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fix(influxdb3): correct Quix Streams guide for Cloud Dedicated (#6828) 

* chore(deps): update yarn dependencies

Run yarn to update lockfile with latest compatible versions.

* fix(influxdb3): correct Quix Streams guide for Cloud Dedicated

Extract downsample-quix content to shared file and fix product-specific
terminology, links, and prerequisites for Cloud Dedicated and Clustered.

- Use "database" terminology for Cloud Dedicated/Clustered
- Remove Docker from prerequisites (not used in guide)
- Add alt_links for cross-product navigation
- Fix broken TOC anchor links
- Add links to admin pages for tokens and databases
- Remove incorrect /reference/regions link for Cloud Dedicated
- Add lint rules for deprecated code-placeholders and py fence

Closes #6825

* Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* fix(influxdb3): fix broken code blocks in Quix Streams guide

Code blocks inside show-in shortcodes were missing closing fences,
causing the markdown to render incorrectly. Added proper fence
boundaries and placeholder key documentation for each code section.

Also adds TODO to content-editing skill about improving automation
for code-placeholder-key workflow.

* Docs v2 docs v2 pr6828 (#6829)

* fix(influxdb): Rename to match other pages. Remove alt_links

* chore(deps): bump ESLint to 10.0.0

* Update content/shared/v3-process-data/downsample/quix.md

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
pull/6830/head
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26
.claude/skills/content-editing/SKILL.md
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@ -157,6 +157,28 @@ function getSharedSource(filePath) {
- Tests will fail because content hasn't changed
- Published site won't reflect your edits
### Check for Path Differences and Add `alt_links`
When creating or editing shared content, check if the URL paths differ between products. If they do, add `alt_links` frontmatter to each product file to cross-reference the equivalent pages.
See [DOCS-FRONTMATTER.md](../../../DOCS-FRONTMATTER.md#alternative-links-alt_links) for syntax and examples.
### Check product resource terms are cross-referenced
When creating or editing content, check that product resource terms link to `admin/` or `reference/` pages that help the user understand and set up the resource.
Product resource terms often appear inside `code-placeholder-key` shortcode text and bullet item text.
Example product resource terms:
- "database token"
- "database name"
**TODOs for CI/config:**
- Add automated check to validate `alt_links` are present when shared content paths differ across products
- Add check for product-specific URL patterns in shared content (e.g., Cloud Serverless uses `/reference/regions` for URLs, Cloud Dedicated/Clustered do not have this page - cluster URLs come from account setup)
- Add check/helper to ensure resource references (tokens, databases, buckets) link to proper admin pages using `/influxdb3/version/admin/` pattern
- Rethink `code-placeholder-key` workflow: `docs placeholders` adds `placeholders` attributes to code blocks but doesn't generate the "Replace the following:" lists with `{{% code-placeholder-key %}}` shortcodes. Either improve automation to generate these lists, or simplify by removing `code-placeholder-key` if the attribute alone is sufficient
## Part 2: Testing Workflow
After making content changes, run tests to validate:
@ -285,6 +307,7 @@ Vale reports three alert levels:
### Fixing Common Vale Issues
**Spelling/vocabulary errors:**
```bash
# If Vale flags a legitimate term, add it to vocabulary
echo "YourTerm" >> .ci/vale/styles/config/vocabularies/InfluxDataDocs/accept.txt
@ -292,6 +315,7 @@ echo "YourTerm" >> .ci/vale/styles/config/vocabularies/InfluxDataDocs/accept.txt
**Style violations:**
Vale will suggest the correct form. For example:
```
content/file.md:25:1: Use 'InfluxDB 3' instead of 'InfluxDB v3'
```
@ -300,6 +324,7 @@ Simply make the suggested change.
**False positives:**
If Vale incorrectly flags something:
1. Check if it's a new technical term that should be in vocabulary
2. See if the rule needs refinement (consult **vale-rule-config** skill)
3. Add inline comments to disable specific rules if necessary:
@ -617,6 +642,7 @@ ls content/influxdb3/core/api/
- [ ] Content created/edited using appropriate method (CLI or direct)
- [ ] If shared content: Sourcing files touched (or used `docs edit`)
- [ ] If shared content: Check for path differences and add `alt_links` if paths vary
- [ ] Technical accuracy verified (MCP fact-check if needed)
- [ ] Hugo builds without errors (`hugo --quiet`)
- [ ] Vale style linting passes (`docker compose run -T vale content/**/*.md`)

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@ -0,0 +1,16 @@
---
title: Downsample data stored in InfluxDB using Quix Streams
description: >
Use [Quix Streams](https://github.com/quixio/quix-streams) to query time series
data stored in InfluxDB and written to Kafka at regular intervals, continuously
downsample it, and then write the downsampled data back to InfluxDB.
menu:
influxdb3_cloud_dedicated:
name: Use Quix
parent: Downsample data
identifier: influxdb-dedicated-downsample-quix
weight: 102
aliases:
- /influxdb3/cloud-dedicated/process-data/downsample-quix/
source: /content/shared/v3-process-data/downsample/quix.md
---

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content/influxdb3/cloud-serverless/process-data/downsample/quix.md
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@ -12,350 +12,5 @@ menu:
weight: 202
related:
- /influxdb3/cloud-serverless/query-data/sql/aggregate-select/, Aggregate or apply selector functions to data (SQL)
source: /content/shared/v3-process-data/downsample/quix.md
---
Use [Quix Streams](https://github.com/quixio/quix-streams) to query time series
data stored in InfluxDB and written to Kafka at regular intervals, continuously
downsample it, and then write the downsampled data back to InfluxDB.
Quix Streams is an open source Python library for building containerized stream
processing applications with Apache Kafka. It is designed to run as a service
that continuously processes a stream of data while streaming the results to a
Kafka topic. You can try it locally, with a local Kafka installation, or run it
in [Quix Cloud](https://quix.io/) with a free trial.
This guide uses [Python](https://www.python.org/) and the
[InfluxDB 3 Python client library](https://github.com/InfluxCommunity/influxdb3-python),
but you can use your runtime of choice and any of the available
[InfluxDB 3 client libraries](/influxdb3/cloud-serverless/reference/client-libraries/v3/).
This guide also assumes you have already
[setup your Python project and virtual environment](/influxdb3/cloud-serverless/query-data/execute-queries/client-libraries/python/#create-a-python-virtual-environment).
## Pipeline architecture
The following diagram illustrates how data is passed between processes as it is downsampled:
{{< html-diagram/quix-downsample-pipeline >}}
> [!Note]
> It is usually more efficient to write raw data directly to Kafka rather than
> writing raw data to InfluxDB first (essentially starting the Quix Streams
> pipeline with the "raw-data" topic). However, this guide assumes that you
> already have raw data in InfluxDB that you want to downsample.
---
1. [Set up prerequisites](#set-up-prerequisites)
2. [Install dependencies](#install-dependencies)
3. [Prepare InfluxDB buckets](#prepare-influxdb-buckets)
4. [Create the downsampling logic](#create-the-downsampling-logic)
5. [Create the producer and consumer clients](#create-the-producer-and-consumer-clients)
1. [Create the producer](#create-the-producer)
2. [Create the consumer](#create-the-consumer)
6. [Get the full downsampling code files](#get-the-full-downsampling-code-files)
## Set up prerequisites
The process described in this guide requires the following:
- An InfluxDB Cloud Serverless account with data ready for downsampling.
- A [Quix Cloud](https://portal.platform.quix.io/self-sign-up/) account or a
local Apache Kafka or Red Panda installation.
- Familiarity with basic Python and Docker concepts.
## Install dependencies
Use `pip` to install the following dependencies:
- `influxdb_client_3`
- `quixstreams<2.5`
- `pandas`
```sh
pip install influxdb3-python pandas quixstreams<2.5
```
## Prepare InfluxDB buckets
The downsampling process involves two InfluxDB buckets.
Each bucket has a [retention period](/influxdb3/cloud-serverless/reference/glossary/#retention-period)
that specifies how long data persists before it expires and is deleted.
By using two buckets, you can store unmodified, high-resolution data in a bucket
with a shorter retention period and then downsampled, low-resolution data in a
bucket with a longer retention period.
Ensure you have a bucket for each of the following:
- One to query unmodified data from
- The other to write downsampled data to
For information about creating buckets, see
[Create a bucket](/influxdb3/cloud-serverless/admin/buckets/create-bucket/).
