Resolving Journaling of Journal Anomaly via Weaving Recovery Information into DB Page. Beomseok Nam

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1 NVRAMOS Resolving Journaling of Journal Anomaly via Weaving Recovery Information into DB Page Beomseok Nam UNIST

2 Outline Motivation Journaling of Journal Anomaly How to resolve Journaling of Journal anomaly Multi-Version B-Tree (MVBT) Optimizations of Multi-Version B-Tree Lazy Split Reserved Buffer Space Lazy Garbage Collection Metadata Embedding Disabling Sibling Redistribution Evaluation Conclusion 2

3 Storage I/O Problems in Android Performance Bottleneck Lifetime of Storage Cause = Excessive IO 3

4 Android I/O Stack Apps Txn Journaling Write() Journaling SQLite Misaligned Interaction EXT4 Insert/Update/Delete/Select Block Device Driver Read/Write 4

5 Journaling of Journal Anomaly Journaling in SQLite (TRUNCATE mode) SQLite Insert a DB entry Open rollback journal. Record the data to journal. Put commit mark to journal. Insert entry to DB Truncate journal. 5

6 EXT4 Journaling (ordered mode) SQLite write() EXT4 Write data block Write EXT4 journal Write journal metadata Write journal commit 6

7 Journaling of Journal Anomaly SQLite insert One insert of 100 Byte 9 Random Writes of 4KByte EXT4 Write data block Write EXT4 journal Write journal metadata Write journal commit 7

8 How to Resolve Journaling of Journal? Database Journaling Database Insertion Insert a DB entry SQLite EXT4 Journaling of journal anomaly. 8

9 How to Resolve Journaling of Journal? fsync() fsync() fsync() Journal DB Journal+DB = Version-based B-Tree (MVBT) 9

10 Version-based B-Tree (MVBT) Versioning Insert 10 Update 10 with 20 Time T1 10 [T1, ) T2 10 [T1, T2) 20 [T2, ) Dead Entry Do not overwrite old version data Do not need a rollback journal 10

11 Node Split in Multi-Version B-Tree key 25, ver=5 P1 5 [3~ ) 10 [2~ ) 12 [4~ ) 40 [1~ ) 11

12 Node Split in Multi-Version B-Tree key 25, ver=5 P1 5 [3~5) 10 [2~5) 12 [4~5) 40 [1~5) Dead Node 12

13 Node Split in Multi-Version B-Tree key 25, ver=5 P1 5 [3~5) 10 [2~5) 12 [4~5) 40 [1~5) 13

14 Node Split in Multi-Version B-Tree key 25, ver=5 P3 P2 P1 5 [5~ ) [3~5) 10 [5~ ) [2~5) 12 [5~ ) [4~5) 40 [5~ ) [1~5) 14

15 Node Split in Multi-Version B-Tree P4 10 [5~ ) : P3 [5~ ) : P2 [0~5) : P1 key 25, ver=5 P3 5 [5~ ) 10 [5~ ) P1 5 [3~5) 10 [2~5) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 15

16 Node Split in Multi-Version B-Tree One more dirty page than original B-Tree P4 dirty 10 [5~ ) : P3 [5~ ) : P2 [0~5) : P1 P3 dirty P1 dirty P2 dirty 5 [5~ ) 5 [3~5) 10 [5~ ) 10 [2~5) 12 [5~ ) 12 [4~5) 25 [5~ ) 40 [1~5) 40 [5~ ) 16

17 I/O Traffic in MVBT DB buffer cache MVBT fsync() Reduce the number of dirty pages! 17

18 I/O Traffic in MVBT DB buffer cache MVBT fsync() DB buffer cache LS -MVBT fsync() 18

19 Optimizations in Android I/O t write >> t read Write Read Write Transaction 1 Write Transaction 2 Write Transaction 3 Multiple Insert Single Insert Transaction 19

20 Lazy Split Multi-Version B-Tree (LS-MVBT) Optimizations Lazy Split Reserved Buffer Space LS-MVBT Disabling Sibling Redistribution Lazy Garbage Collection Metadata Embedding 20

