Trending March 2024 # Spotify Recommendation System Using Pyspark And Kafka Streaming # Suggested April 2024 # Top 4 Popular

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Introduction

We all love listening to our favorite music every day. It is pretty hard to find songs similar to our taste and we would love a system to do this for us. We have music applications like Spotify that uses content-based and collaborative filtering to recommend us songs similar to what we like. In this article, I would like to show you how to implement a content-based music recommendation system, that takes songs from our liked playlist and recommend similar songs from a streaming data source. To carry out this process we use Kafka to stream the data, pyspark data frame, and Spark SQL to carry out the spark operations, and streamlit to visualize everything. We also use MLlib for KMeans and PCA analysis.

Streaming data using Kafka

Initially, we have a CSV file that contains all our song’s data. It has a name, artist, and features associated with the song. I had taken a dataset that has over 5 lakhs songs details which are available on Spotify. The dataset is available here:

Use this CSV and put it in the same directory where you will be having the Kafka producer code to be run. Now use the following code to get the CSV data to the stream. This is the Kafka producer code where you can stream the data as it is sent to a topic we specify. In consumer code, we can retrieve this data with the topic.

import pandas as pd from kafka import KafkaProducer from datetime import datetime import time import random import numpy as np # pip install kafka-python KAFKA_TOPIC_NAME_CONS = "songTopic" KAFKA_BOOTSTRAP_SERVERS_CONS = 'localhost:9092' if __name__ == "__main__": print("Kafka Producer Application Started ... ") kafka_producer_obj = KafkaProducer(bootstrap_servers=KAFKA_BOOTSTRAP_SERVERS_CONS, value_serializer=lambda x: x.encode('utf-8')) filepath = "tracks.csv" # This is the csv which has Spotify data. songs_df = pd.read_csv(filepath) # We use this filter to get popular songs streaming. This can be tuned based on your intrest. songs_df['order_id'] = np.arange(len(songs_df)) songs_df['artists'] = songs_df['artists'].str.replace('[^a-zA-Z]', '') songs_df['id_artists'] = songs_df['id_artists'].str.replace('[^a-zA-Z]', '') # Some pre-processing performed for clean data. song_list = songs_df.to_dict(orient="records") message_list = [] message = None for message in song_list: message_fields_value_list = [] message_fields_value_list.append(message["order_id"]) message_fields_value_list.append(message["id"]) message_fields_value_list.append(message["name"]) message_fields_value_list.append(message["popularity"]) message_fields_value_list.append(message["duration_ms"]) message_fields_value_list.append(message["explicit"]) message_fields_value_list.append(message["artists"]) message_fields_value_list.append(message["id_artists"]) message_fields_value_list.append(message["release_date"]) message_fields_value_list.append(message["danceability"]) message_fields_value_list.append(message["energy"]) message_fields_value_list.append(message["key"]) message_fields_value_list.append(message["loudness"]) message_fields_value_list.append(message["mode"]) message_fields_value_list.append(message["speechiness"]) message_fields_value_list.append(message["acousticness"]) message_fields_value_list.append(message["instrumentalness"]) message_fields_value_list.append(message["liveness"]) message_fields_value_list.append(message["valence"]) message_fields_value_list.append(message["tempo"]) message_fields_value_list.append(message["time_signature"]) message = ','.join(str(v) for v in message_fields_value_list) print("Message Type: ", type(message)) print("Message: ", message) kafka_producer_obj.send(KAFKA_TOPIC_NAME_CONS, message) time.sleep(1) print("Kafka Producer Application Completed. ")

This is the code for the producer and we stream data after running this code. You can see an output like this if the code runs properly on your IDE:

Message: 0,3BFRqZFLSrqtQr6cjHbAxU,Ain’t Misbehavin’,51,237773,0,FatsWaller,DYWCXTkNqGFZIfSrWEa,1926,0.515,0.222,0,-16.918,0,0.0575,0.821,0.00193,0.19,0.35,98.358,4 Message: 1,61znp1Iy11bdJ2YAbwaqw7,Sing, Sing, Sing,51,520133,0,BennyGoodman,pBuKaLHJlIlqYxQQaflve,1928,0.626,0.744,2,-9.189,0,0.0662,0.847,0.892,0.145,0.259,113.117,4 Message: 2,0RNxWy0PC3AyH4ThH3aGK6,Mack the Knife,55,201467,0,LouisArmstrong,eLuQmkaCobbVDHceek,1929,0.673,0.377,0,-14.141,1,0.0697,0.586,0.0,0.332,0.713,88.973,4 # So on…

  Pyspark consumer for streaming data

So, we saw how the data is streamed using Kafka. We had used “songTopic” as the topic name. In the consumer code, we use this same topic name to retrieve the data which is streamed from the producer.

from chúng tôi import SparkSession from pyspark.sql.functions import * from pyspark.ml.feature import Normalizer, StandardScaler import random import time kafka_topic_name = "songTopic" kafka_bootstrap_servers = 'localhost:9092' spark = SparkSession .builder .appName("Spotify Streaming Reccomendation System") .master("local[*]") .getOrCreate() spark.sparkContext.setLogLevel("ERROR")

