In this guide you'll learn how to visualize data from Apache Pinot using Streamlit . Streamlit is a Python library that makes it easy to build interactive data based web applications.
We're going to use Streamlit to build a real-time dashboard to visualize the changes being made to Wikimedia properties.
Real-Time Dashboard Architecture
Startup components
We're going to use the following Docker compose file, which spins up instances of Zookeeper, Kafka, along with a Pinot controller, broker, and server:
Copy version : '3.7'
services :
zookeeper :
image : zookeeper:3.5.6
container_name : "zookeeper-wiki"
ports :
- "2181:2181"
environment :
ZOOKEEPER_CLIENT_PORT : 2181
ZOOKEEPER_TICK_TIME : 2000
kafka :
image : wurstmeister/kafka:latest
restart : unless-stopped
container_name : "kafka-wiki"
ports :
- "9092:9092"
expose :
- "9093"
depends_on :
- zookeeper
environment :
KAFKA_ZOOKEEPER_CONNECT : zookeeper-wiki:2181/kafka
KAFKA_BROKER_ID : 0
KAFKA_ADVERTISED_HOST_NAME : kafka-wiki
KAFKA_ADVERTISED_LISTENERS : PLAINTEXT://kafka-wiki:9093,OUTSIDE://localhost:9092
KAFKA_LISTENERS : PLAINTEXT://0.0.0.0:9093,OUTSIDE://0.0.0.0:9092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP : PLAINTEXT:PLAINTEXT,OUTSIDE:PLAINTEXT
pinot-controller :
image : apachepinot/pinot:0.10.0
command : "StartController -zkAddress zookeeper-wiki:2181 -dataDir /data"
container_name : "pinot-controller-wiki"
volumes :
- ./config:/config
- ./data:/data
restart : unless-stopped
ports :
- "9000:9000"
depends_on :
- zookeeper
pinot-broker :
image : apachepinot/pinot:0.10.0
command : "StartBroker -zkAddress zookeeper-wiki:2181"
restart : unless-stopped
container_name : "pinot-broker-wiki"
volumes :
- ./config:/config
ports :
- "8099:8099"
depends_on :
- pinot-controller
pinot-server :
image : apachepinot/pinot:0.10.0
command : "StartServer -zkAddress zookeeper-wiki:2181"
restart : unless-stopped
container_name : "pinot-server-wiki"
volumes :
- ./config:/config
depends_on :
- pinot-broker docker-compose.yml
Run the following command to launch all the components:
Wikimedia recent changes stream
Wikimedia provides provides a continuous stream of structured event data describing changes made to various Wikimedia properties. The events are published over HTTP using the Server-Side Events (SSE) Protocol.
You can find the endpoint at: stream.wikimedia.org/v2/stream/recentchange
We'll need to install the SSE client library to consume this data:
Copy pip install sseclient-py Next, create a file called wiki.py that contains the following:
Copy import json
import pprint
import sseclient
import requests
def with_requests ( url , headers ):
"""Get a streaming response for the given event feed using requests."""
return requests . get (url, stream = True , headers = headers)
url = 'https://stream.wikimedia.org/v2/stream/recentchange'
headers = { 'Accept' : 'text/event-stream' }
response = with_requests (url, headers)
client = sseclient . SSEClient (response)
for event in client . events ():
stream = json . loads (event.data)
pprint . pprint (stream) wiki.py
The highlighted section shows how we connect to the recent changes feed using the SSE client library.