21 Optimization1: Lazy Split Legacy Split in MVBT P4 10 [5~ ) : P3 [5~ ) : P2 [0~5) : P1 dirty 4 dirty pages P3 dirty P1 dirty P2 dirty 5 [5~ ) 5 [3~5) 10 [5~ ) 10 [2~5) 25 [5~ ) 12 [4~5) 12 [5~ ) 40 [1~5) 40 [5~ ) 21

22 Optimization1: Lazy Split key 25, ver=5 P1 5 [3~ ) 10 [2~ ) 12 [4~ ) 40 [1~ ) 22

23 Optimization1: Lazy Split key 25, ver=5 P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) Lazy Node: Half-dead, Half-live 23

24 Optimization1: Lazy Split key 25, ver=5 P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) 24

25 Optimization1: Lazy Split key 25, ver=5 P2 P1 5 [3~ ) 10 [2~ ) 12 [5~ ) [4~5) 40 [5~ ) [1~5) 25

26 Optimization1: Lazy Split P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 25, ver=5 P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 26

27 Optimization1: Lazy Split Lazy Node Overflow P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 Dead entries P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 27

28 Optimization1: Lazy Split Lazy Node Overflow Garbage collect dead entries P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 28

29 Optimization1: Lazy Split Lazy Node Overflow Garbage collect dead entries P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 P1 5 [3~ ) 8 [6~ ) 10 [2~ ) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 29 But, what if the dead entries are being accessed by other transactions?

30 Optimization2: Reserved Buffer Space Option 1. Wait for read transactions to finish P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 30

31 Optimization2: Reserved Buffer Space Option 2. Split as in legacy MVBT split P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 P1 P3 5 [3~6) [6~ ) 10 [2~6) [6~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 31

32 Optimization2: Reserved Buffer Space Option 2. Split as in legacy MVBT split key 8, ver = 6 P3 10 [5~6) : P1 [0~5) : P1 [5~ ) : P2 10 [6~ ) : P3 P3 5 [6~ ) 8 [6~ ) 10 [6~ ) P1 5 [3~6) 10 [2~6) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) 32

33 Optimization2: Reserved Buffer Space Option 3. Pad some buffer space in tree nodes P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 key 8, ver = 6 Buffer space is used when lazy node is full P1 10 [2~ ) 12 [4~5) 40 [1~5) 9 [6~ ) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) If buffer space is also full, split as in legacy MVBT 33

34 Rollback in LS-MVBT Similar to rollback in MVBT P3 10 [5~ ) : P1 [0~5) : P1 [5~ ) : P2 Rollback Txn 5 crashes P3 [0~5) [0~ ) : P1 Number of dirty nodes touched by rollback of LS- MVBT is also smaller than that of MVBT. P1 5 [3~ ) 10 [2~ ) 12 [4~5) 40 [1~5) P2 12 [5~ ) 25 [5~ ) 40 [5~ ) P1 5 [3~ ) 10 [2~ ) 12 [4~5) [4~ ) 40 [1~5) [1~ ) 34

35 Optimization3: Lazy Garbage Collection Periodic Garbage Collection Transaction 1 P1 Transaction buffer cache P1 P2 P3 35

36 Optimization3: Lazy Garbage Collection Periodic Garbage Collection Transaction 1 Stopped Transaction buffer cache P1 P2 P3 Garbage Collection P1 P2 P3 fsync() Extra Dirty Pages GC buffer cache 36

37 Optimization3: Lazy Garbage Collection Lazy Garbage Collection Do not garabge collect if no space is needed Transaction 1 P1 Insert fsync() Transaction buffer cache P1 P2 P3 No Extra Dirty Page by GC 37

38 Optimization4: Metadata embedding Version = File Change Counter in DB header page Header Page 1 dirty Write Transaction 6 ++ Page 2 65 Commit... 2 dirty pages Read Transaction 5 Page N dirty 15 [6~ ) 25 [5~ ) 40 [1~ ) 38

39 Optimization4: Metadata embedding Flush File Change Counter to the last modified page and RAMDISK Header Page 1 Write Transaction 6 Commit Read Transaction 5 Page RAMDISK 65 Page N 5 15 [6~ ) 25 [5~ ) 40 [1~ ) 6 39

40 Optimization4: Metadata embedding Flush File Change Counter to the last modified page and RAMDISK Header Page 1 CRASH Write Transaction 6 Commit Page Read Transaction 5 RAMDISK 6 Volatile Page N 6 dirty 15 [6~ ) 25 [5~ ) 40 [1~ ) 1 dirty page 40

