Chapter folders renamed
This commit is contained in:
Karan Solanki
@@ -0,0 +1,22 @@
|
||||
#casestudy1: Pi calculater
|
||||
from operator import add
|
||||
from random import random
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
|
||||
spark = SparkSession.builder.master("spark://192.168.64.2:7077") \
|
||||
.appName("Pi claculator app") \
|
||||
.getOrCreate()
|
||||
|
||||
partitions = 2
|
||||
n = 10000000 * partitions
|
||||
|
||||
|
||||
def f(_):
|
||||
x = random() * 2 - 1
|
||||
y = random() * 2 - 1
|
||||
return 1 if x ** 2 + y ** 2 <= 1 else 0
|
||||
|
||||
|
||||
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
|
||||
print("Pi is roughly %f" % (4.0 * count / n))
|
||||
@@ -0,0 +1,32 @@
|
||||
#casestudy2.py: word count application
|
||||
import matplotlib.pyplot as plt
|
||||
from pyspark.sql import SparkSession
|
||||
from wordcloud import WordCloud
|
||||
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("word cloud app")\
|
||||
.getOrCreate()
|
||||
|
||||
wc_threshold = 1
|
||||
wl_threshold = 3
|
||||
textRDD = spark.sparkContext.textFile('wordcloud.txt',3)
|
||||
flatRDD = textRDD.flatMap(lambda x: x.split(' '))
|
||||
wcRDD = flatRDD.map(lambda word: (word, 1)).\
|
||||
reduceByKey(lambda v1, v2: v1 + v2)
|
||||
|
||||
# filter out words with fewer than threshold occurrences
|
||||
filteredRDD = wcRDD.filter(lambda pair: pair[1] >= wc_threshold)
|
||||
filteredRDD2 = filteredRDD.filter(lambda pair:
|
||||
len(pair[0]) > wl_threshold)
|
||||
|
||||
word_freq = dict(filteredRDD2.collect())
|
||||
|
||||
# Create the wordcloud object
|
||||
wordcloud = WordCloud(width=480, height=480, margin=0).\
|
||||
generate_from_frequencies(word_freq)
|
||||
|
||||
# Display the generated cloud image
|
||||
plt.imshow(wordcloud, interpolation='bilinear')
|
||||
plt.axis("off")
|
||||
plt.margins(x=0, y=0)
|
||||
plt.show()
|
||||
@@ -0,0 +1,97 @@
|
||||
Spark Overview
|
||||
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.
|
||||
|
||||
Security
|
||||
Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Please see Spark Security before downloading and running Spark.
|
||||
|
||||
Downloading
|
||||
Get Spark from the downloads page of the project website. This documentation is for Spark version 3.1.2. Spark uses Hadoop’s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions. Users can also download a “Hadoop free” binary and run Spark with any Hadoop version by augmenting Spark’s classpath. Scala and Java users can include Spark in their projects using its Maven coordinates and Python users can install Spark from PyPI.
|
||||
|
||||
If you’d like to build Spark from source, visit Building Spark.
|
||||
|
||||
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java. This should include JVMs on x86_64 and ARM64. It’s easy to run locally on one machine — all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.
|
||||
|
||||
Spark runs on Java 8/11, Scala 2.12, Python 3.6+ and R 3.5+. Java 8 prior to version 8u92 support is deprecated as of Spark 3.0.0. For the Scala API, Spark 3.1.2 uses Scala 2.12. You will need to use a compatible Scala version (2.12.x).
|
||||
|
||||
For Python 3.9, Arrow optimization and pandas UDFs might not work due to the supported Python versions in Apache Arrow. Please refer to the latest Python Compatibility page. For Java 11, -Dio.netty.tryReflectionSetAccessible=true is required additionally for Apache Arrow library. This prevents java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available when Apache Arrow uses Netty internally.
|
||||
|
||||
Running the Examples and Shell
|
||||
Spark comes with several sample programs. Scala, Java, Python and R examples are in the examples/src/main directory. To run one of the Java or Scala sample programs, use bin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, this invokes the more general spark-submit script for launching applications). For example,
|
||||
|
||||
./bin/run-example SparkPi 10
|
||||
You can also run Spark interactively through a modified version of the Scala shell. This is a great way to learn the framework.
