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Payments and commerce
rtb-exchange
real-time bidding ad exchange
Google AdX, OpenRTB
System
Techniques · 14
Primitives · 68
rtb-exchange
Deterministic simulation testing
distributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.
›
Partitioned commit log
distributed · medium: A topic split into replicated append-only partitions: key hash keeps per-key order, offsets give position, and a high watermark limits readers to fully replicated records.
›
Primary-backup replication
distributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.
›
Budget pacing
finance · medium: Spreading an advertiser budget over the day via throttling or bid multipliers with a feedback controller
Ftrl online learning
machine-learning · medium: FTRL-proximal online logistic regression for sparse, high-volume click prediction
Coverage-guided fuzzing
security · medium: Mutate inputs, run an instrumented target, and keep any input that reaches a new code edge; feedback turns blind guessing into incremental search.
›
Point-in-time recovery
storage · medium: Periodic base backups plus a continuously archived change log let you rebuild the state as of any instant, then branch from there.
›
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.
›
Feature hashing (the hashing trick)
machine-learning · low: Hash each feature name to an index in a fixed-size vector, with a second hash for the sign, so no vocabulary is needed and memory is fixed.
›
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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Bloom filter
data-structures · low: Bit array answering "definitely not present" or "probably present" in about 10 bits per key for 1 % false positives; inserts are cheap, deletes impossible.
›
Count-min sketch
data-structures · low: d rows of w counters estimate any item's frequency in a stream, overestimating by at most ε·N with probability 1-δ, in fixed tiny memory.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
Consistent hashing
distributed · low: Hash nodes and keys onto a ring; each key belongs to the next node clockwise, so membership changes move only about 1/n of keys.
›
Sealed bid auction
finance · low: First-price and second-price sealed-bid auctions with reserves and floors
Process supervision
hardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.
›
Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
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epoll event loop
networking · low: One thread waits on thousands of non-blocking sockets with epoll_wait and handles only ready ones; the core of nginx, Redis and tokio.
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HTTP/2
networking · low: Binary multiplexed framing for HTTP over one TCP connection: streams, per-stream and per-connection credit windows, HPACK header compression and the limits that contain abuse.
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Longest prefix match
networking · low: IP prefix lookup structures (DIR-24-8, Poptrie, LC-trie) for routes, ACLs and geo-IP
Zero-copy serialization
networking · low: Wire formats laid out like in-memory structs (FlatBuffers, Cap'n Proto, SBE, rkyv) so readers access fields in place with no decode pass or allocation.
›
Mutual TLS
security · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.
›
Json streaming parser
storage · low: Streaming/zero-copy JSON parsing and in-place field rewriting
+ 7 more techniques
+ 52 more primitives
System
Techniques · 14
Primitives · 68
rtb-exchange
Deterministic simulation testing
distributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.
›
Partitioned commit log
distributed · medium: A topic split into replicated append-only partitions: key hash keeps per-key order, offsets give position, and a high watermark limits readers to fully replicated records.
›
Primary-backup replication
distributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.
›
Budget pacing
finance · medium: Spreading an advertiser budget over the day via throttling or bid multipliers with a feedback controller
Ftrl online learning
machine-learning · medium: FTRL-proximal online logistic regression for sparse, high-volume click prediction
Coverage-guided fuzzing
security · medium: Mutate inputs, run an instrumented target, and keep any input that reaches a new code edge; feedback turns blind guessing into incremental search.
›
Point-in-time recovery
storage · medium: Periodic base backups plus a continuously archived change log let you rebuild the state as of any instant, then branch from there.
›
Delta debugging
compilers · medium: Ddmin minimization of failing inputs or change sets
Fault injection
distributed · medium: Inject latency, errors, kills and partitions via proxies, netem or hooks
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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ARIES recovery
storage · medium: Three-pass crash recovery (analysis, redo, undo) for steal/no-force buffer pools; repeats history, then rolls back losers with compensation log records.
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Syscall interception
security · medium: Intercept syscalls via ptrace, seccomp user notify or a user-space kernel
Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
Buffer pool
storage · medium: The database's own page cache (frames, page table, pin counts, dirty bits, replacement) so it, not the OS, controls eviction, write order and I/O.
›
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.
›
Feature hashing (the hashing trick)
machine-learning · low: Hash each feature name to an index in a fixed-size vector, with a second hash for the sign, so no vocabulary is needed and memory is fixed.
›
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.
›
Bloom filter
data-structures · low: Bit array answering "definitely not present" or "probably present" in about 10 bits per key for 1 % false positives; inserts are cheap, deletes impossible.
