Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Accordion HBaseCon 2017
Similaire à Accordion HBaseCon 2017 (20)
Dernier
Dernier (20)
Accordion HBaseCon 2017
- 1. Accordion: HBase Breathes w ith In-Memor y Compaction Eshcar Hillel, Anastasia Braginsky, Edward Bortnikov ⎪ HBaseCon, Jun 12, 2017
- 2. The Team 2 Edward Bortnikov Anastasia Braginsky (committer) Eshcar Hillel (committer) Michael Stack Anoop Sam John Ramkrishna Vasudevan
- 3. Quest: The User’s Holy Grail 3 Reliable Persistent Storage In-Memory Database Performance
- 4. What is Accordion? 4 Novel Write-Path Algorithm Better Performance of Write-Intensive Workloads Write Throughput ì, Read Latency î Better Disk Use Write amplification î GA in HBase 2.0 (becomes default MemStore implementation)
- 5. In a Nutshell 5 Inspired by Log-Structured-Merge (LSM) Tree Design Transforms random I/O to sequential I/O (efficient!) Governs the HBase storage organization Accordion reapplies the LSM Tree design to RAM data à Efficient resource use – data lives in memory longer à Less disk I/O à Ultimately, higher speed
- 6. How LSM Trees Work 6 MemStore HFile HFile HFile RAM Disk Put Get/Scan Flush Compaction HRegion Data updates stored as versions Compaction eliminates redundancies
- 7. LSM Trees in Action 7 MemStore MemStore MemStore MemStore MemStore HFile HFile HFile HFile HFile HFile HFile HFile Flush! Flush! Flush! Compaction!
- 8. Accordion: In-Memory LSM Tree 8 Active Segment HFile Immutable Segment Immutable Segment Immutable Segment HFile Compacting MemStore Flush Put Get/Scan Compaction RAM Disk
- 9. Accordion in Action 9 Active Segment Active Segment Active Segment Active Segment Active Segment Immutable Segment Immutable Segment Immutable Segment Immutable Segment In-Memory Flush! In-Memory Flush! In-Memory Compaction! Snapshot Disk Flush! Compaction Pipeline
- 10. Flat Immutable Segment Index 10 Hhjs iutkldfk;wjt;w iejerg;iopp Jkkgkykytkt gcccdddeiuy oweuoweiuo ieu Poiuytrewqa sdfaaabbbm nppppbvcxq qaaaxcvb qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkdddfkgbbbd iwpoqqqaaacc cdddeiuyoweu oweiuoieu k;wjt;wiej;iwj opppqqqyrta aajeutkiyt Jkkgkykytcc dddeiuyowe uoweiuoieu Poiuytrewqa hmnppppbv cxqqaaaxcv b qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkkgkykytktjjjjo ooooooqqbyfjt dhghfhfngfhfg bcccdddeiuyo weuoweiuoieu Hhjjuuyrqaa ss iuaaajeutkiyt Jkkgkykytktg kg;diwpoqeu oweiuoieu Poiuytrejkl;; mnppppbvcx qqaaaxcvb qqqwertyuioas dfghjklrtyuioplk jhgfpppwwwm nbvcmnb Jkdddfkaabbb cccdddeiuyow euoweiuoieu utkldfk;ioppp qqqyrtaaaje utkiyt diwpoqqqaa abbbcccddw euoweiuoieu hjkl;;mnpppp bvcxqqaaax cvb qqqwertyuioas dfghjklrtyuioplk jhgfpppwwwm nbvcmnb Jkkgkyaaabbyf jtdhghfhfngfhfg bcccdddeiuyo weuoweiuoieu Cell Storage Cell Storage Flatten Skiplist Index CellArrayMap Index Lean footprint – the smaller the cells the better! KV- Objects
- 11. Redundancy Elimination 11 In-Memory Compaction merges the pipelined segments Get access latency under control (less segments to scan) BASIC compaction Multiple indexes merged into one, cell data remains in place EAGER compaction Redundant data versions eliminated (SQM scan)
- 12. BASIC vs EAGER 12 BASIC: universal optimization, avoids physical data copy EAGER: high value for highly redundant workloads SQM scan is expensive Data relocation cost may be high (think MSLAB!) Configuration BASIC is default, EAGER may be configured Future implementation may figure out the right mode automatically
- 13. Compaction Pipeline: Correctness & Performance 13 Shared Data Structure Read access: Get, Scan, in-memory compaction Write access: in-memory flush, in-memory compaction, disk flush Design Choice: Non-Blocking Reads Read-Only pipeline clone – no synchronization upon read access Copy-on-Write upon modification Versioning prevents compaction concurrent to other updates