Here, we have built a basic spark session and also initialized the topic from which we retrieve the streaming data.

songs_df = spark .readStream .format(“kafka”) .option(“kafka.bootstrap.servers”, kafka_bootstrap_servers) .option(“subscribe”, kafka_topic_name) .option(“startingOffsets”, “latest”) .load() songs_df1 = songs_df.selectExpr(“CAST(value AS STRING)”, “timestamp”) songs_schema_string = “order_id INT,id STRING, name STRING,popularity INT, duration_ms DOUBLE, explicit INT, ” + “artists STRING, id_artists STRING, release_date STRING, ” + “danceability DOUBLE,” + “energy DOUBLE, key INT, loudness DOUBLE, ” + “mode INT,” + “speechiness DOUBLE,” + “acousticness DOUBLE, instrumentalness DOUBLE, liveness DOUBLE, ” + “valence DOUBLE, tempo DOUBLE, time_signature DOUBLE” songs_df2 = songs_df1 .select(from_csv(col(“value”), songs_schema_string) .alias(“song”), “timestamp”) songs_df3 = songs_df2.select(“song.*”, “timestamp”)

Spark SQL View

Next, we create the schema for our streaming data. We create it in such a way that it matches the data coming from our producer. The schema is created with Spark SQL and we finally add a timestamp to each row as data arrives.

songs_df3.createOrReplaceTempView("song_find"); song_find_text = spark.sql("SELECT * FROM song_find") songs_agg_write_stream = song_find_text .writeStream .trigger(processingTime='5 seconds') .outputMode("append") .option("truncate", "false") .format("memory") .queryName("testedTable5") .start() songs_agg_write_stream.awaitTermination(1)

Finally, we create an SQL View so that the data streaming can be put into a View and written to memory. We use a processing time of 5 seconds in append mode to get all the data incoming from the producer.

Favorite song data generated using Spotify API import pandas as pd from spotify_api import getSong song_data = getSong.passs() #song_data.rename(columns={'duration_s': 'duration_ms' }, inplace=True) song_data = song_data.drop(['id', 'added_at', 'time_signature','duration_s'], axis='columns') rand_n = random. randint(0,len(song_data)-1) add_df = song_data.head(rand_n)[-1:]

This code will help us retrieve a random song from our Spotify liked songs playlist. Now, this is an abstraction and the real code is being implemented on a different python file. So, feel free to add the following python file :

#!/usr/bin/env python # coding: utf-8 import os #import my_spotify_credentials as credentials import numpy as np import pandas as pd import ujson import spotipy import spotipy.util import seaborn as sns # fill your credentials here. os.environ["SPOTIPY_CLIENT_ID"] = '' os.environ["SPOTIPY_CLIENT_SECRET"] = '' os.environ["SPOTIPY_REDIRECT_URI"] = '' scope = 'user-library-read' username = '' token = spotipy.util.prompt_for_user_token(username, scope) if token: spotipy_obj = spotipy.Spotify(auth=token) saved_tracks_resp = spotipy_obj.current_user_saved_tracks(limit=50) else: print('Couldn't get token for that username') number_of_tracks = saved_tracks_resp['total'] print('%d tracks' % number_of_tracks) def save_only_some_fields(track_response): return { 'id': str(track_response['track']['id']), 'name': str(track_response['track']['name']), 'artists': [artist['name'] for artist in track_response['track']['artists']], 'duration_ms': track_response['track']['duration_ms'], 'popularity': track_response['track']['popularity'], 'added_at': track_response['added_at'] } tracks = [save_only_some_fields(track) for track in saved_tracks_resp['items']] while saved_tracks_resp['next']: saved_tracks_resp = spotipy_obj.next(saved_tracks_resp) tracks.extend([save_only_some_fields(track) for track in saved_tracks_resp['items']]) tracks_df = pd.DataFrame(tracks) pd.set_option('display.max_rows', len(tracks)) tracks_df['artists'] = tracks_df['artists'].apply(lambda artists: artists[0]) tracks_df['duration_ms'] = tracks_df['duration_ms'].apply(lambda duration: duration/1000) tracks_df = tracks_df.rename(columns = {'duration_ms':'duration_s'}) audio_features = {} for idd in tracks_df['id'].tolist(): audio_features[idd] = spotipy_obj.audio_features(idd)[0] tracks_df['acousticness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['acousticness']) tracks_df['speechiness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['speechiness']) tracks_df['key'] = tracks_df['id'].apply(lambda idd: str(audio_features[idd]['key'])) tracks_df['liveness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['liveness']) tracks_df['instrumentalness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['instrumentalness']) tracks_df['energy'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['energy']) tracks_df['tempo'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['tempo']) tracks_df['time_signature'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['time_signature']) tracks_df['loudness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['loudness']) tracks_df['danceability'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['danceability']) tracks_df['valence'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['valence']) class getSong(): def __init__(self): super(getSong, self).__init__() def passs(): return tracks_df

Using this we can now get a random favorite song from the Spotify playlist.

