Now that we have the fundamentals down for Sentiment Analysis, let's setup the development environment for building the Sentiment Analysis application. First, we will need to create a Twitter Application through Twitter's Developer portal, which will allow us to obtain API KEYS and TOKENS needed to connect to Twitter's API data feed. Lastly, we will setup and configure services for HDF and HDP Sandbox, so later we can use the tools for processing data.
- Enabled Connected Data Architecture
- Create a Twitter Application
- Verify Prerequisites Have Been Covered
- Manually Setup Development Environment
- Further Reading
After creating and registering the Twitter Application, Twitter will provide you authorized access to pull live data through their API as long as you have the Consumer API Keys and Access Tokens. First you must apply for developer account, then once Twitter accepts your application, you will be able to create Twitter applications.
1. To create new applications, Twitter requires you must go to their website -> apply for a developer account
2. Click the purple apply button.
3. Select the Twitter account you want to associate as admin of this developer account, then press blue continue button.
4. Fill in the details for your developer account, then press blue continue button.
5. Provide project details, then press the blue continue button.
6. Read and agree to Terms of Service, then press the blue submit application button.
7. Verify you received the verification email to complete the application.
8. Open your email inbox, select Verify your Twitter Developer Account, then press the yellow orange confirm your email button.
9. Welcome to your new Twitter developer account. Press on Create an app to go to the Apps page.
1. Press the blue Create an app button.
2. Enter your app details. Then press the blue Create button.
3. Review Twitter's Developer Terms, then press the blue Create button.
4. A green checkmark should appear in the top right corner notifying you that the application was created successfully. You will be taken to the page showing summary about your app.
Application at this point was created successfully! Switch to the Permissions tab.
Since we are only going to be reading live data, let's check the permissions.
The access permission shows Read and Write. Press Edit.
The Edit button drops down to Access permission and Additional permissions. Change Read and Write permission to Read Only. Then press the blue Save button.
A green checkmark should appear in the top corner notifying you that the application was updated successfully.
You will need to regenerate access tokens for the changes to take effect. Switch to the Keys and tokens tab.
In order for you to have authorization access to pull data from your application's live feed, we need to get Consumer API Keys and Access Tokens.
1. Initially the Access tokens are not created, so let's create the Access tokens. The changes we made earlier to the permissions will take effect too. Press the blue Create button.
2. The access tokens will be created.
Save the keys and tokens as they appear on "your" web page. The following keys and tokens are just examples from a previous application setup and are no longer usable, so you will need to get the keys and tokens from your developer account.
- Example: Consumer API Key
- Example: Consumer API Secret Key
- Example: Access Token
- Example: Access Token Secret
Map sandbox IP to desired hostname in hosts file
- If you need help mapping Sandbox IP to hostname, reference Environment Setup -> Map Sandbox IP To Your Desired Hostname In The Hosts File in Learning the Ropes of HDP Sandbox
Setup Ambari admin password for "HDF" and "HDP"
If you need help setting the Ambari admin password,
- for HDP, reference Admin Password Reset in Learning the Ropes of HDP Sandbox
- for HDF, reference Admin Password Reset in Learning the Ropes of CDF Sandbox
Started up all required services for "HDF" and "HDP"
If unsure, login to Ambari admin Dashboard
- for HDF at http://sandbox-hdf.hortonworks.com:8080 and verify NiFi starts up, else start it.
- for HDP at http://sandbox-hdp.hortonworks.com:8080 and verify HDFS, Spark2, HBase and Zeppelin starts up, else start them.
The first task will be to setup NiFi service on CDF Sandbox since we will need to use it to build a dataflow for acquiring Twitter data. The second task will be to setup Hive, HDFS, Spark and HBase on HDP Sandbox, so we can focus on cleaning, preprocessing and building a machine learning for delivering visual insight to the user on their Twitter data.
Open CDF Sandbox Web Shell Client at http://sandbox-hdf.hortonworks.com:4200 with login
root/hadoop. If this login is the first one, then you will be prompted to set a new password, remember it.
The following shell code synchronizes CentOS7 System Clock with UTC, which CDF Sandbox runs on and is needed for NiFi's GetTwitter processor. By updating the CentOS7 System Clock, we will avoid running into authorization errors when connecting to the Twitter API Feed through GetTwitter processor.
The second part of the code cleans up the NiFi flow that is already prebuilt into CDF Sandbox by backing up the flow and removing it. Copy and paste the code line by line into the web shell.
echo "Synchronizing CentOS7 System Clock with UTC for GetTwitter Processor" # Install Network Time Protocol yum install -y ntp service ntpd stop # Use the NTPDATE to synchronize CentOS7 sysclock within few ms of UTC ntpdate pool.ntp.org service ntpd start echo "Existing flow on NiFi canvas backed up to flow.xml.gz.bak" mv /var/lib/nifi/conf/flow.xml.gz /var/lib/nifi/conf/flow.xml.gz.bak
For the changes in NiFi to take effect, we need to restart NiFi. Open Ambari at http://sandbox-hdf.hortonworks.com:8080 with user
admin and the password you set.
Click the NiFi service from Ambari stack of services, press Service Actions dropdown, select Restart All.
A Background Operation Running window will pop up with Restart all components for NiFi progress bar loading and turning green once the restart is successful.
If Kafka is off, make sure to turn it on from Ambari.
We will need to create a Kafka topic on HDF for Spark to stream data into from HDP.
