Scaling Log-Structured KV-Stores featuring Monkey and Dostoevsky - PowerPoint PPT Presentation

Scaling Log-Structured KV-Stores featuring Monkey and Dostoevsky SIGMOD17 / SIGMOD18 Niv Dayan

Log-Structured KV-Stores

Log-Structured KV-Stores

Why Log-Structured KV-Stores?

Why Log-Structured KV-Stores? fast writes

Why Log-Structured KV-Stores? memory storage

Why Log-Structured KV-Stores?

Why Log-Structured KV-Stores?

Why Log-Structured KV-Stores? byte -addressable block -addressable

write data

write data

write data

In-Place Writes write data

In-Place Writes B-trees write data

In-Place Writes B-trees write data

Log-Structured Writes

Log-Structured Writes buffer writes

Log-Structured Writes buffer writes

Log-Structured Writes buffer writes

Log-Structured Writes buffer writes

Log-Structured Writes buffer writes

Log-Structured KV-Stores fast writes buffer writes

Log-Structured KV-Stores fast writes fast reads massive data

Background

Background buffer The Log-Structured Merge-Tree

Background buffer LSM-tree

buffer

writes buffer

key value pairs buffer

key value Sherlock: a fictional detective Waldo: an inconspicuous traveler buffer

buffer gets full

level buffer sort & flush 0 1

level buffer sort & flush … sorted runs 0 1

0 buffer 1 sort-merge 2

level 0 buffer exponentially increasing capacities o n e 1 level 1 I / O p e r r u n level 2 2 level 3 3

where’s level Waldo 0 buffer b i n a 1 r y s e a r c h i n g 2 3

where’s level Waldo 0 buffer pointers o n e 1 I / O p e r r u n 2 3

where’s level Waldo Bloom 0 buffer pointers filters 1 2 3

where’s level Waldo Bloom 0 buffer pointers filters true 1 negative 2 3

where’s level Waldo Bloom 0 buffer pointers filters true 1 negative false 2 positive 3

where’s level Waldo Bloom 0 buffer pointers filters true 1 negative false 2 positive true 3 positive

Bloom 0 buffer pointers filters merging frequency 1 2 3

merging writes reads

merging writes reads

merging Leveling Tiering write-optimized read-optimized

Leveling Tiering read-optimized write-optimized

Leveling Tiering read-optimized write-optimized gather

Leveling Tiering read-optimized write-optimized gather merge & flush

Leveling Tiering read-optimized write-optimized gather

Leveling Tiering read-optimized write-optimized gather merge

Leveling Tiering read-optimized write-optimized gather merge flush

Leveling Tiering read-optimized write-optimized gather merge

Leveling Tiering read-optimized write-optimized log R ( N )

Leveling Tiering read-optimized write-optimized 1 run per level R runs per level log R ( N ) size ratio

Leveling Tiering read-optimized write-optimized 1 run per level R runs per level log R ( N ) size ratio

Leveling Tiering read-optimized write-optimized 1 run per level R runs per level size ratio R

Leveling Tiering read-optimized write-optimized 1 run per level 1 run per level size ratio R

Leveling Tiering read-optimized write-optimized 1 run per level T runs per level size ratio R

Leveling Tiering read-optimized write-optimized O(l Nl ) runs per level 1 run per level sorted log array size ratio R

log Tiering Leveling sorted array

log Tiering size ratio R Leveling sorted array

log Tiering size ratio R Leveling sorted array

R log Tiering size ratio R Leveling sorted R array

Monkey Dostoevsky

M onkey: O ptimal N avigable Key -Value Store SIGMOD17

M onkey: O ptimal N avigable Key -Value Store SIGMOD17 Niv Dayan Manos Athanassoulis 
 Stratos Idreos

M onkey: O ptimal N avigable Key -Value Store SIGMOD17 Bloom data filters

Bloom data bits/entry filters x x x

Bloom data bits/entry filters x x x

false Bloom data positive rate filters O(e -x ) O(e -x ) O(e -x )

false Bloom positive rate filters O(e -x ) O( e -x · log R ( N )) I/O O(e -x ) = O(e -x )

false Bloom positive rate filters O(e -x ) O( e -x · log R ( N )) I/O O(e -x ) = O(e -x )

false Bloom positive rate filters O(e -x ) O(e -x ) O(e -x ) most memory

false Bloom positive rate filters O(e -x ) O(e -x ) O(e -x ) most memory saves at most 1 I/O!

reallocate

reallocate

same memory - fewer false positives reallocate

relax false positive rates 0 < p 0 < 1 0 < p 1 < 1 0 < p 2 < 1

model relax read false positive rates = f( p 0 , p 1 …) cost 0 < p 0 < 1 0 < p 1 < 1 memory = f( p 0 , p 1 …) footprint 0 < p 2 < 1

model relax L read ∑ false positive rates = p i cost 1 0 < p 0 < 1 0 < p 1 < 1 L memory T L − i ⋅ ln( p i ) N ∑ = − ln(2) 2 footprint 0 < p 2 < 1 i

model relax optimize L read ∑ false positive rates = p i cost 1 0 < p 0 < 1 0 < p 1 < 1 L memory T L − i ⋅ ln( p i ) N ∑ = in terms of p 0 , p 1 … − ln(2) 2 footprint 0 < p 2 < 1 i

false positive rate p 0 ≈ O( e -x / R 2 ) p 1 ≈ O( e -x / R 1 ) O( e -x / R 0 ) p 2 ≈

false positive rate geometric O( e -x /R 2 ) progression = O(e - x ) I/O O( e -x /R 1 ) O( e -x /R 0 )

O( e -x · log R ( N )) > O( e - x ) I/O

O( e -x · log R ( N )) O( e - x ) I/O

O( e -x · log R ( N )) read latency (ms) RocksDB Monkey O( e - x ) I/O number of entries (log scale)

Existing Monkey

Existing Monkey Dostoevsky

tiering Monkey leveling

I/O overheads with leveling point long range short range writes

point false positive rates O( e - x / R 2 ) exponentially O( e - x / R ) decreasing O( e - x )

false positive rates O(e - x / R 2 ) O(e - x / R ) O(e - x ) largest level point

point long range short range writes largest level O(e - x )