Feature Engineering df = spark.sql("SELECT * FROM testedTable5") df = df.sort(df.release_date.desc()) df_stream = df df = df.drop('order_id', 'id', 'explicit', 'mode', 'release_date', 'id_artists', 'time_signature', 'duration_ms', 'timestamp') df_sp = spark.createDataFrame(add_df) df = df.union(df_sp) from pyspark.ml.feature import VectorAssembler assembler=VectorAssembler(inputCols=[ 'danceability', 'energy', 'loudness', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo'], outputCol='features') assembled_data=assembler.setHandleInvalid("skip").transform(df)

We initially drop the unwanted columns from our spark data frame. We append our favorite song data to this data frame using the sparks union operation. It is important for us the get all features to a ‘features column’ for which we use VectorAssembler from pyspark.ml.feature library. The assembled_data here is a data frame that has the feature vector attached with all other columns.

from pyspark.ml.feature import StandardScaler scale=StandardScaler(inputCol='features',outputCol='standardized') data_scale=scale.fit(assembled_data) df=data_scale.transform(assembled_data)

We use a standard scaler to scale the features column we generated earlier. So that this scaled column can be further used to perform K-Means clustering.

K-Means Clustering from pyspark.ml.clustering import KMeans from pyspark.ml.evaluation import ClusteringEvaluator silhouette_score=[] evaluator = ClusteringEvaluator(predictionCol='prediction', featuresCol='standardized', metricName='silhouette', distanceMeasure='squaredEuclidean') KMeans_algo=KMeans(featuresCol='standardized', k=3) KMeans_fit=KMeans_algo.fit(df) output_df =KMeans_fit.transform(df)

Now, this is a step that we can skip if we want. But when we have very large data incoming it is always better that we perform K-Means so that we can cluster data and then we can use the recommendation on this clustered dataset based on what cluster our favorite song falls. We use MLlib to perform clustering. We can also perform PCA Analysis using MLlib and we had found that almost all numerical features we have used take up to 90% variance. So all features are being included here.

Recommendation system code import numpy as np, pandas as pd import matplotlib.pyplot as plt, seaborn as sns from tqdm import tqdm import warnings warnings.filterwarnings("ignore") class SpotifyRecommender(): def __init__(self, rec_data): self.rec_data_ = rec_data def spotify_recommendations(self, song_name, amount=1): distances = [] song = self.rec_data_[(self.rec_data_.name.str.lower() == song_name.lower())].head(1).values[0] # get details of our fav song from name we pass as x earlier. res_data = self.rec_data_[self.rec_data_.name.str.lower() != song_name.lower()] #dropping the data with our fav song so that it doesnt affect our recommendation. for r_song in tqdm(res_data.values): # tqdm is just used for showing the bar of iteration through our streamed songs. dist = 0 for col in np.arange(len(res_data.columns)): #indeces of non-numerical columns neednt be considered. if not col in [0,1,13]: #calculating the manhettan distances for each numerical feature dist = dist + np.absolute(float(song[col]) - float(r_song[col])) distances.append(dist) # distances are calculated and appended and added to a new column called distances in our dataset. res_data['distance'] = distances #sorting our data to be ascending by 'distance' feature res_data = res_data.sort_values('distance') # resulting dataset have the song similar to our fav song's numerical values and thus recommended. columns = ['name', 'artists', 'acousticness', 'liveness', 'instrumentalness', 'energy', 'danceability', 'valence'] return res_data[columns][:amount] datad = output_df.select('name', 'artists', 'danceability', 'energy', 'key', 'loudness', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'prediction') datf = datad.toPandas() datf.drop(datf[datf['artists'] == '0'].index, inplace = True) datf.drop_duplicates(inplace=True) datf.drop(datf[datf['danceability'] == 0.0000].index, inplace = True) datf.drop(datf[datf['liveness'] == 0.000].index, inplace = True) datf.drop(datf[datf['instrumentalness'] == 0.000000].index, inplace = True) datf.drop(datf[datf['energy'] == 0.0000].index, inplace = True) datf.drop(datf[datf['danceability'] == 0.000].index, inplace = True) datf.drop(datf[datf['valence'] == 0.000].index, inplace = True) y = datf value_pred = datf.iloc[-1:]['prediction'] #datf = datf[datf['prediction'] == list(value_pred)[0]] recommender = SpotifyRecommender(datf) x = add_df['name'].tolist()[0] rec_song = recommender.spotify_recommendations(x, 10) v = add_df[['name', 'artists', 'acousticness', 'liveness', 'instrumentalness', 'energy', 'danceability', 'valence']] rec_song = pd.concat([rec_song, v]) rec_song.to_csv('rec_song.csv') df_rec = spark.createDataFrame(rec_song) df_rec.show()