## Create the downsampling logic
This process reads the raw data from the input Kafka topic that stores data streamed from InfluxDB,
downsamples it, and then sends it to an output topic that is used to write back to InfluxDB.
1. Use the Quix Streams library's `Application` class to initialize a connection to Apache Kafka.
```py
from quixstreams import Application
app = Application(consumer_group='downsampling-process', auto_offset_reset='earliest')
input_topic = app.topic('raw-data')
output_topic = app.topic('downsampled-data')
# ...
```
2. Configure the Quix Streams built-in windowing function to create a tumbling
window that continously downsamples the data into 1-minute buckets.
```py
# ...
target_field = 'temperature' # The field that you want to downsample.
def custom_ts_extractor(value):
# ...
# truncated for brevity - custom code that defines the 'time_recorded'
# field as the timestamp to use for windowing...
topic = app.topic(input_topic, timestamp_extractor=custom_ts_extractor)
sdf = (
sdf.apply(lambda value: value[target_field]) # Extract temperature values
.tumbling_window(timedelta(minutes=1)) # 1-minute tumbling windows
.mean() # Calculate average temperature
.final() # Emit results at window completion
)
sdf = sdf.apply(
lambda value: {
'time': value['end'], # End of the window
'temperature_avg': value['value'], # Average temperature
}
)
sdf.to_topic(output_topic) # Output results to the 'downsampled-data' topic
# ...
```
The results are streamed to the Kafka topic, `downsampled-data`.
> [!Note]
> "sdf" stands for "Streaming Dataframe".
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/Downsampler/main.py).
## Create the producer and consumer clients
Use the `influxdb_client_3` and `quixstreams` modules to instantiate two clients that interact with InfluxDB and Apache Kafka:
- A **producer** client configured to read from your InfluxDB bucket with _unmodified_ data and _produce_ that data to Kafka.
- A **consumer** client configured to _consume_ data from Kafka and write the _downsampled_ data to the corresponding InfluxDB bucket.
### Create the producer client
Provide the following credentials for the producer:
- **host**: [{{< product-name >}} region URL](/influxdb3/cloud-serverless/reference/regions)
_(without the protocol)_
- **org**: InfluxDB organization name
- **token**: InfluxDB API token with read and write permissions on the buckets you
want to query and write to.
- **database**: InfluxDB bucket name
The producer queries for fresh data from InfluxDB at specific intervals. It's configured to look for a specific measurement defined in a variable. It writes the raw data to a Kafka topic called 'raw-data'
{{% code-placeholders "(API|(RAW|DOWNSAMPLED)_BUCKET|ORG)_(NAME|TOKEN)" %}}
```py
from influxdb_client_3 import InfluxDBClient3
from quixstreams import Application
import pandas
# Instantiate an InfluxDBClient3 client configured for your unmodified bucket
influxdb_raw = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='API_TOKEN',
database='RAW_BUCKET_NAME'
)
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams producer application that connects to a local Kafka installation
app = Application(
broker_address=os.environ.get('BROKER_ADDRESS','localhost:9092'),
consumer_group=consumer_group_name,
auto_create_topics=True
)
# Override the app variable if the local development env var is set to false or is not present.
# This causes Quix Streams to use an application configured for Quix Cloud
localdev = os.environ.get('localdev', 'false')
if localdev == 'false':
# Create a Quix platform-specific application instead (broker address is in-built)
app = Application(consumer_group=consumer_group_name, auto_create_topics=True)
topic = app.topic(name='raw-data')
## ... remaining code trunctated for brevity ...
# Query InfluxDB for the raw data and store it in a Dataframe
def get_data():
# Run in a loop until the main thread is terminated
while run:
try:
myquery = f'SELECT * FROM "{measurement_name}" WHERE time >= {interval}'
print(f'sending query {myquery}')
# Query InfluxDB 3 using influxql or sql
table = influxdb_raw.query(
query=myquery,
mode='pandas',
language='influxql')
#... remaining code trunctated for brevity ...
# Send the data to a Kafka topic for the downsampling process to consumer
def main():
"""
Read data from the Query and publish it to Kafka
"""
#... remaining code trunctated for brevity ...
for index, obj in enumerate(records):
print(obj) # Obj contains each row in the table includimng temperature
# Generate a unique message_key for each row
message_key = obj['machineId']
logger.info(f'Produced message with key:{message_key}, value:{obj}')
serialized = topic.serialize(
key=message_key, value=obj, headers={'uuid': str(uuid.uuid4())}
)
# publish each row returned in the query to the topic 'raw-data'
producer.produce(
topic=topic.name,
headers=serialized.headers,
key=serialized.key,
value=serialized.value,
)
```
{{% /code-placeholders %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Source/main.py).
### Create the consumer
As before, provide the following credentials for the consumer:
- **host**: [{{< product-name >}} region URL](/influxdb3/cloud-serverless/reference/regions)
_(without the protocol)_
- **org**: InfluxDB organization name
- **token**: InfluxDB API token with read and write permissions on the buckets you
want to query and write to.
- **database**: InfluxDB bucket name
This process reads messages from the Kafka topic `downsampled-data` and writes each message as a point dictionary back to InfluxDB.
{{% code-placeholders "(API|(RAW|DOWNSAMPLED)_BUCKET|ORG)_(NAME|TOKEN)" %}}
```py
# Instantiate an InfluxDBClient3 client configured for your downsampled database.
# When writing, the org= argument is required by the client (but ignored by InfluxDB).
influxdb_downsampled = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='API_TOKEN',
database='DOWNSAMPLED_BUCKET_NAME',
org=''
)
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams consumer application that connects to a local Kafka installation
app = Application(
broker_address=os.environ.get('BROKER_ADDRESS','localhost:9092'),
consumer_group=consumer_group_name,
auto_create_topics=True
)
# Override the app variable if the local development env var is set to false or is not present.
# This causes Quix Streams to use an application configured for Quix Cloud
localdev = os.environ.get('localdev', 'false')
if localdev == 'false':
# Create a Quix platform-specific application instead (broker address is in-built)
app = Application(consumer_group=consumer_group_name, auto_create_topics=True)
input_topic = app.topic('downsampled-data')
## ... remaining code trunctated for brevity ...
def send_data_to_influx(message):
logger.info(f'Processing message: {message}')
try:
## ... remaining code trunctated for brevity ...
# Construct the points dictionary
points = {
'measurement': measurement_name,
'tags': tags,
'fields': fields,
'time': message['time']
}
influxdb_downsampled.write(record=points, write_precision='ms')
sdf = app.dataframe(input_topic)
sdf = sdf.update(send_data_to_influx) # Continuously apply the 'send_data' function to each message in the incoming stream
## ... remaining code trunctated for brevity ...
```
{{% /code-placeholders %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Sink/main.py).
## Get the full downsampling code files
To get the complete set of files referenced in this tutorial, clone the Quix "downsampling template" repository or use an interactive version of this tutorial saved as a Jupyter Notebook.
### Clone the downsampling template repository
To clone the downsampling template, enter the following command in the command line:
```sh
git clone https://github.com/quixio/template-influxdbv3-downsampling.git
```
This repository contains the following folders which store different parts of the whole pipeline:
- **Machine Data to InfluxDB**: A script that generates synthetic machine data
and writes it to InfluxDB. This is useful if you dont have your own data yet,
or just want to work with test data first.
- It produces a reading every 250 milliseconds.
- This script originally comes from the
[InfluxCommunity repository](https://github.com/InfluxCommunity/Arrow-Task-Engine/blob/master/machine_simulator/src/machine_generator.py)
but has been adapted to write directly to InfluxDB rather than using an MQTT broker.
- **InfluxDB V3 Data Source**: A service that queries for fresh data from
InfluxDB at specific intervals. It's configured to look for the measurement
produced by the previously-mentioned synthetic machine data generator.
It writes the raw data to a Kafka topic called "raw-data".
- **Downsampler**: A service that performs a 1-minute tumbling window operation
on the data from InfluxDB and emits the mean of the "temperature" reading
every minute. It writes the output to a "downsampled-data" Kafka topic.
- **InfluxDB V3 Data Sink**: A service that reads from the "downsampled-data"
topic and writes the downsample records as points back into InfluxDB.
### Use the downsampling Jupyter Notebook
You can use the interactive notebook ["Continuously downsample data using InfluxDB and Quix Streams"](https://github.com/quixio/tutorial-code/edit/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb) to try downsampling code yourself. It is configured to install Apache Kafka within the runtime environment (such as Google Colab).
