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Streaming Academy

This repo contains a code and instructions to support series of episodes of Streaming Academy. This series goal is to teach Data engineers and Software Engineers how to build and develop redistributed fault-tolerant streaming architectures using Kafka and Python.

Curriculum

  1. Episode 1: Intro to stream processing

    • What is stream processing
    • Motivation
    • Landscape overview
    • Kafka fundamentals
      • Topics
      • Partitions
      • Consumer groups
      • Checkpointing
    • Quix Streams fundamentals
      • Basic architecture
      • Streaming DataFrame
    • Practice part
      • Produce message
      • Replaying data
      • Connect to local RedPanda broker
      • Local pipeline
      • Create producer service
      • Deploy service locally
      • Building data normalization service
    • Homework
      • Invite your friends and colleagues to Episode 2

  2. Episode 2: Stateful data transformations basics

    • Use cases
    • State fundamentals
    • Basic state operations
    • Windows
    • Homework

  3. Episode 3: Stateful use case - downsampling (InfluxDB)

    • Pre-episode homework
      • sign up to InfluxDB Serverless
    • Time series data
    • Time series database
    • Downsampling
    • Homework

  4. Episode 4: Deploy to the cloud (with Redpanda)

    • Pre-episode homework
      • Sign up (Redpanda serverless)
      • Sign up (Quix Cloud)
    • Configure Quix Cloud to connect to Redpanda
    • Connect Quix Cloud to your GitHub repository
    • Deploy your repo to production
    • Implementing a new feature
      • Consuming production data
      • Implementation
      • Creating PR
      • Releasing to production
    • Monitoring + Observability
    • Homework

  5. Episode 5: Application development

    • Common ways to integrate stream processing pipelines with third party systems
    • WebAPI ingestion
    • WebSocket ingestion
    • WebSocket consumption
    • In-memory views
    • Consumer lag metrics

  6. Episode 6: Advanced stateful processing

    • State recovery
    • Partition reassignment
    • Changelog topics
    • Group By (repartition)
    • Homework

  7. Episode 7: Realtime ML inference

    • Architecture introduction
    • Model training
    • Model deployment
    • Model KPIs
    • A/B testing
    • Homework

Episode X: GenAI

Episode 1

Link to the video: youtube.com

Create new repo in GitHub

Create new empty repository for your project in GitHub.

You should end up on empty project screen like this:

Empty GitHub project

Install Quix CLI

Quix CLI helps you to connect to your cloud infrastructure seamlessly as well as helps you with managing your pipeline locally.

Installation of Quix CLI

To install the Quix CLI, users have multiple methods depending on their operating system. Here's an expanded installation section including the main ways to install Quix CLI on Linux, macOS, and Windows.

For macOS:

  • Install latest version:

    curl -fsSL https://github.com/quixio/quix-cli/raw/main/install.sh | sudo bash

For Linux:

  • Install latest version:

    curl -fsSL https://github.com/quixio/quix-cli/raw/main/install.sh | sudo bash

For Windows (PowerShell):

  • Install latest version:

    iwr https://github.com/quixio/quix-cli/raw/main/install.ps1 -useb | iex

Setting up local pipeline

We are going to setup local pipeline, running in docker compose. We will start with empty one that contains local broker (Redpanda).

First we need to initialize repository for Quix pipeline

quix local init

and then we will see two new files. .gitignore and quix.yaml.

quix.yaml is place for your pipeline definition and later we will add our services there.

# Quix Project Descriptor
# This file describes the data pipeline and configuration of resources of a Quix Project.

metadata:
  version: 1.0

# This section describes the Deployments of the data pipeline
deployments: []

# This section describes the Topics of the data pipeline
topics: []

Starting the pipeline

In order to run our empty pipeline, we need docker installed. Please install docker desktop if you have no docker installed - https://www.docker.com/products/docker-desktop/

Then you can run:

quix local pipeline up

After your pipeline is built and deploy, you can either check it using command line:

docker compose ps

or use Docker desktop UI:

Decker Desktop UI

There are two services:

Python VENV

Let's start with creating virtual environment for Python

python3 -m venv venv
source venv/bin/activate

Connect local contenxt to local broker

To send data into our local Redpanda broker, first we need to configure local Quix context to use it:

quix contexts broker set localhost:19092 --enable

Data producer

To simulate data source before we connect to real one, let's replay sample file with messages.