Let's run this script as shown below:
We'll see the following (truncated) output:
Output
Copy {'$schema' : '/mediawiki/recentchange/ 1.0 . 0 ' ,
'bot' : False ,
'comment' : '[[:File:Storemyr-Fagerbakken landskapsvernområde HVASSER '
'Oslofjorden Norway (Protected coastal forest Recreational area '
'hiking trails) Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24 .jpg]] removed '
'from category' ,
'id' : 1923506287 ,
'meta' : {'domain' : 'commons.wikimedia.org' ,
'dt' : ' 2022-05-12 T 09 : 57 : 00 Z' ,
'id' : ' 3800228e-43 d 8-440 d -8034 -c 68977742653 ' ,
'offset' : 3855767440 ,
'partition' : 0 ,
'request_id' : ' 930 b 17 cc-f 14 a -4656 -afa 1 -d 15 b 79 a 8 f 666 ' ,
'stream' : 'mediawiki.recentchange' ,
'topic' : 'eqiad.mediawiki.recentchange' ,
'uri' : 'https: //commons.wikimedia.org/wiki/Category:Iron_Age_in_Norway'},
'namespace': 14 ,
'parsedcomment' : '<a '
'href= "/wiki/File:Storemyr-Fagerbakken_landskapsvernomr%C3%A5de_HVASSER_Oslofjorden_Norway_(Protected_coastal_forest_Recreational_area_hiking_trails)_Rituell-kultisk_steinstreng_s%C3%B8r%C3%B8st_i_skogen_(small_archeological_stone_string)_V%C3%A5r_(spring)_2021-04-24.jpg" '
'title= "File:Storemyr-Fagerbakken landskapsvernområde '
'HVASSER Oslofjorden Norway (Protected coastal forest '
'Recreational area hiking trails) Rituell-kultisk '
'steinstreng sørøst i skogen (small archeological stone '
'string) Vår (spring) '
'2021-04-24.jpg" >File:Storemyr-Fagerbakken '
'landskapsvernområde HVASSER Oslofjorden Norway (Protected '
'coastal forest Recreational area hiking trails) '
'Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24 .jpg</a> '
'removed from category' ,
'server_name' : 'commons.wikimedia.org' ,
'server_script_path' : '/w' ,
'server_url' : 'https: //commons.wikimedia.org',
'timestamp': 1652349420 ,
'title' : 'Category:Iron Age in Norway' ,
'type' : 'categorize' ,
'user' : 'Krg' ,
'wiki' : 'commonswiki'}
{'$schema' : '/mediawiki/recentchange/ 1.0 . 0 ' ,
'bot' : False ,
'comment' : '[[:File:Storemyr-Fagerbakken landskapsvernområde HVASSER '
'Oslofjorden Norway (Protected coastal forest Recreational area '
'hiking trails) Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24 .jpg]] removed '
'from category' ,
'id' : 1923506289 ,
'meta' : {'domain' : 'commons.wikimedia.org' ,
'dt' : ' 2022-05-12 T 09 : 57 : 00 Z' ,
'id' : ' 2 b 819 d 20 -beca -46 a 5-8 ce 3 -b 2 f 3 b 73 d 2 cbe' ,
'offset' : 3855767441 ,
'partition' : 0 ,
'request_id' : ' 930 b 17 cc-f 14 a -4656 -afa 1 -d 15 b 79 a 8 f 666 ' ,
'stream' : 'mediawiki.recentchange' ,
'topic' : 'eqiad.mediawiki.recentchange' ,
'uri' : 'https: //commons.wikimedia.org/wiki/Category:Cultural_heritage_monuments_in_F%C3%A6rder'},
'namespace': 14 ,
'parsedcomment' : '<a '
'href= "/wiki/File:Storemyr-Fagerbakken_landskapsvernomr%C3%A5de_HVASSER_Oslofjorden_Norway_(Protected_coastal_forest_Recreational_area_hiking_trails)_Rituell-kultisk_steinstreng_s%C3%B8r%C3%B8st_i_skogen_(small_archeological_stone_string)_V%C3%A5r_(spring)_2021-04-24.jpg" '
'title= "File:Storemyr-Fagerbakken landskapsvernområde '
'HVASSER Oslofjorden Norway (Protected coastal forest '
'Recreational area hiking trails) Rituell-kultisk '
'steinstreng sørøst i skogen (small archeological stone '
'string) Vår (spring) '
'2021-04-24.jpg" >File:Storemyr-Fagerbakken '
'landskapsvernområde HVASSER Oslofjorden Norway (Protected '
'coastal forest Recreational area hiking trails) '
'Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24 .jpg</a> '
'removed from category' ,
'server_name' : 'commons.wikimedia.org' ,
'server_script_path' : '/w' ,
'server_url' : 'https: //commons.wikimedia.org',
'timestamp': 1652349420 ,
'title' : 'Category:Cultural heritage monuments in Færder' ,
'type' : 'categorize' ,
'user' : 'Krg' ,
'wiki' : 'commonswiki'} Ingest recent changes into Kafka
Now we're going to import each of the events into Apache Kafka. First let's create a Kafka topic called wiki_events with 5 partitions:
Copy docker exec -it kafka-wiki kafka-topics.sh \
--bootstrap-server localhost:9092 \
--create \
--topic wiki_events \
--partitions 5 Create a new file called wiki_to_kafka.py and import the following libraries:
Copy import json
import sseclient
import datetime
import requests
import time
from confluent_kafka import Producer wiki_to_kafka.py
Add these functions:
Copy def with_requests ( url , headers ):
"""Get a streaming response for the given event feed using requests."""