Each process is also set up to run in the background so that a running cell does not block the rest of the tutorial.
<a target="_blank" href="https://colab.research.google.com/github/quixio/tutorial-code/blob/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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@ -12,350 +12,5 @@ menu:
weight: 202
related:
- /influxdb3/clustered/query-data/sql/aggregate-select/, Aggregate or apply selector functions to data (SQL)
source: /content/shared/v3-process-data/downsample/quix.md
---
Use [Quix Streams](https://github.com/quixio/quix-streams) to query time series
data stored in InfluxDB and written to Kafka at regular intervals, continuously
downsample it, and then write the downsampled data back to InfluxDB.
Quix Streams is an open source Python library for building containerized stream
processing applications with Apache Kafka. It is designed to run as a service
that continuously processes a stream of data while streaming the results to a
Kafka topic. You can try it locally, with a local Kafka installation, or run it
in [Quix Cloud](https://quix.io/) with a free trial.
This guide uses [Python](https://www.python.org/) and the
[InfluxDB 3 Python client library](https://github.com/InfluxCommunity/influxdb3-python),
but you can use your runtime of choice and any of the available
[InfluxDB 3 client libraries](/influxdb3/clustered/reference/client-libraries/v3/).
This guide also assumes you have already
[setup your Python project and virtual environment](/influxdb3/clustered/query-data/execute-queries/client-libraries/python/#create-a-python-virtual-environment).
## Pipeline architecture
The following diagram illustrates how data is passed between processes as it is downsampled:
{{< html-diagram/quix-downsample-pipeline >}}
> [!Note]
> It is usually more efficient to write raw data directly to Kafka rather than
> writing raw data to InfluxDB first (essentially starting the Quix Streams
> pipeline with the "raw-data" topic). However, this guide assumes that you
> already have raw data in InfluxDB that you want to downsample.
---
1. [Set up prerequisites](#set-up-prerequisites)
2. [Install dependencies](#install-dependencies)
3. [Prepare InfluxDB buckets](#prepare-influxdb-buckets)
4. [Create the downsampling logic](#create-the-downsampling-logic)
5. [Create the producer and consumer clients](#create-the-producer-and-consumer-clients)
1. [Create the producer](#create-the-producer)
2. [Create the consumer](#create-the-consumer)
6. [Get the full downsampling code files](#get-the-full-downsampling-code-files)
## Set up prerequisites
The process described in this guide requires the following:
- An InfluxDB cluster with data ready for downsampling.
- A [Quix Cloud](https://portal.platform.quix.io/self-sign-up/) account or a
local Apache Kafka or Red Panda installation.
- Familiarity with basic Python and Docker concepts.
## Install dependencies
Use `pip` to install the following dependencies:
- `influxdb_client_3`
- `quixstreams<2.5`
- `pandas`
```sh
pip install influxdb3-python pandas quixstreams<2.5
```
## Prepare InfluxDB buckets
The downsampling process involves two InfluxDB databases.
Each database has a [retention period](/influxdb3/clustered/reference/glossary/#retention-period)
that specifies how long data persists before it expires and is deleted.
By using two databases, you can store unmodified, high-resolution data in a database
with a shorter retention period and then downsampled, low-resolution data in a
database with a longer retention period.
Ensure you have a database for each of the following:
- One to query unmodified data from
- The other to write downsampled data to
For information about creating databases, see
[Create a bucket](/influxdb3/clustered/admin/databases/create/).
## Create the downsampling logic
This process reads the raw data from the input Kafka topic that stores data streamed from InfluxDB,
downsamples it, and then sends it to an output topic that is used to write back to InfluxDB.
1. Use the Quix Streams library's `Application` class to initialize a connection to Apache Kafka.
```py
from quixstreams import Application
app = Application(consumer_group='downsampling-process', auto_offset_reset='earliest')
input_topic = app.topic('raw-data')
output_topic = app.topic('downsampled-data')
# ...
```
2. Configure the Quix Streams built-in windowing function to create a tumbling
window that continously downsamples the data into 1-minute buckets.
```py
# ...
target_field = 'temperature' # The field that you want to downsample.
def custom_ts_extractor(value):
# ...
# truncated for brevity - custom code that defines the 'time_recorded'
# field as the timestamp to use for windowing...
topic = app.topic(input_topic, timestamp_extractor=custom_ts_extractor)
sdf = (
sdf.apply(lambda value: value[target_field]) # Extract temperature values
.tumbling_window(timedelta(minutes=1)) # 1-minute tumbling windows
.mean() # Calculate average temperature
.final() # Emit results at window completion
)
sdf = sdf.apply(
lambda value: {
'time': value['end'], # End of the window
'temperature_avg': value['value'], # Average temperature
}
)
sdf.to_topic(output_topic) # Output results to the 'downsampled-data' topic
# ...
```
The results are streamed to the Kafka topic, `downsampled-data`.
> [!Note]
> "sdf" stands for "Streaming Dataframe".
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/Downsampler/main.py).
## Create the producer and consumer clients
Use the `influxdb_client_3` and `quixstreams` modules to instantiate two clients that interact with InfluxDB and Apache Kafka:
- A **producer** client configured to read from your InfluxDB database with _unmodified_ data and _produce_ that data to Kafka.
- A **consumer** client configured to _consume_ data from Kafka and write the _downsampled_ data to the corresponding InfluxDB database.
### Create the producer client
Provide the following credentials for the producer:
- **host**: {{< product-name omit=" Clustered">}} cluster URL
_(without the protocol)_
- **org**: An arbitrary string. {{< product-name >}} ignores the organization.
- **token**: InfluxDB database token with read and write permissions on the databases you
want to query and write to.
- **database**: InfluxDB database name
The producer queries for fresh data from InfluxDB at specific intervals. It's configured to look for a specific measurement defined in a variable. It writes the raw data to a Kafka topic called 'raw-data'
{{% code-placeholders "(RAW|DOWNSAMPLED)_DATABASE_(NAME|TOKEN)" %}}
```py
from influxdb_client_3 import InfluxDBClient3
from quixstreams import Application
import pandas
# Instantiate an InfluxDBClient3 client configured for your unmodified database
influxdb_raw = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='DATABASE_TOKEN',
database='RAW_DATABASE_NAME'
)
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams producer application that connects to a local Kafka installation
app = Application(
broker_address=os.environ.get('BROKER_ADDRESS','localhost:9092'),
consumer_group=consumer_group_name,
auto_create_topics=True
)
# Override the app variable if the local development env var is set to false or is not present.
# This causes Quix Streams to use an application configured for Quix Cloud
localdev = os.environ.get('localdev', 'false')
if localdev == 'false':
# Create a Quix platform-specific application instead (broker address is in-built)
app = Application(consumer_group=consumer_group_name, auto_create_topics=True)
topic = app.topic(name='raw-data')
## ... remaining code trunctated for brevity ...
# Query InfluxDB for the raw data and store it in a Dataframe
def get_data():
# Run in a loop until the main thread is terminated
while run:
try:
myquery = f'SELECT * FROM "{measurement_name}" WHERE time >= {interval}'
print(f'sending query {myquery}')
# Query InfluxDB 3 using influxql or sql
table = influxdb_raw.query(
query=myquery,
mode='pandas',
language='influxql')
#... remaining code trunctated for brevity ...
# Send the data to a Kafka topic for the downsampling process to consumer
def main():
"""
Read data from the Query and publish it to Kafka
"""
#... remaining code trunctated for brevity ...
for index, obj in enumerate(records):
print(obj) # Obj contains each row in the table includimng temperature
# Generate a unique message_key for each row
message_key = obj['machineId']
logger.info(f'Produced message with key:{message_key}, value:{obj}')
serialized = topic.serialize(
key=message_key, value=obj, headers={'uuid': str(uuid.uuid4())}
)
# publish each row returned in the query to the topic 'raw-data'
producer.produce(
topic=topic.name,
headers=serialized.headers,
key=serialized.key,
value=serialized.value,
)
```
{{% /code-placeholders %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Source/main.py).
### Create the consumer
As before, provide the following credentials for the consumer:
- **host**: {{< product-name omit=" Clustered">}} cluster URL
_(without the protocol)_
- **org**: An arbitrary string. {{< product-name >}} ignores the organization.
- **token**: InfluxDB database token with read and write permissions on the databases you
want to query and write to.
