Analyze X data using Apache Hive and Apache Hadoop on HDInsight

Article
08/09/2024

Learn how to use Apache Hive to process X data. The result is a list of X users who sent the most tweets that contain a certain word.

Important

The steps in this document were tested on HDInsight 3.6.

Get the data

X allows you to retrieve the data for each tweet as a JavaScript Object Notation (JSON) document through a REST API. OAuth is required for authentication to the API.

Create an X application

From a web browser, sign in to https://developer.x.com. Select the Sign-up now link if you don't have an X account.
Select Create New App.
Enter Name, Description, Website. You can make up a URL for the Website field. The following table shows some sample values to use:

Field Value

Name MyHDInsightApp

Description MyHDInsightApp

Website https://www.myhdinsightapp.com
Select Yes, I agree, and then select Create your Twitter application.
Select the Permissions tab. The default permission is Read only.
Select the Keys and Access Tokens tab.
Select Create my access token.
Select Test OAuth in the upper-right corner of the page.
Write down consumer key, Consumer secret, Access token, and Access token secret.

Field	Value
Name	MyHDInsightApp
Description	MyHDInsightApp
Website	`https://www.myhdinsightapp.com`

Download tweets

The following Python code downloads 10,000 tweets from X and save them to a file named tweets.txt.

Note

The following steps are performed on the HDInsight cluster, since Python is already installed.

Use ssh command to connect to your cluster. Edit the command below by replacing CLUSTERNAME with the name of your cluster, and then enter the command:
```
ssh sshuser@CLUSTERNAME-ssh.azurehdinsight.net
```

Use the following commands to install Tweepy, Progress bar, and other required packages:

sudo apt install python-dev libffi-dev libssl-dev
sudo apt remove python-openssl
python -m pip install virtualenv
mkdir gettweets
cd gettweets
virtualenv gettweets
source gettweets/bin/activate
pip install tweepy progressbar pyOpenSSL requests[security]

Use the following command to create a file named gettweets.py:
```
nano gettweets.py
```

Edit the code below by replacing Your consumer secret, Your consumer key, Your access token, and Your access token secret with the relevant information from your X application. Then paste the edited code as the contents of the gettweets.py file.

#!/usr/bin/python

from tweepy import Stream, OAuthHandler
from tweepy.streaming import StreamListener
from progressbar import ProgressBar, Percentage, Bar
import json
import sys

#X app information
consumer_secret='Your consumer secret'
consumer_key='Your consumer key'
access_token='Your access token'
access_token_secret='Your access token secret'

#The number of tweets we want to get
max_tweets=100

#Create the listener class that receives and saves tweets
class listener(StreamListener):
    #On init, set the counter to zero and create a progress bar
    def __init__(self, api=None):
        self.num_tweets = 0
        self.pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=max_tweets).start()

    #When data is received, do this
    def on_data(self, data):
        #Append the tweet to the 'tweets.txt' file
        with open('tweets.txt', 'a') as tweet_file:
            tweet_file.write(data)
            #Increment the number of tweets
            self.num_tweets += 1
            #Check to see if we have hit max_tweets and exit if so
            if self.num_tweets >= max_tweets:
                self.pbar.finish()
                sys.exit(0)
            else:
                #increment the progress bar
                self.pbar.update(self.num_tweets)
        return True

    #Handle any errors that may occur
    def on_error(self, status):
        print status

#Get the OAuth token
auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
#Use the listener class for stream processing
twitterStream = Stream(auth, listener())
#Filter for these topics
twitterStream.filter(track=["azure","cloud","hdinsight"])

Tip

Adjust the topics filter on the last line to track popular keywords. Using keywords popular at the time you run the script allows for faster capture of data.

Use Ctrl + X, then Y to save the file.
Use the following command to run the file and download tweets:
```
python gettweets.py
```
A progress indicator appears. It counts up to 100% as the tweets are downloaded.

Note

If it is taking a long time for the progress bar to advance, you should change the filter to track trending topics. When there are many tweets about the topic in your filter, you can quickly get the 100 tweets needed.

Upload the data

To upload the data to HDInsight storage, use the following commands:

hdfs dfs -mkdir -p /tutorials/x/data
hdfs dfs -put tweets.txt /tutorials/x/data/tweets.txt

These commands store the data in a location that all nodes in the cluster can access.

