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Using the Faker with Vagrant
Jason Dugdale edited this page Dec 8, 2015
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The Faker is a simple tool written to allow you to verify data can successfully flow through your instance of the connector. This guide assumes you have set up your instance of the connector via Vagrant (vagrant up
), and SSH'd into your connector instance (vagrant ssh
).
- Start by updating your reader and writer config files to tell the connector where data will be flowing from and to:
1.a. Update the gnip.host
value to http://localhost:5001
sudo vi /etc/datasift/gnip-reader/reader.json
{
"kafka": {
"topic": "twitter",
"servers": "localhost:6667",
"retry-backoff": 1000,
"reconnect-backoff": 1000
},
"gnip": {
"account": "ACCOUNT",
"label": "LABEL",
"product": "PRODUCT",
"username": "USER",
"password": "PASSWORD",
"host": "http://localhost:5001"
},
"hosebird" : {
"retries": 10,
"buffer_size": 10000,
"buffer_timeout": 500
},
"metrics": {
"host": "localhost",
"port": 8125,
"prefix": "hosebird.reader",
"reporting-time": 1
}
}
1.b. Update the datasift.base_url
and datasift.port
values to http://localhost:5002
sudo vi /etc/datasift/datasift-writer/writer.json
{
"zookeeper": {
"socket": "localhost:2181"
},
"kafka": {
"topic": "twitter",
"broker": "localhost",
"port": 6667
},
"datasift": {
"base_url": "http://localhost",
"port": 5002,
"username": "YOUR_DATASIFT_USERNAME",
"api_key": "YOUR_DATASIFT_API_KEY",
"source_id": "YOUR_DATASIFT_SOURCE_ID",
"bulk_size": 100000,
"bulk_items": 1000,
"bulk_interval": 1000
},
"metrics": {
"host": "localhost",
"port": 8125,
"prefix": "datasift.writer",
"reporting-time": 1
}
}
- After saving these changes, restart both the reader and writer services:
sudo supervisorctl restart gnip-reader datasift-writer
- Now the connector knows where your Faker data will flow from and to, we can start the Faker services:
3.a. Start the gnip-faker
with default settings
cd /vagrant/gnip-faker/
./server.js
3.b. Start the datasift-ingestion-faker
with default settings
node /vagrant/datasift-ingestion-faker/server.js
- Take a look at your Grafana Dashboard at localhost:3000; you should now see data flowing through your connector