- **database**: InfluxDB database name
This process reads messages from the Kafka topic `downsampled-data` and writes each message as a point dictionary back to InfluxDB.
{{% code-placeholders "(RAW|DOWNSAMPLED)_DATABASE_(NAME|TOKEN)" %}}
```py
# Instantiate an InfluxDBClient3 client configured for your downsampled database.
# When writing, the org= argument is required by the client (but ignored by InfluxDB).
influxdb_downsampled = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='DATABASE_TOKEN',
database='DOWNSAMPLED_DATABASE_NAME',
org=''
)
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams consumer application that connects to a local Kafka installation
app = Application(
broker_address=os.environ.get('BROKER_ADDRESS','localhost:9092'),
consumer_group=consumer_group_name,
auto_create_topics=True
)
# Override the app variable if the local development env var is set to false or is not present.
# This causes Quix Streams to use an application configured for Quix Cloud
localdev = os.environ.get('localdev', 'false')
if localdev == 'false':
# Create a Quix platform-specific application instead (broker address is in-built)
app = Application(consumer_group=consumer_group_name, auto_create_topics=True)
input_topic = app.topic('downsampled-data')
## ... remaining code trunctated for brevity ...
def send_data_to_influx(message):
logger.info(f'Processing message: {message}')
try:
## ... remaining code trunctated for brevity ...
# Construct the points dictionary
points = {
'measurement': measurement_name,
'tags': tags,
'fields': fields,
'time': message['time']
}
influxdb_downsampled.write(record=points, write_precision='ms')
sdf = app.dataframe(input_topic)
sdf = sdf.update(send_data_to_influx) # Continuously apply the 'send_data' function to each message in the incoming stream
## ... remaining code trunctated for brevity ...
```
{{% /code-placeholders %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Sink/main.py).
## Get the full downsampling code files
To get the complete set of files referenced in this tutorial, clone the Quix "downsampling template" repository or use an interactive version of this tutorial saved as a Jupyter Notebook.
### Clone the downsampling template repository
To clone the downsampling template, enter the following command in the command line:
```sh
git clone https://github.com/quixio/template-influxdbv3-downsampling.git
```
This repository contains the following folders which store different parts of the whole pipeline:
- **Machine Data to InfluxDB**: A script that generates synthetic machine data
and writes it to InfluxDB. This is useful if you dont have your own data yet,
or just want to work with test data first.
- It produces a reading every 250 milliseconds.
- This script originally comes from the
[InfluxCommunity repository](https://github.com/InfluxCommunity/Arrow-Task-Engine/blob/master/machine_simulator/src/machine_generator.py)
but has been adapted to write directly to InfluxDB rather than using an MQTT broker.
- **InfluxDB V3 Data Source**: A service that queries for fresh data from
InfluxDB at specific intervals. It's configured to look for the measurement
produced by the previously-mentioned synthetic machine data generator.
It writes the raw data to a Kafka topic called "raw-data".
- **Downsampler**: A service that performs a 1-minute tumbling window operation
on the data from InfluxDB and emits the mean of the "temperature" reading
every minute. It writes the output to a "downsampled-data" Kafka topic.
- **InfluxDB V3 Data Sink**: A service that reads from the "downsampled-data"
topic and writes the downsample records as points back into InfluxDB.
### Use the downsampling Jupyter Notebook
You can use the interactive notebook ["Continuously downsample data using InfluxDB and Quix Streams"](https://github.com/quixio/tutorial-code/edit/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb) to try downsampling code yourself. It is configured to install Apache Kafka within the runtime environment (such as Google Colab).
Each process is also set up to run in the background so that a running cell does not block the rest of the tutorial.
<a target="_blank" href="https://colab.research.google.com/github/quixio/tutorial-code/blob/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

193
content/influxdb3/cloud-dedicated/process-data/downsample/downsample-quix.md → content/shared/v3-process-data/downsample/quix.md
View File

@ -1,17 +1,3 @@
---
title: Downsample data stored in InfluxDB using Quix Streams
description: >
Use [Quix Streams](https://github.com/quixio/quix-streams) to query time series
data stored in InfluxDB and written to Kafka at regular intervals, continuously
downsample it, and then write the downsampled data back to InfluxDB.
menu:
influxdb3_cloud_dedicated:
name: Use Quix
parent: Downsample data
identifier: influxdb-dedicated-downsample-quix
weight: 102
---
Use [Quix Streams](https://github.com/quixio/quix-streams) to query time series
data stored in InfluxDB and written to Kafka at regular intervals, continuously
downsample it, and then write the downsampled data back to InfluxDB.
@ -24,9 +10,9 @@ in [Quix Cloud](https://quix.io/) with a free trial.
This guide uses [Python](https://www.python.org/) and the
[InfluxDB 3 Python client library](https://github.com/InfluxCommunity/influxdb3-python),
but you can use your runtime of choice and any of the available
[InfluxDB 3 client libraries](/influxdb3/cloud-dedicated/reference/client-libraries/v3/).
[InfluxDB 3 client libraries](/influxdb3/version/reference/client-libraries/v3/).
This guide also assumes you have already
[setup your Python project and virtual environment](/influxdb3/cloud-dedicated/query-data/execute-queries/client-libraries/python/#create-a-python-virtual-environment).
[setup your Python project and virtual environment](/influxdb3/version/query-data/execute-queries/client-libraries/python/#create-a-python-virtual-environment).
## Pipeline architecture
@ -44,21 +30,31 @@ The following diagram illustrates how data is passed between processes as it is
1. [Set up prerequisites](#set-up-prerequisites)
2. [Install dependencies](#install-dependencies)
{{% show-in "cloud-serverless" %}}
3. [Prepare InfluxDB buckets](#prepare-influxdb-buckets)
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
3. [Prepare InfluxDB databases](#prepare-influxdb-databases)
{{% /show-in %}}
4. [Create the downsampling logic](#create-the-downsampling-logic)
5. [Create the producer and consumer clients](#create-the-producer-and-consumer-clients)
1. [Create the producer](#create-the-producer)
2. [Create the consumer](#create-the-consumer)
1. [Create the producer client](#create-the-producer-client)
2. [Create the consumer client](#create-the-consumer-client)
6. [Get the full downsampling code files](#get-the-full-downsampling-code-files)
## Set up prerequisites
The process described in this guide requires the following:
{{% show-in "cloud-serverless" %}}
- An InfluxDB Cloud Serverless account with data ready for downsampling.
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
- An InfluxDB cluster with data ready for downsampling.
{{% /show-in %}}
- A [Quix Cloud](https://portal.platform.quix.io/self-sign-up/) account or a
local Apache Kafka or Red Panda installation.
- Familiarity with basic Python and Docker concepts.
local Apache Kafka or Redpanda installation.
- Familiarity with basic Python concepts.
## Install dependencies
@ -72,22 +68,33 @@ Use `pip` to install the following dependencies:
pip install influxdb3-python pandas quixstreams<2.5
```
{{% show-in "cloud-serverless" %}}
## Prepare InfluxDB buckets
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
## Prepare InfluxDB databases
{{% /show-in %}}
The downsampling process involves two InfluxDB buckets.
Each bucket has a [retention period](/influxdb3/cloud-dedicated/reference/glossary/#retention-period)
The downsampling process involves two InfluxDB {{% show-in "cloud-serverless" %}}buckets{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}databases{{% /show-in %}}.
Each {{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}} has a [retention period](/influxdb3/version/reference/glossary/#retention-period)
that specifies how long data persists before it expires and is deleted.
By using two buckets, you can store unmodified, high-resolution data in a bucket
By using two {{% show-in "cloud-serverless" %}}buckets{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}databases{{% /show-in %}}, you can store unmodified, high-resolution data in a {{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}}
with a shorter retention period and then downsampled, low-resolution data in a
bucket with a longer retention period.
{{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}} with a longer retention period.
Ensure you have a bucket for each of the following:
Ensure you have a {{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}} for each of the following:
- One to query unmodified data from
- The other to write downsampled data to
{{% show-in "cloud-serverless" %}}
For information about creating buckets, see
[Create a bucket](/influxdb3/cloud-dedicated/admin/buckets/create-bucket/).
[Create a bucket](/influxdb3/cloud-serverless/admin/buckets/create-bucket/).
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
For information about creating databases, see
[Create a database](/influxdb3/version/admin/databases/create/).
