG Scheduler at the backend)
    • The driver program request resources from the clusters
    • Assign task to workers, one task per partition
    • Knows each step of the application for in-memory processing
  • Worker node
    • processing slave (node manager) grands the request to usage of resources from resource manager
    • The request is called Container, within the Container, executor process is launched to apply tasks to its unit of work to the dataset in its partition and outputs a new partition dataset
    • because iterative algorithms apply operations repeated to the data, the benefit from caching datasets across iterations


  • Results are sent back: worker node -> container -> manager -> driver program/disk
  • The entire execution is lazily evaluated (transformations not evaluated until an action is called)

What is a Parquet file and what are its advantages

  • Parquet is a columnar format that is supported byh several data processing systems (default data type for spark)
  • Advisable to use if not all fields/columns in the data are used
  • Advantages
    • able to fetch specific columns for access
    • consumes less space
    • follow type-specific encoding
    • limited I/O operations

What is shuffling in Spark? When does it occur?

  • Shuffling is the process of redistributing data across partitions that may lead to data movement across executors
  • Occurs while joining two tables or while performing byKey operations such as GroupByKey or ReduceByKey

Notes on Big Data Learning Journey (for those who truly want a Big Data job and for my future )

To excel in the Big Data domain, you should master the following skills:

  1. Java & Scala Understand source code for related package/API development
  2. Linux Everyone should know about shell scripts, bash and linux commands
  3. Hadoop It's a broad topic, but first of all, the ability to read whatever source code for an API is a must
  4. Hive Know how to use it, understand how the SQL is converted in base code and how to optimize the query process or MapReduce/Spark Operations
  5. Spark The core developement process (But honestly, most of the time it is still SQL)
  6. Kafka High-volumn stream data processing; Good to use when you have high concurrency
  7. Flink Faster than Spark sometimes. However, you should not discard Spark. Learn based on what you need.
  8. HBase Know your database knowledge. Understand its fundamental knowledge
  9. Zookeeper Distributation cluster data coordination services; Know how to use, better to understand the basic
  10. YARN Cluster resources management; Know how to use
  11. Sqoop, Flume, Oozie/Azkaban Know how to use

Different cluster managers

  1. Spark Standalone mode
    • by default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes
  2. Apache Mesos
    • an open sources project to manage computer clusters, and can also run Hadoop applications
  3. YARN
    • Apache YARN is the cluster resource manager of Hadoop 2.
  4. Kubernetes
    • an open-source system for automating deployment, scaling and management of containerized applications

Zhenlin Wang

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