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Payments and commerce
dynamic-pricing
dynamic pricing
airline fares
System
Techniques · 14
Primitives · 44
dynamic-pricing
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.
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Revenue management emsr
finance · medium: EMSR-b booking limits and bid prices for perishable capacity (seats, rooms)
Feature store
machine-learning · medium: One definition per feature feeds a history store for training and a latest-value store for serving, so training and inference see the same values as of the same moment.
›
Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
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Multi-armed bandits
machine-learning · medium: Choose among K options repeatedly while learning their payoffs: epsilon-greedy, UCB1 and Thompson sampling keep total regret low where a fixed split or a fixed exploration rate pays forever.
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A/B testing (online controlled expe…
statistics · medium: Randomly split units into arms by a salted hash, log exposure, compare metrics with a confidence interval, and guard the result with sample-ratio, guardrail and peeking checks.
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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.
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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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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.
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RCU (read-copy-update)
concurrency · low: Readers access shared data with no locks or atomic writes; writers publish a new copy and free the old one after a grace period.
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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.
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Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
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W-TinyLFU
systems · low: An LRU window plus segmented LRU, guarded by a 4-bit Count-Min sketch that admits a newcomer only if it beats the victim's frequency.
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Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
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.
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Columnar file format
storage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)
Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
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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Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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Bayesian rate estimation
statistics · low: Estimate a conversion or event rate from few observations with a Beta-Binomial or Gamma-Poisson update: two counters, an honest interval, and P(B beats A) directly.
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Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
CUPED (variance reduction with pre-…
statistics · low: Subtract the part of each unit's outcome that a pre-experiment covariate predicts; the effect estimate stays unbiased and its variance shrinks by 1 - rho^2.
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+ 7 more techniques
+ 28 more primitives
System
Techniques · 14
Primitives · 44
dynamic-pricing
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.
›
Revenue management emsr
finance · medium: EMSR-b booking limits and bid prices for perishable capacity (seats, rooms)
Feature store
machine-learning · medium: One definition per feature feeds a history store for training and a latest-value store for serving, so training and inference see the same values as of the same moment.
›
Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
›
Multi-armed bandits
machine-learning · medium: Choose among K options repeatedly while learning their payoffs: epsilon-greedy, UCB1 and Thompson sampling keep total regret low where a fixed split or a fixed exploration rate pays forever.
›
A/B testing (online controlled expe…
statistics · medium: Randomly split units into arms by a salted hash, log exposure, compare metrics with a confidence interval, and guard the result with sample-ratio, guardrail and peeking checks.
›
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.
›
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
Hypothesis testing
statistics · medium: Pick a test statistic, compare it to its distribution under "no effect", read off a p-value, and control error rates across one test or many.
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Sequential hypothesis testing
statistics · medium: Group-sequential and alpha-spending designs for interim analyses without inflating error
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.
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Point in time join
storage · medium: As-of join that picks the latest feature value at or before each event timestamp
Sequential probability ratio test
statistics · medium: Add up the log-likelihood ratio per observation and stop when it crosses a fixed bound; the least data of any test with the same error rates (for two point hypotheses).
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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.
›
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.
›
RCU (read-copy-update)
concurrency · low: Readers access shared data with no locks or atomic writes; writers publish a new copy and free the old one after a grace period.
›
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.
›
Linear and logistic regression
machine-learning · low: Fit a weighted sum of features to a number (least squares) or a probability (logistic); the baseline every model must beat.
›
W-TinyLFU
systems · low: An LRU window plus segmented LRU, guarded by a 4-bit Count-Min sketch that admits a newcomer only if it beats the victim's frequency.
›
Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
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.
›
Columnar file format
storage · low: Row groups, column chunks, pages, encodings and footer stats (Parquet/ORC style) (listed in Moved to the wiki)
Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
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.
›
Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Bayesian rate estimation
statistics · low: Estimate a conversion or event rate from few observations with a Beta-Binomial or Gamma-Poisson update: two counters, an honest interval, and P(B beats A) directly.
›
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
CUPED (variance reduction with pre-…
statistics · low: Subtract the part of each unit's outcome that a pre-experiment covariate predicts; the effect estimate stays unbiased and its variance shrinks by 1 - rho^2.
›
Experiment layering (overlapping ex…
statistics · low: Partition the tunable parameters into layers, hash each unit with a per-layer salt so layers are independent, and give experiments inside a layer disjoint bucket ranges.
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Deterministic bucketing
systems · low: Hash a stable unit id with a per-purpose salt into a bucket; "in" means bucket below the percentage, so assignment is stateless, repeatable and only grows on ramp-up.
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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.
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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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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.
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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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Wilson score interval
statistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samples
Sort-merge join
storage · low: Sort both inputs on the key, then advance two cursors in lockstep and emit matching runs; cheap on sorted inputs and good for range joins.
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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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Window functions
storage · low: ROW_NUMBER, rank, running and frame aggregates via sort and segment trees
Loglikelihood scoring
machine-learning · low: Scoring choices by summed token log-probabilities, normalization by length, perplexity computation
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
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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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Largest remainder rounding
finance · low: Splitting an amount into parts that sum exactly (largest remainder / Hamilton) for allocations, splits and prorations
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.
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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
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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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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Copy for AI
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