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slo-platform

SLOs and error budgets

Nobl9

SystemTechniques · 8Primitives · 27 slo-platform Windowed stream aggregationdistributed · medium: Group events by event-time window per key, keep one partial aggregate per slice, and emit a window when the watermark passes its end.›Time-series forecasting (baselines,…statistics · medium: The end-to-end recipe: fix horizon and uncertainty, beat naive baselines, choose a model family, and validate with rolling-origin backtests and scale-free metrics.›Rollup aggregationstorage · medium: Fold raw events into one mergeable partial aggregate per time bucket and dimension key, then merge fine buckets into coarser ones, so long ranges read few rows and stay exact.›Alert groupingsystems · medium: Group, deduplicate, inhibit and silence alerts by label sets before notifyingBurn rate alertingsystems · medium: Multi-window multi-burn-rate alerts on SLO error-budget consumptionWatermarksdistributed · medium: Event-time progress markers that bound lateness and trigger window resultsArimastatistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fitSliding-window counterssystems · low: Count events in the last W seconds with a ring of sub-bucket counters; the two-bucket weighted estimate gives rate limits in 16 bytes per key.›Quantile sketchesdata-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histogramsJSON Schema validationsecurity · low: Compile a JSON Schema once, then check every untrusted document against it, within hard size and depth limits, and report failures as JSON Pointers.›Hash aggregationstorage · low: GROUP BY via hash tables with partitioning and spill to diskHierarchical timing wheelsystems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.›Croston methodstatistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)Exponential smoothing (ETS, Holt-Wi…statistics · low: Forecast by keeping a few running states (level, trend, seasonal) updated by a fraction of each forecast error; O(1) memory per series and a strong baseline.›STL decomposition (seasonal-trend w…statistics · low: Split a series into trend, seasonal and remainder with iterated loess smoothing; the seasonal shape may drift and robust weights keep outliers out of it.›Time-series cross-validation (rolli…statistics · low: Estimate forecast error by repeatedly training on the past and testing on the next h steps, moving the origin forward; never trains on the future.›Time partitioningstorage · low: Cut a table into fixed-interval time blocks so queries read only overlapping blocks and retention drops a whole block as one metadata change.›Swiss tabledata-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.›Glob matchingsearch-sort · low: Glob/gitignore pattern matching without backtracking blowupd-ary heapdata-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.›Consumer offsetsdistributed · low: Each consumer group stores one integer per partition, the next position to read, so a restarted consumer resumes where it left off with at-least-once delivery.›Finite differencesnumerics · low: Replace derivatives with weighted differences of grid values; simple stencils plus a stability condition (CFL) turn PDEs like heat, advection and waves into array loops.›L-BFGSnumerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.›+ 1 more techniques+ 11 more primitives

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