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Observability and operations
experimentation
feature flags and A/B testing
LaunchDarkly, Statsig
System
Techniques · 7
Primitives · 20
experimentation
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.
›
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.
›
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.
›
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).
›
Feature flag evaluation
systems · medium: Local evaluation of targeting rules and percentage rollouts from a flag snapshot
Watermarks
distributed · medium: Event-time progress markers that bound lateness and trigger window results
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.
›
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
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.
›
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.
›
HyperLogLog
data-structures · low: Counts distinct elements in kilobytes with about 1 % error by tracking the longest run of leading zero bits in hashed values per bucket.
›
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.
›
Server-sent events
networking · low: One long-lived HTTP response carries text events one way; each event may carry an id, so a reconnecting client names its last one and the server resumes.
›
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.
›
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.
›
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.
›
Wilson score interval
statistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samples
Loglikelihood scoring
machine-learning · low: Scoring choices by summed token log-probabilities, normalization by length, perplexity computation
Stale while revalidate
systems · low: Serving cached data past TTL while refreshing in the background, with request coalescing
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.
›
+ 4 more primitives
System
Techniques · 7
Primitives · 20
experimentation
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.
›
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.
›
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.
›
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).
›
Feature flag evaluation
systems · medium: Local evaluation of targeting rules and percentage rollouts from a flag snapshot
Watermarks
distributed · medium: Event-time progress markers that bound lateness and trigger window results
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.
›
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
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.
›
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.
›
HyperLogLog
data-structures · low: Counts distinct elements in kilobytes with about 1 % error by tracking the longest run of leading zero bits in hashed values per bucket.
›
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.
›
Server-sent events
networking · low: One long-lived HTTP response carries text events one way; each event may carry an id, so a reconnecting client names its last one and the server resumes.
›
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.
›
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.
›
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.
›
Wilson score interval
statistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samples
Loglikelihood scoring
machine-learning · low: Scoring choices by summed token log-probabilities, normalization by length, perplexity computation
Stale while revalidate
systems · low: Serving cached data past TTL while refreshing in the background, with request coalescing
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.
›
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".
›
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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