Dynamic Repartitioning for Time Series Workloads
Netflix's TimeSeries Abstraction uses Apache Cassandra to handle large-scale temporal data, addressing wide partition challenges through dynamic partitioning strategies, improving read latency and system stability.
MAIN POINTS
- Netflix's TimeSeries Abstraction manages petabytes of data with millisecond latency using Apache Cassandra.
- Wide partitions in Cassandra lead to high read latencies, timeouts, and increased CPU usage.
- TimeSeries partitioning strategy divides data into manageable chunks, reducing latency and improving query efficiency.
- Dynamic partitioning detects and splits wide partitions at the ID level, enhancing performance and reducing read timeouts.
TAKEAWAYS
- Dynamic partitioning significantly reduces average read latency from seconds to milliseconds.
- The strategy involves detection, planning, splitting, and serving reads for wide partitions.
- Bloom filters and metadata tables ensure efficient read operations post-partitioning.
- Future work includes addressing mutable partitions and refining split strategies for failed cases.