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social-feed
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X, Instagram
System
Techniques · 26
Primitives · 82
social-feed
CDN and edge
cloud · medium: CDNs cache content in hundreds of PoPs near users, cutting latency and egress bills; edge compute runs small code there. Bandwidth price spreads 10x between providers.
›
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.
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Fan-out on write
distributed · medium: Append each new item id to every follower's precomputed, capped list so a read is one fetch; pull the few huge authors at read time instead.
›
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.
›
Deep ranking models for recommendat…
machine-learning · medium: Embedding tables plus a feature-interaction net predict several feedback signals per candidate; a weighted sum of them is the ranking score.
›
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.
›
Sharded counters
concurrency · low: Split one hot counter into per-core or per-thread cells on separate cache lines; increments stay local and reads sum the cells.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
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.
›
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.
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Distributed ID generation
distributed · low: Mint unique, roughly time-sorted IDs on many nodes with no per-ID coordination: Snowflake (time, worker, sequence) or UUIDv7 (time, random).
›
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.
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Mutual TLS
security · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.
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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.
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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.
›
HTTP caching semantics
networking · low: The rules (RFC 9111) that decide whether a stored response is still fresh, and how a cheap conditional request revalidates it when it is not.
›
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.
›
Range partitioning
distributed · low: Cut the sorted key space into contiguous half-open ranges kept in a boundary map; split hot or big ranges, merge small ones, move ranges between nodes.
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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.
›
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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+ 19 more techniques
+ 66 more primitives
System
Techniques · 26
Primitives · 82
social-feed
CDN and edge
cloud · medium: CDNs cache content in hundreds of PoPs near users, cutting latency and egress bills; edge compute runs small code there. Bandwidth price spreads 10x between providers.
›
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.
›
Fan-out on write
distributed · medium: Append each new item id to every follower's precomputed, capped list so a read is one fetch; pull the few huge authors at read time instead.
›
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.
›
Deep ranking models for recommendat…
machine-learning · medium: Embedding tables plus a feature-interaction net predict several feedback signals per candidate; a weighted sum of them is the ranking score.
›
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.
›
Near-duplicate detection with MinHa…
machine-learning · medium: Estimate set Jaccard similarity from k min-hashes and find similar pairs with banded LSH buckets; the standard dedup for web crawls, training corpora and sequence collections.
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Two-tower retrieval
machine-learning · medium: Two separate encoders map users/queries and items into one vector space; item vectors are precomputed and searched by nearest neighbour, so recall over millions costs one small forward pass.
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Mobile push delivery
networking · medium: Sending notifications through APNs and FCM: HTTP/2 requests, token hygiene, collapse keys, TTL and priority, and the retry rules that keep you un-throttled.
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HNSW
search-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.
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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.
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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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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 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.
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Delta debugging
compilers · medium: Ddmin minimization of failing inputs or change sets
Durable job queue
distributed · medium: Persisted tasks that workers lease for a visibility timeout, ack on success and retry with backoff; at-least-once execution that survives worker and queue crashes.
›
Fault injection
distributed · medium: Inject latency, errors, kills and partitions via proxies, netem or hooks
Contrastive learning
machine-learning · medium: Train an encoder so two views of the same thing score higher than every other pair in the batch; InfoNCE with a temperature, no labels needed.
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TLS 1.3
security · medium: The standard encrypted, authenticated transport; one round trip, only forward-secret AEAD suites, and little left to misconfigure.
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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
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
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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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.
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Sharded counters
concurrency · low: Split one hot counter into per-core or per-thread cells on separate cache lines; increments stay local and reads sum the cells.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
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.
›
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.
›
Distributed ID generation
distributed · low: Mint unique, roughly time-sorted IDs on many nodes with no per-ID coordination: Snowflake (time, worker, sequence) or UUIDv7 (time, random).
›
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.
›
Mutual TLS
security · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.
›
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.
›
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.
›
HTTP caching semantics
networking · low: The rules (RFC 9111) that decide whether a stored response is still fresh, and how a cheap conditional request revalidates it when it is not.
›
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.
›
Range partitioning
distributed · low: Cut the sorted key space into contiguous half-open ranges kept in a boundary map; split hot or big ranges, merge small ones, move ranges between nodes.
›
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.
›
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.
›
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.
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Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
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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.
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Mixture of experts (MoE)
machine-learning · low: Replace one FFN with many expert FFNs and a router sending each token to its top-k; parameters scale with E, per-token compute with k.
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Embedding table sharding
ml-training · low: Sharding huge embedding tables across devices (row/column/table-wise) with all-to-all lookups and caching
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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Rolling hash
data-structures · low: Rabin-Karp polynomial rolling hashes and winnowing for substring fingerprints and document matching
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
›
Negative sampling
machine-learning · low: Train by contrasting each observed positive with a few sampled negatives instead of normalising over every candidate; the choice of sampler decides bias and quality.
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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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JSON Web Token (JWT)
security · low: A signed, URL-safe token carrying claims; the verifier's job is the checklist (pinned algorithm, key by kid, exp, aud, iss), not the format.
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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.
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Token bucket
systems · low: A bucket of up to b tokens refilled at rate r; requests spend tokens, enforcing an average rate while allowing bursts of b.
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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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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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Scalar quantization
search-sort · low: Per-dimension int8/binary quantization of vectors with rescoring
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.
›
Sanitizers
security · low: ASan/UBSan/TSan shadow memory and instrumentation to catch memory bugs
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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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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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
Dead-letter queue
distributed · low: A side queue for messages that keep failing: after N delivery attempts or a permanent error, park them with the reason so the main queue keeps moving.
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Idempotency keys
finance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.
›
Transparent interception
networking · low: Redirect traffic to a local proxy (iptables TPROXY/REDIRECT, eBPF) without app changes
Data augmentation
machine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.
›
AEAD (AES-GCM, ChaCha20-Poly1305)
security · low: Authenticated encryption that encrypts and detects tampering in one call; the only kind of symmetric encryption you should use.
›
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
›
ECDH key exchange (X25519, P-256)
security · low: Each side multiplies the other's public curve point by its own scalar and both get the same point; always validate the peer key and run the result through a KDF.
›
HKDF key derivation (extract-and-ex…
security · low: HMAC-based two-step key derivation: extract a uniform pseudorandom key from any secret, then expand it into as many context-bound keys as you need.
›
X509 certificates
security · low: Certificate chains, path validation, SANs, revocation and short-lived issuance
Wilson score interval
statistics · low: Wilson lower bound for ranking by up/down votes and proportions with few samples
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.
›
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
Loglikelihood scoring
machine-learning · low: Scoring choices by summed token log-probabilities, normalization by length, perplexity computation
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.
›
False sharing
concurrency · low: Independent variables written by different cores on one cache line force the line to bounce between cores; pad or regroup hot data to fix it.
›
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.
›
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.
›
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.
›
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.
›
CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
›
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.
›
AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
›
Collective communication
ml-training · low: The family of group exchanges (broadcast, reduce, all-gather, reduce-scatter, all-to-all) and the latency-plus-bytes cost model that picks ring, tree or direct algorithms by message size and topology.
›
Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
›
Transformer block
ml-training · low: The decoder-only pre-norm block (attention plus MLP on a residual stream) behind every modern LLM, with exact shapes, parameter, FLOP and KV-cache counts.
›
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.
›
Constant time code
security · low: Writing secret-dependent code without branches or memory lookups on secrets; constant-time compare and select
Csprng
security · low: OS-backed cryptographically secure random numbers (getrandom), and when a fast PRNG is not enough
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