Lsm tree — where it appears
Named by 15 essays across one field — each of them below, with the objects they name alongside it.
The writes nobody counted
Sixteen thousand keys inserted into a B-tree write 49.3 elements' worth of blocks for every key stored. The same keys into a log-structured store write 2.0. Every operation counter reports the two as the same work — the same insertions, the same comparisons, the same number of updates — and the factor of 24 decides which structure a storage engine is built from.
One dial between two structures
A B-tree writes 226 elements of block for every key stored and a log-structured store writes two. They are presented as rival designs. They are one design at two settings of an exponent that nothing in either description mentions, and every setting between them is available.
The filter each run carries
A log-structured store turns every lookup for a missing key into a read of every level, and a Bloom filter on each run buys those reads back with memory. Spread five bits a key evenly across three levels and a missing key still wastes 0.279 reads. Give the small levels more bits and the large one fewer — the same memory — and it wastes 0.201. At four levels the gap is 0.382 against 0.209, because sized filters stop the waste growing with the number of levels.
The read a filter has no key for
A Bloom filter on every run of a log-structured store turns a lookup for a missing key from a read of every level into a fraction of one — 0.72 transfers across eight levels at five bits a key, and 0.00027 at twenty. A range query over the same store reads nine transfers at five bits and nine at twenty, because a filter answers whether one named key is in a run and a range has no key to name.
The runs a range query can skip
A Bloom filter over key prefixes can tell a range query that a run holds nothing it wants, which a filter over keys cannot. On a four-level store of a million keys, a range expected to hold one key costs 4.76 transfers with no such filter and 2.41 with one built on 20-bit prefixes. The best length moves two bits for every tenfold longer range. By a hundred keys the most any range filter could save is a fifth of a transfer, because only the smallest level is ever empty. And the same memory spent on 20-bit prefixes makes a lookup for an absent key three times dearer.
The small levels' share of a range filter
Prefix filters on a four-level store were proposed a prefix length each, on the reasoning that one length is a compromise between levels whose densities differ 43-fold. Each level does want its own length, 18 to 22 bits, and that recovers a quarter of the gap to a perfect range filter. The gap is mostly not about length. Three fifths of it is false positives. Moving filter bits from the largest level to the small ones — 12, 12, 8 and 3 bits a key instead of 5 each — recovers more than half of it at the same memory, and matches one shared length given twice the memory.
The resolution a range filter can afford
A prefix filter on each level of a log-structured store answers a range by asking one question for every prefix the range touches, and each can be wrong. A filter holding prefixes at every length from 16 to 24 bits could ask each range at the resolution it needs, and was proposed as the repair. At the store's five bits a key it costs 5.69 transfers a range query where one length a level costs 4.32, because a short range is asked only at the finest length, and that filter now holds a fraction of the memory. Asked at every length it recovers most of that, and seems to overtake the single length at twelve bits a key — but only a single length chosen for five bits. Chosen again for each memory, one length a level beats every multi-resolution design at every memory measured, because the resolution a level can afford is itself set by its memory.
One length for the gaps and the clusters
On uniform keys one prefix length a level, chosen for its memory, beat a filter holding every length from 12 to 24 bits. Clustering the keys was predicted to reverse that, because coarse prefixes between clusters are empty and a coarse filter can then say no to a long range in one question. Clustering alone does not: whether ranges fall in the gaps or at the keys, one length re-chosen for that workload still wins, since it moves coarse for the gaps and fine for the keys. The multi-resolution filter wins only where a clustered store is asked both — by 0.17 transfers a query at ten bits a key on sixteen clusters — and it wins on short ranges inside the clusters, not on long ones between them.
Two lengths that do the work of eleven
On keys that cluster, asked both between the clusters and at them, a prefix filter holding every length from 12 to 24 bits beat one length a level by 0.17 transfers a range query, and a pair of lengths — one coarse, one fine — was proposed to recover nearly all of that. It recovers more than all of it: 0.23 transfers at ten bits a key, and at four bits, where the eleven lengths lose to one, the pair still saves 0.18. The coarse filter takes under 2% of a level's memory, and the eleven-length filter was leaving its finest length as little as 14% and cannot give it more without starving the lengths its descent passes through. The store predicted to defeat a pair, with gaps at two scales, does not. The saving is bought in questions: the pair asks its filters more often than the eleven lengths do.
