# Connect to Dash
In this guide you'll learn how to visualize data from Apache Pinot using Plotly's [Dash](https://github.com/plotly/dash) web framework. Dash is the most downloaded, trusted Python framework for building ML & data science web apps.
We're going to use Dash 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:
```yaml
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:
```bash
docker-compose up
```
## 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](https://stream.wikimedia.org/v2/stream/recentchange)
We'll need to install the SSE client library to consume this data:
```bash
pip install sseclient-py
```
Next, create a file called `wiki.py` that contains the following:
```python
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:
```bash
python wiki.py
```
We'll see the following (truncated) output:
**Output**
```json
{'$schema': '/mediawiki/recentchange/1.0.0',
'bot': False,
'comment': '[[:File:Storemyr-Fagerbakken landskapsvernområde HVASSER '
'Oslofjorden Norway (Protected coastal forest Recreational area '
'hiking trails) Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24.jpg]] removed '
'from category',
'id': 1923506287,
'meta': {'domain': 'commons.wikimedia.org',
'dt': '2022-05-12T09:57:00Z',
'id': '3800228e-43d8-440d-8034-c68977742653',
'offset': 3855767440,
'partition': 0,
'request_id': '930b17cc-f14a-4656-afa1-d15b79a8f666',
'stream': 'mediawiki.recentchange',
'topic': 'eqiad.mediawiki.recentchange',
'uri': 'https://commons.wikimedia.org/wiki/Category:Iron_Age_in_Norway'},
'namespace': 14,
'parsedcomment': 'File:Storemyr-Fagerbakken '
'landskapsvernområde HVASSER Oslofjorden Norway (Protected '
'coastal forest Recreational area hiking trails) '
'Rituell-kultisk steinstreng sørøst i skogen (small '
'archeological stone string) Vår (spring) 2021-04-24.jpg '
'removed from category',
'server_name': 'commons.wikimedia.org',
'server_script_path': '/w',
'server_url': 'https://commons.wikimedia.org',
'timestamp': 1652349420,
'title': 'Category:Iron Age in Norway',
'type': 'categorize',
'user': 'Krg',
'wiki': 'commonswiki'}
{'$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': '2022-05-12T09:57:00Z',
'id': '2b819d20-beca-46a5-8ce3-b2f3b73d2cbe',
'offset': 3855767441,
'partition': 0,
'request_id': '930b17cc-f14a-4656-afa1-d15b79a8f666',
'stream': 'mediawiki.recentchange',
'topic': 'eqiad.mediawiki.recentchange',
'uri': 'https://commons.wikimedia.org/wiki/Category:Cultural_heritage_monuments_in_F%C3%A6rder'},
'namespace': 14,
'parsedcomment': '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',
'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:
```bash
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:
```python
import json
import sseclient
import datetime
import requests
import time
from confluent_kafka import Producer
```
*wiki\_to\_kafka.py*
Add these functions:
```python
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:
```python
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:
```bash
python wiki_to_kafka.py
```
We'll see a message every time 100 messages are pushed to Kafka, as shown below:
**Output**
```
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:
```bash
docker exec -it kafka-wiki kafka-run-class.sh kafka.tools.GetOffsetShell \
--broker-list localhost:9092 \
--topic wiki_events
```
**Output**
```
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:
```bash
docker exec -it kafka-wiki kafka-console-consumer.sh \
--bootstrap-server localhost:9092 \
--topic wiki_events \
--from-beginning
```
**Output**
```
...
