https://github.com/imankulov/time-series-caching

Sample dashboard application to demonstrate practical time series caching with Python and Redis. The app shows a plot of all BTC transactions for a specific Bitcoin wallet in a specific date range.
https://github.com/imankulov/time-series-caching

elasticsearch plotly-dash python redis time-series

Last synced: 19 days ago
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Sample dashboard application to demonstrate practical time series caching with Python and Redis. The app shows a plot of all BTC transactions for a specific Bitcoin wallet in a specific date range.

• Host: GitHub
• URL: https://github.com/imankulov/time-series-caching
• Owner: imankulov
• License: mit
• Created: 2021-10-27T09:55:23.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-27T11:17:19.000Z (about 3 years ago)
• Last Synced: 2024-10-12T07:32:51.834Z (about 1 month ago)
• Topics: elasticsearch, plotly-dash, python, redis, time-series
• Language: Python
• Homepage: https://roman.pt/posts/time-series-caching/
• Size: 55.7 KB
• Stars: 13
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Time series caching

Sample dashboard application to demonstrate practical time series caching with Python and Redis. The app shows a plot of all BTC transactions for a specific Bitcoin wallet in a specific date range.

![](./screenshot.png)

See [roman.pt/posts/time-series-caching/](https://roman.pt/posts/time-series-caching/) for more details and explanation of what's going on inside.

## How to get started

Create a new virtual environment with Python3.9 or newer and install the dependencies.

```shell

python3.9 -m venv env

source env/bin/activate

python -m pip install -r requirements.txt

```

Create a new free account at https://www.anyblockanalytics.com/ that acts as the upstream data source.

Start a local Redis server with Docker compose:

```shell

docker-compose up

```

Copy .env.sample to .env and update the file with ElasticSearch requisites.

```shell

cp .env.sample .env

edit .env

```

Start a new cron job populating the cache in the background

```shell

python ./app.py cron

```

Start a web server.

```shell

python ./app.py server

```

Open the browser for the dashboard: http://127.0.0.1:8050/