arXiv subjects

Ramnatthan Alagappan

Publications and source records attributed to Ramnatthan Alagappan.

5 recordsLinked to original sources

arXiv2026

Sophrosyne: Agentic Exploration of Relational Data Systems Needs Moderation

Text2SQL agents powered by LLMs translate natural language intent into SQL by exploring the data system through tool calls before formulating the query. However, to ensure secure and scoped access, data systems construct environments with explicit API surfaces. We study and categorize these APIs exposed today as either coarse-grained or fine-grained and posit that choosing between them presents a fundamental tradeoff between cost-efficient exploration and accurate SQL generation. Most data systems expose fine-grained APIs, but this inadvertently disadvantages agents: they over-explore, incorporating irrelevant schema elements into their query formulation and produce inaccurate results. We argue that curbing over-exploration is key to the effective use of these API surfaces, and propose Sophrosyne, a data system environment that augments API responses with directives that guide the agent's exploration process. Initial results show that directives reduce over-exploration by 4.6x and boost accuracy by up to 12.4% (approx. 4 percentage points).

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arXiv2026

AgileLog: A Forkable Shared Log for Agents on Data Streams

In modern data-streaming systems, alongside traditional programs, a new type of entity has emerged that can interact with streaming data: AI agents. Unlike traditional programs, AI agents use LLM reasoning to accomplish high-level tasks specified in natural language over streaming data. Unfortunately, current streaming systems cannot fully support agents: they lack the fundamental mechanisms to avoid the performance interference caused by agentic tasks and to safely handle agentic writes. We argue that the shared log, the core abstraction underlying streaming data, must support creating forks of itself, and that such a forkable shared log serves as a great substrate for agents acting on streaming data. We propose AgileLog, a new shared log abstraction that provides novel forking primitives for agentic use cases. We design Bolt, an implementation of the AgileLog abstraction, that uses novel techniques to make forks cheap, and provide logical and performance isolation.

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arXiv2026

HEAL: Online Incremental Recovery for Leaderless Distributed Systems Across Persistency Models

Ensuring resilience in distributed systems has become an acute concern. In today's environment, it is crucial to develop light-weight mechanisms that recover a distributed system from faults quickly and with only a small impact on the live-system throughput. To address this need, this paper proposes a new low-overhead, general recovery scheme for modern non-transactional leaderless distributed systems. We call our scheme HEAL. On a node failure, HEAL performs an optimized online incremental recovery. This paper presents HEAL's algorithms for settings with Linearizable consistency and different memory persistency models. We implement HEAL on a 6-node Intel cluster. Our experiments running TAOBench workloads show that HEAL is very effective. HEAL recovers the cluster in 120 milliseconds on average, while reducing the throughput of the running workload by an average of 8.7%. In contrast, a conventional recovery scheme for leaderless systems needs 360 seconds to recover, reducing the throughput of the system by 16.2%. Finally, compared to an incremental recovery scheme for a state-of-the-art leader-based system, HEAL reduces the average recovery latency by 20.7x and the throughput degradation by 62.4%.

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arXiv2026

Towards CXL Resilience to CPU Failures

Compute Express Link (CXL) 3.0 and beyond allows the compute nodes of a cluster to share data with hardware cache coherence and at the granularity of a cache line. This enables shared-memory semantics for distributed computing, but introduces new resilience challenges: a node failure leads to the loss of the dirty data in its caches, corrupting application state. Unfortunately, the CXL specification does not consider processor failures. Moreover, when a component fails, the specification tries to isolate it and continue application execution; there is no attempt to bring the application to a consistent state. To address these limitations, this paper extends the CXL specification to be resilient to node failures, and to correctly recover the application after node failures. We call the system ReCXL. To handle the failure of nodes, ReCXL augments the coherence transaction of a write with messages that propagate the update to a small set of other nodes (i.e., Replicas). Replicas save the update in a hardware Logging Unit. Such replication ensures resilience to node failures. Then, at regular intervals, the Logging Units dump the updates to memory. Recovery involves using the logs in the Logging Units to bring the directory and memory to a correct state. Our evaluation shows that ReCXL enables fault-tolerant execution with only a 30% slowdown over the same platform with no fault-tolerance support.

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arXiv2020

From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees

We introduce BOURBON, a log-structured merge (LSM) tree that utilizes machine learning to provide fast lookups. We base the design and implementation of BOURBON on empirically-grounded principles that we derive through careful analysis of LSM design. BOURBON employs greedy piecewise linear regression to learn key distributions, enabling fast lookup with minimal computation, and applies a cost-benefit strategy to decide when learning will be worthwhile. Through a series of experiments on both synthetic and real-world datasets, we show that BOURBON improves lookup performance by 1.23x-1.78x as compared to state-of-the-art production LSMs.

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