Create new service from template

quix local apps create demo-data-source -p raw-replay
cd raw-replay/

Then download sample data file:

wget https://raw.githubusercontent.com/tomas-quix/streaming-academy/main/demo_stream.json

pip install

Install service Python dependencies specified in requirements.txt:

pip install -r requirements.txt

If your IDE is not highlighting code properly, you might need to select default Python interpreter for file or workspace. For VSCode type following command: > Python: Select Interpreter

Edit service settings and code

To configure this new service, edit app.yaml variables to configure output topic to send data to raw-data:

name: RAW data replay
language: python
variables:
  - name: output
    inputType: OutputTopic
    description: Name of the output topic to write into
    defaultValue: raw-data
    required: true
dockerfile: dockerfile
runEntryPoint: main.py
defaultFile: main.py

then we create local .env file to inject environment variables into the runtime using:

quix local vars export

This will create .env file:

Quix__Broker__Address=localhost:19092
output=raw-data

These environment variables are then injected into the runtime because of these lines at the top of the main.py:

from dotenv import load_dotenv
load_dotenv()

main.py

Change main.py to send data from demo_stream.json that we donwloaded with wget command couple of steps above:

from quixstreams import Application
import json
import time
import os

# import the dotenv module to load environment variables from a file
from dotenv import load_dotenv
load_dotenv(override=False)

# Create an Application.
app = Application.Quix()

# Define the topic using the "output" environment variable
topic_name = os.getenv("output", "")
if topic_name == "":
    raise ValueError("The 'output' environment variable is required. This is the output topic that data will be published to.")

topic = app.topic(topic_name)

with app.get_producer() as producer:
    with open("demo_stream.json", 'r') as file:
        for line in file:
            # Remove newline characters from the message
            message = json.loads(line.strip())
            
            # Publish message to Kafka
            producer.produce(topic.name, json.dumps(message), "demo_stream.json")

            print(message)
            time.sleep(1)

    producer.flush(30)  # Wait for all messages to be delivered
    print('All messages have been flushed to the Kafka topic')

Running replay

Now last step is to run replay service and produce some sample data into raw-data topic so we can continue building our pipeline:

python3 main.py

Inspecting data

Go to topics tab in Redpanda console on http://localhost:8080/topics to inspect data being send to topic:

Topic inspection

Data seems to flow into the selected topic so let's add our new service into the pipeline:

quix local pipeline update

This results in modified quix.yaml file:

# Quix Project Descriptor
# This file describes the data pipeline and configuration of resources of a Quix Project.

metadata:
  version: 1.0

# This section describes the Deployments of the data pipeline
deployments:
  - name: raw-replay
    application: raw-replay
    deploymentType: Service
    version: latest
    resources:
      cpu: 200
      memory: 500
      replicas: 1
    variables:
      - name: output
        inputType: OutputTopic
        description: Name of the output topic to write into
        required: true
        value: raw-data

# This section describes the Topics of the data pipeline
topics: []

after this we update running docker compose pipeline using:

quix local pipeline up

This will build our new service into container and deploy it. We can verify it by checking logs using:

docker compose logs raw-replay -f

Now bacause data are in rather unpractical format for our timeseries database InfluxDb and also not great for any downstream processing we plan in next episodes, we need to normalize it.

Creating Data normalization service

Now we have sucesfully created ingestion pipeline for our data, we can proccess them.

Note

Later we will replace Replay service with real data producer, a Flask Web API gateway ingesting real data from phones.

