We dwell in a world the place numerous techniques—social networks, monitoring, inventory exchanges, web sites, IoT units—all repeatedly generate volumes of knowledge within the type of occasions, captured in techniques like Apache Kafka and Amazon Kinesis. One can carry out all kinds of analyses, like aggregations, filtering, or sampling, on these occasion streams, both on the document stage or over sliding time home windows. On this weblog, I’ll present how Rockset can serve a dwell dashboard, which surfaces analytics on real-time Twitter knowledge ingested into Rockset from a Kinesis stream.
Establishing a Kinesis Stream
The Python code snippet beneath exhibits learn how to create a Kinesis stream programmatically. This can be achieved via the AWS Console or the AWS CLI.
import boto3
kinesis = boto3.consumer('kinesis') # requires AWS credentials to be current in env
kinesis.create_stream(StreamName="twitter-stream", ShardCount=5)
Writing Tweets to Kinesis
Right here, I will likely be utilizing the Tweepy module to fetch tweets via a streaming search API. This API permits me to specify a listing of phrases that I wish to embody in my search (e.g. “music”, “fb”, “apple”). You want to have a Twitter developer account so as to get entry to the Twitter Streaming API. Right here, I’ve a StreamListener, which is registered to be notified on a tweet arrival. Upon receiving a tweet, it writes the tweet knowledge to one of many 5 random shards of the Kinesis stream.
# twitter api credentials
access_token=...
access_token_secret=...
consumer_key=...
consumer_secret=...
class TweetListener(StreamListener):
def __init__(self, stream_name):
self.kinesis = boto3.consumer('kinesis')
self.stream_name = stream_name
def on_data(self, knowledge):
document = {}
document['Data'] = knowledge
document['PartitionKey'] = ''.be part of(random.selection(chars) for _ in vary(measurement))
self.kinesis.put_records(Data=[record], StreamName=self.stream_name)
auth=OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
stream=Stream(auth, TweetListener("twitter-stream"))
search_terms=["music", "facebook", "apple"]
stream.filter(observe=search_terms)
Connecting Kinesis to Rockset
The next snippet exhibits learn how to create a set in Rockset, backed by a Kinesis stream. Be aware: You want to create an Integration (an object that represents your AWS credentials) and arrange related permissions on the Kinesis stream, which permits Rockset to carry out sure learn operations on that stream.
from rockset import Consumer, Q, F
rs=Consumer(api_key=...)
aws_integration=rs.Integration.retrieve(...)
sources=[
rs.Source.kinesis(
stream_name="twitter-stream",
integration=aws_integration)]
twitter_kinesis_demo=rs.Assortment.create("twitter-kinesis-demo", sources=sources)
Alternatively, collections can be created from the Rockset console, as proven beneath.
Constructing the Reside Dashboard
Now that I’ve a producer writing tweets to a Kinesis stream and a set to ingest them into Rockset, I can construct a dashboard on prime of this assortment. My dashboard has two views.
Tweets View
The primary view shows analytics on all of the tweets coming into Rockset and has 3 panels, every of which makes its personal question to Rockset.
Reside Tweets
The Reside Tweets panel continually refreshes to indicate the most recent tweets showing within the assortment. A question is made at a hard and fast refresh interval to fetch tweets that have been tweeted within the final minute. Right here, I’m deciding on required fields to indicate on the feed and filtering out tweets older than a minute.
SELECT t.timestamp_ms,
t.created_at AS created_at,
t.textual content AS textual content,
t.consumer.screen_name AS screen_name
FROM "twitter-kinesis-demo" t
WHERE CAST(timestamp_ms AS INT) > UNIX_MILLIS(current_timestamp() - minutes(1))
ORDER BY timestamp_ms DESC
LIMIT 100;
Prime Hashtags
The Prime Hashtags panel exhibits trending hashtags, which have been present in most variety of tweets within the final hour, together with the related tweet depend. On this question, all hashtags showing within the final one hour are filtered into a short lived relation latest_hashtags. Utilizing a WITH clause, latest_hashtags is used it the primary question, the place we group by all of the hashtags and order by tweet_count to acquire the trending hashtags.
WITH lastest_hashtags AS
(SELECT decrease(ht.textual content) AS hashtag
FROM "twitter-kinesis-demo" t,
unnest(t.extended_tweet.entities.hashtags) ht
WHERE CAST(t.timestamp_ms AS INT) > UNIX_MILLIS(current_timestamp() - hours(1)))
SELECT depend(hashtag) AS tweet_count,
hashtag
FROM latest_hashtags
GROUP BY hashtag
ORDER BY tweet_count DESC
LIMIT 10;
Incoming Tweets
The final panel is a chart which exhibits the speed at which customers are tweeting. We get hold of knowledge factors for the variety of incoming tweets each 2 seconds and plot them in a chart.
SELECT depend(*)
FROM "twitter-kinesis-demo"
WHERE forged(timestamp_ms AS INT) > unix_millis(current_timestamp() - seconds(2));
Hashtags View
The second view shows analytics on tweets with a selected hashtag and in addition has 3 panels: Reside Tweets, Associated Hashtags, and Influencers. Every panel within the dashboard makes a question to Rockset. That is similar to the primary dashboard view however narrows the analytics to a specific hashtag of curiosity.
Influencers
As we’ve narrowed our evaluation to a single hashtag, it might be fascinating to see who essentially the most influential customers are round this subject. For this, we outline influencers as customers with the very best variety of followers who’re tweeting the hashtag of curiosity.
SELECT t.consumer.screen_name,
t.consumer.followers_count AS fc
FROM "twitter-kinesis-demo" t
WHERE 'music' IN
(SELECT hashtags.textual content
FROM unnest(t.entities.hashtags) hashtags)
GROUP BY (t.consumer.screen_name,
t.consumer.followers_count)
ORDER BY t.consumer.followers_count DESC
LIMIT 5;
Associated Hashtags
This part is considerably much like the Prime Hashtags panel we noticed within the Tweets view of the dashboard. It exhibits the hashtags that co-occur most frequently together with our hashtag of curiosity.
SELECT hashtags.textual content as hashtag,
depend(*) AS occurence_count
FROM "twitter-kinesis-demo" t,
unnest(t.entites.hashtags) hashtags
WHERE 'music' IN
(SELECT ht.textual content
FROM unnest(t.entities.hashtags) ht)
AND hashtags.textual content != 'music'
GROUP BY hashtags.textual content
ORDER BY occurence_count DESC
LIMIT 10;
Reside Tweets
The Reside Tweets panel is similar to one we noticed within the Tweets view of the dashboard. The one distinction is a brand new filter is utilized so as to choose these tweets which include our hashtag of curiosity. I already used this filter for the opposite two panels within the Hashtags view.
The place to Go from Right here
Whereas I created this instance dwell dashboard as an instance how real-time analytics may very well be carried out on knowledge from Kinesis streams, Rockset helps Kafka, as a streaming supply, and customary visualization instruments, like Tableau, Apache Superset, Redash, and Grafana, as nicely.
You’ll be able to seek advice from the complete supply code for this instance right here, in case you are considering constructing on streaming knowledge utilizing Rockset and Kinesis. Joyful constructing!