| name| artists|acousticness|liveness|instrumentalness|energy|danceability|valence| | Tennessee Whiskey| ChrisStapleton| 0.205| 0.0821| 0.0096| 0.37| 0.392| 0.512| | Element| PopSmoke| 0.0301| 0.251| 2.18E-6| 0.878| 0.772| 0.305| | River| BishopBriggs| 0.302| 0.0579| 2.97E-6| 0.477| 0.733| 0.545| | Edelweiss|BillLeeCharmianCarr| 0.785| 0.126| 4.64E-4| 0.156| 0.233| 0.354| | Cradles| SubUrban| 0.27| 0.179| 6.48E-5| 0.585| 0.581| 0.63| |Make You Feel My …| Adele| 0.907| 0.105| 3.83E-4| 0.172| 0.317| 0.0963| | Lover| TaylorSwift| 0.492| 0.118| 1.58E-5| 0.543| 0.359| 0.453| | SAD!| XXXTENTACION| 0.258| 0.123| 0.00372| 0.613| 0.74| 0.473| |I Got It Bad And …| OscarPetersonTrio| 0.971| 0.0882| 0.911|0.0527| 0.488| 0.193| | Sweet Caroline| NeilDiamond| 0.611| 0.237| 1.09E-4| 0.127| 0.529| 0.578| | Naina – Lofi Flip| Mrunal Meena| 0.72| 0.299| 0.897| 0.258| 0.641| 0.321|

This is the final recommendation based on the song we provide. As you can see to check how good it is I passed “Naina-Lofi” as my favorite song as it’s Hindi music and slow song but most of the songs that were recommended were slow music with the same kind of attributes. To visualize everything we can use streamlit.

Visualization using Streamlit

Just run streamlit using this command:

streamlit run dashboard.py

The code for the dashboard is here:

import pandas as pd import numpy as np import streamlit as st import plotly.graph_objects as go import plotly.express as px import os # import my_spotify_credentials as credentials import numpy as np import pandas as pd import ujson import spotipy import spotipy.util import seaborn as sns # fill credentials here. os.environ["SPOTIPY_CLIENT_ID"] = '' os.environ["SPOTIPY_CLIENT_SECRET"] = '' os.environ["SPOTIPY_REDIRECT_URI"] = '' scope = 'user-library-read' username = '' token = spotipy.util.prompt_for_user_token(username, scope) if token: spotipy_obj = spotipy.Spotify(auth=token) saved_tracks_resp = spotipy_obj.current_user_saved_tracks(limit=50) else: print('Couldn't get token for that username') number_of_tracks = saved_tracks_resp['total'] print('%d tracks' % number_of_tracks) def save_only_some_fields(track_response): return { 'id': str(track_response['track']['id']), 'name': str(track_response['track']['name']), 'artists': [artist['name'] for artist in track_response['track']['artists']], 'duration_ms': track_response['track']['duration_ms'], 'popularity': track_response['track']['popularity'], 'added_at': track_response['added_at'] } tracks = [save_only_some_fields(track) for track in saved_tracks_resp['items']] while saved_tracks_resp['next']: saved_tracks_resp = spotipy_obj.next(saved_tracks_resp) tracks.extend([save_only_some_fields(track) for track in saved_tracks_resp['items']]) tracks_df = pd.DataFrame(tracks) pd.set_option('display.max_rows', len(tracks)) #pd.reset_option('display.max_rows') tracks_df['artists'] = tracks_df['artists'].apply(lambda artists: artists[0]) tracks_df['duration_ms'] = tracks_df['duration_ms'].apply(lambda duration: duration/1000) tracks_df = tracks_df.rename(columns = {'duration_ms':'duration_s'}) audio_features = {} for idd in tracks_df['id'].tolist(): audio_features[idd] = spotipy_obj.audio_features(idd)[0] tracks_df['acousticness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['acousticness']) tracks_df['speechiness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['speechiness']) tracks_df['key'] = tracks_df['id'].apply(lambda idd: str(audio_features[idd]['key'])) tracks_df['liveness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['liveness']) tracks_df['instrumentalness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['instrumentalness']) tracks_df['energy'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['energy']) tracks_df['tempo'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['tempo']) tracks_df['time_signature'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['time_signature']) tracks_df['loudness'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['loudness']) tracks_df['danceability'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['danceability']) tracks_df['valence'] = tracks_df['id'].apply(lambda idd: audio_features[idd]['valence']) df = tracks_df st.set_page_config(layout="wide") hide_streamlit_style = """ footer {visibility: hidden;} """ st.markdown(hide_streamlit_style, unsafe_allow_html=True) st.title('Spotify User Dashboard') col1, col2 = st.beta_columns(2) # col1.header("Your Latest added Song") top_5_songs = df[['name', 'artists']].head(5) col1.table(top_5_songs) # col2.header("Your Top 10 Artists") df1 = df['artists'].value_counts()[:11].to_frame() df1['Name'] = df1.index df1.rename(columns={'artists': 'Songs'}, inplace=True) fig = px.pie(df1, values='Songs', names='Name', hole=0.2) fig.update_traces(textposition='inside', textinfo='label') col2.plotly_chart(fig, use_container_width=True) #### col3, col4, col5 = st.beta_columns(3) # ur_favourite_artist = df[['artists']].value_counts().index[0][0] st.markdown(""" .big-font { font-size:30px !important; font-Weight: bold; } """, unsafe_allow_html=True) col3.header("Your Favourite Artist") # col4.header("Total Time of Songs") time = round(df.duration_s.sum() / 3600, 2) # col5.header("Total Number of Songs") # #### col6,col7 = st.beta_columns(2) col6.header("Your Recommended Songs") df2 = rec_df[['name','artists']] print(df2) col6.table(df2.head(10)) # col7.header("Features of your Latest Songs") df3 = rec_df.loc[:10, ['name', 'artists', 'acousticness', 'liveness', 'instrumentalness', 'energy', 'danceability', 'valence']] df3 = df3.T.reset_index() df3.rename(columns={'index': 'theta', 0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four',5:'five',6:'six',7:'seven',8:'eight',9:'nine',10:'ten',11:'eleven',12:'twelve'}, inplace=True) df3_cols = df3.columns[1:] len_cols = len(df3_cols) categories = df3['theta'].tolist()[2:] fig1 = go.Figure() for i in range(0, len_cols): fig1.add_trace(go.Scatterpolar( r=df3[df3_cols[i]][2:].tolist(), theta=categories, fill='toself', name=df3[df3_cols[i]][0])) fig1.update_layout( polar=dict( radialaxis=dict( visible=True, range=[0, 1] )), showlegend=True ) col7.plotly_chart(fig1, use_container_width=True)