Let's start with creating new service from starter-transformation template:

cd ..
quix local apps create starter-transformation -p data-normalization
cd data-normalization
pip install -r requirements.txt

Open main.py and edit app definition to adjust our app for development. We change following code:

  1. Change consumer group to randonmly generated so every time we run our code, we recieve data from the beggining of topic and input records are predictable. We need to put stable string back when we deploy this to production.
  2. We disable changelog topics as we need to polute Kafka with dev changelog topics.
  3. Comment out output topic defition
  4. Comment out output until we have output data in shape
app = Application.Quix(
    str(uuid.uuid4()),
    auto_offset_reset="earliest",
    use_changelog_topics=False)

input_topic = app.topic(os.environ["input"])
#output_topic = app.topic(os.environ["output"])

and at the end of file:

#sdf = sdf.to_topic(output_topic)

Local environment preparation

In order to run your code against Quix Cloud infrastructure, we must sync our local environment again with Quix Cloud.

Now lets go to app.yaml and change input and output topics as following:

name: data-normalization
language: Python
variables:
  - name: input
    inputType: InputTopic
    description: Name of the input topic to listen to.
    defaultValue: raw-data
    required: true
  - name: output
    inputType: OutputTopic
    description: Name of the output topic to write to.
    defaultValue: table-data
    required: true
dockerfile: dockerfile
runEntryPoint: main.py
defaultFile: main.py

then we create local .env file to inject environment variables into the runtime:

quix local vars export

Data normalization

We are left with rest of main.py as following:

sdf = app.dataframe(input_topic)

# Here we are going to add our logic

sdf = sdf.update(lambda row: print(row))

if __name__ == "__main__":
    app.run(sdf)

If we run service now, it will print messages into console:

python3 main.py
Example of conssole output 
[2024-05-13 12:44:58,714] [INFO] : Starting the Application with the config: broker_address="kafka-k1.quix.io:9093,kafka-k2.quix.io:9093,kafka-k3.quix.io:9093" consumer_group="tomas-academytest1-episode1-addf57c7-37eb-4cdb-9245-b1645c488fa0" auto_offset_reset="earliest"
[2024-05-13 12:44:58,715] [INFO] : Topics required for this application: "tomas-academytest1-episode1-raw-data"
[2024-05-13 12:44:58,715] [INFO] : Attempting to create topics...
[2024-05-13 12:44:58,739] [INFO] : No topic creations required!
[2024-05-13 12:44:58,739] [INFO] : Validating Kafka topics exist and are configured correctly...
[2024-05-13 12:44:59,043] [INFO] : Kafka topics validation complete
[2024-05-13 12:44:59,043] [INFO] : Initializing state directory at "/workspaces/streaming-academy-test1/data-normalization/state/tomas-academytest1-episode1-addf57c7-37eb-4cdb-9245-b1645c488fa0"
[2024-05-13 12:44:59,044] [INFO] : Waiting for incoming messages
{'messageId': 10992, 'sessionId': 'f562d95c-4ab4-4843-8d55-cb6f6e559594', 'deviceId': '15f99ef1-5dc0-46c5-ae41-ef4a99d58734', 'payload': [{'name': 'accelerometer', 'time': 1715254448336798500, 'values': {'z': 0.03500760049521923, 'y': 0.010823591831326484, 'x': -0.04537117359936237}}, {'name': 'accelerometeruncalibrated', 'time': 1715254448340549400, 'values': {'z': -0.61737060546875, 'y': -0.677886962890625, 'x': -0.3983306884765625}}, {'name': 'accelerometer', 'time': 1715254448386809600, 'values': {'z': -0.026910218757390976, 'y': 0.03680851243734359, 'x': -0.0001911386579275131}}]}
That is a bit hard to read. Let's improve message formating by adding JSON indent: ```python import json sdf = sdf.update(lambda row: print(json.dumps(row, indent=4))) ```

Running code again, we can now read messages clearly. As you can see in the example message, multiple sensors and multiple timestamps are included in one message from

