Spark tutorial

Learning PySparkA Tutorial

By:Maria Mestre

(@mariarmestre)Sahan Bulathwela

(@in4maniac)Erik Pazos (@zerophewl)

This tutorial

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● Some key Spark concepts (2 minute crash course)

● First part: Spark core○ Notebook: basic operations○ Spark execution model

● Second part: Dataframes and SparkSQL○ Notebook : using DataFrames and Spark SQL ○ DataFrames execution model

● Final note on Spark configs and useful areas to go from here

How to setup the tutorial

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● Directions and resources to setup the tutorial in your local environment can be found at the below mentioned blog post

https://in4maniac.wordpress.com/2016/10/09/spark-tutorial/

● Data Extracted from Amazon Dataset o Image-based recommendations on styles and substitutes , J. McAuley, C. Targett, J.

Shi, A. van den Hengel, SIGIR, 2015o Inferring networks of substitutable and complementary products, J. McAuley, R.

Pandey, J. Leskovec, Knowledge Discovery and Data Mining, 2015

● sample of Amazon product reviewso fashion.json, electronics.json, sports.jsono fields: ASIN, review text, reviewer name, …

● sample of product metadatao sample_metadata.jsono fields: ASIN, price, category, ...

The datasets

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Some Spark definitions (1)

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● An RDD is a distributed dataset● The dataset is divided into partitions● It is possible to cache data in memory

Some Spark definitions (2)

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● A cluster = a master node and slave nodes● Transformations through the Spark context● Only the master node has access to the Spark context● Actions and transformations

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Notebook - Spark core parts 1-3

Why understanding Spark internals?

● essential to understand failures and improve performance

This section is a condensed version of: https://spark-summit.org/2014/talk/a-deeper-understanding-of-spark-internals

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From code to computations

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rd = sc.textFile(‘product_reviews.txt’)

rd.map(lambda x: (x[‘asin’], x[‘overall’]))

.groupByKey()

.filter(lambda x: len(x[1])> 1)

.count()

From code to computations

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1. You write code using RDDs

2. Spark creates a graph of RDDs

rd = sc.textFile(‘product_reviews.txt’)rd..map(lambda x: (x[‘asin’], x[‘overall’]))

.groupByKey().filter(lambda x: len(x[1])> 1).count()

Execution model

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Stage 1

3. Spark figures out logical execution plan for each computation

Stage 2

Execution model

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4. Schedules and executes individual tasks

Skimlinks | Spark… A view from the trenches

If your shuffle fails...● Shuffles are usually the bottleneck:

o if very large tasks ⇒ memory pressureo if too many tasks ⇒ network overheado if too few tasks ⇒ suboptimal cluster utilisation

● Best practices:o always tune the number of partitions!o between 100 and 10,000 partitionso lower bound: at least ~2x number of coreso upper bound: task should take at least 100 ms

● https://spark.apache.org/docs/latest/tuning.html

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Other things failing...

● I’m trying to save a file but it keeps failing...○ Turn speculation off!

● I get an error “no space left on device”!○ Make sure the SPARK_LOCAL_DIRS use the right disk

partition on the slaves

● I keep losing my executors○ could be a memory problem: increase executor

memory, or reduce the number of cores

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Notebook - Spark core part 4

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Apache Spark

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DataFrames API

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DataFrames API

DataFrames and Spark SQL

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A DataFrame is a collection of data that is organized with named columns.

● API very similar to Pandas/R DataFrames

Spark SQL is a functionality that allows to query from DataFrames using SQL-like schematic language

● Catalyst SQL engine

● Hive Context opens up most of HQL functionality with DataFrames

RDDs and DataFrames

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RDDData is stored as independent objects in partitions

Does process optimization on RDD level

More focus on “HOW” to obtain the required data

DataFrameData has higher level column information in

addition to partitioning

Does optimizations on schematic structure

More focus on “WHAT” data is required

Transformable

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Notebook - Spark DataFrames

How do DataFrames work?

●WHY DATAFRAMES??●Overview

This section is inspired by: http://www.slideshare.net/databricks/introducing-dataframes-in-spark-for-large-scale-data-science

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Main Considerations

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Chart extracted from : https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html

Fundamentals

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Un Resolved Logical

Plan Logical Plan

Optimized Logical

Plan

Efficient Physical

Plan

PhysicalPlans

SELECT colsFROM tablesWHERE cond

Code: more_code more() Code=1

DataFrame SparkSQL

RDD

Catalog

COMPANYNAME.COM | PRESENTATION

Notebook - Spark SQL

New stuff: Data Source APIs●Schema Evolution

o In parquet, you can start from a basic schema and keep adding new fields.

●Run SQL directly on the fileo In Parquet files, run the SQL on the file itself

as parquet has got structure

Data Source APIs●Partition Discovery

oTable partitioning is used in systems like HiveoData is normally stored in different directories

spark-sklearn ●Parameter Tuning is the problem

oDataset is smalloGrid search is BIG

More info: https://databricks.com/blog/2016/02/08/auto-scaling-scikit-learn-with-apache-spark.html

New stuff: DataSet API● Spark : Complex

analyses with minimal programming effort

● Run Spark applications faster o Closely knit to Catalyst

engine and Tungsten Engine

● Extension of DataFrame API: type safe, object oriented programming interface

More info: https://databricks.com/blog/2016/01/04/introducing-spark-datasets.html

Spark 2.0● API Changes● A lot of work on

Tungsten Execution engine

● Support of Dataset API

● Unification of DataFrame & Dataset APIs

More info: https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-

rdds-dataframes-and-datasets.html

Important Links

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● Amazon Dataset : https://snap.stanford.edu/data/web-Amazon.html● Spark DataFrames :

https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html● More resources about Apache Spark:

○ http://www.slideshare.net/databricks○ https://www.youtube.com/channel/UC3q8O3Bh2Le8Rj1-Q-_UUbA

● Spark SQL programming guide for 1.6.1:https://spark.apache.org/docs/latest/sql-programming-guide.html● Using Apache Spark in real world applications:

http://files.meetup.com/13722842/Spark%20Meetup.pdf ● Tungsten

https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html ● Further Questions:

○ Maria : @mariarmestre○ Erik : @zerophewl○ Sahan : @in4maniac

Skimlinks is hiring Data Scientists and Senior Software Engineers !!

● Machine Learning● Apache Spark and Big Data

Get in touch with: ● Sahan : sahan@skimlinks.com● Erik : erik@skimlinks.com