Run the HiveQL job

Use the following command to create a file containing HiveQL statements:

nano x.hql

Use the following text as the contents of the file:

set hive.exec.dynamic.partition = true;
set hive.exec.dynamic.partition.mode = nonstrict;
-- Drop table, if it exists
DROP TABLE tweets_raw;
-- Create it, pointing toward the tweets logged from X
CREATE EXTERNAL TABLE tweets_raw (
    json_response STRING
)
STORED AS TEXTFILE LOCATION '/tutorials/x/data';
-- Drop and recreate the destination table
DROP TABLE tweets;
CREATE TABLE tweets
(
    id BIGINT,
    created_at STRING,
    created_at_date STRING,
    created_at_year STRING,
    created_at_month STRING,
    created_at_day STRING,
    created_at_time STRING,
    in_reply_to_user_id_str STRING,
    text STRING,
    contributors STRING,
    retweeted STRING,
    truncated STRING,
    coordinates STRING,
    source STRING,
    retweet_count INT,
    url STRING,
    hashtags array<STRING>,
    user_mentions array<STRING>,
    first_hashtag STRING,
    first_user_mention STRING,
    screen_name STRING,
    name STRING,
    followers_count INT,
    listed_count INT,
    friends_count INT,
    lang STRING,
    user_location STRING,
    time_zone STRING,
    profile_image_url STRING,
    json_response STRING
);
-- Select tweets from the imported data, parse the JSON,
-- and insert into the tweets table
FROM tweets_raw
INSERT OVERWRITE TABLE tweets
SELECT
    cast(get_json_object(json_response, '$.id_str') as BIGINT),
    get_json_object(json_response, '$.created_at'),
    concat(substr (get_json_object(json_response, '$.created_at'),1,10),' ',
    substr (get_json_object(json_response, '$.created_at'),27,4)),
    substr (get_json_object(json_response, '$.created_at'),27,4),
    case substr (get_json_object(json_response,    '$.created_at'),5,3)
        when "Jan" then "01"
        when "Feb" then "02"
        when "Mar" then "03"
        when "Apr" then "04"
        when "May" then "05"
        when "Jun" then "06"
        when "Jul" then "07"
        when "Aug" then "08"
        when "Sep" then "09"
        when "Oct" then "10"
        when "Nov" then "11"
        when "Dec" then "12" end,
    substr (get_json_object(json_response, '$.created_at'),9,2),
    substr (get_json_object(json_response, '$.created_at'),12,8),
    get_json_object(json_response, '$.in_reply_to_user_id_str'),
    get_json_object(json_response, '$.text'),
    get_json_object(json_response, '$.contributors'),
    get_json_object(json_response, '$.retweeted'),
    get_json_object(json_response, '$.truncated'),
    get_json_object(json_response, '$.coordinates'),
    get_json_object(json_response, '$.source'),
    cast (get_json_object(json_response, '$.retweet_count') as INT),
    get_json_object(json_response, '$.entities.display_url'),
    array(
        trim(lower(get_json_object(json_response, '$.entities.hashtags[0].text'))),
        trim(lower(get_json_object(json_response, '$.entities.hashtags[1].text'))),
        trim(lower(get_json_object(json_response, '$.entities.hashtags[2].text'))),
        trim(lower(get_json_object(json_response, '$.entities.hashtags[3].text'))),
        trim(lower(get_json_object(json_response, '$.entities.hashtags[4].text')))),
    array(
        trim(lower(get_json_object(json_response, '$.entities.user_mentions[0].screen_name'))),
        trim(lower(get_json_object(json_response, '$.entities.user_mentions[1].screen_name'))),
        trim(lower(get_json_object(json_response, '$.entities.user_mentions[2].screen_name'))),
        trim(lower(get_json_object(json_response, '$.entities.user_mentions[3].screen_name'))),
        trim(lower(get_json_object(json_response, '$.entities.user_mentions[4].screen_name')))),
    trim(lower(get_json_object(json_response, '$.entities.hashtags[0].text'))),
    trim(lower(get_json_object(json_response, '$.entities.user_mentions[0].screen_name'))),
    get_json_object(json_response, '$.user.screen_name'),
    get_json_object(json_response, '$.user.name'),
    cast (get_json_object(json_response, '$.user.followers_count') as INT),
    cast (get_json_object(json_response, '$.user.listed_count') as INT),
    cast (get_json_object(json_response, '$.user.friends_count') as INT),
    get_json_object(json_response, '$.user.lang'),
    get_json_object(json_response, '$.user.location'),
    get_json_object(json_response, '$.user.time_zone'),
    get_json_object(json_response, '$.user.profile_image_url'),
    json_response
WHERE (length(json_response) > 500);

Press Ctrl + X, then press Y to save the file.
Use the following command to run the HiveQL contained in the file:
```
beeline -u 'jdbc:hive2://headnodehost:10001/;transportMode=http' -i x.hql
```
This command runs the x.hql file. Once the query completes, you see a jdbc:hive2//localhost:10001/> prompt.
From the beeline prompt, use the following query to verify that data was imported:
```
SELECT name, screen_name, count(1) as cc
FROM tweets
WHERE text like "%Azure%"
GROUP BY name,screen_name
ORDER BY cc DESC LIMIT 10;
```
This query returns a maximum of 10 tweets that contain the word Azure in the message text.

Note

If you changed the filter in the gettweets.py script, replace Azure with one of the filters you used.

Next steps

You've learned how to transform an unstructured JSON dataset into a structured Apache Hive table. To learn more about Hive on HDInsight, see the following documents:

Share via