Database / Apache Cassandra Intermediate and Advanced interview questions
What is Time Window Compaction Strategy used for?
Time Window Compaction Strategy (TWCS) is purpose-built for time-series or append-mostly data where rows naturally expire together, such as metrics, logs, or IoT sensor readings written with a TTL.
- SSTables are grouped into fixed-size time windows (e.g. one window per day), and compaction only merges SSTables within the same window rather than across the whole table's history.
- Once every row in a window's SSTables has expired (past its TTL), the entire SSTable can often be dropped outright, without needing to scan or rewrite individual tombstones row by row.
- This avoids the classic time-series problem under STCS/LCS where old and new data get compacted together repeatedly, wasting I/O on data that's about to expire anyway.
CREATE TABLE iot_readings ( device_id text, reading_time timestamp, value double, PRIMARY KEY (device_id, reading_time) ) WITH compaction = {'class': 'TimeWindowCompactionStrategy', 'compaction_window_unit': 'HOURS', 'compaction_window_size': 6} AND default_time_to_live = 604800; -- 7 days
The compaction window size should roughly match your query and TTL patterns — too small creates excessive small SSTables, too large delays reclaiming disk space from expired data.
TWCS is best suited for which kind of data?
What efficient cleanup does TWCS enable once a whole time window expires?
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