Final Output   References

 

Conclusion

In any case, you need help regarding setting up pyspark and Kafka on your Windows machine feel free to contact me at [email protected] or on my LinkedIn page:

The entire code above can be followed on this GitHub repo:

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

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Working And Examples Of Pyspark Collect

Introduction to PySpark collect

PYSPARK COLLECT is an action in PySpark that is used to retrieve all the elements from the nodes of the Data Frame to the driver node. It is an operation that is used to fetch data from RDD/ Data Frame. The operation involves data that fetches the data and gets it back to the driver node.

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The collect operation returns the data as an Array of Row Types to the driver; the result is collected and further displayed for PySpark operation. The data, once is available on the node, can be used in the loops and displayed. The collect operation is widely used for smaller Data Set the data which can be fit upon memory or post that can cause some certain memory exception too. Let’s check the Collect operation in detail and try to understand the functionality for the same.

The syntax for the COLLECT function is:-

cd = spark.sparkContext.parallelize(data1) cd.collect()

explanation:

Cd:- The RDD made from the Data

.collect () :- The function used for Collecting the RDD.

Screenshot:

Working of Collect in Pyspark

Let us see somehow the COLLECT operation works in PySpark:-

Collect is an action that returns all the elements of the dataset, RDD of a PySpark, to the driver program. It is basically used to collect the data from the various node to the driver program that is further returned to the user for analysis.

Retrieving the huge data set can sometimes cause an out-of-memory issue over data collection.

This is a network movement action call where all the elements from the different nodes are sent to the driver memory where the data is collected, so the data movement is much over the collect operation. Since it is an action call of PySpark so every time it is called, all the transformations are done prior to implementing its action.

It retrieves the element in the form of Array [Row] to the driver program.

Let’s check the creation and usage with some coding examples.

Example of PySpark collect

Let us see some Example of how the PYSPARK  COLLECT operation works:-

Let’s start by creating simple data in PySpark.

data1  = [{'Name':'Jhon','ID':2,'Add':'USA'},{'Name':'Joe','ID':3,'Add':'USA'},{'Name':'Tina','ID':2,'Add':'IND'},{'Name':'Jhon','ID':2, 'Add':'USA'},{'Name':'Joe','ID':5,'Add':'INA'}]

A sample data is created with Name, ID, and ADD as the field.

a = sc.parallelize(data1)

RDD is created using sc. parallelize.

b = spark.createDataFrame(a) b.show()

Screenshot:

Now let us try to collect the elements from the RDD.

a=sc.parallelize(data1) a.collect()

This collects all the data back to the driver node, and the result is then displayed as a result at the console.

Screenshot:

a.collect()[0] a.collect()[1] a.collect()[2]

The above code shows that we can also select a selected number of the column from an RDD/Data Frame using collect with index. The index is used to retrieve elements from it.

Screenshot:

Let’s try to understand this with more Example:-

data3 = sc.parallelize(data2) data2 = [1,2,3,4,5,6,7,8,9,10] data3 = sc.parallelize(data2) data3.collect()

This is a very simple way to understand more about collect where we have made a simple RDD of type Int. Post collecting, we can get the data back to driver memory as a result. All the data Frames are called back to the driver, and the result is displayed back. Once the data is available, we can use the data back for our purpose, data analysis and data modeling.

