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What this does: Maintains feature vectors as materialized views over live event streams. At inference time, queries return pre-computed, always-current features — no aggregation triggered at query time. When to use this: Your ML model or scoring system needs fresh features at low latency, computed over a rolling window of recent events.

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Setup

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1. Ingest events from Kafka

CREATE SOURCE bidding_events (
  ad_id            VARCHAR,
  bid_amount       DOUBLE PRECISION,
  bid_won          BOOLEAN,
  response_time_ms DOUBLE PRECISION,
  event_time       TIMESTAMPTZ,
  WATERMARK FOR event_time AS event_time - INTERVAL '5 SECONDS'
) WITH (
  connector = 'kafka',
  topic = 'bidding-events',
  properties.bootstrap.server = 'localhost:9092',
  scan.startup.mode = 'earliest'
) FORMAT PLAIN ENCODE JSON;

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2. Define features as a materialized view

RisingWave maintains this view incrementally as events arrive. The result is always current — no recomputation on read. 
CREATE MATERIALIZED VIEW ad_features AS
SELECT
  ad_id,
  AVG(bid_amount)                              AS avg_bid,
  MAX(bid_amount)                              AS max_bid,
  COUNT(*)                                     AS bid_count,
  SUM(CASE WHEN bid_won THEN 1 ELSE 0 END)     AS win_count,
  AVG(response_time_ms)                        AS avg_response_ms
FROM bidding_events
WHERE event_time >= NOW() - INTERVAL '1 day'
GROUP BY ad_id;

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3. Query features at inference time

Point queries against the MV return in milliseconds — the work is already done. 
SELECT avg_bid, max_bid, bid_count, win_count, avg_response_ms
FROM ad_features
WHERE ad_id = $1;

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4. Chain a UDF for live inference (optional)

CREATE MATERIALIZED VIEW live_predictions AS
SELECT
  ad_id,
  PREDICT_BID(avg_bid, max_bid, bid_count, win_count, avg_response_ms) AS predicted_bid
FROM ad_features;

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Key points

  • The MV is maintained incrementally — a new event triggers an incremental update, not a full recompute
  • WHERE event_time >= NOW() - INTERVAL '1 day' creates a rolling window: old data falls out automatically
  • For multi-entity features, add more columns to GROUP BY; for cross-entity features, use a JOIN between two sources

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Next steps