{
  "messageId": 10994,
  "sessionId": "f562d95c-4ab4-4843-8d55-cb6f6e559594",
  "deviceId": "15f99ef1-5dc0-46c5-ae41-ef4a99d58734",
  "payload": [
    {
      "name": "accelerometer",
      "time": 1715254448786894800,
      "values": {
        "z": 0.4370160372585058,
        "y": -0.3171194917678833,
        "x": -0.3749550101429224
      }
    },
    {
      "name": "accelerometeruncalibrated",
      "time": 1715254448790645800,
      "values": {
        "z": -0.60107421875,
        "y": -0.68988037109375,
        "x": -0.382659912109375
      }
    },
    {
      "name": "accelerometer",
      "time": 1715254448836904700,
      "values": {
        "z": -0.5078478153288364,
        "y": -0.8010843272060155,
        "x": 1.3548703023672104
      }
    }
  ]
}

We don't need payload metadata envelope for now, so we extract payload:

sdf = app.dataframe(input_topic)

sdf = sdf.apply(lambda row: row["payload"], expand=True)

sdf = sdf.update(lambda row: print(json.dumps(row, indent=4)))

apply method with take incoming message and convert it to something else. First predicate will be called for each incomming message. Setting expand to True will flatten messages containing multiple records to one level:

{
    "name": "accelerometer",
    "time": 1715254446586427000,
    "values": {
        "z": -0.0675555328786373,
        "y": 0.00914718305170536,
        "x": -0.04088555261790752
    }
}
{
    "name": "battery",
    "time": 1715254446599000000,
    "values": {
        "batteryLevel": 0.75,
        "batteryState": "unplugged",
        "lowPowerMode": "False"
    }
}
{
    "name": "accelerometeruncalibrated",
    "time": 1715254446640188000,
    "values": {
        "z": -0.6206207275390625,
        "y": -0.6722259521484375,
        "x": -0.387420654296875
    }
}

Now is a time to convert nested JSON to streaming table.

def transpose(row: dict):

    result = {
        "time": row["time"]
    }

    for key in row["values"]:
        result[row["name"] + "-" + key] = row["values"][key]

    return result
        
sdf = sdf.apply(transpose)

Here we join name of the sensor and its dimension to one string that defines column in output table. As a result, we get this output:

{
    "time": 1715254449140719600,
    "accelerometeruncalibrated-z": -0.8031158447265625,
    "accelerometeruncalibrated-y": -0.7025299072265625,
    "accelerometeruncalibrated-x": 0.11285400390625
}
{
    "time": 1715254449186978800,
    "accelerometer-z": -0.7760480856269597,
    "accelerometer-y": 0.022184943801164626,
    "accelerometer-x": -0.2300181131537072
}
{
    "time": 1715254449190730800,
    "accelerometeruncalibrated-z": -0.807373046875,
    "accelerometeruncalibrated-y": -0.679962158203125,
    "accelerometeruncalibrated-x": 0.0048980712890625
}

That is almost there, let's disable JSON formatting and print it as table:

sdf = sdf.update(lambda row: print(list(row.values())))

this will result in console output like this:

[1715254448786894800, 0.4370160372585058, -0.3171194917678833, -0.3749550101429224]
[1715254448790645800, -0.60107421875, -0.68988037109375, -0.382659912109375]
[1715254448836904700, -0.5078478153288364, -0.8010843272060155, 1.3548703023672104]
[1715254448840656000, -0.7664794921875, -0.7050628662109375, -0.144500732421875]
[1715254448886915600, -0.07118775190114975, 0.7032201653033494, 0.42249665964916344]
[1715254448890666800, -0.8001556396484375, -0.517425537109375, -0.108062744140625]
[1715254448936925700, 0.33777298821806906, -0.5094838920980692, -1.1883429386049509]
[1715254448940677600, -0.802093505859375, -0.6744384765625, -0.1807708740234375]
[1715254448986936800, -1.3308470372229815, 0.024103929165005682, -1.2672994248114526]
[1715254448990687700, -0.9002838134765625, -0.64801025390625, -0.17950439453125]
[1715254449036947700, 0.8130968365609645, 1.4198895947664976, -0.31100747516099364]
[1715254449040699000, -0.59027099609375, -0.563751220703125, -0.0431976318359375]
[1715254449086957600, 1.587524627509713, 0.2660755332291126, 0.7324434833087027]
[1715254449007135000, 247.4679606668651, 0.30848966493495394, -1, 50.113308514642924, 203.781837772578, -1, 4.736579546382497, 3.374811288587825, 14.38960443938865, 0]
[1715254449090708700, -0.56170654296875, -0.6567840576171875, 0.1042022705078125]
[1715254449136969000, -0.45388358542919155, 0.13930676388144492, 0.7338478702139108]
[1715254449140719600, -0.8031158447265625, -0.7025299072265625, 0.11285400390625]
[1715254449186978800, -0.7760480856269597, 0.022184943801164626, -0.2300181131537072]
[1715254449190730800, -0.807373046875, -0.679962158203125, 0.0048980712890625]

