Efficient way to store daily data with tags?

I use a possible very inefficient way to store data in InfluxDB 1.2

  • Python 3.3
  • Pandas DataFrame
  • daily sales of articles sold in stores)
  • I need to add tags because the data needs to be filtered in Grafana by article ID and store ID
  • I loop over stores and articles and use write_points()

from influxdb import DataFrameClient
influx_client = DataFrameClient(...)

# Loop over stores
for store in stores:

# Loop over articles
for article in articles:

    # Select data
    df_influx = df.loc[(df['store_id'] == store) & df['article_id'] == article]

    # Write data to InfluxDB
    influx_client.write_points(df_influx, 'measurement', tags={'tag_store':store, 'tag_article':article})


  • thousands of InfluxDB API calls
  • loop takes very long
  • I can’t write the whole DataFrame and subsequently add tags because article-store combinations would be overwritten with daily timestamps

Is there a way to increase efficiency of writing daily data with tags?