Executive Summary

Many SREs and core database engineers at enterprises running LSM-based stores struggle with fast-write regimes that trigger compactions and “deceptive reads” — read latency spikes that originate from internal compaction, bloom-filter inefficiency, or cache thrashing rather than query patterns. These problems are widespread among the roughly 50,000 enterprises that allocate about $200K each per year to database and observability budgets, producing a $10B addressable market and frequent costly firefights and overprovisioning. You could build a focused telemetry and diagnostics platform that understands LSM internals: lightweight agents and OpenTelemetry-compatible collectors that emit per-memtable, SSTable, compaction, bloom-filter and I/O backpressure signals plus correlated distributed traces that tie writes and compactions to user-visible latency. Layered on top would be signature-based root-cause detection, compaction heatmaps, actionable runbooks and tuned sampling to keep runtime overhead low (targeting <1% CPU and <5% memory), with connectors into SIEM/APM and managed DB APIs for incremental adoption. This market is attractive now because LSM engines (RocksDB, Cassandra, Scylla, TiKV) are becoming dominant in cloud-native stacks, creating a concentrated set of failure modes, while teams increasingly prefer platform-native observability (OpenTelemetry) and managed services that make plug-and-play telemetry viable. The product could stand out through deep protocol-level signals and compact, high-signal telemetry that meaningfully shortens diagnosis time, but be realistic about challenges: variability across LSM implementations, limited visibility in closed managed offerings, and competition from generalist APM vendors—so go in with strong case studies and measurable MTTR reductions to win enterprise buy-in.

Executive Summary

Market Opportunity

LSM-backed DBs: fix fast writes and deceptive reads with targeted telemetry targets a $10.0B = 50,000 enterprises x $200K ACV (enterprise DB/observability spend addressing DB-specific tooling and SRE budgets) total addressable market with medium saturation and a year-over-year growth rate of 15%.

Key trends driving demand: Cloud-native adoption -- more teams run managed/hosted clusters that rely on LSM-based stores, increasing demand for specialized observability.; LSM dominance in storage engines -- growth of RocksDB/Cassandra/Scylla/TiKV creates a concentrated set of root causes that tooling can target.; Consolidation of observability stacks -- teams prefer platform-native integrations (OpenTelemetry), making plug-and-play telemetry ingestion viable.; AI for ops -- improved ML for anomaly detection and causal inference enables automated diagnosis and actionable remediation suggestions..

Key competitors include Datadog, New Relic, Percona (PMM / Percona Support), ScyllaDB (Scylla Monitoring Stack / Enterprise).

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LSM-backed DBs: fix fast writes and deceptive reads with targeted telemetry

Executive Summary

LSM-backed DBs: fix fast writes and deceptive reads with targeted telemetry

Executive Summary

Market Validation

Market Opportunity

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