Data projection

Now as you can see, there are different columns between messages. That is because phone was sending accelerometer data as well as GPS data. This is not good for any downstream service. We can normalise data using hopping/rolling windwos but that is content of next episode because it requires state. For now lets just filter rows where we have accelerometer data and select only columns we are interested in:

sdf = sdf[sdf.contains("accelerometer-x")]
sdf = sdf[["time", "accelerometer-x", 'accelerometer-y', "accelerometer-z"]]

will output:

[1715254448886915600, 0.42249665964916344, 0.7032201653033494, -0.07118775190114975]
[1715254448936925700, -1.1883429386049509, -0.5094838920980692, 0.33777298821806906]
[1715254448986936800, -1.2672994248114526, 0.024103929165005682, -1.3308470372229815]
[1715254449036947700, -0.31100747516099364, 1.4198895947664976, 0.8130968365609645]
[1715254449086957600, 0.7324434833087027, 0.2660755332291126, 1.587524627509713]
[1715254449136969000, 0.7338478702139108, 0.13930676388144492, -0.45388358542919155]

Feature computation

To calculate new column that inherit value from other columns, we can use familiar aproach from Pandas. Here we calculate total ammount of G forces applied to the device:

sdf["accelerometer-total"] = sdf["accelerometer-x"].abs() + sdf["accelerometer-y"].abs() + sdf["accelerometer-z"].abs()

Rows filtering

Now we are going to filter all stationary rows and just ouptut rows where there were serieous forces measured:

sdf = sdf[sdf["accelerometer-total"] > 50]

This results in this output:

[2024-05-17 12:45:01,321] [INFO] : Waiting for incoming messages
[1715254416230436600, 35.178867746984956, 49.542382594442365, -7.282223292562365, 92.00347363398969]
[1715254416280445700, 50.36172329382896, 44.70135827553272, -16.822199793440102, 111.88528136280179]
[1715254416330454500, -11.002032335722445, 47.5974117254734, 10.359507169878482, 68.95895123107432]
[1715254416380463600, -69.43010393121241, -2.0125345851540564, -33.9619773438096, 105.40461586017608]
[1715254416430472700, -54.148906083559986, -3.226132376524806, -38.182569086217875, 95.55760754630268]
[1715254416580499700, 55.57413411674499, 55.24722271428108, -17.788064933001994, 128.60942176402807]
[1715254416630508500, 33.90870401878357, 61.184908297920224, 3.890451441025734, 98.98406375772953]
[1715254416680517600, -38.64342484874725, 32.89080590964556, 6.14267624130845, 77.67690699970126]
[1715254416730526700, -68.56867297959327, -7.656873182278871, -56.968401194477075, 133.1939473563492]
[1715254416780535800, -42.41844023082256, 2.629225637039542, -20.428836630928515, 65.47650249879061]
[1715254416930563600, 40.01750487849712, 43.55564860999584, -21.342058560061453, 104.91521204855442]
[1715254416980572400, 36.60124565143585, 46.63288514103889, -1.8561786329247056, 85.09030942539945]
[1715254417030581500, -18.095978205007313, 35.07651328792572, 12.269182269060611, 65.44167376199364]
[1715254417080590600, -38.67960909180641, -1.859741147711873, -14.49934136800766, 55.03869160752595]
^C[2024-05-17 12:45:04,208] [INFO] : Stop processing of StreamingDataFrame