The filter rebuilt with every write
A log-structured store chooses a range filter's two prefix lengths when it writes a level, and a filling store's small levels hold only the keys that arrived last. Choosing at every write was predicted to keep the lead a pair chosen once on the full store has, except where keys arrive a cluster at a time and a small level's choice goes stale when the next batch lands elsewhere. It cannot go stale: a levelled store changes a level's keys only by rewriting it, and rewriting it rebuilds the filter. Choosing at every write and choosing once then differ by at most 2.3% of what a perfect filter saves, on both arrival orders, at two bits a key and at ten, and choosing costs twenty-eight times the hashing. What the order of arrival changes is the store: filled a cluster at a time, its small levels hold one cluster each, and at two bits a key the same filter captures 94% of a perfect filter's saving against 68% shuffled.
One bit a key, divided
A levelled store of a million clustered keys, each level's range filter a pair of prefix lengths chosen when the level is written, captures 68% of a perfect filter's saving at two bits a key spread evenly when its keys arrive shuffled, and 94% when they arrive a cluster at a time. The proposal was to divide the memory by what each level's filter saves per bit, and it predicted the memory would leave the small levels, whose keys the arrival order had already sorted out. It goes the other way, as it did for single prefix lengths, and much further: the small levels take twelve to sixteen bits a key, the deepest level one, and the store captures 99% of a perfect filter's saving on both orders while leaving part of the budget unspent. Divided, one bit a key on average does what four bits spread evenly cannot.
A division chosen before the queries arrive
A levelled store's range-filter memory, divided among its levels by trying every assignment against the store's own queries, captures 99% of a perfect filter's saving on both arrival orders, with the small levels at twelve to sixteen bits a key and the deepest at one. A store writing a level cannot try every assignment, but it knows its levels' sizes, and a rule giving each level δ more bits a key for every factor of four it is smaller was predicted to match the fitted division on the shuffled store and to land near the even split's 94% on the store filled a cluster at a time. It matches the fitted division on both: 97.2% to 99.0% shuffled and 99.7% to 99.9% filled a cluster at a time from one bit a key up, with δ chosen on a sample of queries and charged on others. One δ of four, set before any query, is within a point and a half everywhere from one bit a key up. The rule fails exactly where the proposal said: a δ of six or more at one bit a key gives the deepest level nothing, and the shuffled store falls from 97.2% to 73.9%.
The sizes a store passes through
A levelled store's range-filter memory can be divided by its levels' sizes alone, a few more bits a key for every factor of four a level is smaller than the deepest, and on the full store that rule matched the division fitted to the store's own queries at every budget. Through the fill it does not. At a bit a key or less it falls up to 17 points of a perfect filter's saving behind on the shuffled store and 26 on the store filled a cluster at a time, and every large loss has the same cause: part way through the fill the deepest level is a smaller share of the store, and the rule gives it nothing.
A floor under every level
A levelled store's filter memory divided by its levels' sizes lost up to 26 points of a perfect filter's saving part way through its fill, every large loss a level given no filter at all. A floor of half a bit a key under every level was predicted to close every gap above five points at one bit a key and cost nothing at two and four. It closes them: the worst gap at one bit falls from 17 points to 4 on the shuffled store and from 25 to under 1 on the clustered one, and nothing moves at two bits and up. At half a bit a key the floor is the whole budget and the rule becomes the even split, which the clustered store wants half way through and the shuffled store, at the same sizes, loses 35 points by.
A span the queries do not see
A rule for dividing a levelled store's range-filter memory by its levels' sizes, with a floor of half a bit a key, came within five points of the division fitted to the store's own queries at every budget from one bit up — but at half a bit the shuffled and the clustered store wanted opposite divisions of identically sized levels. A level's fences, its first and last keys, say how much of the key space it spans, and a clustered level spans little. Scaling each level's bits by that span was predicted to close the half-bit gap and change nothing above. It changes nothing at half a bit — where the floor is the whole budget, so there is nothing left to scale — and from one bit up it puts the clustered store 30 points behind the fitted division, where the floored rule was within one. A level holding one cluster spans 0.4% of the key space and meets 4.4% of the queries, because half of them start at a stored key: the fences measure the key space, and the queries are aimed at the keys.
Named alongside it
The objects these essays reach for when they reach for this one.
Bloom filterDesign parameterPer levelBlock transferRange queryRead amplificationSpace accountingFalse-positiveFalse-positive ratePredictionWorkloadWrite amplification