📔
Data Science with Apache Spark
  • Preface
  • Contents
  • Basic Prerequisite Skills
  • Computer needed for this course
  • Spark Environment Setup
  • Dev environment setup, task list
  • JDK setup
  • Download and install Anaconda Python and create virtual environment with Python 3.6
  • Download and install Spark
  • Eclipse, the Scala IDE
  • Install findspark, add spylon-kernel for scala
  • ssh and scp client
  • Summary
  • Development environment on MacOS
  • Production Spark Environment Setup
  • VirtualBox VM
  • VirtualBox only shows 32bit on AMD CPU
  • Configure VirtualBox NAT as Network Adapter on Guest VM and Allow putty ssh Through Port Forwarding
  • Docker deployment of Spark Cluster
  • Create customized Apache Spark Docker container
  • Dockerfile
  • docker-compose and docker-compose.yml
  • Launch custom built Docker container with docker-compose
  • Entering Docker Container
  • Setup Hadoop, Hive and Spark on Linux without docker
  • Hadoop Preparation
  • Hadoop setup
  • Configure $HADOOP_HOME/etc/hadoop
  • HDFS
  • Start and stop Hadoop
  • Work with Hadoop and HDFS file system
  • Connect to Hadoop web interface port 50070 and 8088
  • Install Hive
  • hive home
  • Initialize hive schema
  • Start hive metastore service.
  • hive-site.xml
  • Hive client
  • Setup Apache Spark
  • Spark Home
  • Jupyter-notebook server
  • Python 3 Warm Up
  • Basics
  • Iterables/Collections
  • Strings
  • List
  • Tuple
  • Dictionary
  • Set
  • Conditional statement
  • for loop
  • while loop
  • Functions and methods
  • map and filter
  • map and filter takes function as input
  • lambda
  • Python Class
  • Input and if statement
  • Input from a file
  • Output to a file
  • try except
  • Python coding exercise
  • Scala Warm Up
  • Start Spylon-kernel on Jupyter-notebook
  • Type of Variable: Mutable or immutable
  • Block statement
  • Scala Data Type
  • Array in Scala
  • Methods
  • Functions
  • Anonymous function
  • Scala map and filter methods
  • Class
  • Objects
  • Trait
  • Tuple in Scala
  • List/Seq
  • Set in Scala
  • Scala Map
  • Scala if statement
  • Scala for loop
  • Scala While Loop
  • Scala Exceptions + try catch finally
  • Scala coding exercise
  • Run a program to estimate pi
  • Common Spark command line
  • Run Scala code with spark-submit
  • Python with Apache Spark using Jupyter notebook
  • Spark Core Introduction
  • Spark and Scala Version
  • Basic Spark Package
  • Resilient Distributed Datasets (RDDs)
  • RDD Operations
  • Passing Function to Spark
  • Printing elements of an RDD
  • Working with key value pair
  • RDD Transformation Functions
  • RDD Action Functions
  • SPARK SQL
  • SQL
  • Datasets and DataFrames
  • SparkSession
  • Creating DataFrames
  • Running SQL Queries Programmatically
  • Issue from running Cartesian Join Query
  • Creating Datasets
  • Interoperating with RDD
  • Untyped User-Defined Aggregate Functions
  • Generic Load/Save Functions
  • Manually specify file option
  • Run SQL on files directly
  • Save Mode
  • Saving to Persistent Tables
  • Bucketing, Sorting and Partitioning
  • Apache Arrow
  • Install Python Arrow Module PyArrow
  • Issue might happen import PyArrow
  • Enabling for Conversion to/from Pandas in Python
  • Connect to any data source the same consistent way
  • Spark SQL Implementation Example in Scala
  • Run scala code in Eclipse IDE
  • Hive Integration, run SQL or HiveQL queries on existing warehouses.
  • Example: Enrich JSON
  • Integrate Tableau Data Visualization with Hive Data Warehouse and Apache Spark SQL
  • Connect Tableau to Spark SQL running in VM with VirtualBox with NAT
  • Issues with connecting from Tableau to Spark SQL
  • SPARK Streaming
  • Discretized Streams (DStreams)
  • Transformations on DStreams
  • map(func)
  • filter(func)
  • repartition(numPartitions)
  • union(otherStream)
  • reduce(func)
  • count()
  • countByValue()
  • reduceByKey(func, [numTasks])
  • join(otherStream, [numTasks])
  • cogroup(otherStream, [numTasks])
  • transform(func)
  • updateStateByKey(func)
  • Scala Tips for updateStateByKey
  • repartition(numPartitions)
  • DStream Window Operations
  • DStream Window Transformation
  • countByWindow(windowLength, slideInterval)