At this point we are happy with the ouptut, so it is time to uncomment data output and set static consumer group:

import os
from quixstreams import Application
import uuid
import json

# for local dev, load env vars from a .env file
from dotenv import load_dotenv
load_dotenv()

app = Application(consumer_group="data-normalisation-v1.1", auto_offset_reset="earliest")

input_topic = app.topic(os.environ["input"])
output_topic = app.topic(os.environ["output"])

sdf = app.dataframe(input_topic)

sdf = sdf.apply(lambda message: message["payload"], expand=True)

def transpose(row: dict):

    new_row = {
        "time": row["time"]
    }

    for key in row["values"]:
        new_row[row["name"] + "-" + key] = row["values"][key]

    return new_row


sdf = sdf.apply(transpose)

sdf = sdf[sdf.contains("accelerometer-x")]
sdf = sdf[["time", "accelerometer-x", 'accelerometer-y', "accelerometer-z"]]

sdf["accelerometer-total"] = sdf["accelerometer-x"].abs() + sdf["accelerometer-y"].abs() + sdf["accelerometer-z"].abs()

sdf = sdf[sdf["accelerometer-total"] > 50]
sdf = sdf.update(lambda row: print(list(row.values())))

sdf = sdf.to_topic(output_topic)

if __name__ == "__main__":
    app.run(sdf)

Pipeline deployment

To fininalize our Episode 1, we will equally as with raw-replay service, we will deploy the service to our local pipeline:

quix local pipeline update

resulting:

# Quix Project Descriptor
# This file describes the data pipeline and configuration of resources of a Quix Project.

metadata:
  version: 1.0

# This section describes the Deployments of the data pipeline
deployments:
  - name: raw-replay
    application: raw-replay
    deploymentType: Service
    version: latest
    resources:
      cpu: 200
      memory: 500
      replicas: 1
    variables:
      - name: output
        inputType: OutputTopic
        description: Name of the output topic to write into
        required: true
        value: raw-data
  - name: data-normalisation
    application: data-normalisation
    deploymentType: Service
    version: latest
    resources:
      cpu: 200
      memory: 500
      replicas: 1
    variables:
      - name: input
        inputType: InputTopic
        description: Name of the input topic to listen to.
        required: false
        value: raw-data
      - name: output
        inputType: OutputTopic
        description: Name of the output topic to write to.
        required: false
        value: table-data

# This section describes the Topics of the data pipeline
topics: []

and

quix local pipeline up

Episode 2

Link to the video: youtube.com

In this episode we are going to use stateful processing to normalize our misaligned data using hopping windows.

In order to improve visualization of the data I have added PrettyTable to Data Normalization service. PrettyTable

You need file console_sink.py in order to use it. Otherwise just use sdf = sdf.update(print)

First let's get rid of filters and projections we added at the end of Episode 1. This is our starting point then:

import os
from quixstreams import Application, State
from console_sink import ConsoleSink

# for local dev, load env vars from a .env file
from dotenv import load_dotenv
load_dotenv()


app = Application(consumer_group="odometer-v1.5", auto_offset_reset="earliest")

input_topic = app.topic(os.environ["input"])
output_topic = app.topic(os.environ["output"])
console_sink = ConsoleSink()


sdf = app.dataframe(input_topic)

sdf = sdf.apply(lambda message: message["payload"], expand=True)

def transpose(row: dict):

    new_row = {
        "time": row["time"]
    }

    for key in row["values"]:
        new_row[row["name"] + "-" + key] = row["values"][key]

    return new_row

sdf = sdf.apply(transpose)

sdf = sdf[sdf.contains("accelerometer-x")]

sdf["accelerometer-total"] = sdf["accelerometer-x"].abs() + sdf["accelerometer-y"].abs() + sdf["accelerometer-z"].abs()

sdf = sdf.update(console_sink.print)

#sdf = sdf.to_topic(output_topic)

if __name__ == "__main__":
    app.run(sdf)

This will render data like this:

