DuckDB Internals Part 1

I can see applications having valid reasons to use both. You can use SQLite as the catalog in duck lake systems, for example. SQLite is your metadata record, DuckDB is your ingestion/scanning/aggregating/joining engine.

SQL has been around since the dawn of databases. I am happy to see a trend away from pandas.

  WITH lagged AS (
      SELECT 
          *, 
          LAG(event_time) OVER (PARTITION BY user_id ORDER BY event_time) AS prev_time
      FROM events
  ),
  sessions AS (
      SELECT 
          *, 
          SUM(COALESCE((date_diff('minute', prev_time, event_time) > 30)::INT, 1)) 
              OVER (PARTITION BY user_id ORDER BY event_time) AS session_id
      FROM lagged
  )
  SELECT
      user_id,
      session_id,
      MIN(event_time) AS session_start,
      MAX(event_time) AS session_end,
      COUNT(*) AS event_count
  FROM sessions
  GROUP BY ALL
  ORDER BY user_id, session_start;

  result = (
      df.sort(["user_id", "event_time"])
      .with_columns(
          session_id=(
              pl.when(pl.col("event_time").diff().is_null())
              .then(1)
              .when(pl.col("event_time").diff().dt.total_minutes() > 30)
              .then(1)
              .otherwise(0)
              .cum_sum()
              .over("user_id")
          )
      )
      .group_by(["user_id", "session_id"])
      .agg(
          session_start=pl.col("event_time").min(),
          session_end=pl.col("event_time").max(),
          event_count=pl.col("event_time").count(),
      )
      .sort(["user_id", "session_start"])
  )