/usr/hdf/current/kafka-broker/bin/kafka-topics.sh --create --zookeeper sandbox-hdf.hortonworks.com:2181 --replication-factor 1 --partitions 10 --topic tweetsSentiment
Open HDP Sandbox Web Shell Client at http://sandbox-hdp.hortonworks.com:4200 with login
root/hadoop. On first login, you will be prompted to update the password.
If Kafka is off, make sure to turn it on from Ambari at http://sandbox-hdp.hortonworks.com:8080 with user
admin and the password you set.
We will need to enter the HDP sandbox web shell to create a Kafka topic on HDP for NiFi to publish messages to the Kafka queue.
/usr/hdp/current/kafka-broker/bin/kafka-topics.sh --create --zookeeper sandbox-hdp.hortonworks.com:2181 --replication-factor 1 --partitions 10 --topic tweets
The first set of commands will setup Hive, so we can create, read and write to tables built on top of JSON data by installing maven, downloading Hive-JSON-Serde library and compiling that library. Copy and paste the code line by line in the HDP sandbox web shell.
echo "Setting Up Maven needed for Compiling and Installing Hive JSON-Serde Lib" wget http://repos.fedorapeople.org/repos/dchen/apache-maven/epel-apache-maven.repo -O /etc/yum.repos.d/epel-apache-maven.repo yum install -y apache-maven mvn -version echo "Setting up Hive JSON-Serde Libary" git clone https://github.com/rcongiu/Hive-JSON-Serde cd Hive-JSON-Serde # Compile JsonSerDe source to create JsonSerDe library jar file mvn -Phdp23 clean package # Give JsonSerDe library jar file to Hive and Hive2 library cp json-serde/target/json-serde-1.3.9-SNAPSHOT-jar-with-dependencies.jar /usr/hdp/220.127.116.11-1634/hive/lib cd ~/
For the updates in Hive to take effect, we need to restart Hive, so it can access Hive-JSON-Serde library. Open Ambari at http://sandbox-hdp.hortonworks.com:8080 with user
admin and the password you set.
Click the Hive service from Ambari stack of services, press Service Actions dropdown, select Restart All.
A Background Operation Running window will pop up with Restart all components for Hive progress bar loading and turning green once the restart is successful.
The next set of commands will setup HDFS for holding the Tweet data that will be accessed by Spark. We will create tweets folder that will hold a zipped file. This data will be copied over to HDFS where it will be later loaded by Spark to refine the historical data for creating a machine learning model. Run the code line by line in your HDP sandbox web shell.
echo "Setting up HDFS for Tweet Data" HDFS_TWEET_STAGING="/sandbox/tutorial-files/770/tweets_staging" LFS_TWEETS_PACKAGED_PATH="/sandbox/tutorial-files/770/tweets" sudo -u hdfs mkdir -p $LFS_TWEETS_PACKAGED_PATH # Create tweets_staging hdfs directory ahead of time for hive sudo -u hdfs hdfs dfs -mkdir -p $HDFS_TWEET_STAGING # Change HDFS ownership of tweets_staging dir to maria_dev sudo -u hdfs hdfs dfs -chown -R maria_dev $HDFS_TWEET_STAGING # Change HDFS tweets_staging dir permissions to everyone sudo -u hdfs hdfs dfs -chmod -R 777 $HDFS_TWEET_STAGING # give anyone rwe permissions to /sandbox/tutorial-files/770 sudo -u hdfs hdfs dfs -chmod -R 777 /sandbox/tutorial-files/770 sudo -u hdfs wget https://github.com/hortonworks/data-tutorials/raw/master/tutorials/cda/building-a-sentiment-analysis-application/application/setup/data/tweets.zip -O $LFS_TWEETS_PACKAGED_PATH/tweets.zip sudo -u hdfs unzip $LFS_TWEETS_PACKAGED_PATH/tweets.zip -d $LFS_TWEETS_PACKAGED_PATH sudo -u hdfs rm -rf $LFS_TWEETS_PACKAGED_PATH/tweets.zip # Remove existing (if any) copy of data from HDFS. You could do this with Ambari file view. sudo -u hdfs hdfs dfs -rm -r -f $HDFS_TWEET_STAGING/* -skipTrash # Move downloaded JSON file from local storage to HDFS sudo -u hdfs hdfs dfs -put $LFS_TWEETS_PACKAGED_PATH/* $HDFS_TWEET_STAGING
For Spark Structured Streaming, we will need to leverage SBT package manager. The commands below install SBT. Run the code line by line in the HDP web shell.
curl https://bintray.com/sbt/rpm/rpm | sudo tee /etc/yum.repos.d/bintray-sbt-rpm.repo yum install -y sbt
For HBase since we will be storing streams of data into it from NiFi, we will need to create a table ahead of time, so we wont' have to switch between both applications:
#!/bin/bash # -e: causes echo to process escape sequences, build confirmation into it # -n: tells hbase shell this is a non-interactive session echo -e "create 'tweets_sentiment','social_media_sentiment'" | hbase shell -n
Congratulations! The development environment is setup and configured, so we can began acquiring the customer sentiment data via NiFi ingesting the Twitter API feed.
- NiFi System Administrator's Guide
- Hive SerDe Library Doc
- HDFS Commands Guide Doc
- Setting Up a Spark Development Environment with Scala
- Administering Ambari
- Solr's Banana Installation and Quick Start