  • reduceByWindow(func, windowLength, slideInterval)
  • reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])
  • reduceByKeyAndWindow(func, invFunc, windowLength, slideInterval, [numTasks])
  • countByValueAndWindow(windowLength, slideInterval, [numTasks])
  • window(windowLength, slideInterval)
  • Window DStream print(n)
  • saveAsTextFiles(prefix, [suffix])
  • saveAsObjectFiles(prefix, [suffix])
  • saveAsHadoopFiles(prefix, [suffix])
  • foreachRDD(func)
  • Build Twitter Scala API Library for Spark Streaming using sbt
  • Spark Streaming with Twitter, you can get public tweets by using Twitter API.
  • Spark streaming use case with Python
  • Spark Graph Computing
  • Spark Graph Computing Continue
  • Graphx
  • Package org.apache.spark.graphx
  • Edge Class
  • EdgeContext Class
  • EdgeDirection Class
  • EdgeRDD Class
  • EdgeTriplet Class
  • Graph Class
  • GraphLoader Object
  • GraphOps Class
  • GraphXUtils Object
  • PartitionStrategy Trait
  • Pregel Object
  • TripletFields Class
  • VertexRDD Class
  • Package org.apache.spark.graphx.impl
  • AggregatingEdgeContext Class
  • EdgeRDDImpl Class
  • Class GraphImpl<VD,ED>
  • Class VertexRDDImpl<VD>
  • Package org.apache.spark.graphx.lib
  • Class ConnectedComponents
  • Class LabelPropagation
  • Class PageRank
  • Class ShortestPaths
  • Class StronglyConnectedComponents
  • Class SVDPlusPlus
  • Class SVDPlusPlus.Conf
  • Class TriangleCount
  • Package org.apache.spark.graphx.util
  • Class BytecodeUtils
  • Class GraphGenerators
  • Graphx Example 1
  • Graphx Example 2
  • Graphx Example 3
  • Spark Graphx Describes Organization Chart Easy and Fast
  • Page Rank with Apache Spark Graphx
  • bulk synchronous parallel with Google Pregel Graphx Implementation Use Cases
  • Tree and Graph Traversal with and without Spark Graphx
  • Graphx Graph Traversal with Pregel Explained
  • Spark Machine Learning
  • Binary Classification
  • Multiclass Classification
  • Regression
  • Correlation
  • Image Data Source
  • ML DataFrame is SQL DataFrame
  • ML Transformer
  • ML Estimator
  • ML Pipeline
  • Transformer/Estimator Parameters
  • Extracting, transforming and selecting features
  • TF-IDF
  • Word2Vec
  • FeatureHasher
  • Tokenizer
  • CountVectorizer
  • StopWordRemover
  • n-gram
  • Binarizer
  • PCA
  • PolynomialExpansion
  • StringIndexer
  • Discrete Cosine Transform (DCT)
  • One-hot encoding
  • StandardScaler
  • IndexToString
  • VectorIndexer
  • Interaction
  • Normalizer
  • MinMaxScaler
  • MaxAbScaler
  • Bucketizer
  • ElementwiseProduct
  • SQLTransformer
  • VectorAssembler
  • VectorSizeHint
  • QuantileDiscretizer
  • Imputer
  • VectorSlicer
  • RFormula
  • ChiSqSelector
  • Locality Sensitive Hashing
  • MinHash for Jaccard Distance
  • Classification and Regression
  • LogisticRegression
  • OneVsRest
  • Naive Bayes classifiers
  • Decision trees
  • Random forests
  • Gradient-boosted trees (GBTs)
  • Multilayer perceptron classifier
  • Linear Support Vector Machine
  • Linear Regression
  • Generalized linear regression
  • Isotonic regression
  • Decision Tree Regression
  • Random Forest Regression
  • Gradient-boosted tree regression
  • Survival regression
  • Clustering
  • k-means
  • Latent Dirichlet allocation or LDA
  • Bisecting k-means
  • A Gaussian Mixture Model
  • Collaborative filtering
  • Frequent Pattern Mining
  • FP-Growth
  • PrefixSpan
  • ML Tuning: model selection and hyperparameter tuning
  • Model selection (a.k.a. hyperparameter tuning)
  • Cross-Validation
  • Train-Validation Split
  • Spark Machine Learning Applications
  • Apache Spark SQL & Machine Learning on Genetic Variant Classifications
  • Data Visualization with Vegas Viz and Scala with Spark ML
  • Apache Spark Machine Learning with Dremio Data Lake Engine
  • Dremio Data Lake Engine Apache Arrow Flight Connector with Spark Machine Learning
  • Neural Network with Apache Spark Machine Learning Multilayer Perceptron Classifier
  • Setup TensorFlow, Keras, Theano, Pytorch/torchvision on the CentOS VM
  • Virus Xray Image Classification with Tensorflow Keras Python and Apache Spark Scala
  • Appendix -- Video Presentations
  • References
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Enabling for Conversion to/from Pandas in Python

Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call toPandas() and when creating a Spark DataFrame from a Pandas DataFrame with createDataFrame(pandas_df). To use Arrow when executing these calls, users need to first set the Spark configuration spark.sql.execution.arrow.enabled to true. This is disabled by default.

In addition, optimizations enabled by

spark.sql.execution.arrow.enabled 

could fallback automatically to non-Arrow optimization implementation if an error occurs before the actual computation within Spark. This can be controlled by

spark.sql.execution.arrow.fallback.enabled.

Example Python code

import findspark
findspark.init()
import pandas as pd

from pyspark.sql.functions import col, pandas_udf
from pyspark.sql.types import LongType
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# Declare the function and create the UDF
def multiply_func(a, b):
    return a * b

multiply = pandas_udf(multiply_func, returnType=LongType())

# The function for a pandas_udf should be able to execute with local Pandas data
x = pd.Series([1, 2, 3])
print(multiply_func(x, x))
# 0    1
# 1    4
# 2    9
# dtype: int64

# Create a Spark DataFrame, 'spark' is an existing SparkSession

df = spark.createDataFrame(pd.DataFrame(x, columns=["x"]))

# Execute function as a Spark vectorized UDF
df.select(col("x")*col("x")).show()

'''
0    1
1    4
2    9
dtype: int64
+-------+
|(x * x)|
+-------+
|      1|
|      4|
|      9|
+-------+
'''

Some issue:

import numpy as np
import pandas as pd

# Enable Arrow-based columnar data transfers
spark.conf.set("spark.sql.execution.arrow.enabled", "true")

# Generate a Pandas DataFrame
pdf = pd.DataFrame(np.random.rand(100, 3))

# Create a Spark DataFrame from a Pandas DataFrame using Arrow
df = spark.createDataFrame(pdf)

# Convert the Spark DataFrame back to a Pandas DataFrame using Arrow
result_pdf = df.select("*").toPandas()

here is the error when running creaeDataframe from pandas dataframe, when spark.sql.execution.arrow.enabled is true

/home/dv6/spark/spark/python/pyspark/sql/session.py:714: UserWarning: createDataFrame attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true; however, failed by the reason below:
  An error occurred while calling z:org.apache.spark.sql.api.python.PythonSQLUtils.readArrowStreamFromFile.
: java.lang.IllegalArgumentException
	at java.nio.ByteBuffer.allocate(ByteBuffer.java:334)
	at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$$anon$3.readNextBatch(ArrowConverters.scala:243)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$$anon$3.<init>(ArrowConverters.scala:229)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$.getBatchesFromStream(ArrowConverters.scala:228)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$$anonfun$readArrowStreamFromFile$2.apply(ArrowConverters.scala:216)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$$anonfun$readArrowStreamFromFile$2.apply(ArrowConverters.scala:214)
	at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2543)
	at org.apache.spark.sql.execution.arrow.ArrowConverters$.readArrowStreamFromFile(ArrowConverters.scala:214)
	at org.apache.spark.sql.api.python.PythonSQLUtils$.readArrowStreamFromFile(PythonSQLUtils.scala:46)
	at org.apache.spark.sql.api.python.PythonSQLUtils.readArrowStreamFromFile(PythonSQLUtils.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)

Attempting non-optimization as 'spark.sql.execution.arrow.fallback.enabled' is set to true.
  warnings.warn(msg)

Work around, set OS environment variable

export ARROW_PRE_0_15_IPC_FORMAT=1

Then run Python code

(spark) dv6@dv6:~$ export ARROW_PRE_0_15_IPC_FORMAT=1
(spark) dv6@dv6:~$ python
Python 3.6.10 |Anaconda, Inc.| (default, Jan  7 2020, 21:14:29)
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import findspark
>>> findspark.init()
>>> import pandas as pd
>>>
>>> from pyspark.sql.functions import col, pandas_udf
>>> from pyspark.sql.types import LongType
>>> from pyspark.sql import SparkSession
>>>
>>> spark = SparkSession.builder.getOrCreate()
20/04/12 12:29:18 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/04/12 12:29:20 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
20/04/12 12:29:20 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.
20/04/12 12:29:20 WARN Utils: Service 'SparkUI' could not bind on port 4042. Attempting port 4043.
20/04/12 12:29:20 WARN Utils: Service 'SparkUI' could not bind on port 4043. Attempting port 4044.
>>> import numpy as np
>>> import pandas as pd
>>>
>>> # Enable Arrow-based columnar data transfers
... spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>>
>>> # Generate a Pandas DataFrame
... pdf = pd.DataFrame(np.random.rand(100, 3))
>>> pdf
           0         1         2
0   0.937892  0.387147  0.590136
1   0.007276  0.961907  0.156945
2   0.212474  0.048127  0.936995
3   0.074513  0.579899  0.803862
4   0.324786  0.352669  0.602877
..       ...       ...       ...
95  0.164290  0.376453  0.388663
96  0.014815  0.709746  0.615609
97  0.797867  0.563372  0.132668
98  0.755495  0.589192  0.793425
99  0.505420  0.672960  0.452064

[100 rows x 3 columns]
>>> df = spark.createDataFrame(pdf)
>>> # Convert the Spark DataFrame back to a Pandas DataFrame using Arrow
... result_pdf = df.select("*").toPandas()
>>>

PreviousIssue might happen import PyArrowNextConnect to any data source the same consistent way

Last updated 5 years ago

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