Screenshot:-

These are some of the Examples of PYSPARK ROW Function in PySpark.

Note:-

COLLECT is an action in PySpark.

COLLECT collects the data back to the driver node.

PySpark COLLECT returns the type as Array[Row].

COLLECT can return data back to memory so that excess data collection can cause Memory issues.

PySpark COLLECT causes the movement of data over the network and brings it back to the driver memory.

COLLECTASLIST() is used to collect the same but the result as List.

Conclusion

From the above article, we saw the use of collect Operation in PySpark. We tried to understand how the COLLECT method works in PySpark and what is used at the programming level from various examples and classification.

Recommended Articles

This is a guide to the PySpark collect. Here we discuss the use of collect Operation in PySpark with various examples and classification. You may also have a look at the following articles to learn more –

Spotify Problems And How To Fix Them

Potential solutions:

Spotify not working could be because of problems with the app itself. Like any streaming service or website, Spotify can have the occasional planned or unplanned service outage. So the issue might not be with your device. You can see if there are widespread reports of Spotify problems by checking sites like Down Detector or Down for Everyone or Just Me. The Spotify Status Twitter account also shares updates when the service is facing any downtime.

Spotify lets users download songs for offline access, but for the most part, using the app requires an active Internet connection. Check your network connectivity to ensure it isn’t causing any problems. A simple test is to open another app or website that requires data to see if it’s loading. We have helpful guides on fixing Wi-Fi problems and what to do if your phone won’t connect to Wi-Fi if you have run into network connectivity problems.

Most of the time, simply shutting down and opening the app or turning your device off and on again helps fix the problem. If it doesn’t, you can try clearing the app or browser cache and cookies on Android or clearing the cache on any platform to see if the problem goes away. If nothing works, it’s best to delete and reinstall the app. Clearing the app storage or uninstalling it will completely reset the app, so you might lose data like saved playlists if it isn’t synced across devices.

Don’t forget to update the app to the latest version since software updates usually bring bug fixes that should help solve some problems.

Potential solutions:

Music streaming doesn’t require particularly fast Internet connections. But fluctuations and unstable connections could cause problems with audio quality. Spotify sets the audio quality to Auto by default and changes it depending on the connection speed. You can set the audio quality to a particular level to circumvent the issue.

Potential solutions:

Potential solutions:

This feature is only available for Premium users, so ensure that your subscription is active. Log in to your Spotify account page and check your subscription details in the Your Plan section.

Spotify lets you download songs on up to five devices, but there is a limit of 10,000 songs. This may seem like a lot, but you might surpass the limit if you download multiple playlists and albums across different devices. You cannot download any more music until you remove the downloaded content.

You may also be unable to download songs if your device doesn’t have enough storage. Spotify recommends having at least 1GB of free storage available and more if you download a lot of songs. Check out our guide on how to free up storage if you run into this problem.

Potential solutions:

First, ensure that you have an active subscription. Go to your Spotify account page and check your subscription details in the Your Plan section. You may have logged in with the wrong account if you see Spotify Free. Remember to use the same login method as when you signed up. So if you did so using Facebook, Google, or Apple ID, log in the same way. If your login credentials are correct, go to Check your payment status to see if a payment is pending or has failed.

If you see an active subscription, Spotify recommends simply logging out and signing back in.

When someone invites you to a Duo or Family plan, the primary account holder has control and may have accidentally revoked your access. Check to ensure that you haven’t been removed from the plan. Spotify will also automatically remove you from the plan if you don’t verify your information, like your address, within seven days of getting the verification email. Remember that if verification fails or you don’t respond within a week, you will not be able to join another Duo or Family plan for 12 months.

Potential solutions:

Potential solutions:

Remember that the device you want to listen to music from has to be connected to the same Wi-Fi network as the control device. Your target device may have dropped off Wi-Fi because of a weak signal or an oversaturated router. Wi-Fi 5 routers, in particular, can have trouble with the dozens of connections in modern homes, in which case it may be necessary to disconnect some products or upgrade to a router with Wi-Fi 6.

When the app detects compatible speakers and other devices, a Devices Available will show at the bottom left on mobile and bottom right on desktop. If the Devices Available icon isn’t there, go to Settings and select Show Available Devices. If you don’t see your target device on this list, ensure it is on and connected to the same Wi-Fi network. Keep in mind that some devices, like Echo speakers, require additional steps.

How To Make Spotify Louder And Sound Better

Spotify is one of the most widely-used music streaming services available, with thousands of artists to listen to and discover. If you’re a music enthusiast who uses Spotify, you should know that there are actually settings you can tweak to get the best audio experience. 

These are settings in Spotify such as boosting bass or treble, selecting audio settings for different types of music, and changing the overall volume level. 

Table of Contents

This article on how to make Spotify louder is written for those using the Spotify app on iOS. You’ll need Spotify Premium to change these settings. 