{"$schema": "/mediawiki/recentchange/1.0.0", "meta": {"uri": "https://en.wikipedia.org/wiki/Super_Wings", "request_id": "6f82e64d-220f-41f4-88c3-2e15f03ae504", "id": "c30cd735-1ead-405e-94d1-49fbe7c40411", "dt": "2022-05-12T10:05:36Z", "domain": "en.wikipedia.org", "stream": "mediawiki.recentchange", "topic": "eqiad.mediawiki.recentchange", "partition": 0, "offset": 3855779703}, "type": "log", "namespace": 0, "title": "Super Wings", "comment": "", "timestamp": 1652349936, "user": "2001:448A:50E0:885B:FD1D:2D04:233E:7647", "bot": false, "log_id": 0, "log_type": "abusefilter", "log_action": "hit", "log_params": {"action": "edit", "filter": "550", "actions": "tag", "log": 32575794}, "log_action_comment": "2001:448A:50E0:885B:FD1D:2D04:233E:7647 triggered [[Special:AbuseFilter/550|filter 550]], performing the action \"edit\" on [[Super Wings]]. Actions taken: Tag ([[Special:AbuseLog/32575794|details]])", "server_url": "https://en.wikipedia.org", "server_name": "en.wikipedia.org", "server_script_path": "/w", "wiki": "enwiki", "parsedcomment": ""}
{"$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": ""}
{"$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": ""}
^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:
```json
{
"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:
```json
{
"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:
```bash
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](http://localhost:9000/#/query?query=select+domain%2C+count%28*%29+%0Afrom+wikievents+%0Agroup+by+domain%0Aorder+by+count%28*%29+DESC%0Alimit+10\&tracing=false\&pqlSyntax=false) and run the following query to check that the data has made its way into Pinot:
```sql
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 Dash Dashboard
Now let's write some more queries against Pinot and display the results in Dash.
First, install the following libraries:
```bash
pip install dash pinotdb plotly pandas
```
Create a file called `dashboard.py` and import libraries and write a header for the page:
```python
import pandas as pd
from dash import Dash, html, dcc
import plotly.graph_objects as go
from pinotdb import connect
import plotly.express as px
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = Dash(__name__, external_stylesheets=external_stylesheets)
app.title = "Wiki Recent Changes Dashboard"
```
*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:
```python
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.
First, let's create a couple of helper functions for creating these metrics:
```python
from dash import html, dash_table
import plotly.graph_objects as go
def add_delta_trace(fig, title, value, last_value, row, column):
fig.add_trace(go.Indicator(
mode = "number+delta",
title= {'text': title},
value = value,
delta = {'reference': last_value, 'relative': True},
domain = {'row': row, 'column': column})
)
def add_trace(fig, title, value, row, column):
fig.add_trace(go.Indicator(
mode = "number",
title= {'text': title},
value = value,
domain = {'row': row, 'column': column})
)
```
*dash\_utils.py*
And now let's add the following import to `app.py`:
```python
from dash_utils import add_delta_trace, add_trace
```
*app.py*
And the following code at the end of the file:
```python
fig = go.Figure(layout=go.Layout(height=300))
if df_summary["events1Min"][0] > 0:
if df_summary["events1Min"][0] > 0:
add_delta_trace(fig, "Changes", df_summary["events1Min"][0], df_summary["events1Min2Min"][0], 0, 0)
add_delta_trace(fig, "Users", df_summary["users1Min"][0], df_summary["users1Min2Min"][0], 0, 1)
add_delta_trace(fig, "Domain", df_summary["domains1Min"][0], df_summary["domains1Min2Min"][0], 0, 2)
else:
add_trace(fig, "Changes", df_summary["events1Min"][0], 0, 0)
add_trace(fig, "Users", df_summary["users1Min2Min"][0], 0, 1)
add_trace(fig, "Domains", df_summary["domains1Min2Min"][0], 0, 2)
fig.update_layout(grid = {"rows": 1, "columns": 3, 'pattern': "independent"},)
else:
fig.update_layout(annotations = [{"text": "No events found", "xref": "paper", "yref": "paper", "showarrow": False, "font": {"size": 28}}])
app.layout = html.Div([
html.H1("Wiki Recent Changes Dashboard", style={'text-align': 'center'}),
html.Div(id='content', children=[
dcc.Graph(figure=fig)
])
])
if __name__ == '__main__':
app.run_server(debug=True)
```
*app.py*
Go back to the terminal and run the following command:
```bash
python dashboard.py
```
Navigate to [localhost:8501](http://localhost:8051) to see the Dash app. You should see something like the following:
 *Dash Metrics*
### Changes per minute
Next, let's add a line chart that shows the number of changes being done to Wikimedia per minute. Update `app.py` as follows:
```python
query = """
select ToDateTime(DATETRUNC('minute', ts), 'yyyy-MM-dd hh:mm:ss') AS dateMin, count(*) AS changes,
distinctcount(user) AS users,
distinctcount(domain) AS domains
from wikievents
where ts > ago('PT2M')
group by dateMin
order by dateMin desc
LIMIT 30
"""
curs.execute(query)
df_ts = pd.DataFrame(curs, columns=[item[0] for item in curs.description])
df_ts_melt = pd.melt(df_ts, id_vars=['dateMin'], value_vars=['changes', 'users', 'domains'])
line_chart = px.line(df_ts_melt, x='dateMin', y="value", color='variable', color_discrete_sequence =['blue', 'red', 'green'])
line_chart['layout'].update(margin=dict(l=0,r=0,b=0,t=40), title="Changes/Users/Domains per minute")
line_chart.update_yaxes(range=[0, df_ts["changes"].max() * 1.1])
app.layout = html.Div([
html.H1("Wiki Recent Changes Dashboard", style={'text-align': 'center'}),
html.Div(id='content', children=[
dcc.Graph(figure=fig),
dcc.Graph(figure=line_chart),
])
])
```
*app.py*
Go back to the web browser and you should see something like this:
 *Dash Time Series*
### Auto Refresh
At the moment we need to refresh our web browser to update the metrics and line chart, but it would be much better if that happened automatically. Let's now add auto refresh functionality.