- 14. More Memory Efficiency - KV Object Elimination 14 Hhjs iutkldfk;wjt;w iejerg;iopp Jkkgkykytkt gcccdddeiuy oweuoweiuo ieu Poiuytrewqa sdfaaabbbm nppppbvcxq qaaaxcvb qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkdddfkgbbbd iwpoqqqaaacc cdddeiuyoweu oweiuoieu k;wjt;wiej;iwj opppqqqyrta aajeutkiyt Jkkgkykytcc dddeiuyowe uoweiuoieu Poiuytrewqa hmnppppbv cxqqaaaxcv b qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkkgkykytktjjjjo ooooooqqbyfjt dhghfhfngfhfg bcccdddeiuyo weuoweiuoieu Cell Storage CellArrayMap Index Lean Footprint (no KV-Objects). Friendly to Off-Heap Implementation. Hhjs iutkldfk;wjt;w iejerg;iopp Jkkgkykytkt gcccdddeiuy oweuoweiuo ieu Poiuytrewqa sdfaaabbbm nppppbvcxq qaaaxcvb qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkdddfkgbbbd iwpoqqqaaacc cdddeiuyoweu oweiuoieu k;wjt;wiej;iwj opppqqqyrta aajeutkiyt Jkkgkykytcc dddeiuyowe uoweiuoieu Poiuytrewqa hmnppppbv cxqqaaaxcv b qqqwertyuioas dfghjklrtyuiopl kjhgfpppwww mnbvcmnb Jkkgkykytktjjjjo ooooooqqbyfjt dhghfhfngfhfg bcccdddeiuyo weuoweiuoieu Cell Storage CellChunkMap Index
- 15. The Software Side: What’s New? 15 CompactingMemStore: BASIC and EAGER configurations DefaultMemStore: NONE configuration Segment Class Hierarchy: Mutable, Immutable, Composite NavigableMap Implementations: CellArrayMap, CellChunkMap MemStoreCompactor: compaction algorithms implementation
- 16. CellChunkMap Support (Experimental) 16 Cell objects embedded directly into CellChunkMap (CCM) New cell type - reference data by unique ChunkID ChunkCreator: Chunk allocation + ChunkID management Stores mapping of ChunkID’s to Chunk references Strong references to chunks managed by CCM’s, weak to the rest The CCM’s themselves are allocated via the same mechanism Some exotic use cases E.g., jumbo cells allocated in one-time chunks outside chunk pools
- 17. Evaluation Setup 17 System 2-node HBase on top of 3-node HDFS, 1Gbps interconnect Intel Xeon E5620 (12-core), 2.8TB SSD storage, 48GB RAM RS config: 16GB RAM (40% Cache/40% MemStore), on-heap, no MSLAB Data 1 table (100 regions, 50 columns), 30GB-100GB Workload Driver YCSB (1 node, 12 threads) Batched (async) writes (10KB buffer)
- 18. Experiments 18 Metrics Write throughput, read latency (distribution), disk footprint/amplification Workloads (varied at client side) Write-Only (100% Put) vs Mixed (50% Put/50% Get) Uniform vs Zipfian Key Distributions Small Values (100B) vs Big Values (1K) Configurations (varied at server side) Most experiments exercise Async WAL
- 19. 19 Write Throughput +25% +44% 100GB Dataset 100% Writes 100B Values Every write updates a single column Gains less pronounced with big values (1KB) +11% (why?) - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 Zipf Uniform Throughput,ops/sec NONE BASIC EAGER
- 20. 20 Single-Key Write Latency 0 1 2 3 4 5 6 7 50% (median) 75% 95% 99% (tail) Latency,ms NONE BASIC EAGER 100GB Dataset Zipf distribution 100% Writes 100B Values
- 21. 21 Single-Key Read Latency 30GB Dataset Zipf Distribution 50% Writes/50% Reads 100B Values 0 1 2 3 4 5 6 50% (median) 75% 95% 99% (tail) Latency,ms NONE BASIC EAGER +9% (why?) -13%
- 22. 22 Disk Footprint/Write Amplification 100GB Dataset Zipf Distribution 100% Writes 100B Values -29% 0 200 400 600 800 1000 1200 Flushes Compactions Data Written (GB) NONE BASIC EAGER
- 23. Status 23 In-Memory Compaction GA in HBase 2.0 Master JIRA HBASE-14918 complete (~20 subtasks) Major refactoring/extension of the MemStore code Many details in Apache HBase blog posts CellChunkMap Index, Off-Heap support in progress Master JIRA HBASE-16421
- 24. Summary 24 Accordion = a leaner and faster write path Space-Efficient Index + Redundancy Elimination à less I/O Less Frequent Flushes à increased write throughput Less On-Disk Compaction à reduced write amplification Data stays longer in RAM à reduced tail read latency Edging Closer to In-Memory Database Performance
- 25. Thanks to Our Partners for Being Awesome 25