How to Make Spotify Louder

Once you open Spotify, find the gear symbol in the top right corner, which is where you’ll find your settings. If you scroll down, you’ll see a section labeled Volume. There are three settings: Loud, Normal, or Quiet.

Spotify sets the default volume level to Normal. You can set it to Loud. You may notice that besides the loud option it says “might reduce dynamics.” Dynamics refers to the changes between loud and soft in the music. Adjusting the volume to Loud reduces the dynamics.

However, there isn’t a huge difference in sound quality and, to the untrained ear, the music will simply sound louder than it is when set to Normal. 

Another setting you’ll want to look at is Audio Normalization. This is a feature Spotify has in order to set the volume of all songs the same, so that songs that are louder or softer don’t sound out of place. If you turn this off, you won’t be able to change the Volume level. Songs will play true to the original mix. 

How to Make Your Music Sound Better

In your Spotify Playback settings, you’ll also find an option called Equalizer. Tap this to view the Equalizer bar. The Equalizer adjusts the audio frequency settings for different types of music.

You can set it manually by tapping and dragging on the white dots to alter the audio at that frequency. Or, you can choose from among  multiple presets. These include Bass Booster, Electronic, Hip-Hop, Rock, Pop, and more. You can change these settings while playing a song so that you can hear the differences among the different settings.. 

Other Spotify Settings for Better Playback

There are more options you can change within the playback settings to enhance the audio and make Spotify louder.

Crossfade

This is the rate at which the songs you’re playing fade into each other when one ends and another begins. You can use the slider to change this from 0 seconds to up to 12 seconds. 

You can also turn Gapless Playback on or off, which means there won’t be a gap of silence between songs being played and one will start immediately after another. You can turn on Automix, which changes the transitions between songs if a certain playlist uses it. 

Play Feedback Sounds

If there are any feedback sounds in a song, turning this on keeps them in the song. Whether it’s better to keep it on or off just depends on what kind of music you’re listening to and how you want to hear it. 

Music Quality

You can find the Music Quality settings on the main Spotify settings page under Playback. Here you can change the general quality of your music playback when it is either streamed or downloaded. 

For both streaming and downloaded music, you can choose from Automatic, Low, Normal, High, or Very High. Low quality is at 24 kbit/s, Normal is 96 kbit/s, High is 160 kbit/s, and Very High is at 320 kbit/s. The higher quality setting you choose, the more data or bandwidth you’ll use g. 

5 Best Websites For Streaming Free And Legal Movies

The internet is full of illegal movie streaming sites, so it’s a must to understand where you can go to find completely legal, free movies to stream. Free movie streaming sites are websites that offer new and old movies free of charge, usually for a limited time.

Below are our favorite movie streaming sites that work from a computer or mobile device, and often directly from smart TVs and TV streaming devices, too. You can be confident that the movies you watch through these sites are 100% legal to stream as often as you want.

Table of Contents

Vudu

Vudu has tons of free movies separated into categories like action, crime and suspense, comedy, anime, sci-fi and fantasy, drama, family and kids, documentary, and horror. There are so many free movies here that you’ll find yourself coming back again and again to see what’s new.

at Vudu, you can also rent and purchase movies, even the ones that are free. You might do this so that you can secure the movie as your own in case it leaves the website.

You can watch Vudu’s free movies from your phone, tablet, or computer. You must make a user account in order to watch these movies for free.

Tubi

Everything on Tubi is free to stream, so unlike Vudu, you can watch every video you find. Plus, there are lots of genres, including not only typical ones but interesting categories like Not on Netflix, Only Free on Tubi, Cult Classics, Indie Films, and Martial Arts.

If you’re not sure where to start to find something fun to watch on Tubi, try the Most Popular page to check out what everyone else is watching. You can even jump in right now without having to make a user account, which is great if you’re in need of some movie streaming right away.

The New Releases page and Recently Added page are some other helpful sections of this movie streaming site because you can monitor hot movies and what the site just recently added to its catalog, but don’t forget to occasionally visit their Leaving Soon! page; you don’t want to miss out on these awesome free movies!

Go full screen, adjust the quality, and enable video captions if you want to. Tubi’s videos are always smooth to load both on a computer and through the mobile app.

The Roku Channel

The Roku is usually viewed as a streaming device only, but you can access free movies through their website, too, at The Roku Channel. Just pick a movie, log in to your user account (anyone can make one), and hit Play.

What sets Roku’s streaming movies apart from other legal movie sites is that nearly every movie is high-def. You can be confident that when you watch a movie online through The Roku Channel, you can stream crisp movies and even turn on captions.

There’s a search bar to find movies by their title, or you can browse through sections of the site like featured, new this more. There are even free TV shows and live TV streaming here.

The Roku Channel works from a computer only. There are Roku apps for mobile devices but they don’t support streaming movies on the go.

Sony Crackle

Sony Crackle is a free movie streaming site that you’ll surely love. It’s easy to filter the free movie listing by genres like action, comedy, drama, thriller, or Crackle Original, as well as sort the list by recently added to get the most updated list of free films.

