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Pyspark_read_write_stream.py
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Pyspark_read_write_stream.py
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# Last amended: 15th Dec, 2021
# Ref: https://spark.apache.org/docs/latest/streaming-programming-guide.html#a-quick-example
# Ref: https://github.com/apache/spark/blob/master/docs/structured-streaming-programming-guide.md
# API ref: http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html
# Structured Spark streaming and aggregation
#
# Objective:
# 1. Analyse data streaming into spark from files
# Use Structured spark streaming
# Usage Steps:
# Step 1. Start hadoop
# Step 2. On one terminal, first run the filegenerator program as:
# (copy and paste). Leave this terminal open.
# Sample of data generated is:
# Kor;38
# Eth;65
# Bur;39
"""
# First, install 'Faker', if not installed: pip install Faker
rm -f $HOME/Documents/streamresults.txt
cd $HOME/Documents/spark/3.streaming/2.streamfromfiles
chmod +x file_gen.sh
./file_gen.sh
"""
#
# Step3. Next, open another terminal and type:
# This step also stores file in hadoop. But not sure where.
"""
cd ~
spark-submit $HOME/Documents/spark/3.streaming/2.streamfromfiles/1.stream_data_fromfiles1.py
"""
# OR, Better still
# Step3. Direct output to a file
"""
cd ~
export resultfile=$HOME/Documents/streamresults.txt
rm -f $resultfile
spark-submit $HOME/Documents/spark/3.streaming/2.streamfromfiles/1.stream_data_fromfiles1.py > $resultfile
"""
# ELSE,
# run on pyspark (py_spark: see .bashrc)
# from para #2.1 onwards
#
# Then examine on terminal,
# file 'streamresults.txt' and you will find counts
# The following bash-script outputs contents of file
# periodically on console
"""
while true; do
echo "Next reading of file..........."
echo "==================== "
echo " "
sleep 2
cat $HOME/Documents/streamresults.txt
sleep 3
done
hdfs dfs -ls /user/ashok/data_files/fake
"""
### Call libraries
# 1.0 Library to generate Spark context
# whether local or on hadoop
from pyspark.context import SparkContext
# 1.1 Library to generate SparkSession
from pyspark.sql.session import SparkSession
# 1.2 Some type to define data schema
from pyspark.sql.types import StructType
# 2.0 Create spark context and session:
sc = SparkContext('local')
spark = SparkSession(sc)
# 2.1 Where will be my csv files which spark will analyse
# Path should be on hadoop
datafilesPath = 'hdfs://localhost:9000/user/ashok/data_files/fake'
# 3.0 Define CSV file structure:
"""
There are many ways to define a StructType()
One way is to use add() method. There are
many add methods(). See the reference, below:
https://spark.apache.org/docs/1.5.0/api/java/org/apache/spark/sql/types/StructType.html#add(java.lang.String,%20org.apache.spark.sql.types.DataType)
One add() method is:
StructType() \
.add("a", "int") \
.add("b", "long") \
.add("c", "string")
Another add() as:
StructType() \
.add("a", "int", true) \
.add("b", "long", false ) \
.add("c", "string", true)
Another add() as:
StructType() .add("a", IntegerType, true) .add("b", LongType, false) .add("c", StringType, true)
And there are others also.
"""
# 3.0 Define data schema in either of the following ways.
# Schema definition is MUST. inferSchema will not work.
## 3.1
from pyspark.sql.types import StructType
# 3.1.1
userSchema = StructType() \
.add("name", "string") \
.add("balance", "integer")
# # 3.2.1 OR, better, as:
# Note just using StringType instead of StringType()
# will give error. Last 'boolean' refers to nullable
# or not.
from pyspark.sql.types import StructType, IntegerType, StringType
# 3.2.2
userSchema = StructType() \
.add("name", StringType(), True) \
.add("balance",IntegerType(),True)
# 4.0
# Stream files from folder /home/ashok/Documents/spark/data.
# New content must be added to new files.
# Ref: https://stackoverflow.com/questions/45086501/how-to-process-new-records-only-from-file
## Micro-batch trigger time?
# Trigger time for micro-batches can be specified. There are a number of options
# See here: https://stackoverflow.com/questions/57760563/how-to-specify-batch-interval-in-spark-structured-streaming
# if you do not set a batch interval, Spark will look for data as soon as it has written last batch.
# Trigger option is available with writeStream() method.
csvDF = spark \
.readStream \
.option("sep", ";") \
.schema(userSchema) \
.csv(datafilesPath)
"""
Another way to read stream:
Ref: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader
csvDF = spark.readStream.csv(
datafilesPath,
schema = userSchema,
sep = ",",
inferSchema=False
)
"""
# 4.1 Print data schema
csvDF.printSchema();
# 5.0 Perform some selection and aggregation using dataframe csvDF
# Multiple streaming aggregations are not supported
abc = csvDF \
.select("name", "balance") \
.where("balance > 40")
abc = abc \
.groupby("name") \
.agg({ 'balance' : 'avg' , '*' : 'count' })
# 5.1 Print result to console. 'append' mode also exists but is not supported when
# there are aggregations. Output is sent to console but
# directed to a file.
result = abc.writeStream \
.format("memory") \
.outputMode("complete") \
.format("console") \
.start()
# 5.2
result.awaitTermination()
##################################################################