+---------------------+---------------------+----------------------+---------------------+---------------------+
| accelerometer-total |   accelerometer-x   |   accelerometer-y    |   accelerometer-z   |         time        |
+---------------------+---------------------+----------------------+---------------------+---------------------+
|         ...         |         ...         |         ...          |         ...         |         ...         |
|  10.162986818912625 |  4.907982470417022  | -1.3199994799941777  | -3.9350048685014247 | 1715254407478859000 |
|  9.694509183202683  |  3.3571129183486104 |  3.3870921692878007  |  2.9503040955662727 | 1715254407528867800 |
|  1.590272172652185  | -0.7619710449576378 | -0.04575783482939005 | -0.7825432928651571 | 1715254407578877000 |
|  8.793086997456848  |  -4.751639231538772 | -0.4465983968570828  |  -3.594849369060993 | 1715254407628886000 |
|  13.906442110598086 |  -7.645883001708984 |  0.5143506213515997  |  -5.746208487537503 | 1715254407678895000 |
+---------------------+---------------------+----------------------+---------------------+---------------------+

Odometer (Sum aggregation)

def odometer(row:dict, state: State):

    odometer = state.get("odo", 0)
    odometer += row["accelerometer-total"]
    state.set("odo", odometer)

    return odometer

sdf = sdf.apply(odometer, stateful=True)
sdf = sdf.update(print)

This will render forever increasing number:

979.179732379717
4979.239616939577
4979.45256761634
4979.556884023123
4979.746151626481
4979.796649641575
4979.953991828363
4980.082425316052
4980.228895980214
4980.301952448596
4980.4522055539055
4980.580047797704
4980.908325227823
4981.154737444684
4981.224264569896
4981.398837000687
4981.614090985957
4981.704197373339
4981.790215030103
4982.019386189305
4982.142447628567
4982.22129144818
4982.4055146276305
4982.569420410718
4982.660622776643
4982.724532646496
4982.849878981825
4982.911196497097
4983.004259396198

Tumbling windows

Lets perform tumbling window with mean aggregation over the accelerometer-total column:

sdf = sdf.apply(lambda row: row["accelerometer-total"]) \
    .tumbling_window(2000, 1000) \
    .mean() \
    .final()

# Just for better visualization, remove when writing to output topic.
sdf["start"] = sdf["start"].apply(lambda epoch: str(datetime.fromtimestamp(epoch / 1000)))
sdf["end"] = sdf["end"].apply(lambda epoch: str(datetime.fromtimestamp(epoch / 1000)))

sdf = sdf.update(console_sink.print)

this will render:

+---------------------+---------------------+---------------------+
|        start        |         end         |        value        |
+---------------------+---------------------+---------------------+
|         ...         |         ...         |         ...         |
| 2024-07-23 08:34:32 | 2024-07-23 08:34:34 | 0.14932685365689918 |
| 2024-07-23 08:34:34 | 2024-07-23 08:34:36 | 0.09565322246797382 |
| 2024-07-23 08:34:36 | 2024-07-23 08:34:38 |  0.1288766977590881 |
| 2024-07-23 08:34:38 | 2024-07-23 08:34:40 | 0.12598890389570966 |
| 2024-07-23 08:34:40 | 2024-07-23 08:34:42 | 0.09342559128006919 |
+---------------------+---------------------+---------------------+

Custom reduce function

If you need custom aggregation, you can use .reduce() API:

def mean_reduce(window: dict, value: float):
    window["sum"] += value
    window["count"] += 1
    return window

def mean_init(value: float):
    return {
        "sum": value,
        "count": 1
    }


sdf = sdf.apply(lambda row: row["accelerometer-total"]) \
    .tumbling_window(2000, 1000) \
    .reduce(mean_reduce, mean_init) \
    .final()

sdf["value"] = sdf["value"]["sum"] / sdf["value"]["count"]

# Just for better visualization, remove when writing to output topic.
sdf["start"] = sdf["start"].apply(lambda epoch: str(datetime.fromtimestamp(epoch / 1000)))
sdf["end"] = sdf["end"].apply(lambda epoch: str(datetime.fromtimestamp(epoch / 1000)))

sdf = sdf.update(console_sink.print)

This will render exactly the same data as previous example.