|
||||
|
||||
./bin/spark-shell --master local[2]
|
||||
The --master option specifies the master URL for a distributed cluster, or local to run locally with one thread, or local[N] to run locally with N threads. You should start by using local for testing. For a full list of options, run Spark shell with the --help option.
|
||||
|
||||
Spark also provides a Python API. To run Spark interactively in a Python interpreter, use bin/pyspark:
|
||||
|
||||
./bin/pyspark --master local[2]
|
||||
Example applications are also provided in Python. For example,
|
||||
|
||||
./bin/spark-submit examples/src/main/python/pi.py 10
|
||||
Spark also provides an R API since 1.4 (only DataFrames APIs included). To run Spark interactively in an R interpreter, use bin/sparkR:
|
||||
|
||||
./bin/sparkR --master local[2]
|
||||
Example applications are also provided in R. For example,
|
||||
|
||||
./bin/spark-submit examples/src/main/r/dataframe.R
|
||||
Launching on a Cluster
|
||||
The Spark cluster mode overview explains the key concepts in running on a cluster. Spark can run both by itself, or over several existing cluster managers. It currently provides several options for deployment:
|
||||
|
||||
Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
|
||||
Apache Mesos
|
||||
Hadoop YARN
|
||||
Kubernetes
|
||||
Where to Go from Here
|
||||
Programming Guides:
|
||||
|
||||
Quick Start: a quick introduction to the Spark API; start here!
|
||||
RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
|
||||
Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
|
||||
Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
|
||||
Spark Streaming: processing data streams using DStreams (old API)
|
||||
MLlib: applying machine learning algorithms
|
||||
GraphX: processing graphs
|
||||
SparkR: processing data with Spark in R
|
||||
PySpark: processing data with Spark in Python
|
||||
API Docs:
|
||||
|
||||
Spark Scala API (Scaladoc)
|
||||
Spark Java API (Javadoc)
|
||||
Spark Python API (Sphinx)
|
||||
Spark R API (Roxygen2)
|
||||
Spark SQL, Built-in Functions (MkDocs)
|
||||
Deployment Guides:
|
||||
|
||||
Cluster Overview: overview of concepts and components when running on a cluster
|
||||
Submitting Applications: packaging and deploying applications
|
||||
Deployment modes:
|
||||
Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
|
||||
Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
|
||||
Mesos: deploy a private cluster using Apache Mesos
|
||||
YARN: deploy Spark on top of Hadoop NextGen (YARN)
|
||||
Kubernetes: deploy Spark on top of Kubernetes
|
||||
Other Documents:
|
||||
|
||||
Configuration: customize Spark via its configuration system
|
||||
Monitoring: track the behavior of your applications
|
||||
Tuning Guide: best practices to optimize performance and memory use
|
||||
Job Scheduling: scheduling resources across and within Spark applications
|
||||
Security: Spark security support
|
||||
Hardware Provisioning: recommendations for cluster hardware
|
||||
Integration with other storage systems:
|
||||
Cloud Infrastructures
|
||||
OpenStack Swift
|
||||
Migration Guide: Migration guides for Spark components
|
||||
Building Spark: build Spark using the Maven system
|
||||
Contributing to Spark
|
||||
Third Party Projects: related third party Spark projects
|
||||
External Resources:
|
||||
|
||||
Spark Homepage
|
||||
Spark Community resources, including local meetups
|
||||
StackOverflow tag apache-spark
|
||||
Mailing Lists: ask questions about Spark here
|
||||
AMP Camps: a series of training camps at UC Berkeley that featured talks and exercises about Spark, Spark Streaming, Mesos, and more. Videos, slides and exercises are available online for free.
