Partitioning can provide several benefits to a sharding system, including faster query execution, by splitting data into non-overlapping partitions that can be read and processed in parallel, reducing unnecessary reads and improving data retention policies. Partitioning effects include data pruning, intra-node parallelism, and fast deletion, allowing for efficient management of hot and cold data, with hot partitions containing recent data and cold partitions containing older data. Storing and managing partitions optimizes query workloads by minimizing the number of files in a partition, reducing I/O operations while reading data, and improving query execution speed, while optimizing ingest workloads through low latency ingestion and compaction processes that keep the accumulation of small files to a minimum. Hierarchical partitioning provides answers to questions about quantifying hot, less hot, cold, and more cold data, and can be relaxed by defining an active partition, reducing the number of partitions in the system, making it easier to manage, and reducing the number of partitions that need to be read when querying larger and older chunks.