41 Optimization5: Disable Sibling Redistribution Sibling redistribution hurts insertion performance P5: : P : P3 : P2 dirty Insertion of key 20 4 dirty pages P4: 5 10 dirty P3: dirty P2: dirty Disable sibling redistribution avoid dirtying 4 pages 41

42 Optimization5: Disable Sibling Redistribution Disabled sibling redistribution P5: dirty 10 : P : P3 P5 90 : P2 Insertion of key 20 P5: P4: dirty P3: dirty P2: dirty pages 42

43 Summary Optimizations Lazy Split Reduce the probability of node split Avoid dirtying an extra page when split occurs Disabling Reserved Sibling Buffer Space Redistribution LS-MVBT Do not touch siblings to make search faster Lazy Garbage Collection Delete dead entries on the next mutation Metadata Embedding Avoid dirtying header page 43

44 Performance Evaluation Implemented LS-MVBT in SQLite Testbed: Samsung Galaxy-S4 (JellyBean 4.3) Exynos 5 Octa 5410 Quad Core 1.6GHz 2GB Memory 16GB emmc Performance Analysis Tools Mobibench MOST 44

45 WAL (Write-Ahead-Logging) mode Default mode in Jelly Bean and KitKat Commit -> Write to a log file Checkpointing -> Flush logs to DB pages DRAM dirty DB Page 1 WAL Log 2 DB Page

46 Analysis of insert Performance SQLite Insert (Response Time) LS-MVBT: 30~78% faster than WAL mode LS-MVBT fsync WAL fsync() B-tree computation WAL Checkpointing Interval 46

47 Analysis of insert Performance SQLite Insert (# of Dirty Pages per Insert) LS-MVBT flushes only one dirty page 47

48 Analysis of Block I/O Behavior SQLite Insert (# of Accesses to Block Device) 20 block accesses 34 block accesses 10 insertions (LS-MVBT) 10 insertions (WAL) 148 block accesses 218 block accesses 100 insertions (LS-MVBT) 100 insertions (WAL) 48

49 I/O Traffic at Block Device Level SQLite Insert (I/O Traffic per 10 msec) 1000 insertions (LS-MVBT) 1000 insertions (WAL) LS-MVBT saves 67% I/O traffic: 9.9 MB (LS-MVBT) vs 31 MB (WAL) x3 longer life time of NAND flash 49

50 How about Search performance? LS-MVBT is optimized for write performance. 50

51 Search Performance Transaction Throughput with varying Search/Insert ratio LS-MVBT wins unless more than 93% of transactions are search 51

52 How about recovery latency? WAL mode exhibits longer recovery latency due to log replay. How about LS-MVBT? 52

53 Recovery Recovery Time with varying # of insert operations to rollback LS-MVBT is x5~x6 faster than WAL. 53

54 How much performance improvement for each optimization? 54

55 Effect of Disabling Sibling Redistribution SQLite Insert: (Response Time and # of Dirty Pages) Disabling sibling redistribution 20% faster insertion 55

56 Performance Quantification Throughput of SQLite: Combining All Optimizations 70% 51% improvemnet improvement 40% improvement X1.7 performance improvement LS-MVBT: 704 insertions/sec WAL : 417 insertions/sec 56

57 Conclusion LS-MVBT resolves the Journaling of Journal anomaly via Reducing the number of fsync() s with Version based B-tree. Reducing the overhead of single fsync() via lazy split, disabling sibling redistribution, metadata embedding, reserved buffer space, lazy garbage collection. LS-MVBT achieves 70% performance gain against WAL mode (417 ins/sec 704 ins/sec) solely via software optimization. 57

58 Future/Ongoing Works 1. Improve the performance of WAL mode 3 dirty pages per insertion not really necessary Easier work than replacing the entire B-tree Compatible with current SQLite database files We expect the performance benefit would be similar to LS-MVBT 2. SQLite for Non-Volatile Memory Logging/Journaling in NVRAM 3. SQLite for multi-cores 58

59 Credit Woo-Hee Kim Beomseok Nam Dongil Park Youjip Won 59

60 Thank you

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