mhtirogla
Home
Systems
API
Pricing
About
Log in
← Back
Home
›
Systems
›
Cloud platform and compute
usage-metering
usage metering pipeline for billing
AWS billing
System
Techniques · 7
Primitives · 37
usage-metering
Exactly once processing
distributed · medium: Idempotent producers plus transactional offset+output commits for effectively-once results
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.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
›
Two-phase commit
distributed · medium: Atomic commit across shards: participants durably vote yes, then the coordinator logs and broadcasts one decision; blocks if the coordinator is lost.
›
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.
›
Vectorized execution
storage · medium: Query operators pass batches of about 1-2K values per column instead of single tuples, amortizing interpretation overhead and enabling tight SIMD loops.
›
Event deduplication window
distributed · low: Keep the ids of recently accepted events for a time window and drop repeats; memory stays bounded by rotating time buckets instead of per-entry timers.
›
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.
›
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.
›
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.
›
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).
›
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.
›
Double-entry ledger
finance · low: Append-only journal of transactions whose postings sum to zero per currency; balances are derived, so money can only move, never appear or vanish.
›
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.
›
Reconciliation
finance · low: Match internal ledger records against bank and processor statements by reference, then amount and date window; every unmatched or mismatched item becomes an exception.
›
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
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.
›
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.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Dictionary encoding
compression · low: Map values to small integer codes via a dictionary for compact, comparable columns
+ 21 more primitives
System
Techniques · 7
Primitives · 37
usage-metering
Exactly once processing
distributed · medium: Idempotent producers plus transactional offset+output commits for effectively-once results
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.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
›
Two-phase commit
distributed · medium: Atomic commit across shards: participants durably vote yes, then the coordinator logs and broadcasts one decision; blocks if the coordinator is lost.
›
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.
›
Vectorized execution
storage · medium: Query operators pass batches of about 1-2K values per column instead of single tuples, amortizing interpretation overhead and enabling tight SIMD loops.
›
Event deduplication window
distributed · low: Keep the ids of recently accepted events for a time window and drop repeats; memory stays bounded by rotating time buckets instead of per-entry timers.
›
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.
›
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.
›
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.
›
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).
›
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.
›
Double-entry ledger
finance · low: Append-only journal of transactions whose postings sum to zero per currency; balances are derived, so money can only move, never appear or vanish.
›
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.
›
Reconciliation
finance · low: Match internal ledger records against bank and processor statements by reference, then amount and date window; every unmatched or mismatched item becomes an exception.
›
Resource usage accounting
hardware-os · low: Cgroup/procfs/GPU counters sampled into per-workload usage
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.
›
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.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Dictionary encoding
compression · low: Map values to small integer codes via a dictionary for compact, comparable columns
Zone maps
storage · low: Per-block min/max (and sparse primary) indexes to skip data during scans
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.
›
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.
›
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.
›
Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
›
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.
›
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.
›
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.
›
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.
›
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".
›
Largest remainder rounding
finance · low: Splitting an amount into parts that sum exactly (largest remainder / Hamilton) for allocations, splits and prorations
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.
›
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.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
›
Csprng
security · low: OS-backed cryptographically secure random numbers (getrandom), and when a fast PRNG is not enough
Hash join
storage · low: Build a hash table on the smaller input's join key and probe it with every row of the larger one; the linear-time workhorse for equi-joins.
›
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.
›
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.
›
Copy for AI
Similar systems
Share the most building blocks with this one.
error-tracking
23 %
cloud-cost
21 %
affiliate-tracking
21 %
influencer-marketing
19 %
energy-retail-billing
19 %