{{% /show-in %}}
## Create the downsampling logic
@ -96,7 +103,7 @@ downsamples it, and then sends it to an output topic that is used to write back
1. Use the Quix Streams library's `Application` class to initialize a connection to Apache Kafka.
```py
```python
from quixstreams import Application
app = Application(consumer_group='downsampling-process', auto_offset_reset='earliest')
@ -107,9 +114,9 @@ downsamples it, and then sends it to an output topic that is used to write back
```
2. Configure the Quix Streams built-in windowing function to create a tumbling
window that continously downsamples the data into 1-minute buckets.
window that continuously downsamples the data into 1-minute buckets.
```py
```python
# ...
target_field = 'temperature' # The field that you want to downsample.
@ -148,26 +155,36 @@ You can find the full code for this process in the
## Create the producer and consumer clients
Use the `influxdb_client_3` and `quixstreams` modules to instantiate two clients that interact with InfluxDB and Apache Kafka:
Use the `influxdb_client_3` and `quixstreams` modules to instantiate two clients that interact with InfluxDB and Apache Kafka:
- A **producer** client configured to read from your InfluxDB bucket with _unmodified_ data and _produce_ that data to Kafka.
- A **consumer** client configured to _consume_ data from Kafka and write the _downsampled_ data to the corresponding InfluxDB bucket.
- A **producer** client configured to read from your InfluxDB {{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}} with _unmodified_ data and _produce_ that data to Kafka.
- A **consumer** client configured to _consume_ data from Kafka and write the _downsampled_ data to the corresponding InfluxDB {{% show-in "cloud-serverless" %}}bucket{{% /show-in %}}{{% show-in "cloud-dedicated,clustered" %}}database{{% /show-in %}}.
### Create the producer client
Provide the following credentials for the producer:
- **host**: [{{< product-name >}} region URL](/influxdb3/cloud-dedicated/reference/regions)
{{% show-in "cloud-serverless" %}}
- **host**: Your [{{% product-name %}} region URL](/influxdb3/cloud-serverless/reference/regions)
_(without the protocol)_
- **org**: InfluxDB organization name
- **token**: InfluxDB API token with read and write permissions on the buckets you
- **org**: Your InfluxDB organization name
- **token**: Your InfluxDB [API token](/influxdb3/version/admin/tokens/) with read and write permissions on the buckets you
want to query and write to.
- **database**: InfluxDB bucket name
- **database**: Your InfluxDB [bucket](/influxdb3/version/admin/buckets/) name
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
- **host**: Your {{% product-name omit=" Clustered" %}} cluster URL
_(without the protocol)_
- **org**: An arbitrary string (InfluxDB ignores this parameter, but the client requires it)
- **token**: Your InfluxDB [database token](/influxdb3/version/admin/tokens/database/) with read and write permissions on the databases you
want to query and write to.
- **database**: Your InfluxDB [database](/influxdb3/version/admin/databases/) name
{{% /show-in %}}
The producer queries for fresh data from InfluxDB at specific intervals. It's configured to look for a specific measurement defined in a variable. It writes the raw data to a Kafka topic called 'raw-data'
{{% code-placeholders "(API|(RAW|DOWNSAMPLED)_BUCKET|ORG)_(NAME|TOKEN)" %}}
```py
{{% show-in "cloud-serverless" %}}
```python { placeholders="API_TOKEN|RAW_BUCKET_NAME" }
from influxdb_client_3 import InfluxDBClient3
from quixstreams import Application
import pandas
@ -178,7 +195,24 @@ influxdb_raw = InfluxDBClient3(
token='API_TOKEN',
database='RAW_BUCKET_NAME'
)
```
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
```python { placeholders="DATABASE_TOKEN|RAW_DATABASE_NAME" }
from influxdb_client_3 import InfluxDBClient3
from quixstreams import Application
import pandas
# Instantiate an InfluxDBClient3 client configured for your unmodified database
influxdb_raw = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='DATABASE_TOKEN',
database='RAW_DATABASE_NAME'
)
```
{{% /show-in %}}
```python
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams producer application that connects to a local Kafka installation
@ -215,7 +249,7 @@ def get_data():
#... remaining code trunctated for brevity ...
# Send the data to a Kafka topic for the downsampling process to consumer
# Send the data to a Kafka topic for the downsampling process to consumer
def main():
"""
Read data from the Query and publish it to Kafka
@ -223,7 +257,7 @@ def main():
#... remaining code trunctated for brevity ...
for index, obj in enumerate(records):
print(obj) # Obj contains each row in the table includimng temperature
print(obj) # Obj contains each row in the table including temperature
# Generate a unique message_key for each row
message_key = obj['machineId']
logger.info(f'Produced message with key:{message_key}, value:{obj}')
@ -241,27 +275,47 @@ def main():
)
```
{{% /code-placeholders %}}
Replace the following:
{{% show-in "cloud-serverless" %}}
- {{% code-placeholder-key %}}`API_TOKEN`{{% /code-placeholder-key %}}: your InfluxDB [API token](/influxdb3/version/admin/tokens/) with read permission on the bucket
- {{% code-placeholder-key %}}`RAW_BUCKET_NAME`{{% /code-placeholder-key %}}: the name of your InfluxDB [bucket](/influxdb3/version/admin/buckets/) with unmodified data
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
- {{% code-placeholder-key %}}`DATABASE_TOKEN`{{% /code-placeholder-key %}}: your InfluxDB [database token](/influxdb3/version/admin/tokens/database/) with read permission on the database
- {{% code-placeholder-key %}}`RAW_DATABASE_NAME`{{% /code-placeholder-key %}}: the name of your InfluxDB [database](/influxdb3/version/admin/databases/) with unmodified data
{{% /show-in %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Source/main.py).
### Create the consumer
### Create the consumer client
As before, provide the following credentials for the consumer:
- **host**: [{{< product-name >}} region URL](/influxdb3/cloud-dedicated/reference/regions)
{{% show-in "cloud-serverless" %}}
- **host**: Your [{{% product-name %}} region URL](/influxdb3/cloud-serverless/reference/regions)
_(without the protocol)_
- **org**: InfluxDB organization name
- **token**: InfluxDB API token with read and write permissions on the buckets you
- **org**: Your InfluxDB organization name
- **token**: Your InfluxDB [API token](/influxdb3/version/admin/tokens/) with read and write permissions on the buckets you
want to query and write to.
- **database**: InfluxDB bucket name
- **database**: Your InfluxDB [bucket](/influxdb3/version/admin/buckets/) name
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
- **host**: Your {{% product-name omit=" Clustered" %}} cluster URL
_(without the protocol)_
- **org**: An arbitrary string (InfluxDB ignores this parameter, but the client requires it)
- **token**: Your InfluxDB [database token](/influxdb3/version/admin/tokens/database/) with read and write permissions on the databases you
want to query and write to.
- **database**: Your InfluxDB [database](/influxdb3/version/admin/databases/) name
{{% /show-in %}}
This process reads messages from the Kafka topic `downsampled-data` and writes each message as a point dictionary back to InfluxDB.
{{% code-placeholders "(API|(RAW|DOWNSAMPLED)_BUCKET|ORG)_(NAME|TOKEN)" %}}
```py
# Instantiate an InfluxDBClient3 client configured for your downsampled database.
{{% show-in "cloud-serverless" %}}
```python { placeholders="API_TOKEN|DOWNSAMPLED_BUCKET_NAME" }
# Instantiate an InfluxDBClient3 client configured for your downsampled bucket.
# When writing, the org= argument is required by the client (but ignored by InfluxDB).
influxdb_downsampled = InfluxDBClient3(
host='{{< influxdb/host >}}',
@ -269,7 +323,22 @@ influxdb_downsampled = InfluxDBClient3(
database='DOWNSAMPLED_BUCKET_NAME',
org=''
)
```
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
```python { placeholders="DATABASE_TOKEN|DOWNSAMPLED_DATABASE_NAME" }
# Instantiate an InfluxDBClient3 client configured for your downsampled database.
# When writing, the org= argument is required by the client (but ignored by InfluxDB).
influxdb_downsampled = InfluxDBClient3(
host='{{< influxdb/host >}}',
token='DATABASE_TOKEN',
database='DOWNSAMPLED_DATABASE_NAME',
org=''
)
```
{{% /show-in %}}
```python
# os.environ['localdev'] = 'true' # Uncomment if you're using local Kafka rather than Quix Cloud
# Create a Quix Streams consumer application that connects to a local Kafka installation
@ -310,9 +379,19 @@ def send_data_to_influx(message):
sdf = app.dataframe(input_topic)
sdf = sdf.update(send_data_to_influx) # Continuously apply the 'send_data' function to each message in the incoming stream
