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Window Features at Scale

Reference for time-based-feature-builder · 55 lines

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20264 references2 scripts
Data sizeToolApproach
< 1M eventspandas / pit_features.pygroupby + rolling(closed="left") or bisect per entity
1M - 500M eventspolars, DuckDBrolling(..., group_by=, closed="left"); SQL joins bounded by the longest window
> 500M eventsSpark, BigQuery, Snowflakepre-aggregate to daily tables, then window over days

Pre-aggregate to daily (or hourly) tables

Window features over days do not need raw events:

sql
CREATE TABLE daily_card AS
SELECT card_id, DATE(ts) AS d, COUNT(*) AS n, SUM(amount) AS amt
FROM txns GROUP BY 1, 2;

SELECT s.card_id, s.score_date,
       SUM(CASE WHEN dc.d >= s.score_date - 7  THEN dc.n END)   AS n_7d,
       SUM(CASE WHEN dc.d >= s.score_date - 30 THEN dc.amt END) AS amt_30d
FROM spine s
JOIN daily_card dc ON dc.card_id = s.card_id AND dc.d < s.score_date AND dc.d >= s.score_date - 30
GROUP BY 1, 2;

Cost drops from events x snapshots to days x snapshots. Distinct counts cannot be summed across days: keep HyperLogLog sketches (BigQuery HLL_COUNT, Snowflake HLL) or compute them from raw events.

DuckDB from Python

python
import duckdb
con = duckdb.connect()
sql = open("features.sql").read()          # from gen_window_sql.py --dialect duckdb
feats = con.execute(sql).df()               # tables can be CSV/Parquet: FROM read_parquet('events/*.parquet')

Spark

python
from pyspark.sql import functions as F, Window
w = Window.partitionBy("card_id").orderBy(F.col("ts").cast("long")).rangeBetween(-86400, -1)
df = df.withColumn("n_24h", F.count("*").over(w)).withColumn("sum_24h", F.sum("amount").over(w))

rangeBetween(-86400, -1) on epoch seconds excludes the current second (and the current row).

Incremental computation

For daily batch scoring, update yesterday's aggregates with today's new events instead of recomputing history: maintain per-entity daily partials and roll windows by adding the newest day and dropping the oldest.

Memory tips for pandas

  • Sort once by (entity, time); use groupby(..., sort=False).
  • Use categoricals for entity IDs, float32 for amounts.
  • Process entities in chunks (hash partition) and concatenate results.
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