In scenarios such as high-frequency trading, tick-by-tick market data distribution, and risk control linkage, in-memory databases are involved in order status storage, market snapshot queries, risk control key value operations, and important tasks such as time-series data statistics. Compared to financial trading loads, conventional internet OLTP business has stronger time locality, bursty updates, and P99 tail latency requirements. In recent years, research on adaptive radix trees, hybrid tree hash indexes, split in-memory indexes, and compact perfect hashes has continued to develop, providing new avenues for index improvement in microsecond-level trading systems. This paper, based on English literature from the past three years, designs an index optimization framework suitable for financial trading in-memory databases, focusing on four aspects: low tail latency, low write amplification, low memory consumption, and controllable range query performance. A composite design, mainly composed of upper-layer ordered routing, leaf-layer segmented hashing, cache-friendly node layout, version-aware concurrency control, and range rollback mechanism, achieves better performance in balanced point lookups, updates, and range scans. This paper reconstructs statistical graphs based on publicly available experimental results and provides a multi-objective optimization model and engineering implementation suggestions. The research results show that, for financial transaction loads, it is not necessary to choose only one type of tree or hash structure. It is better to determine the layered collaborative design method based on access distribution, key growth pattern and tail latency.

Lizhi Wang. Research on Optimization of In-Memory Database Index Structure for Microsecond-Level Matching and Market Data Processing. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 37-47. https://doi.org/10.38007/SESR.2026.070105.

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