Real-time state management techniques using RocksDB: A high-performance approach to scalable stream processing

SANDEEP PAMARTHI *

Principal Data Engineer, AI/ML Expert, CGI Inc.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(01), 3180-3190.
Article DOI: 10.30574/ijsra.2024.12.1.0867
Publication history: 
Received on 02 April 2024; revised on 15 May 2024; accepted on 18 May 2024
 
Abstract: 
The proliferation of real-time artificial intelligence (AI) and machine learning (ML) systems has amplified the demand for robust, low-latency state management techniques capable of operating at scale. From streaming feature extraction to online model inference and complex event processing, stateful operations lie at the core of intelligent data-driven pipelines. However, managing this state in distributed environments presents numerous challenges, including fault tolerance, efficient recovery, incremental updates, and tight latency budgets.
This paper explores RocksDB, a high-performance, embeddable key-value store based on a log-structured merge-tree (LSM) architecture, as a state backend solution for real-time applications. We delve into the internal mechanisms that make RocksDB particularly well-suited for low-latency, high-throughput workloads, such as background compaction, memory/disk tiering, and custom serialization strategies. The article details practical integration techniques with distributed stream processing engines such as Apache Flink and Kafka Streams, emphasizing checkpointing, state TTL, and asynchronous snapshotting.
We also introduce a set of design patterns for real-time AI/ML applications — including online feature stores, real-time recommender systems, and stateful anomaly detection — and show how RocksDB enables efficient, fault-tolerant management of evolving application state. Through empirical evaluations, we benchmark performance trade-offs between RocksDB and alternative backends (e.g., in-memory, Redis, Cassandra), and present optimizations that significantly improve state access latency, recovery time, and disk footprint.
By providing a comprehensive review of RocksDB’s role in real-time state management, this work serves as both a scholarly reference and a practical guide for engineers, researchers, and system architects building the next generation of AI/ML-driven streaming systems. 
 
Keywords: 
Real-Time State Management; Rocksdb; Apache Flink; Stream Processing; AI/ML Pipelines; Stateful Computation; Low-Latency Storage; Embedded Key-Value Store; LSM Tree; Fault Tolerance; Checkpointing; Feature Store; Model Inference; Complex Event Processing
 
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