## ... remaining code trunctated for brevity ...
## ... remaining code truncated for brevity ...
```
{{% /code-placeholders %}}
Replace the following:
{{% show-in "cloud-serverless" %}}
- {{% code-placeholder-key %}}`API_TOKEN`{{% /code-placeholder-key %}}: your InfluxDB [API token](/influxdb3/version/admin/tokens/) with write permission on the bucket
- {{% code-placeholder-key %}}`DOWNSAMPLED_BUCKET_NAME`{{% /code-placeholder-key %}}: the name of your InfluxDB [bucket](/influxdb3/version/admin/buckets/) for downsampled data
{{% /show-in %}}
{{% show-in "cloud-dedicated,clustered" %}}
- {{% code-placeholder-key %}}`DATABASE_TOKEN`{{% /code-placeholder-key %}}: your InfluxDB [database token](/influxdb3/version/admin/tokens/database/) with write permission on the database
- {{% code-placeholder-key %}}`DOWNSAMPLED_DATABASE_NAME`{{% /code-placeholder-key %}}: the name of your InfluxDB [database](/influxdb3/version/admin/databases/) for downsampled data
{{% /show-in %}}
You can find the full code for this process in the
[Quix GitHub repository](https://github.com/quixio/template-influxdbv3-downsampling/blob/dev/InfluxDB%20V3%20Data%20Sink/main.py).
@ -335,24 +414,24 @@ This repository contains the following folders which store different parts of th
and writes it to InfluxDB. This is useful if you dont have your own data yet,
or just want to work with test data first.
- It produces a reading every 250 milliseconds.
- It produces a reading every 250 milliseconds.
- This script originally comes from the
[InfluxCommunity repository](https://github.com/InfluxCommunity/Arrow-Task-Engine/blob/master/machine_simulator/src/machine_generator.py)
but has been adapted to write directly to InfluxDB rather than using an MQTT broker.
- **InfluxDB V3 Data Source**: A service that queries for fresh data from
- **InfluxDB v3 Data Source**: A service that queries for fresh data from
InfluxDB at specific intervals. It's configured to look for the measurement
produced by the previously-mentioned synthetic machine data generator.
produced by the synthetic machine data generator.
It writes the raw data to a Kafka topic called "raw-data".
- **Downsampler**: A service that performs a 1-minute tumbling window operation
on the data from InfluxDB and emits the mean of the "temperature" reading
every minute. It writes the output to a "downsampled-data" Kafka topic.
- **InfluxDB V3 Data Sink**: A service that reads from the "downsampled-data"
- **InfluxDB v3 Data Sink**: A service that reads from the "downsampled-data"
topic and writes the downsample records as points back into InfluxDB.
### Use the downsampling Jupyter Notebook
You can use the interactive notebook ["Continuously downsample data using InfluxDB and Quix Streams"](https://github.com/quixio/tutorial-code/edit/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb) to try downsampling code yourself. It is configured to install Apache Kafka within the runtime environment (such as Google Colab).
You can use the interactive notebook ["Continuously downsample data using InfluxDB and Quix Streams"](https://github.com/quixio/tutorial-code/edit/main/notebooks/Downsampling_viaKafka_Using_Quix_Influx.ipynb) to try downsampling code yourself. It is configured to install Apache Kafka within the runtime environment (such as Google Colab).
Each process is also set up to run in the background so that a running cell does not block the rest of the tutorial.

19
lefthook.yml
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@ -5,6 +5,25 @@
pre-commit:
parallel: true
commands:
deprecated-markdown-patterns:
tags: lint
glob: "content/**/*.md"
run: |
errors=0
# Check for deprecated code-placeholders shortcode
if grep -l '{{% code-placeholders' {staged_files} 2>/dev/null; then
echo "❌ Found deprecated {{% code-placeholders %}} shortcode."
echo " Use \`\`\`language { placeholders=\"...\" } instead."
errors=1
fi
# Check for abbreviated 'py' language identifier
if grep -lE '^\s*```py(\s|$)' {staged_files} 2>/dev/null; then
echo "❌ Found abbreviated 'py' code fence language."
echo " Use 'python' instead of 'py' for code fences."
errors=1
fi
exit $errors
fail_text: "Deprecated markdown patterns found. See messages above for details."
eslint-debug-check:
glob: "assets/js/*.js"
run: yarn eslint {staged_files}

2
package.json
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@ -23,7 +23,7 @@
"@vvago/vale": "^3.12.0",
"autoprefixer": ">=10.2.5",
"cypress": "^14.0.1",
"eslint": "^9.18.0",
"eslint": "^10.0.0",
"eslint-config-prettier": "^10.1.5",
"eslint-plugin-import": "^2.31.0",
"eslint-plugin-jsdoc": "^50.6.17",

186
yarn.lock
View File

@ -190,65 +190,50 @@
dependencies:
eslint-visitor-keys "^3.4.3"
"@eslint-community/regexpp@^4.12.1", "@eslint-community/regexpp@^4.12.2":
"@eslint-community/regexpp@^4.12.2":
version "4.12.2"
resolved "https://registry.yarnpkg.com/@eslint-community/regexpp/-/regexpp-4.12.2.tgz#bccdf615bcf7b6e8db830ec0b8d21c9a25de597b"
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"@eslint/config-array@^0.21.1":
version "0.21.1"
resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.21.1.tgz#7d1b0060fea407f8301e932492ba8c18aff29713"
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"@eslint/config-array@^0.23.0":
version "0.23.1"
resolved "https://registry.yarnpkg.com/@eslint/config-array/-/config-array-0.23.1.tgz#908223da7b9148f1af5bfb3144b77a9387a89446"
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dependencies:
"@eslint/object-schema" "^2.1.7"
"@eslint/object-schema" "^3.0.1"
debug "^4.3.1"
minimatch "^3.1.2"
minimatch "^10.1.1"
"@eslint/config-helpers@^0.4.2":
version "0.4.2"
resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.4.2.tgz#1bd006ceeb7e2e55b2b773ab318d300e1a66aeda"
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"@eslint/config-helpers@^0.5.2":
version "0.5.2"
resolved "https://registry.yarnpkg.com/@eslint/config-helpers/-/config-helpers-0.5.2.tgz#314c7b03d02a371ad8c0a7f6821d5a8a8437ba9d"
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dependencies:
"@eslint/core" "^0.17.0"
"@eslint/core" "^1.1.0"
"@eslint/core@^0.17.0":
version "0.17.0"
resolved "https://registry.yarnpkg.com/@eslint/core/-/core-0.17.0.tgz#77225820413d9617509da9342190a2019e78761c"
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"@eslint/core@^1.1.0":
version "1.1.0"
resolved "https://registry.yarnpkg.com/@eslint/core/-/core-1.1.0.tgz#51f5cd970e216fbdae6721ac84491f57f965836d"
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dependencies:
"@types/json-schema" "^7.0.15"
"@eslint/eslintrc@^3.3.1":
version "3.3.3"
resolved "https://registry.yarnpkg.com/@eslint/eslintrc/-/eslintrc-3.3.3.tgz#26393a0806501b5e2b6a43aa588a4d8df67880ac"
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dependencies:
ajv "^6.12.4"
debug "^4.3.2"
espree "^10.0.1"
globals "^14.0.0"
ignore "^5.2.0"
import-fresh "^3.2.1"
js-yaml "^4.1.1"
minimatch "^3.1.2"
strip-json-comments "^3.1.1"
"@eslint/js@9.39.2", "@eslint/js@^9.18.0":
"@eslint/js@^9.18.0":
version "9.39.2"
resolved "https://registry.yarnpkg.com/@eslint/js/-/js-9.39.2.tgz#2d4b8ec4c3ea13c1b3748e0c97ecd766bdd80599"
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"@eslint/object-schema@^2.1.7":
version "2.1.7"
resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-2.1.7.tgz#6e2126a1347e86a4dedf8706ec67ff8e107ebbad"
integrity sha512-VtAOaymWVfZcmZbp6E2mympDIHvyjXs/12LqWYjVw6qjrfF+VK+fyG33kChz3nnK+SU5/NeHOqrTEHS8sXO3OA==
"@eslint/object-schema@^3.0.1":
version "3.0.1"
resolved "https://registry.yarnpkg.com/@eslint/object-schema/-/object-schema-3.0.1.tgz#9a1dc9af00d790dc79a9bf57a756e3cb2740ddb9"
integrity sha512-P9cq2dpr+LU8j3qbLygLcSZrl2/ds/pUpfnHNNuk5HW7mnngHs+6WSq5C9mO3rqRX8A1poxqLTC9cu0KOyJlBg==
"@eslint/plugin-kit@^0.4.1":
version "0.4.1"
resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.4.1.tgz#9779e3fd9b7ee33571a57435cf4335a1794a6cb2"
integrity sha512-43/qtrDUokr7LJqoF2c3+RInu/t4zfrpYdoSDfYyhg52rwLV6TnOvdG4fXm7IkSB3wErkcmJS9iEhjVtOSEjjA==
"@eslint/plugin-kit@^0.6.0":
version "0.6.0"
resolved "https://registry.yarnpkg.com/@eslint/plugin-kit/-/plugin-kit-0.6.0.tgz#e0cb12ec66719cb2211ad36499fb516f2a63899d"
integrity sha512-bIZEUzOI1jkhviX2cp5vNyXQc6olzb2ohewQubuYlMXZ2Q/XjBO0x0XhGPvc9fjSIiUN0vw+0hq53BJ4eQSJKQ==
dependencies:
"@eslint/core" "^0.17.0"
"@eslint/core" "^1.1.0"
levn "^0.4.1"
"@evilmartians/lefthook@^1.7.1":
@ -352,6 +337,11 @@
wrap-ansi "^8.1.0"
wrap-ansi-cjs "npm:wrap-ansi@^7.0.0"
"@isaacs/cliui@^9.0.0":
version "9.0.0"
resolved "https://registry.yarnpkg.com/@isaacs/cliui/-/cliui-9.0.0.tgz#4d0a3f127058043bf2e7ee169eaf30ed901302f3"
integrity sha512-AokJm4tuBHillT+FpMtxQ60n8ObyXBatq7jD2/JA9dxbDDokKQm8KMht5ibGzLVU9IJDIKK4TPKgMHEYMn3lMg==
"@isaacs/fs-minipass@^4.0.0":
version "4.0.1"
resolved "https://registry.yarnpkg.com/@isaacs/fs-minipass/-/fs-minipass-4.0.1.tgz#2d59ae3ab4b38fb4270bfa23d30f8e2e86c7fe32"
@ -624,7 +614,12 @@
dependencies:
"@types/ms" "*"
"@types/estree@^1.0.6":
"@types/esrecurse@^4.3.1":
version "4.3.1"
resolved "https://registry.yarnpkg.com/@types/esrecurse/-/esrecurse-4.3.1.tgz#6f636af962fbe6191b830bd676ba5986926bccec"
integrity sha512-xJBAbDifo5hpffDBuHl0Y8ywswbiAp/Wi7Y/GtAgSlZyIABppyurxVueOPE8LUQOxdlgi6Zqce7uoEpqNTeiUw==
"@types/estree@^1.0.6", "@types/estree@^1.0.8":
version "1.0.8"
resolved "https://registry.yarnpkg.com/@types/estree/-/estree-1.0.8.tgz#958b91c991b1867ced318bedea0e215ee050726e"
integrity sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==
@ -1097,6 +1092,13 @@ balanced-match@^1.0.0:
resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
balanced-match@^4.0.2:
version "4.0.2"
resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-4.0.2.tgz#241591ea634702bef9c482696f2469406e16d233"
integrity sha512-x0K50QvKQ97fdEz2kPehIerj+YTeptKF9hyYkKf6egnwmMWAkADiO0QCzSp0R5xN8FTZgYaBfSaue46Ej62nMg==
dependencies:
jackspeak "^4.2.3"
bare-events@^2.5.4, bare-events@^2.7.0:
version "2.8.2"
resolved "https://registry.yarnpkg.com/bare-events/-/bare-events-2.8.2.tgz#7b3e10bd8e1fc80daf38bb516921678f566ab89f"
@ -1216,6 +1218,13 @@ brace-expansion@^2.0.1:
dependencies:
balanced-match "^1.0.0"
brace-expansion@^5.0.2:
version "5.0.2"
resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-5.0.2.tgz#b6c16d0791087af6c2bc463f52a8142046c06b6f"
integrity sha512-Pdk8c9poy+YhOgVWw1JNN22/HcivgKWwpxKq04M/jTmHyCZn12WPJebZxdjSa5TmBqISrUSgNYU3eRORljfCCw==
dependencies:
balanced-match "^4.0.2"
braces@~3.0.2:
version "3.0.3"
resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.3.tgz#490332f40919452272d55a8480adc0c441358789"
@ -1315,7 +1324,7 @@ chainsaw@~0.1.0:
dependencies:
traverse ">=0.3.0 <0.4"
chalk@^4.0.0, chalk@^4.1.0:
chalk@^4.1.0:
version "4.1.2"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==
@ -2401,11 +2410,13 @@ eslint-plugin-jsx-a11y@^6.10.2:
safe-regex-test "^1.0.3"
string.prototype.includes "^2.0.1"
eslint-scope@^8.4.0:
version "8.4.0"
resolved "https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-8.4.0.tgz#88e646a207fad61436ffa39eb505147200655c82"
integrity sha512-sNXOfKCn74rt8RICKMvJS7XKV/Xk9kA7DyJr8mJik3S7Cwgy3qlkkmyS2uQB3jiJg6VNdZd/pDBJu0nvG2NlTg==
eslint-scope@^9.1.0:
version "9.1.0"
resolved "https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-9.1.0.tgz#dfcb41d6c0d73df6b977a50cf3e91c41ddb4154e"
integrity sha512-CkWE42hOJsNj9FJRaoMX9waUFYhqY4jmyLFdAdzZr6VaCg3ynLYx4WnOdkaIifGfH4gsUcBTn4OZbHXkpLD0FQ==
dependencies:
"@types/esrecurse" "^4.3.1"
"@types/estree" "^1.0.8"
esrecurse "^4.3.0"
estraverse "^5.2.0"
@ -2419,32 +2430,34 @@ eslint-visitor-keys@^4.2.1:
resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-4.2.1.tgz#4cfea60fe7dd0ad8e816e1ed026c1d5251b512c1"
integrity sha512-Uhdk5sfqcee/9H/rCOJikYz67o0a2Tw2hGRPOG2Y1R2dg7brRe1uG0yaNQDHu+TO/uQPF/5eCapvYSmHUjt7JQ==
eslint@^9.18.0:
version "9.39.2"
resolved "https://registry.yarnpkg.com/eslint/-/eslint-9.39.2.tgz#cb60e6d16ab234c0f8369a3fe7cc87967faf4b6c"
integrity sha512-LEyamqS7W5HB3ujJyvi0HQK/dtVINZvd5mAAp9eT5S/ujByGjiZLCzPcHVzuXbpJDJF/cxwHlfceVUDZ2lnSTw==
eslint-visitor-keys@^5.0.0:
version "5.0.0"
resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-5.0.0.tgz#b9aa1a74aa48c44b3ae46c1597ce7171246a94a9"
integrity sha512-A0XeIi7CXU7nPlfHS9loMYEKxUaONu/hTEzHTGba9Huu94Cq1hPivf+DE5erJozZOky0LfvXAyrV/tcswpLI0Q==
eslint@^10.0.0:
version "10.0.0"
resolved "https://registry.yarnpkg.com/eslint/-/eslint-10.0.0.tgz#c93c36a96d91621d0fbb680db848ea11af56ab1e"
integrity sha512-O0piBKY36YSJhlFSG8p9VUdPV/SxxS4FYDWVpr/9GJuMaepzwlf4J8I4ov1b+ySQfDTPhc3DtLaxcT1fN0yqCg==
dependencies:
"@eslint-community/eslint-utils" "^4.8.0"
"@eslint-community/regexpp" "^4.12.1"
"@eslint/config-array" "^0.21.1"
"@eslint/config-helpers" "^0.4.2"
"@eslint/core" "^0.17.0"
"@eslint/eslintrc" "^3.3.1"
"@eslint/js" "9.39.2"
"@eslint/plugin-kit" "^0.4.1"
"@eslint-community/regexpp" "^4.12.2"
"@eslint/config-array" "^0.23.0"
"@eslint/config-helpers" "^0.5.2"
"@eslint/core" "^1.1.0"
"@eslint/plugin-kit" "^0.6.0"
"@humanfs/node" "^0.16.6"
"@humanwhocodes/module-importer" "^1.0.1"
"@humanwhocodes/retry" "^0.4.2"
"@types/estree" "^1.0.6"
ajv "^6.12.4"
chalk "^4.0.0"
cross-spawn "^7.0.6"
debug "^4.3.2"
escape-string-regexp "^4.0.0"
eslint-scope "^8.4.0"
eslint-visitor-keys "^4.2.1"
espree "^10.4.0"
esquery "^1.5.0"
eslint-scope "^9.1.0"
eslint-visitor-keys "^5.0.0"
espree "^11.1.0"
esquery "^1.7.0"
esutils "^2.0.2"
fast-deep-equal "^3.1.3"
file-entry-cache "^8.0.0"
@ -2454,12 +2467,11 @@ eslint@^9.18.0:
imurmurhash "^0.1.4"
is-glob "^4.0.0"
json-stable-stringify-without-jsonify "^1.0.1"
lodash.merge "^4.6.2"
minimatch "^3.1.2"
minimatch "^10.1.1"
natural-compare "^1.4.0"
optionator "^0.9.3"
espree@^10.0.1, espree@^10.3.0, espree@^10.4.0:
espree@^10.3.0:
version "10.4.0"
resolved "https://registry.yarnpkg.com/espree/-/espree-10.4.0.tgz#d54f4949d4629005a1fa168d937c3ff1f7e2a837"
integrity sha512-j6PAQ2uUr79PZhBjP5C5fhl8e39FmRnOjsD5lGnWrFU8i2G776tBK7+nP8KuQUTTyAZUwfQqXAgrVH5MbH9CYQ==
@ -2468,12 +2480,21 @@ espree@^10.0.1, espree@^10.3.0, espree@^10.4.0:
acorn-jsx "^5.3.2"
eslint-visitor-keys "^4.2.1"
espree@^11.1.0:
version "11.1.0"
resolved "https://registry.yarnpkg.com/espree/-/espree-11.1.0.tgz#7d0c82a69f8df670728dba256264b383fbf73e8f"
integrity sha512-WFWYhO1fV4iYkqOOvq8FbqIhr2pYfoDY0kCotMkDeNtGpiGGkZ1iov2u8ydjtgM8yF8rzK7oaTbw2NAzbAbehw==
dependencies:
acorn "^8.15.0"
acorn-jsx "^5.3.2"
eslint-visitor-keys "^5.0.0"
esprima@^4.0.0, esprima@^4.0.1:
version "4.0.1"
resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A==
esquery@^1.5.0, esquery@^1.6.0:
esquery@^1.6.0, esquery@^1.7.0:
version "1.7.0"
resolved "https://registry.yarnpkg.com/esquery/-/esquery-1.7.0.tgz#08d048f261f0ddedb5bae95f46809463d9c9496d"
integrity sha512-Ap6G0WQwcU/LHsvLwON1fAQX9Zp0A2Y6Y/cJBl9r/JbW90Zyg4/zbG6zzKa2OTALELarYHmKu0GhpM5EO+7T0g==
@ -2885,11 +2906,6 @@ global-dirs@^3.0.0:
dependencies:
ini "2.0.0"
globals@^14.0.0:
version "14.0.0"
resolved "https://registry.yarnpkg.com/globals/-/globals-14.0.0.tgz#898d7413c29babcf6bafe56fcadded858ada724e"
integrity sha512-oahGvuMGQlPw/ivIYBjVSrWAfWLBeku5tpPE2fOPLi+WHffIWbuh2tCjhyQhTBPMf5E9jDEH4FOmTYgYwbKwtQ==
globals@^15.14.0:
version "15.15.0"
resolved "https://registry.yarnpkg.com/globals/-/globals-15.15.0.tgz#7c4761299d41c32b075715a4ce1ede7897ff72a8"
@ -3046,7 +3062,7 @@ ignore@^7.0.5:
resolved "https://registry.yarnpkg.com/ignore/-/ignore-7.0.5.tgz#4cb5f6cd7d4c7ab0365738c7aea888baa6d7efd9"
integrity sha512-Hs59xBNfUIunMFgWAbGX5cq6893IbWg4KnrjbYwX3tx0ztorVgTDA6B2sxf8ejHJ4wz8BqGUMYlnzNBer5NvGg==
import-fresh@^3.2.1, import-fresh@^3.3.0:
import-fresh@^3.3.0:
version "3.3.1"
resolved "https://registry.yarnpkg.com/import-fresh/-/import-fresh-3.3.1.tgz#9cecb56503c0ada1f2741dbbd6546e4b13b57ccf"
integrity sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==
@ -3378,6 +3394,13 @@ jackspeak@^3.1.2:
optionalDependencies:
"@pkgjs/parseargs" "^0.11.0"
jackspeak@^4.2.3:
version "4.2.3"
resolved "https://registry.yarnpkg.com/jackspeak/-/jackspeak-4.2.3.tgz#27ef80f33b93412037c3bea4f8eddf80e1931483"
integrity sha512-ykkVRwrYvFm1nb2AJfKKYPr0emF6IiXDYUaFx4Zn9ZuIH7MrzEZ3sD5RlqGXNRpHtvUHJyOnCEFxOlNDtGo7wg==
dependencies:
"@isaacs/cliui" "^9.0.0"
jquery@^3.7.1:
version "3.7.1"
resolved "https://registry.yarnpkg.com/jquery/-/jquery-3.7.1.tgz#083ef98927c9a6a74d05a6af02806566d16274de"
@ -3697,11 +3720,6 @@ lodash-es@4.17.21, lodash-es@^4.17.21, lodash-es@^4.17.23:
resolved "https://registry.yarnpkg.com/lodash-es/-/lodash-es-4.17.23.tgz#58c4360fd1b5d33afc6c0bbd3d1149349b1138e0"
integrity sha512-kVI48u3PZr38HdYz98UmfPnXl2DXrpdctLrFLCd3kOx1xUkOmpFPx7gCWWM5MPkL/fD8zb+Ph0QzjGFs4+hHWg==
lodash.merge@^4.6.2:
version "4.6.2"
resolved "https://registry.yarnpkg.com/lodash.merge/-/lodash.merge-4.6.2.tgz#558aa53b43b661e1925a0afdfa36a9a1085fe57a"
integrity sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ==
lodash.once@^4.1.1:
version "4.1.1"
resolved "https://registry.yarnpkg.com/lodash.once/-/lodash.once-4.1.1.tgz#0dd3971213c7c56df880977d504c88fb471a97ac"
@ -4253,6 +4271,13 @@ mimic-fn@^2.1.0:
resolved "https://registry.yarnpkg.com/mimic-fn/-/mimic-fn-2.1.0.tgz#7ed2c2ccccaf84d3ffcb7a69b57711fc2083401b"
integrity sha512-OqbOk5oEQeAZ8WXWydlu9HJjz9WVdEIvamMCcXmuqUYjTknH/sqsWvhQ3vgwKFRR1HpjvNBKQ37nbJgYzGqGcg==
minimatch@^10.1.1:
version "10.2.1"
resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-10.2.1.tgz#9d82835834cdc85d5084dd055e9a4685fa56e5f0"
integrity sha512-MClCe8IL5nRRmawL6ib/eT4oLyeKMGCghibcDWK+J0hh0Q8kqSdia6BvbRMVk6mPa6WqUa5uR2oxt6C5jd533A==
dependencies:
brace-expansion "^5.0.2"
minimatch@^3.1.1, minimatch@^3.1.2:
version "3.1.2"
resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
@ -5483,11 +5508,6 @@ strip-final-newline@^2.0.0:
resolved "https://registry.yarnpkg.com/strip-final-newline/-/strip-final-newline-2.0.0.tgz#89b852fb2fcbe936f6f4b3187afb0a12c1ab58ad"
integrity sha512-BrpvfNAE3dcvq7ll3xVumzjKjZQ5tI1sEUIKr3Uoks0XUl45St3FlatVqef9prk4jRDzhW6WZg+3bk93y6pLjA==
strip-json-comments@^3.1.1:
version "3.1.1"
resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==
stylis@^4.3.6:
version "4.3.6"
resolved "https://registry.yarnpkg.com/stylis/-/stylis-4.3.6.tgz#7c7b97191cb4f195f03ecab7d52f7902ed378320"