suman6565 / Spark-Tutorials

Spark and Python for Big Data with PySpark taken from Udemy

PySpark and Spark Tutorials

Following along with Jose Portilla on Udemy Course

Hadoop and MapReduce

• Hadoop is method for distributing very large files across multiple machines
• uses Hadoop Distributed File System (HDFS) to allow users to work with large data sets
• Hadoop uses MapReduce to allow for computation across the distributed data set
• HDFS uses blocks of data (default 128MB) that are replicated 3 times, distributed to support fault tolerance
• smaller blocks provide more parralelization, multiple copies prevent loss of data
• MapReduce splits computational task to distributed set of files
• MapReduce consists ofJob Tracker and Task Tracker
• Job Tracker to send code to run on Task Trackers
• Task Trackers allocate CPU and Memory for the task and monitors the tasks on the worker node

Spark

• Spark is one of latest frameworks to quickly and easily handle big data
• first released February 2013, created at Berkley
• written in Scala, so Scala normally gets the latest features
• Scala is written in Java, so Java API normally does well too!
• Python and R APIs are slowest to catch up
• flexible alternative to MapReduce - i.e. it handles splitting of computational tasks across nodes

Spark vs MapReduce

• Hadoop and MapReduce are bound because MapReduce requires HDFS
• Spark can perform operations up to 100X faster than MapReduce
• Spark can work on HDFS, and other formats
• MapReduce writes most data to disk, while Spark keeps it in RAM and spills over to disk only when necessary. This makes Spark faster!

Spark RDDs

• Resilient Distributed Dataset (RDD)
  • distributed collection of data
  • fault tolerant
  • parallel operation, partitioned
  • ability to use many data sources
• immutable
• lazily evaluated
• cacheable
• even if working with DataFrames, they are still RDDs under the hood

Spark DataFrames

• Spark 2.0 shifted towards DataFrame syntax
• are now the standard way of using Spark's ML Capabilties
• Spark Docs are still new
• DataFrame is very familiar to Pandas DataFrames
• Columns = features
• Rows = records

Local vs Distributed Systems

• local process is limited to computation resources on a single machine
• distributed process process has access to computational resources across a number of machines connected through a network
• after certain point, it is easier to scale out to many low cpu machnes than it is to scale up a single machine
• distributed system is fault tolerant - if one machine fails, network still runs