HBase: Extreme Makeover
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The document presents an overview of the BigBase project, a multi-level caching solution for HBase, aimed at overcoming HBase's limitations such as single level caching and inefficient memory utilization. It details the architecture and features of BigBase, including its row and block caching mechanisms, performance benchmarks, and comparisons with HBase versions. The findings indicate significant performance improvements in operations per second and latency reduction when using BigBase compared to standard HBase implementations.
HBase: Extreme Makeover
- 1. HBase: Extreme makeover Vladimir Rodionov Hadoop/HBase architect Founder of BigBase.org HBaseCon 2014 Features & Internal Track
- 2. Agenda
- 3. About myself • Principal PlaKorm Engineer @Carrier IQ, Sunnyvale, CA • Prior to Carrier IQ, I worked @ GE, EBay, Plumtree/BEA. • HBase user since 2009. • HBase hacker since 2013. • Areas of experTse include (but not limited to) Java, HBase, Hadoop, Hive, large-‐scale OLAP/AnalyTcs, and in-‐ memory data processing. • Founder of BigBase.org
- 4. What?
- 7. BigBase = EM(HBase) EM(*) = ?
- 8. BigBase = EM(HBase) EM(*) =
- 9. BigBase = EM(HBase) EM(*) = Seriously?
- 10. BigBase = EM(HBase) EM(*) = Seriously? for HBase It’s a MulT-‐Level Caching soluTon
- 11. Real Agenda • Why BigBase? • Brief history of BigBase.org project • BigBase MLC high level architecture (L1/L2/L3) • Level 1 -‐ Row Cache. • Level 2/3 -‐ Block Cache RAM/SSD. • YCSB benchmark results • Upcoming features in R1.5, 2.0, 3.0. • Q&A
- 13. HBase • STll lacks some original BigTable’s features. • STll not able to uTlize efficiently all RAM. • No good mixed storage (SSD/HDD) support. • Single Level Caching only. Simple. • HBase + Large JVM Heap (MemStore) = ?
- 14. BigBase • Adds Row Cache and block cache compression. • UTlizes efficiently all RAM (TBs). • Supports mixed storage (SSD/HDD). • Has MulT Level Caching. Not that simple. • Will move MemStore off heap in R2.
- 15. BigBase History
- 16. Koda (2010) • Koda -‐ Java off heap object cache, similar to Terracola’s BigMemory. • Delivers 4x Tmes more transacTons … • 10x Tmes beler latencies than BigMemory 4. • Compression (Snappy, LZ4, LZ4HC, Deflate). • Disk persistence and periodic cache snapshots. • Tested up to 240GB.
- 17. Karma (2011-‐12) • Karma -‐ Java off heap BTree implementaTon to support fast in memory queries. • Supports extra large heaps, 100s millions – billions objects. • Stores 300M objects in less than 10G of RAM. • Block Compression. • Tested up to 240GB. • Off Heap MemStore in R2.
- 18. Yamm (2013) • Yet Another Memory Manager. – Pure 100% Java memory allocator. – Replaced jemalloc in Koda. – Now Koda is 100% Java. – Karma is the next (sTll on jemalloc). – Similar to memcached slab allocator. • BigBase project started (Summer 2013).
- 20. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache
- 21. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Disk JVM RAM Bucket cache One level of caching : • RAM (L2)
- 22. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Bucket cache JVM RAM One level of caching : • RAM (L2) • Or DISK (L3)
- 23. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Disk JVM RAM Bucket cache BigBase 1.0 Block Cache L3 SSD JVM RAM Row Cache L1 Block Cache L2
- 24. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Disk JVM RAM Bucket cache BigBase 1.0 JVM RAM Row Cache L1 Block Cache L2 BlockCache L3 Network
- 25. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Disk JVM RAM Bucket cache BigBase 1.0 JVM RAM Row Cache L1 Block Cache L2 BlockCache L3 memcached
- 26. MLC – MulT-‐Level Caching HBase 0.94 Disk JVM RAM LRUBlockCache HBase 0.96 Disk JVM RAM Bucket cache BigBase 1.0 JVM RAM Row Cache L1 Block Cache L2 BlockCache L3 DynamoDB
- 27. BigBase Row Cache (L1)
- 28. Where is BigTable’s Scan Cache? • Scan Cache caches hot rows data. • Complimentary to Block Cache. • STll missing in HBase (as of 0.98). • It’s very hard to implement in Java (off heap). • Max GC pause is ~ 0.5-‐2 sec per 1GB of heap • G1 GC in Java 7 does not resolve the problem. • We call it Row Cache in BigBase.
