Every essay — page 9
What a bound is Counting The floors What the machine does Structures Two parameters The other axis When the algorithm flips a coin What the libraries do When it does not fit One pass, and no room The data that is not a number When the algorithm is a table The index that replaces the text What is taught wrongly Ladders Objects Search
One pass, and no room
The data goes past once and there is not room to keep it. What survives is a summary of a few hundred bits, and the answer it gives is wrong — the whole design is a choice about how wrong, and every structure here buys accuracy with bits at a rate that can be measured.
The counter with no window in it
A counter that fades by half every H settles, on a steady stream, at exactly the count of a window of 1.44H. That correspondence holds in the mean, on a steady stream, and nowhere else — and it is the reason a decayed counter is not an estimate of a windowed count for any window.
The summary that has to forget
Every structure in this field so far accumulates. Ask instead for the count over only the last thousand arrivals and no counter will do, because a counter has no record of which of its increments are old — and the repair is a row of buckets whose whole error is the oldest one.
The bits that say when
A windowed cardinality estimator holds 4,592 bits and 2,392 of them are clocks. Every summary in this collection has reported its size from the shape of its own structure, and not one of those numbers has ever been asked what the bits were for — so the resource that half of these structures spend most of their state on has been invisible while being counted.
A register that became a list
HyperLogLog replaces a key per distinct item with a five-bit register, and over a whole stream that is a saving of a hundred times. Ask it about the last four thousand arrivals instead and the same comparison against the same exact structure comes out at five. The estimator did not get worse. The exact answer got cheap.
An error measured against the answer
A quantile summary asked for the 99.9th percentile answered 9,694 where the truth was 256, and violated nothing — its promise was a rank error under one per cent of the stream and it delivered a tenth of one per cent. One per cent of the stream is a thousand per cent of the tail, and no amount of extra state changes that.
The floor a histogram already knows
A summary of thirty-two counters settles at a smallest counter of 119, and the number can be computed from the shard's key frequencies before a single counter is allocated. The obvious way to compute it is wrong by a factor of two, and the reason is that the heavy counters carry no error at all.
The tuples a merge does not give back
A merge of thirty-two quantile summaries keeps seven times the tuples of one summary over the same values, and sixty-four keeps ten and a half. Fitted across the sweep the count goes as the shard number to the power 0.56, which answers what it converges to — it does not.
The window that is even in the wrong currency
A window of four thousand arrivals in eight blocks retires a block every 5.1 seconds on a steady stream and anywhere between 0.57 and 14.8 seconds on a stream whose rate moves. The structure cannot tell, because it is counting arrivals, and the alert written against it is in seconds.
The period that is not a promise
Greenwald–Khanna's ε appears twice — once as the rank tolerance the structure promises, and once as ⌊1/2ε⌋, the number of updates between compressions. Unhook the second from the first and sweep it across a thousand-fold range. The tuples held move by 10%, the worst rank error by 21%, the peak by ten times and the housekeeping by seventy.
A decay measured from where it started
An exponentially decayed counter is one number because its weights fade by elapsed time alone. Forward decay keeps a polynomial weight in one number too, by measuring each arrival from a fixed landmark. Its memory is then a share of the time since that landmark: at β = 2 an arrival counts half at 29% of that time. Anchored at the start of a stream, it takes 5.3 seconds to register a fourfold rise twenty seconds in and 83 seconds when the rise comes at five minutes.
Structures
Heaps, trees, hash tables and dynamic arrays — each with its advertised bound put through the same measurement as everything else here.
What amortised means
Appending to a dynamic array is O(1) amortised. It is also, on 512 appends, an operation that costs one unit 503 times and 257 units once. The amortised bound is a true statement about the sequence and a false one about any append in it, and the picture that shows why is a sawtooth nobody draws.
Choosing a growth factor
When a dynamic array fills up, how much bigger should the new one be? Doubling costs 2.02 units per append and leaves 39% of the allocation empty. Growing by an eighth costs 9.89 and leaves 10%. Every factor is a trade between time and space, no factor wins on both, and real implementations disagree about the answer for reasons that are measurable.
The order nobody fixed
The same eight summaries, combined pairwise in a tree or folded in one at a time, produce tables that differ on forty keys — the largest by 964 arrivals. Nothing in a deployment fixes which shape is used, and both answers are inside the guarantee.
The tree that is a list
A binary search tree gives logarithmic lookup. Build one from 128 keys in sorted order and it has height 127 — every node has one child, and a lookup is a linear scan. The failure is not gradual and it happens on the input people try first, which makes "O(log n) lookup" a claim about the insertion order rather than about the structure.