return requests . get (url, stream = True , headers = headers)
def acked ( err , msg ):
if err is not None :
print ( "Failed to deliver message: {0} : {1} "
. format (msg. value (), err. str ()))
def json_serializer ( obj ):
if isinstance (obj, (datetime.datetime, datetime.date)):
return obj . isoformat ()
raise "Type %s not serializable" % type (obj) wiki_to_kafka.py
And now let's add the code that calls the recent changes API and imports events into the wiki_events topic:
Copy producer = Producer ({ 'bootstrap.servers' : 'localhost:9092' })
url = 'https://stream.wikimedia.org/v2/stream/recentchange'
headers = { 'Accept' : 'text/event-stream' }
response = with_requests (url, headers)
client = sseclient . SSEClient (response)
events_processed = 0
while True :
try :
for event in client . events ():
stream = json . loads (event.data)
payload = json . dumps (stream, default = json_serializer, ensure_ascii = False ). encode ( 'utf-8' )
producer . produce (topic = 'wiki_events' ,
key = str (stream[ 'meta' ][ 'id' ]), value = payload, callback = acked)
events_processed += 1
if events_processed % 100 == 0 :
print ( f " {str (datetime.datetime. now ()) } Flushing after { events_processed } events" )
producer . flush ()
except Exception as ex :
print ( f " {str (datetime.datetime. now ()) } Got error:" + str (ex))
response = with_requests (url, headers)
client = sseclient . SSEClient (response)
time . sleep ( 2 ) wiki_to_kafka.py
The highlighted parts of this script indicate where events are ingested into Kafka and then flushed to disk.
If we run this script:
Copy python wiki_to_kafka.py We'll see a message every time 100 messages are pushed to Kafka, as shown below:
Output
Copy 2022-05-12 10:58:34.449326 Flushing after 100 events
2022-05-12 10:58:39.151599 Flushing after 200 events
2022-05-12 10:58:43.399528 Flushing after 300 events
2022-05-12 10:58:47.350277 Flushing after 400 events
2022-05-12 10:58:50.847959 Flushing after 500 events
2022-05-12 10:58:54.768228 Flushing after 600 events Explore Kafka
Let's check that the data has made its way into Kafka.
The following command returns the message offset for each partition in the wiki_events topic:
Copy docker exec -it kafka-wiki kafka-run-class.sh kafka.tools.GetOffsetShell \
--broker-list localhost:9092 \
--topic wiki_events Output
Copy wiki_events:0:42
wiki_events:1:61
wiki_events:2:52
wiki_events:3:56
wiki_events:4:58 Looks good. We can also stream all the messages in this topic by running the following command:
Copy docker exec -it kafka-wiki kafka-console-consumer.sh \
--bootstrap-server localhost:9092 \
--topic wiki_events \
--from-beginning Output
Copy ...
{"$schema": "/mediawiki/recentchange/1.0.0", "meta": {"uri": "https://en.wikipedia.org/wiki/Super_Wings", "request_id": "6f82e64d-220f-41f4-88c3-2e15f03ae504", "id": "c30cd735-1ead-405e-94d1-49fbe7c40411", "dt": "2022-05-12T10:05:36Z", "domain": "en.wikipedia.org", "stream": "mediawiki.recentchange", "topic": "eqiad.mediawiki.recentchange", "partition": 0, "offset": 3855779703}, "type": "log", "namespace": 0, "title": "Super Wings", "comment": "", "timestamp": 1652349936, "user": "2001:448A:50E0:885B:FD1D:2D04:233E:7647", "bot": false, "log_id": 0, "log_type": "abusefilter", "log_action": "hit", "log_params": {"action": "edit", "filter": "550", "actions": "tag", "log": 32575794}, "log_action_comment": "2001:448A:50E0:885B:FD1D:2D04:233E:7647 triggered [[Special:AbuseFilter/550|filter 550]], performing the action \"edit\" on [[Super Wings]]. Actions taken: Tag ([[Special:AbuseLog/32575794|details]])", "server_url": "https://en.wikipedia.org", "server_name": "en.wikipedia.org", "server_script_path": "/w", "wiki": "enwiki", "parsedcomment": ""}