›
Count-min sketch
data-structures · low: d rows of w counters estimate any item's frequency in a stream, overestimating by at most ε·N with probability 1-δ, in fixed tiny memory.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
Consistent hashing
distributed · low: Hash nodes and keys onto a ring; each key belongs to the next node clockwise, so membership changes move only about 1/n of keys.
›
Sealed bid auction
finance · low: First-price and second-price sealed-bid auctions with reserves and floors
Process supervision
hardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.
›
Calibration
machine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.
›
epoll event loop
networking · low: One thread waits on thousands of non-blocking sockets with epoll_wait and handles only ready ones; the core of nginx, Redis and tokio.
›
HTTP/2
networking · low: Binary multiplexed framing for HTTP over one TCP connection: streams, per-stream and per-connection credit windows, HPACK header compression and the limits that contain abuse.
›
Longest prefix match
networking · low: IP prefix lookup structures (DIR-24-8, Poptrie, LC-trie) for routes, ACLs and geo-IP
Zero-copy serialization
networking · low: Wire formats laid out like in-memory structs (FlatBuffers, Cap'n Proto, SBE, rkyv) so readers access fields in place with no decode pass or allocation.
›
Mutual TLS
security · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.
›
Json streaming parser
storage · low: Streaming/zero-copy JSON parsing and in-place field rewriting
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.
›
Hedged requests
systems · low: Sending a backup request after a latency percentile and taking the first answer to cut tail latency
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.
›
Cooperative scheduling
concurrency · low: Green threads/coroutines yielding at await points on a user-space scheduler
Record replay
distributed · low: Record every nondeterministic input (time, randomness, I/O, model and tool outputs, per-tick player input) and re-execute deterministically for debugging, replays and state rebuild
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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Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
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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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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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Zero-copy I/O
networking · low: Move bytes between files, sockets and NICs by passing page references instead of copying through user space: sendfile, splice, MSG_ZEROCOPY, kTLS.
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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.
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Write-ahead log
storage · low: Append each change to a checksummed sequential log and fsync it before touching data pages; the basis of crash durability in nearly every database.
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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.
›
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.
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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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Code coverage instrumentation
compilers · low: Insert counters into compiled code to record which blocks, edges or lines ran; a spanning tree of the control-flow graph cuts the counters needed.
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Sanitizers
security · low: ASan/UBSan/TSan shadow memory and instrumentation to catch memory bugs
Object storage
cloud · low: S3-compatible blob storage at cents per GB-month with 11 nines durability; pick by egress price, request price and latency, not by storage price alone.
›
Copy on write snapshots
storage · low: Point-in-time snapshots by sharing pages/extents and copying only on write
Failure bisection
search-sort · low: Binary search over commits, batches or inputs to isolate a failure
Transparent interception
networking · low: Redirect traffic to a local proxy (iptables TPROXY/REDIRECT, eBPF) without app changes
Croston method
statistics · 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.
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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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Ebpf
hardware-os · low: Verified in-kernel programs on hooks (kprobes, XDP, tc) with maps
Ptrace process control
hardware-os · low: Ptrace: attach, read and write registers and memory, single-step, intercept signals
Seccomp bpf
security · low: Syscall filtering with seccomp BPF programs
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.
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fsync, O_DSYNC and O_DIRECT
hardware-os · low: What fsync, fdatasync, O_DSYNC, O_DIRECT and F_FULLFSYNC really guarantee on Linux and macOS, and the exact call sequences for crash-safe files.
›
LRU and CLOCK
systems · low: LRU evicts the least recently used entry via hash map plus linked list; CLOCK approximates it with a reference bit, so hits take no lock.
›
S3-FIFO
systems · low: Three FIFO queues (small, main, ghost) that evict one-hit wonders quickly and keep reused objects; low miss ratios with no list moves on hits.
›
Control-flow graph
compilers · low: Split a linear instruction list into basic blocks and connect them by jumps; the graph every optimizer, SSA builder and dataflow pass walks.
›
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.
›
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.
›
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".
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
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Minimum spanning tree
graphs · low: Cheapest set of edges connecting all vertices; Kruskal sorts edges and joins components with union-find, Prim grows one tree, Boruvka merges in parallel rounds.
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Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
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CRC32C checksums
hardware-os · low: The Castagnoli CRC-32 with dedicated instructions on x86 (SSE4.2) and ARMv8. It is the standard fast integrity check for storage blocks, logs and network frames.
›
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.
›
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.
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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.
›
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.
›
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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