Calculating State Execution Overhead In Continuous Runtime Systems
State execution overhead measures runtime compute capacity consumed by serialization, synchronization, and storage engine interaction during event transitions.
log-structured merge-tree
Meaning
Data structures organized into hierarchical levels optimize high-volume write operations by deferring and batching storage updates. A log-structured merge-tree accepts incoming data into a fast memory buffer before periodically writing it to disk as immutable files. This approach avoids the high cost of random disk writes by transforming them into sequential operations.
It is commonly used in databases where the rate of data entry exceeds the speed of traditional indexing methods. The structure stops being effective when the frequency of data updates is low and read speed is the only priority for the application.
Write Performance
Sequential access to storage media is noticeably faster than jumping between different locations. By using a log-structured merge-tree, a system can ingest data at the maximum physical speed of the storage device. This provides a cardinal advantage in applications like logging or real-time telemetry where the stream of information is constant.
High performance is maintained even as the total volume of data grows.
Compaction Logic
Because data is written in immutable chunks, the same record may exist in multiple files on disk. The compaction logic of the log-structured merge-tree periodically merges these files into larger ones while removing duplicate or deleted entries. This process reclaims disk space and keeps the total number of files manageable.
It runs in the background to minimize the impact on active read and write operations.
Read Penalty
Retrieving a specific record may require searching through multiple levels of the structure. This read penalty occurs because the most recent version of a data point could be in memory or in any of several disk files. Bloom filters are often used to narrow the search and reduce the number of disk accesses.
Despite these optimizations, the read speed is generally lower than that of a traditional B-tree.