{"$schema": "/mediawiki/recentchange/1.0.0", "meta": {"uri": "https://no.wikipedia.org/wiki/Brukerdiskusjon:Haros", "request_id": "a20c9692-f301-4faf-9373-669bebbffff4", "id": "566ee63e-8e86-4a7e-a1f3-562704306509", "dt": "2022-05-12T10:05:36Z", "domain": "no.wikipedia.org", "stream": "mediawiki.recentchange", "topic": "eqiad.mediawiki.recentchange", "partition": 0, "offset": 3855779714}, "id": 84572581, "type": "edit", "namespace": 3, "title": "Brukerdiskusjon:Haros", "comment": "/* Stor forbokstav / ucfirst */", "timestamp": 1652349936, "user": "Asav", "bot": false, "minor": false, "patrolled": true, "length": {"old": 110378, "new": 110380}, "revision": {"old": 22579494, "new": 22579495}, "server_url": "https://no.wikipedia.org", "server_name": "no.wikipedia.org", "server_script_path": "/w", "wiki": "nowiki", "parsedcomment": "<span dir=\"auto\"><span class=\"autocomment\"><a href=\"/wiki/Brukerdiskusjon:Haros#Stor_forbokstav_/_ucfirst\" title=\"Brukerdiskusjon:Haros\">→Stor forbokstav / ucfirst</a></span></span>"}
{"$schema": "/mediawiki/recentchange/1.0.0", "meta": {"uri": "https://es.wikipedia.org/wiki/Campo_de_la_calle_Industria", "request_id": "d45bd9af-3e2c-4aac-ae8f-e16d3340da76", "id": "7fb3956e-9bd2-4fa5-8659-72b266cdb45b", "dt": "2022-05-12T10:05:35Z", "domain": "es.wikipedia.org", "stream": "mediawiki.recentchange", "topic": "eqiad.mediawiki.recentchange", "partition": 0, "offset": 3855779718}, "id": 266270269, "type": "edit", "namespace": 0, "title": "Campo de la calle Industria", "comment": "/* Historia */", "timestamp": 1652349935, "user": "Raimon will", "bot": false, "minor": false, "length": {"old": 7566, "new": 7566}, "revision": {"old": 143485393, "new": 143485422}, "server_url": "https://es.wikipedia.org", "server_name": "es.wikipedia.org", "server_script_path": "/w", "wiki": "eswiki", "parsedcomment": "<span dir=\"auto\"><span class=\"autocomment\"><a href=\"/wiki/Campo_de_la_calle_Industria#Historia\" title=\"Campo de la calle Industria\">→Historia</a></span></span>"}
^CProcessed a total of 269 messages Configure Pinot
Now let's configure Pinot to consume the data from Kafka.
We'll have the following schema:
Copy {
"schemaName" : "wikipedia" ,
"dimensionFieldSpecs" : [
{
"name" : "id" ,
"dataType" : "STRING"
} ,
{
"name" : "wiki" ,
"dataType" : "STRING"
} ,
{
"name" : "user" ,
"dataType" : "STRING"
} ,
{
"name" : "title" ,
"dataType" : "STRING"
} ,
{
"name" : "comment" ,
"dataType" : "STRING"
} ,
{
"name" : "stream" ,
"dataType" : "STRING"
} ,
{
"name" : "domain" ,
"dataType" : "STRING"
} ,
{
"name" : "topic" ,
"dataType" : "STRING"
} ,
{
"name" : "type" ,
"dataType" : "STRING"
} ,
{
"name" : "uri" ,
"dataType" : "STRING"
} ,
{
"name" : "bot" ,
"dataType" : "BOOLEAN"
} ,
{
"name" : "metaJson" ,
"dataType" : "STRING"
}
] ,
"dateTimeFieldSpecs" : [
{
"name" : "ts" ,
"dataType" : "TIMESTAMP" ,
"format" : "1:MILLISECONDS:EPOCH" ,
"granularity" : "1:MILLISECONDS"
}
]
} schema.json
And the following table config:
Copy {
"tableName" : "wikievents" ,
"tableType" : "REALTIME" ,
"segmentsConfig" : {
"timeColumnName" : "ts" ,
"schemaName" : "wikipedia" ,
"replication" : "1" ,
"replicasPerPartition" : "1"
} ,
"tableIndexConfig" : {
"invertedIndexColumns" : [] ,
"rangeIndexColumns" : [] ,
"autoGeneratedInvertedIndex" : false ,
"createInvertedIndexDuringSegmentGeneration" : false ,
"sortedColumn" : [] ,
"bloomFilterColumns" : [] ,
"loadMode" : "MMAP" ,
"streamConfigs" : {
"streamType" : "kafka" ,
"stream.kafka.topic.name" : "wiki_events" ,
"stream.kafka.broker.list" : "kafka-wiki:9093" ,
"stream.kafka.consumer.type" : "lowlevel" ,
"stream.kafka.consumer.prop.auto.offset.reset" : "smallest" ,