Hopping window

sdf = sdf.apply(lambda row: row["accelerometer-total"]) \
    .hopping_window(4000, 2000, 1000) \
    .reduce(mean_reduce, mean_init) \
    .final()

this will render:

+---------------------+---------------------+---------------------+
|        start        |         end         |        value        |
+---------------------+---------------------+---------------------+
|         ...         |         ...         |         ...         |
| 2024-07-23 08:56:26 | 2024-07-23 08:56:30 | 0.10751399724688382 |
| 2024-07-23 08:56:28 | 2024-07-23 08:56:32 | 0.12998267893308774 |
| 2024-07-23 08:56:30 | 2024-07-23 08:56:34 |  0.1276151241645776 |
| 2024-07-23 08:56:32 | 2024-07-23 08:56:36 | 0.09845571632133795 |
| 2024-07-23 08:56:34 | 2024-07-23 08:56:38 | 0.08961228006960824 |
+---------------------+---------------------+---------------------+

Data Normalization

Data are arriving particularly missaligned. Data from the device looks like this:

+-----------------+-----------------+----------------------+-------------------+
|       time      | accelerometer-z | battery-batteryLevel | location-altitude |
+-----------------+-----------------+----------------------+-------------------+
| 171525441132... | -0.021757073... |                      |                   |
| 171525441137... | -0.040846974... |                      |                   |
| 171525441142... | 0.0231324537... |                      |                   |
| 171525441147... | -0.058454527... |                      |                   |
| 171525441152... | 0.0354296252... |                      |                   |
| 171525441157... |                 |         0.75         |                   |
| 171525441157... | -0.011672902... |                      |                   |
| 171525441162... | -0.057761868... |                      |                   |
| 171525441167... | 0.0527004935... |                      |                   |
| 171525441172... | -0.057727381... |                      |                   |
| 171525441177... | -0.012996844... |                      |                   |
| 171525441182... | 0.0204348852... |                      |                   |
| 171525441187... | -0.075468790... |                      |                   |
| 171525441192... | 0.0370294617... |                      |                   |
| 171525441197... | -0.025990181... |                      |                   |
| 171525441202... | -0.017441548... |                      |                   |
| 171525441207... | -0.024626491... |                      |                   |
| 171525441200... |                 |                      |  249.04045523...  |
| 171525441212... | -0.041283612... |                      |                   |
| 171525441217... | 0.0210392808... |                      |                   |
+-----------------+-----------------+----------------------+-------------------+

This might be difficult to use in service where we want to use accelerometer, location or battery modules at the same time. For that reason we are going to normalize data to uniform frequency and use last value known for that timestamp to build evenly distributed table looking more like this:

+---------------+-----------------+----------------------+----------------------+-----+
|      time     | accelerometer-x | battery-batteryLevel | battery-batteryState | ... |
+---------------+-----------------+----------------------+----------------------+-----+
| 1721727161000 | -7.645883001... |         0.75         |      unplugged       | ... |
| 1721727162000 | 26.581488322... |         0.75         |      unplugged       | ... |
| 1721727163000 | -44.99809431... |         0.75         |      unplugged       | ... |
| 1721727164000 | 53.154980386... |         0.75         |      unplugged       | ... |
| 1721727165000 | 0.4243703445... |         0.75         |      unplugged       | ... |
| 1721727166000 | -23.91999649... |         0.75         |      unplugged       | ... |
| 1721727167000 | 4.1758863392... |         0.75         |      unplugged       | ... |
| 1721727168000 | 1.9415555075... |         0.75         |      unplugged       | ... |
| 1721727169000 | -1.295200846... |         0.75         |      unplugged       | ... |
| 1721727170000 | 0.2961559795... |         0.75         |      unplugged       | ... |
+---------------+-----------------+----------------------+----------------------+-----+

We are going to use hopping windows to normalize them:

sdf = sdf.hopping_window(10000, 1000, 1000) \
        .reduce(lambda window, row: {**window, **row}, lambda row: row) \
        .final()

sdf = sdf.apply(lambda row: {
    **row["value"],
    "time": row["end"]
})