How do you model time-series data in Cassandra?

Good time-series modeling in Cassandra centers on two goals: keep partitions bounded in size, and keep the most commonly queried time range fast to retrieve.

CREATE TABLE sensor_readings (
  sensor_id text,
  day       text,        -- bucket, e.g. '2026-07-21'
  reading_time timestamp,
  value     double,
  PRIMARY KEY ((sensor_id, day), reading_time)
) WITH CLUSTERING ORDER BY (reading_time DESC);

1. Bucket the partition key by a time unit (day, hour, or week depending on write volume) combined with the natural entity id, so no single partition grows without bound.
2. Cluster by timestamp, typically descending, so the most recent readings — the ones usually queried — are at the front of the partition and returned fastest.
3. Use TTLs to expire old data automatically if it doesn't need to be kept forever, pairing well with Time Window Compaction Strategy so whole SSTables age out together.
4. Choose bucket size based on write rate: a high-frequency sensor might bucket hourly, while a low-frequency one might bucket monthly, so each bucket stays a reasonable, similar size.

This pattern — bucketed partition key, descending clustering key, TTL, TWCS — is the standard playbook for metrics, logs, events, and IoT data in Cassandra.