- 29. Row Cache vs. Block Cache HFile Block HFile Block HFile Block HFile Block HFile Block
- 30. Row Cache vs. Block Cache
- 31. Row Cache vs. Block Cache BLOCK CACHE ROW CACHE
- 32. Row Cache vs. Block Cache ROW CACHE BLOCK CACHE
- 33. Row Cache vs. Block Cache ROW CACHE BLOCK CACHE
- 34. BigBase Row Cache • Off Heap Scan Cache for HBase. • Cache size: 100’s of GBs to TBs. • EvicTon policies: LRU, LFU, FIFO, Random. • Pure 100% -‐ compaTble Java. • Sub-‐millisecond latencies, zero GC. • Implemented as RegionObserver coprocessor. Row Cache YAMM Codecs Kryo SerDe KODA
- 35. BigBase Row Cache • Read through cache. • It caches rowkey:CF. • Invalidates key on every mutaTon. • Can be enabled/disabled per table and per table:CF. • New ROWCACHE alribute. • Best for small rows (< block size) Row Cache YAMM Codecs Kryo SerDe KODA
- 36. Performance-‐Scalability • GET (small rows < 100 bytes): 175K operaTons per sec per one Region Server (from cache). • MULTI-‐GET (small rows < 100 bytes): > 1M records per second (network limited) per one Region Server. • LATENCY : 99% < 1ms (for GETs) with 100K ops. • VerTcal scalability: tested up to 240GB (the maximum available in Amazon EC2). • Horizontal scalability: limited by HBase scalability. • No more memcached farms in front of HBase clusters.
- 37. BigBase Block Cache (L2, L3)
- 38. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 39. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 40. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 41. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps ? SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 42. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 43. What is wrong with Bucket Cache? Scalability LIMITED MulT-‐Level Caching (MLC) NOT SUPPORTED Persistence (‘ozeap’ mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (‘file’ mode) NOT FRIENDLY Compression NOT SUPPORTED
- 44. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 45. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 46. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 47. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 48. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 49. Here comes BigBase Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE
- 50. Wait, there are more … Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE Non disk–based L3 cache SUPPORTED RAM Cache opTmizaTon IBCO
- 51. Wait, there are more … Scalability HIGH MulT-‐Level Caching (MLC) SUPPORTED Persistence (‘ozeap’ mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (‘file’ mode) SSD-‐FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE Non disk–based L3 cache SUPPORTED RAM Cache opTmizaTon IBCO
- 52. BigBase 1.0 vs. HBase 0.98 BigBase HBase 0.98 Row Cache (L1) YES NO Block Cache RAM (L2) YES (fully off heap) YES (parTally off heap) Block Cache (L3) DISK YES (SSD-‐ friendly) YES (not SSD – friendly) Block Cache (L3) NON DISK YES NO Compression YES NO RAM Cache persistence YES (both L1 and L2) NO Low Latency opTmized YES NO MLC support YES (L1, L2, L3) NO (either L2 or L3) Scalability HIGH MEDIUM (limited by JVM heap)
- 53. YCSB Benchmark
- 54. Test setup (AWS) • HBase 0.94.15 – RS: 11.5GB heap (6GB LruBlockCache on heap); Master: 4GB heap. • Clients: 5 (30 threads each), collocated with Region Servers. • Data sets: 100M and 200M. 120GB / 240GB approximately. Only 25% fits in a cache. • Workloads: 100% read (read100, read200, hotspot100), 100% scan (scan100, scan200) –zipfian. • YCSB 0.1.4 (modified to generate compressible data). We generated compressible data (with factor of 2.5x) only for scan workloads to evaluate effect of compression in BigBase block cache implementaTon. • Common – Whirr 0.8.2; 1 (Master + Zk) + 5 RS; m1.xlarge: 15GB RAM, 4 vCPU, 4x420 HDD • BigBase 1.0 (0.94.15) – RS: 4GB heap (6GB off heap cache); Master: 4GB heap. • HBase 0.96.2 – RS: 4GB heap (6GB Bucket Cache off heap); Master: 4GB heap.
- 55. Test setup (AWS) • HBase 0.94.15 – RS: 11.5GB heap (6GB LruBlockCache on heap); Master: 4GB heap. • Clients: 5 (30 threads each), collocated with Region Servers. • Data sets: 100M and 200M. 120GB / 240GB approximately. Only 25% fits in a cache. • Workloads: 100% read (read100, read200, hotspot100), 100% scan (scan100, scan200) –zipfian. • YCSB 0.1.4 (modified to generate compressible data). We generated compressible data (with factor of 2.5x) only for scan workloads to evaluate effect of compression in BigBase block cache implementaTon. • Common – Whirr 0.8.2; 1 (Master + Zk) + 5 RS; m1.xlarge: 15GB RAM, 4 vCPU, 4x420 HDD • BigBase 1.0 (0.94.15) – RS: 4GB heap (6GB off heap cache); Master: 4GB heap. • HBase 0.96.2 – RS: 4GB heap (6GB Bucket Cache off heap); Master: 4GB heap.