Building a heap from the bottom
Bottom-up heap construction is Θ(n) and repeated insertion is Θ(n log n), and the second of those is a worst case quoted as a behaviour. On random input, repeated insertion measures linear too — 2.22 comparisons per element against 1.87 — and the famous logarithmic factor never appears. On ascending input it appears in full, and it is a factor of six.
The priority nobody supplied
Insert 4,096 sorted keys into a binary search tree and it reaches height 4,095, costing 8,386,560 comparisons to build. Give every key a second, random key and keep the tree heap-ordered on that instead, and the same insertion reaches height 26 for 32,750 comparisons. Nothing detected the imbalance, and nothing rebalanced.
The probe nobody waits for
Robin Hood hashing makes an inserting key steal a slot from a key that has probed less far. The mean number of probes afterwards is 4.817, and before it was 4.817 — identical, and it cannot be otherwise, because the total displacement is fixed by the hash. What changes is the worst case, from 114 slots from home to 19, and a table reported by its average lookup cost shows no difference at all.
A bucket that becomes a tree
Java's HashMap converts a chained bucket into a red-black tree once it holds eight entries. The comment in the source computes the probability of that happening under a decent hash at about six in a hundred million, so the mechanism is written never to run. Under a hash that fails, the worst lookup falls from 192 comparisons to 8 — and the whole value of the tree is in a case its author does not control.
A tree with nodes the size of a block
A B-tree is a binary search tree that has read the hardware manual. Its node holds as many keys as fit in one transfer, so the height falls from log₂ n to log_B n — and the measured cost falls further still, to 1.01 transfers over four million keys, because the top of the tree is small enough to stay in memory. The comparison count goes up.
The index that is the text
A suffix array sorts all 4,097 suffixes of a text — 8.4 million characters of string, in total — and examines exactly zero characters doing it. It then answers a search in 91 characters where a scan costs 1,472, and the whole thing pays for itself at six queries. Both halves of that are worth the same amount of attention, and the first is the one that is usually skipped.
The items that survive k counters
Misra-Gries keeps k counters, decrements all of them on a miss, and never returns a count above the truth — with no hashing, no randomness and no failure probability. At equal state it is more accurate than the randomised sketch on the question both are usually asked, at every size measured.
The summaries that add
Two sketches built over two streams and merged are, for three of the four structures here, byte for byte the summary the concatenated stream would have produced. For the fourth the guarantee survives and the state does not, and calling both properties mergeability hides the difference that matters.
Every substring, in fewer states than substrings
A text of 512 characters has 129,416 distinct substrings. A machine that recognises every one of them, and nothing else, needs 831 states — and the bound it is under, 2n − 1, is reached exactly by a string one line long.
A sketch that is allowed to be under
Count-Min's estimate is never below the truth, and it pays for that with an error proportional to the whole stream. Give every key a sign and take a median instead, and the same table is 2.7 times more accurate on the keys anybody asks about — and wrong in both directions.
The count that outlives its arrivals
A Misra-Gries counter holding six thousand is not a record of six thousand arrivals. It is a number that has been added to and taken from, and nothing in the structure says when any of it happened — so when the key stops arriving the counter stays, and goes on reporting a key with nothing in the window as the heaviest thing in it.
The counter that takes the smallest slot
Space-Saving keeps two numbers per key and they bracket the truth from both sides. On the twenty heaviest keys of a stream its mean error is a tenth of one arrival, against a hundred and ten for Misra-Gries at the same bits — and on the keys ranked past a hundred the ordering reverses.
The bill a partition only divides
The two predicted damages for any key sum to the same number under every partition — 967 arrivals here, whatever the arrangement. Round-robin hands nearly all of it to Misra-Gries and hashing hands most of it to Space-Saving, and neither of them is paying more than the other in total.
The shape that moves the bill
Thirty-two quantile summaries combined pairwise keep 3,637 tuples and the same thirty-two folded in one at a time keep 2,616, for answers that differ by nothing at all. The counter tables measured for the same thing do the opposite — their order moves the answer and leaves the space alone.
The fold that minimises the wrong thing
A fold charges per level and a survivor pays the cuts on its path, so the bill looks like a weighted external path length — and Huffman's construction minimises that quantity by proof. Built and measured on thirty-two uneven shards it does minimise it, 181,407 against a balanced tree's 200,000, and leaves more damage than the tree does.
The shape one structure will not fold
Folding thirty-two shards largest-pair-first keeps 2,556 quantile tuples against a balanced tree's 3,211 — a fifth of the space saved. The same fold on the counter tables beside them leaves 403 counts of error against the tree's 148. A deployment holding both cannot fold once and be right twice.