Processing tweets with Spark: Difference between revisions

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Created page with "== Processing tweets with Spark === Continuing the examples from [Getting started with Apache Spark]: * load the tweets in ‘tweet-id-text-345/’ as JSON objects * collect..."
 
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== Processing tweets with Spark ===
== Processing tweets with Spark ==


Continuing the examples from [Getting started with Apache Spark]:
Continuing the examples from [[Getting started with Apache Spark]]:
* load the tweets in ‘tweet-id-text-345/’ as JSON objects
* load the tweets in ‘tweet-id-text-345/’ as JSON objects
* collect only the texts from the tweets
* collect only the texts from the tweets

Revision as of 14:13, 1 September 2022

Processing tweets with Spark

Continuing the examples from Getting started with Apache Spark:

  • load the tweets in ‘tweet-id-text-345/’ as JSON objects
  • collect only the texts from the tweets
  • split the texts into words and select all the hashtags
  • the step where you go from a column of lists-of-words to a columns of words is a little harder
 * you can write this step in Python (using collect() and then creating a new DataFrame)
 * you can also go via a Pandas frame, but like the Python solution, this breaks the parallel processing
 * you can also use the file all-tweet-words.txt in mitt.uib.no to skip this step
 * two other solutions, which we will present later, are: 
   * going via an RDD and using a flatMap()
   * writing a user-defined Spark function (UDF)
  • split the tweets into two sets of 80% and 20% size
  • find URLs in the texts and download a few image files