mizvol

def getFollowers(userId, nextCursor):
    params = {
        'cursor': nextCursor,
        'access_token': INSTAGRAM_ACCESS_TOKEN
    }
    
    session = Session()
    session.mount("https://", adapters.HTTPAdapter(max_retries=50))
    response = session.get("https://api.instagram.com/v1/users/" + userId + "/followed-by", params = params, verify = True)
    

mizvol

db.allPosts.aggregate([
    {$group: {_id: "$user.id"}},
    {$out: "users"}
]);

mizvol

from requests import get, Session, adapters

def getInstaPosts(latitude, longitude, distance, minTimestamp, maxTimestamp, count):
    params = {
        'lat': latitude,
        'lng': longitude,
        'distance': distance, # radius of requested area
        'min_timestamp': str(minTimestamp), #start date
        'max_timestamp': str(maxTimestamp), #end date
        'count': COUNT, # number of posts(100 max)

mizvol

topicIndices = ldaModel.describeTopics(maxTermsPerTopic=5)
vocablist = vectorizer.vocabulary

topicsRDD = sc.parallelize(topicIndices)
termsRDD = topicsRDD.map(lambda topic: (zip(itemgetter(*topic[0])(vocablist), topic[1])))

indexedTermsRDD = termsRDD.zipWithIndex()
termsRDD = indexedTermsRDD.flatMap(lambda term: [(t[0], t[1], term[1]) for t in term[0]])
termDF = termsRDD.toDF(['term', 'probability', 'topicId'])

mizvol

from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.feature import IDF
from pyspark.ml.feature import CountVectorizer

#vectorize tags array for each user
vectorizer = CountVectorizer(inputCol="tokens", outputCol="features").fit(tagsListDF)
countVectors = vectorizer.transform(tagsListDF).select("id", "features")

#find TF-IDF coefficients for each tag
frequencyVectors = countVectors.map(lambda vector: vector[1])

mizvol

import pymongo as pm
import unicodedata

client = pm.MongoClient()
db = client.instagram
tagsDB = db.tags

tagsList = []
for tag in tagsDB.find():
    tagsList.append((str(tag['_id']), [unicodedata.normalize('NFKD', t).encode('ascii','ignore') 

mizvol

db.allPosts.aggregate([
    {$group: {_id: "$user.id", tags: {$addToSet: "$tags"}}}, 
    {$unwind: "$tags"}, 
    {$unwind: "$tags"},
    {$group: {_id: "$_id", tags: {$addToSet: "$tags"}}},
    {$out: "tags"}
])

mizvol

name := "SparkScalaTest"

version := "1.0"

scalaVersion := "2.11.12"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "2.3.2",
  "org.apache.spark" %% "spark-sql" % "2.3.2"
)

mizvol

import org.apache.spark.sql.SparkSession 

object SparkWordCount extends App {

  val spark = SparkSession.builder
    .master("local[*]")
    .appName("Spark Word Count")
    .getOrCreate()

  val lines = spark.sparkContext.parallelize(

mizvol