You’ll find tons of older movies at Sony Crackle as well as some newer ones. Every movie page has a full synopsis, the list of cast members, and some other details like the producers and writers.

The free movies at Sony Crackle can be watched in full screen mode and streamed from your computer, tablet, or phone. While don’t need a user account to watch them.

However, you can make a free user account if you want, to save your favorite movies to a “Watch Later” list so you can keep track of what you want to watch at another time.

Popcornflix

The free movies at Popcornflix are categorized in fun sections like Staff Picks, Popcornflix Originals, Old School Cool, and Date Night, but you can browse by regular genres, too, to find standup comedy films, Asian action movies, documentaries, foreign films, drama, action, etc.

Like most websites that offer free streaming movies, you can search for films by title or look through genre pages to find something new to watch. Another way is to open the page of new movies or the list of Popcornflix’s most popular films.

you leave are saved along with a timestamp of when during the movie you left it. This is a fun way to see what others think of the movie as you’re watching it.

You can also generate GIFs and watch in full screen mode. The free movies can be streamed from a mobile device or your computer, and you don’t need to make an account.

Learn The Internal Working Of Pyspark Foreach

Introduction to PySpark foreach

PySpark foreach is explained in this outline. PySpark foreach is an active operation in the spark that is available with DataFrame, RDD, and Datasets in pyspark to iterate over each and every element in the dataset. The For Each function loops in through each and every element of the data and persists the result regarding that. The PySpark ForEach Function returns only those elements which meet up the condition provided in the function of the For Each Loop. A simple function that applies to each and every element in a data frame is applied to every element in a For Each Loop. ForEach partition is also used to apply to each and every partition in RDD. We can create a function and pass it with for each loop in pyspark to apply it over all the functions in Spark. This is an action operation in Spark used for Data processing in Spark. In this topic, we are going to learn about PySpark foreach.

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Syntax for PySpark foreach

The syntax for the PYSPARK WHEN function is:-

def function(x): Dataframe.foreach(function) def f(x): print(x) b=a.foreach(f)

ScreenShot:

Working of PySpark foreach

Let us see somehow the ForEach function works in PySpark:-

The ForEach function in Pyspark works with each and every element in the Spark Application. We have a function that is applied to each and every element in a Spark Application.

The loop is iterated for each and every element in Spark. The function is executed on each and every element in an RDD and the result is evaluated.

Every Element in the loop is iterated and the given function is executed the result is then returned back to the driver and the action is performed.

The ForEach loop works on different stages for each stage performing a separate action in Spark. The loop in for Each iterate over items that is an iterable item, One Item is selected from the loop and the function is applied to it, if the functions satisfy the predicate for the loop it is returned back as the action.

The number of times the loop will iterate is equal to the length of the elements in the data.

If the data is not there or the list or data frame is empty the loop will not iterate.

The same can be applied with RDD, DataFrame, and Dataset in PySpark.

Example of PySpark foreach

Let us see some Example of how PYSPARK ForEach function works:

Create a DataFrame in PYSPARK:

Let’s first create a DataFrame in Python.

CreateDataFrame is used to create a DF in Python

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name") a.show()

Now let’s create a simple function first that will print all the elements in and will pass it in a For Each Loop.

def f(x) : print(x)

This is a simple Print function that prints all the data in a DataFrame.

def f(x): print(x)

Code SnapShot:

Let’s iterate over all the elements using for Each loop.

b = a.foreach(f)

This is simple for Each Statement that iterates and prints through all the elements of a Data Frame.

b = a.foreach(f)

Stages are defined and the action is performed.

Row(Name=’ROBIN’) Row(Name=’ANAND’) Row(Name=’AND’) Row(Name=’JOHN’) Row(Name=’SAM’)

Code Snapshot:

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name") b=a.foreach(print) Example #2

Let us check the type of element inside a Data Frame. For This, we will proceed with the same DataFrame as created above and will try to pass a function that defines the type of variable inside.

Create a DataFrame in PYSPARK:-

Let’s first create a DataFrame in Python.

CreateDataFrame is used to create a DF in Python

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name").show()

Code SnapShot:

This function defines the type of the variable inside.

def f(x): print(type(x))

Let’s use ForEach Statement and print the type in the DataFrame.

b = a.foreach(f)

Output:

This will print the Type of every element it iterates.

Code SnapShot:

We can also build complex UDF and pass it with For Each loop in PySpark.

From the above example, we saw the use of the ForEach function with PySpark

Note:

For Each is used to iterate each and every element in a PySpark

We can pass a UDF that operates on each and every element of a DataFrame.

ForEach is an Action in Spark.

It doesn’t have any return value.

Conclusion

From the above article, we saw the use of FOR Each in PySpark. From various examples and classification, we tried to understand how the FOREach method works in PySpark and what are is used at the programming level.

Recommended Articles

We hope that this EDUCBA information on “PySpark foreach” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

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