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Cloud platform and compute
cloud-cost
cloud cost allocation
Kubecost
System
Techniques · 10
Primitives · 37
cloud-cost
Windowed stream aggregation
distributed · 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.
›
Cost allocation models
finance · medium: Split shared resource costs across tenants by requests, usage or proportional share
Time-series anomaly detection
statistics · medium: Predict the expected value from a seasonal baseline, scale the residual by a robust spread (MAD), threshold at a chosen false-alarm rate, then merge flags into incidents.
›
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.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
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Watermarks
distributed · medium: Event-time progress markers that bound lateness and trigger window results
Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
Money representation
finance · low: Store money as integer minor units with an explicit currency exponent, compute in wider integers, and round once with a named rounding mode.
›
Bin packing
optimization · low: Pack items of given sizes into the fewest fixed-capacity bins; sort descending, then first-fit, gives at most 11/9 of optimal and runs in O(n log n).
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Simplex method
optimization · low: Solves linear programs by walking along the edges of the feasible polytope from vertex to vertex, one basis swap (pivot) at a time; the workhorse behind MIP solvers.
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Hierarchical timing wheel
systems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.
›
Largest remainder rounding
finance · low: Splitting an amount into parts that sum exactly (largest remainder / Hamilton) for allocations, splits and prorations
Isolation forest
machine-learning · low: Score each record by how few random axis-aligned splits it takes to isolate it in a small forest of random trees; anomalies are few and different, so they isolate fast.
›
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.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
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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.
›
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
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.
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Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Dictionary encoding
compression · low: Map values to small integer codes via a dictionary for compact, comparable columns
+ 3 more techniques
+ 21 more primitives
System
Techniques · 10
Primitives · 37
cloud-cost
Windowed stream aggregation
distributed · 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.
›
Cost allocation models
finance · medium: Split shared resource costs across tenants by requests, usage or proportional share
Time-series anomaly detection
statistics · medium: Predict the expected value from a seasonal baseline, scale the residual by a robust spread (MAD), threshold at a chosen false-alarm rate, then merge flags into incidents.
›
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.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
›
Watermarks
distributed · medium: Event-time progress markers that bound lateness and trigger window results
Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
LSM-tree
storage · medium: Buffer writes in memory, flush them as immutable sorted files and merge in the background; turns random writes into sequential I/O.
›
Rollup aggregation
storage · 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.
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Vectorized execution
storage · medium: Query operators pass batches of about 1-2K values per column instead of single tuples, amortizing interpretation overhead and enabling tight SIMD loops.
›
Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
Money representation
finance · low: Store money as integer minor units with an explicit currency exponent, compute in wider integers, and round once with a named rounding mode.
›
Bin packing
optimization · low: Pack items of given sizes into the fewest fixed-capacity bins; sort descending, then first-fit, gives at most 11/9 of optimal and runs in O(n log n).
›
Simplex method
optimization · low: Solves linear programs by walking along the edges of the feasible polytope from vertex to vertex, one basis swap (pivot) at a time; the workhorse behind MIP solvers.
›
Hierarchical timing wheel
systems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.
›
Largest remainder rounding
finance · low: Splitting an amount into parts that sum exactly (largest remainder / Hamilton) for allocations, splits and prorations
Isolation forest
machine-learning · low: Score each record by how few random axis-aligned splits it takes to isolate it in a small forest of random trees; anomalies are few and different, so they isolate fast.
›
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.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
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.
›
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
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.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Dictionary encoding
compression · low: Map values to small integer codes via a dictionary for compact, comparable columns
Zone maps
storage · low: Per-block min/max (and sparse primary) indexes to skip data during scans
d-ary heap
data-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 offsets
distributed · 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.
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Finite differences
numerics · 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.
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L-BFGS
numerics · 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.
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Kalman filter
robotics · low: Optimal recursive estimator for a linear system with Gaussian noise: fuses a motion model with noisy measurements into a state estimate plus its covariance.
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Time partitioning
storage · 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.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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Fencing tokens
distributed · low: A number that grows with every lock grant and is checked by the protected resource, so a paused ex-holder's late write is rejected.
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Message sequence numbers
distributed · low: A per-stream counter on every message lets the receiver detect loss, duplicates and reordering, ack cheaply and resume after reconnect with "send from N".
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Segmented log
distributed · low: Split an append-only log into files named by their first offset, write only the newest, and find any record by binary search plus a sparse index.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
›
Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Explicit Euler
numerics · low: Step an ODE forward by its current slope, y += h·f(t, y); first-order, one evaluation per step, the baseline every other integrator is measured against.
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Floating-point error
numerics · low: How IEEE 754 rounding error accumulates and how to contain it: compensated (Kahan/Neumaier) and pairwise summation, error-free transformations with FMA, ULP comparisons.
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LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
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Thomas tridiagonal solver
numerics · low: O(n) tridiagonal linear solve used in implicit finite-difference and spline schemes
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
Slab allocator
systems · low: Pre-carve pages into equal-size slots per size class with an in-place free list: O(1) alloc and free, no external fragmentation, cache-warm reuse.
›
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