|
||||
Code Examples: more are also available in the examples subfolder of Spark (Scala, Java, Python, R)
|
||||
@@ -0,0 +1,6 @@
|
||||
firstname,middlename,lastname,department,gender,salary
|
||||
James,,Bylsma,HR,M,40000
|
||||
Kamal,Rahim,,HR,M,41000
|
||||
Robert,,Zaine,Finance,M,35000
|
||||
Sophia,Anne,Richer,Finance,F,4000
|
||||
John,Will,Brown,Engineering,F,65000
|
||||
|
@@ -0,0 +1,24 @@
|
||||
#dfcreate1.py: create a df from a collection
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("DataFrame Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [('James','','Bylsma','HR','M',40000),
|
||||
('Kamal','Rahim','','HR','M',41000),
|
||||
('Robert','','Zaine','Finance','M',35000),
|
||||
('Sophia','Anne','Richer','Finance','F',4000),
|
||||
('John','Will','Brown','Engineering','F',65000)
|
||||
]
|
||||
|
||||
columns = ["firstname","middlename","lastname",
|
||||
"department","gender","salary"]
|
||||
df = spark.createDataFrame(data=data, schema = columns)
|
||||
print(df.printSchema())
|
||||
print(df.show())
|
||||
|
||||
time.sleep(60)
|
||||
@@ -0,0 +1,26 @@
|
||||
#dfcreate2.py: create a df from a csv file
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
|
||||
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("DataFrame Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
schemas = StructType([ \
|
||||
StructField("firstname",StringType(),True), \
|
||||
StructField("middlename",StringType(),True), \
|
||||
StructField("lastname",StringType(),True), \
|
||||
StructField("department", StringType(), True), \
|
||||
StructField("gender", StringType(), True), \
|
||||
StructField("salary", IntegerType(), True) \
|
||||
])
|
||||
|
||||
df = spark.read.csv('df2.csv', header=True, nullValue='NA', schema=schemas)
|
||||
print(df.printSchema())
|
||||
print(df.show())
|
||||
|
||||
time.sleep(60)
|
||||
@@ -0,0 +1,43 @@
|
||||
#dfcreate1.py: create a df from a collection
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql.functions import regexp_replace, lit, when
|
||||
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("DataFrame Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [('James','','Bylsma','HR','M',40000),
|
||||
('Kamal','Rahim','','HR','M',41000),
|
||||
('Robert','','Zaine','Finance','M',35000),
|
||||
('Sophia','Anne','Richer','Finance','F',47000),
|
||||
('John','Will','Brown','Engineering','F',65000)
|
||||
]
|
||||
|
||||
columns = ["firstname","middlename","lastname",
|
||||
"department","gender","salary"]
|
||||
df = spark.createDataFrame(data=data, schema = columns)
|
||||
|
||||
#show two columns
|
||||
print(df.select([df.firstname, df.salary]).show())
|
||||
|
||||
#replacing values of a columm
|
||||
myDict = {'F':'Female','M':'Male'}
|
||||
df2 = df.replace(myDict, subset=['gender'])
|
||||
|
||||
#Another way of replacing column values
|
||||
#df1 = df.withColumn('gender',regexp_replace('gender','M', 'Male'))
|
||||
#df2 = df1.withColumn('gender',regexp_replace('gender','F', 'Female'))
|
||||
|
||||
#adding a new colum Pay Level based on an existing column values
|
||||
df3 = df2.withColumn("Pay Level",
|
||||
when((df2.salary < 40000), lit("10")) \
|
||||
.when((df.salary >= 40000) & (df.salary <= 50000), lit("11")) \
|
||||
.otherwise(lit("12")) \
|
||||
)
|
||||
print(df3.show())
|
||||
|
||||
time.sleep(60)
|
||||
@@ -0,0 +1,24 @@
|
||||
#rddaction1.py: rdd action functions
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("RDD Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [5, 4, 6, 3, 2, 8, 9, 2, 8, 7,
|
||||
8, 4, 4, 8, 2, 7, 8, 9, 6, 9]
|
||||
rdd1 = spark.sparkContext.parallelize(data)
|
||||
|
||||
print("RDD contents with partitions: "+rdd1.glom().collect())