- 56. Benchmark results (RPS) 11405 6123 5553 6265 4086 3850 15150 3512 2855 3224 1500 709 820 434 228 0 2000 4000 6000 8000 10000 12000 14000 16000 BigBase R1.0 HBase 0.96.2 HBase 0.94.15 read100 read200 hotspot100 scan100 scan200
- 57. Average latency (ms) 13 24 27 23 36 39 10 44 52 48 102 223 187 375 700 0 100 200 300 400 500 600 700 800 BigBase R1.0 HBase 0.96.2 HBase 0.94.15 read100 read200 hotspot100 scan100 scan200
- 58. 95% latency (ms) 51 91 100 88 124 138 38 152 197 175 405 950 729 0 100 200 300 400 500 600 700 800 900 1000 BigBase R1.0 HBase 0.96.2 HBase 0.94.15 read100 read200 hotspot100 scan100 scan200
- 59. 99% latency (ms) 133 190 213 225 304 338 111 554 632 367 811 0 100 200 300 400 500 600 700 800 900 BigBase R1.0 HBase 0.96.2 HBase 0.94.15 read100 read200 hotspot100 scan100 scan200
- 60. YCSB 100% Read 3621 1308 2281 1111 1253 770 0 500 1000 1500 2000 2500 3000 3500 4000 BigBase R1.0 HBase 0.94.15 Per Server 50M 100M 200M • 50M = 2.77X • 100M = 2.05X • 200M = 1.63X • 50M = 40% fits cache • 100M = 20% fits cache • 200M = 10% fits cache • What is the maximum?
- 61. YCSB 100% Read 3621 1308 2281 1111 1253 770 0 500 1000 1500 2000 2500 3000 3500 4000 BigBase R1.0 HBase 0.94.15 Per Server 50M 100M 200M • 50M = 2.77X • 100M = 2.05X • 200M = 1.63X • 50M = 40% fits cache • 100M = 20% fits cache • 200M = 10% fits cache • What is the maximum? • ~ 75X (hotspot 2.5/100) • 56K (BB) vs. 750 (HBase) • 100% in cache
- 62. All data in cache • Setup: BigBase 1.0, 48G RAM, (8/16) CPU cores – 5 nodes (1+ 4) • Data set: 200M (300GB) • Test: Read 100%, hotspot • YCSB 0.1.4 – 4 clients • 40 threads – 100K • 100 threads – 168K • 200 threads – 224K • 400 threads -‐ 262K 100,000 168,000 224,000 262,000 99% 1 2 3 7 95% 1 1 2 3 avg 0.4 0.6 0.9 1.5 0 1 2 3 4 5 6 7 8 Latency (ms) Hotspot (2.5/100 – 200M data)
- 63. All data in cache • Setup: BigBase 1.0, 48G RAM, (8/16) CPU cores – 5 nodes (1+ 4) • Data set: 200M (300GB) • Test: Read 100%, hotspot • YCSB 0.1.4 – 4 clients • 40 threads – 100K • 100 threads – 168K • 200 threads – 224K • 400 threads -‐ 262K 100,000 168,000 224,000 262,000 99% 1 2 3 7 95% 1 1 2 3 avg 0.4 0.6 0.9 1.5 0 1 2 3 4 5 6 7 8 Latency (ms) Hotspot (2.5/100 – 200M data) 100K ops: 99% < 1ms
- 64. What is next? • Release 1.1 (2014 Q2) – Support HBase 0.96, 0.98, trunk – Fully tested L3 cache (SSD) • Release 1.5 (2014 Q3) – YAMM: memory allocator compacTng mode . – IntegraTon with Hadoop metrics. – Row Cache: merge rows on update (good for counters). – Block Cache: new evicTon policy (LRU-‐2Q). – File read posix_fadvise ( bypass OS page cache). – Row Cache: make it available for server-‐side apps
- 65. What is next? • Release 2.0 (2014 Q3) – HBASE-‐5263: Preserving cache data on compacTon – Cache data blocks on memstore flush (configurable). – HBASE-‐10648: Pluggable Memstore. Off heap implementaTon, based on Karma (off heap BTree lib). • Release 3.0 (2014 Q4) – Real Scan Cache – caches results of Scan operaTons on immutable store files. – Scan Cache integraTon with Phoenix and with other 3rd party libs provided rich query API for HBase.
- 66. Download/Install/Uninstall • Download BigBase 1.0 from www.bigbase.org • InstallaTon/upgrade takes 10-‐20 minutes • BeaTficaTon operator EM(*) is inverTble: HBase = EM-‐1(BigBase) (the same 10-‐20 min)
- 67. Q & A Vladimir Rodionov Hadoop/HBase architect Founder of BigBase.org HBase: Extreme makeover Features & Internal Track