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Cloud platform and compute
autoscaler
node and pod autoscaler
Karpenter, HPA
System
Techniques · 9
Primitives · 27
autoscaler
Desired-state reconciliation
distributed · medium: Declare the target in a store; controllers repeatedly diff actual against desired from full state and apply idempotent fixes, so missed events only delay convergence.
›
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.
›
Autoscaling
systems · medium: Target-tracking scaler with stabilization windows, cooldowns and hysteresis
Priority and preemption
systems · medium: Priority classes, quota and evicting lower-priority work to admit higher
Spot capacity allocation
systems · medium: Diversify across instance pools by price and interruption risk
Warm pool provisioning
systems · medium: Keep pre-initialized instances ready to cut cold starts
Watch notifications
distributed · medium: A store numbers every write; clients list at revision R, then watch from R+1 and receive an ordered change stream with no gap, no duplicate, and a resume point.
›
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.
›
PID controller
robotics · low: Drive a measured value to a setpoint with three terms on the error: proportional, integral, derivative. The default feedback loop for motors, heaters, drones and gimbals.
›
Graceful draining
systems · low: On a termination signal, stop taking new work, finish in-flight work until a deadline, tell clients to reconnect elsewhere, then exit; clients resume from a token.
›
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).
›
Leases
distributed · low: A lock that expires on its own: bounded-time ownership that survives crashes and partitions, given bounded clock drift and fencing tokens on writes.
›
Conditional writes
distributed · low: Put-if-absent / if-match compare-and-swap on objects or keys as a commit primitive
Exponential backoff with jitter
systems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.
›
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
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.
›
Sliding-window counters
systems · 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.
›
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.
›
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
Health checking
systems · low: Decide per backend whether to route to it using probes and real-traffic errors, with consecutive-count hysteresis, capped ejection and a panic fallback.
›
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".
›
Grpc protocol
networking · low: GRPC framing over HTTP/2: unary and streaming RPCs, deadlines, status
+ 2 more techniques
+ 11 more primitives
System
Techniques · 9
Primitives · 27
autoscaler
Desired-state reconciliation
distributed · medium: Declare the target in a store; controllers repeatedly diff actual against desired from full state and apply idempotent fixes, so missed events only delay convergence.
›
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.
›
Autoscaling
systems · medium: Target-tracking scaler with stabilization windows, cooldowns and hysteresis
Priority and preemption
systems · medium: Priority classes, quota and evicting lower-priority work to admit higher
Spot capacity allocation
systems · medium: Diversify across instance pools by price and interruption risk
Warm pool provisioning
systems · medium: Keep pre-initialized instances ready to cut cold starts
Watch notifications
distributed · medium: A store numbers every write; clients list at revision R, then watch from R+1 and receive an ordered change stream with no gap, no duplicate, and a resume point.
›
Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
MVCC
storage · medium: Keep timestamped versions of each row so readers see a consistent snapshot without blocking writers; the default concurrency control in modern databases.
›
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.
›
PID controller
robotics · low: Drive a measured value to a setpoint with three terms on the error: proportional, integral, derivative. The default feedback loop for motors, heaters, drones and gimbals.
›
Graceful draining
systems · low: On a termination signal, stop taking new work, finish in-flight work until a deadline, tell clients to reconnect elsewhere, then exit; clients resume from a token.
›
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).
›
Leases
distributed · low: A lock that expires on its own: bounded-time ownership that survives crashes and partitions, given bounded clock drift and fencing tokens on writes.
›
Conditional writes
distributed · low: Put-if-absent / if-match compare-and-swap on objects or keys as a commit primitive
Exponential backoff with jitter
systems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.
›
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
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.
›
Sliding-window counters
systems · 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.
›
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.
›
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
Health checking
systems · low: Decide per backend whether to route to it using probes and real-traffic errors, with consecutive-count hysteresis, capped ejection and a panic fallback.
›
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".
›
Grpc protocol
networking · low: GRPC framing over HTTP/2: unary and streaming RPCs, deadlines, status
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
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
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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