|
||||
print("Count by values: "+rdd1.countByValue())
|
||||
print("reduce function"+rdd1.reduce(lambda a,b: a+b))
|
||||
print("Sum of RDD contents"+rdd1.sum())
|
||||
print(""+rdd1.top(5))
|
||||
print(rdd1.count())
|
||||
print(rdd1.max())
|
||||
print(rdd1.min())
|
||||
|
||||
time.sleep(60)
|
||||
@@ -0,0 +1,16 @@
|
||||
#rddcreate.py: to create rdd from a collection and from a file
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
from pyspark.sql import SparkSession
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("RDD Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [5, 4, 6, 3, 2, 8, 9, 2, 8, 7,
|
||||
8, 4, 4, 8, 2, 7, 8, 9, 6, 9]
|
||||
rdd1 = spark.sparkContext.parallelize(data)
|
||||
print(rdd1.getNumPartitions())
|
||||
|
||||
|
||||
rdd2 = spark.sparkContext.textFile('sample.txt')
|
||||
print(rdd2.getNumPartitions())
|
||||
@@ -0,0 +1,19 @@
|
||||
#rddtransform1.py: rdd tranformation function
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("RDD Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
rdd1 = spark.sparkContext.textFile('sample.txt')
|
||||
#print(rdd1.getNumPartitions())
|
||||
rdd2 = rdd1.map(lambda lines: lines.lower())
|
||||
rdd3 = rdd1.map(lambda lines: lines.upper())
|
||||
|
||||
print(rdd2.collect())
|
||||
print(rdd3.collect())
|
||||
|
||||
time.sleep(60)
|
||||
@@ -0,0 +1,13 @@
|
||||
#rddtransform2.py: rdd tranformation function-map
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
from pyspark.sql import SparkSession
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("RDD Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [5, 4, 6, 3, 2, 8, 9, 2, 8, 7,
|
||||
8, 4, 4, 8, 2, 7, 8, 9, 6, 9]
|
||||
rdd1 = spark.sparkContext.parallelize(data)
|
||||
rdd2 = rdd1.filter(lambda x: x % 2 !=0 )
|
||||
print(rdd2.collect())
|
||||
@@ -0,0 +1,4 @@
|
||||
Spark Read Text File | RDD | DataFrame — SparkByExampleshttps://sparkbyexamples.com › spark › spark-read-text-...
|
||||
Complete example — txt files, for example, sparkContext.textFile() and sparkContext.wholeTextFiles() methods to read into RDD and spark.read.text() ...
|
||||
Quick Start - Spark 2.2.1 Documentation - Apache Sparkhttps://spark.apache.org › docs › quick-start
|
||||
scala> val textFile = spark.read.textFile("README.md") textFile: org.apache.spark.sql. ... For example, we can easily call functions declared elsewhere. We'll use ...
|
||||
@@ -0,0 +1,34 @@
|
||||
#dfcreate1.py: create a df from a collection
|
||||
|
||||
#please ignore next 2 statements if running directly in PySpark shell
|
||||
import time
|
||||
|
||||
from pyspark.sql import SparkSession
|
||||
from pyspark.sql.functions import regexp_replace, lit, when
|
||||
|
||||
spark = SparkSession.builder.master("local[*]")\
|
||||
.appName("DataFrame Test app")\
|
||||
.getOrCreate()
|
||||
|
||||
data = [('James','','Bylsma','HR','M',40000),
|
||||
('Kamal','Rahim','','HR','M',41000),
|
||||
('Robert','','Zaine','Finance','M',35000),
|
||||
('Sophia','Anne','Richer','Finance','F',47000),
|
||||
('John','Will','Brown','Engineering','F',65000)
|
||||
]
|
||||
|
||||
columns = ["firstname","middlename","lastname",
|
||||
"department","gender","salary"]
|
||||
df = spark.createDataFrame(data=data, schema = columns)
|
||||
|
||||
df.createOrReplaceTempView("EMP_DATA")
|
||||
|
||||
df2 = spark.sql("SELECT * FROM EMP_DATA")
|
||||
print(df2.show())
|
||||
|
||||
df3 = spark.sql("SELECT firstname, middlename, lastname, "
|
||||
"salary FROM EMP_DATA WHERE SALARY > 45000")
|
||||
print(df3.show())
|
||||
|
||||
df4 = spark.sql(("SELECT gender, count(*) from EMP_DATA group by gender"))
|
||||
print(df4.show())
|
||||
Reference in New Issue
Block a user