This will require some restructuring of our application so that each component is rendered from a function annotated with a callback that causes the function to be called on an interval.
The app layout now looks like this:
```python
app.layout = html.Div([
html.H1("Wiki Recent Changes Dashboard", style={'text-align': 'center'}),
html.Div(id='latest-timestamp', style={"padding": "5px 0", "text-align": "center"}),
dcc.Interval(
id='interval-component',
interval=1 * 1000,
n_intervals=0
),
html.Div(id='content', children=[
dcc.Graph(id="indicators"),
dcc.Graph(id="time-series"),
])
])
```
*app.py*
* `interval-component` is configured to fire a callback every 1,000 milliseconds.
* `latest-timestamp` is a container that will contain the latest timestamp.
* `indicators` will contain indicators with the latest counts of users, domains, and changes.
* `time-series` will contain the time series line chart.
The timestamp is refreshed by the following callback function:
```python
@app.callback(
Output(component_id='latest-timestamp', component_property='children'),
Input('interval-component', 'n_intervals'))
def timestamp(n):
return html.Span(f"Last updated: {datetime.datetime.now()}")
```
*app.py*
The indicators are refreshed by this function:
```python
@app.callback(Output(component_id='indicators', component_property='figure'),
Input('interval-component', 'n_intervals'))
def indicators(n):
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 = connection.cursor()
curs.execute(query)
df_summary = pd.DataFrame(curs, columns=[item[0] for item in curs.description])
curs.close()
fig = go.Figure(layout=go.Layout(height=300))
if df_summary["events1Min"][0] > 0:
if df_summary["events1Min"][0] > 0:
add_delta_trace(fig, "Changes", df_summary["events1Min"][0], df_summary["events1Min2Min"][0], 0, 0)
add_delta_trace(fig, "Users", df_summary["users1Min"][0], df_summary["users1Min2Min"][0], 0, 1)
add_delta_trace(fig, "Domain", df_summary["domains1Min"][0], df_summary["domains1Min2Min"][0], 0, 2)
else:
add_trace(fig, "Changes", df_summary["events1Min"][0], 0, 0)
add_trace(fig, "Users", df_summary["users1Min2Min"][0], 0, 1)
add_trace(fig, "Domains", df_summary["domains1Min2Min"][0], 0, 2)
fig.update_layout(grid = {"rows": 1, "columns": 3, 'pattern': "independent"},)
else:
fig.update_layout(annotations = [{"text": "No events found", "xref": "paper", "yref": "paper", "showarrow": False, "font": {"size": 28}}])
return fig
```
*app.py*
And finally, the following function refreshes the line chart:
```python
@app.callback(Output(component_id='time-series', component_property='figure'),
Input('interval-component', 'n_intervals'))
def time_series(n):
query = """
select ToDateTime(DATETRUNC('minute', ts), 'yyyy-MM-dd hh:mm:ss') AS dateMin, count(*) AS changes,
distinctcount(user) AS users,
distinctcount(domain) AS domains
from wikievents
where ts > ago('PT1H')
group by dateMin
order by dateMin desc
LIMIT 30
"""
curs = connection.cursor()
curs.execute(query)
df_ts = pd.DataFrame(curs, columns=[item[0] for item in curs.description])
curs.close()
df_ts_melt = pd.melt(df_ts, id_vars=['dateMin'], value_vars=['changes', 'users', 'domains'])
line_chart = px.line(df_ts_melt, x='dateMin', y="value", color='variable', color_discrete_sequence =['blue', 'red', 'green'])
line_chart['layout'].update(margin=dict(l=0,r=0,b=0,t=40), title="Changes/Users/Domains per minute")
line_chart.update_yaxes(range=[0, df_ts["changes"].max() * 1.1])
return line_chart
```
*app.py*
If we navigate back to our web browser, we'll see the following:
 *Dash Auto Refresh*
The full script used in this example is shown below:
```python
import pandas as pd
from dash import Dash, html, dash_table, dcc, Input, Output
import plotly.graph_objects as go
from pinotdb import connect
from dash_utils import add_delta_trace, add_trace
import plotly.express as px
import datetime
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = Dash(__name__, external_stylesheets=external_stylesheets)
app.title = "Wiki Recent Changes Dashboard"
connection = connect(host="localhost", port="8099", path="/query/sql", scheme=( "http"))
@app.callback(Output(component_id='indicators', component_property='figure'),
Input('interval-component', 'n_intervals'))
def indicators(n):
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 = connection.cursor()
curs.execute(query)
df_summary = pd.DataFrame(curs, columns=[item[0] for item in curs.description])
curs.close()
fig = go.Figure(layout=go.Layout(height=300))
if df_summary["events1Min"][0] > 0:
if df_summary["events1Min"][0] > 0:
add_delta_trace(fig, "Changes", df_summary["events1Min"][0], df_summary["events1Min2Min"][0], 0, 0)
add_delta_trace(fig, "Users", df_summary["users1Min"][0], df_summary["users1Min2Min"][0], 0, 1)
add_delta_trace(fig, "Domain", df_summary["domains1Min"][0], df_summary["domains1Min2Min"][0], 0, 2)
else:
add_trace(fig, "Changes", df_summary["events1Min"][0], 0, 0)
add_trace(fig, "Users", df_summary["users1Min2Min"][0], 0, 1)
add_trace(fig, "Domains", df_summary["domains1Min2Min"][0], 0, 2)
fig.update_layout(grid = {"rows": 1, "columns": 3, 'pattern': "independent"},)
else:
fig.update_layout(annotations = [{"text": "No events found", "xref": "paper", "yref": "paper", "showarrow": False, "font": {"size": 28}}])
return fig
@app.callback(Output(component_id='time-series', component_property='figure'),
Input('interval-component', 'n_intervals'))
def time_series(n):
query = """
select ToDateTime(DATETRUNC('minute', ts), 'yyyy-MM-dd hh:mm:ss') AS dateMin, count(*) AS changes,
distinctcount(user) AS users,
distinctcount(domain) AS domains
from wikievents
where ts > ago('PT1H')
group by dateMin
order by dateMin desc
LIMIT 30
"""
curs = connection.cursor()
curs.execute(query)
df_ts = pd.DataFrame(curs, columns=[item[0] for item in curs.description])
curs.close()
df_ts_melt = pd.melt(df_ts, id_vars=['dateMin'], value_vars=['changes', 'users', 'domains'])
line_chart = px.line(df_ts_melt, x='dateMin', y="value", color='variable', color_discrete_sequence =['blue', 'red', 'green'])
line_chart['layout'].update(margin=dict(l=0,r=0,b=0,t=40), title="Changes/Users/Domains per minute")
line_chart.update_yaxes(range=[0, df_ts["changes"].max() * 1.1])
return line_chart
@app.callback(
Output(component_id='latest-timestamp', component_property='children'),
Input('interval-component', 'n_intervals'))
def timestamp(n):
return html.Span(f"Last updated: {datetime.datetime.now()}")
app.layout = html.Div([
html.H1("Wiki Recent Changes Dashboard", style={'text-align': 'center'}),
html.Div(id='latest-timestamp', style={"padding": "5px 0", "text-align": "center"}),
dcc.Interval(
id='interval-component',
interval=1 * 1000,
n_intervals=0
),
html.Div(id='content', children=[
dcc.Graph(id="indicators"),
dcc.Graph(id="time-series"),
])
])
if __name__ == '__main__':
app.run_server(debug=True)
```
*dashboard.py*
## Summary
In this guide we've learnt how to publish data into Kafka from Wikimedia's event stream, ingest it from there into Pinot, and finally make sense of the data using SQL queries run from Dash.