"stream.kafka.consumer.factory.class.name" : "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory" ,
"stream.kafka.decoder.class.name" : "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder" ,
"realtime.segment.flush.threshold.rows" : "1000" ,
"realtime.segment.flush.threshold.time" : "24h" ,
"realtime.segment.flush.segment.size" : "100M"
} ,
"tenants" : {
"broker" : "DefaultTenant" ,
"server" : "DefaultTenant" ,
"tagOverrideConfig" : {}
} ,
"noDictionaryColumns" : [] ,
"onHeapDictionaryColumns" : [] ,
"varLengthDictionaryColumns" : [] ,
"enableDefaultStarTree" : false ,
"enableDynamicStarTreeCreation" : false ,
"aggregateMetrics" : false ,
"nullHandlingEnabled" : false
} ,
"metadata" : {} ,
"quota" : {} ,
"routing" : {} ,
"query" : {} ,
"ingestionConfig" : {
"transformConfigs" : [
{
"columnName" : "metaJson" ,
"transformFunction" : "JSONFORMAT(meta)"
} ,
{
"columnName" : "id" ,
"transformFunction" : "JSONPATH(metaJson, '$.id')"
} ,
{
"columnName" : "stream" ,
"transformFunction" : "JSONPATH(metaJson, '$.stream')"
} ,
{
"columnName" : "domain" ,
"transformFunction" : "JSONPATH(metaJson, '$.domain')"
} ,
{
"columnName" : "topic" ,
"transformFunction" : "JSONPATH(metaJson, '$.topic')"
} ,
{
"columnName" : "uri" ,
"transformFunction" : "JSONPATH(metaJson, '$.uri')"
} ,
{
"columnName" : "ts" ,
"transformFunction" : "\"timestamp\" * 1000"
}
]
} ,
"isDimTable" : false
} table.json
The highlighted lines are how we connect Pinot to the Kafka topic that contains the events. Create the schema and table by running the following commnad:
Copy docker exec -it pinot-controller-wiki bin/pinot-admin.sh AddTable \
-tableConfigFile /config/table.json \
-schemaFile /config/schema.json \
-exec Once you've done that, navigate to the Pinot UI and run the following query to check that the data has made its way into Pinot:
Copy select domain, count ( * )
from wikievents
group by domain
order by count ( * ) DESC
limit 10 As long as you see some records, everything is working as expected.
Building a Streamlit Dashboard
Now let's write some more queries against Pinot and display the results in Streamlit.
First, install the following libraries:
Copy pip install streamlit pinotdb plotly pandas Create a file called app.py and import libraries and write a header for the page:
Copy import pandas as pd
import streamlit as st
from pinotdb import connect
import plotly . express as px
st . set_page_config (layout = "wide" )
st . header ( "Wikipedia Recent Changes" ) app.py
Connect to Pinot and write a query that returns recent changes, along with the users who made the changes, and domains where they were made:
Copy conn = connect (host = 'localhost' , port = 8099 , path = '/query/sql' , scheme = 'http' )
query = """select
count(*) FILTER(WHERE ts > ago('PT1M')) AS events1Min,
count(*) FILTER(WHERE ts <= ago('PT1M') AND ts > ago('PT2M')) AS events1Min2Min,
distinctcount(user) FILTER(WHERE ts > ago('PT1M')) AS users1Min,
distinctcount(user) FILTER(WHERE ts <= ago('PT1M') AND ts > ago('PT2M')) AS users1Min2Min,
distinctcount(domain) FILTER(WHERE ts > ago('PT1M')) AS domains1Min,
distinctcount(domain) FILTER(WHERE ts <= ago('PT1M') AND ts > ago('PT2M')) AS domains1Min2Min
from wikievents
where ts > ago('PT2M')
limit 1
"""
curs = conn . cursor ()
curs . execute (query)
df_summary = pd . DataFrame (curs, columns = [item[ 0 ] for item in curs.description]) app.py
The highlighted part of the query shows how to count the number of events from the last minute and the minute before that. We then do a similar thing to count the number of unique users and domains.
Metrics
Now let's create some metrics based on that data:
Copy metric1 , metric2 , metric3 = st . columns ( 3 )
