HBase: Extreme makeover

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BigBase is a read-optimized version of HBase NoSQL data store and is FULLY, 100% HBase compatible. 100% compatibility means that the upgrade from HBase to BigBase and other way around does not involve data migration and even can be made without stopping the cluster (via rolling restart).

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<ul><li> 1. HBase: Extreme makeover Vladimir Rodionov Hadoop/HBase architect Founder of BigBase.org HBaseCon 2014 Features &amp; Internal Track </li></ul> <p> 2. Agenda 3. About myself Principal Platform 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 expertise include (but not limited to) Java, HBase, Hadoop, Hive, large-scale OLAP/Analytics, and in- memory data processing. Founder of BigBase.org 4. What? 5. BigBase = EM(HBase) 6. BigBase = EM(HBase) EM(*) = ? 7. BigBase = EM(HBase) EM(*) = 8. BigBase = EM(HBase) EM(*) = Seriously? 9. BigBase = EM(HBase) EM(*) = Seriously? for HBase Its a Multi-Level Caching solution 10. 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&amp;A 11. HBase Still lacks some original BigTables features. Still not able to utilize efficiently all RAM. No good mixed storage (SSD/HDD) support. Single Level Caching only. Simple. HBase + Large JVM Heap (MemStore) = ? 12. BigBase Adds Row Cache and block cache compression. Utilizes efficiently all RAM (TBs). Supports mixed storage (SSD/HDD). Has Multi Level Caching. Not that simple. Will move MemStore off heap in R2. 13. BigBase History 14. Koda (2010) Koda - Java off heap object cache, similar to Terracottas BigMemory. Delivers 4x times more transactions 10x times better latencies than BigMemory 4. Compression (Snappy, LZ4, LZ4HC, Deflate). Disk persistence and periodic cache snapshots. Tested up to 240GB. 15. Karma (2011-12) Karma - Java off heap BTree implementation 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. 16. Yamm (2013) Yet Another Memory Manager. Pure 100% Java memory allocator. Replaced jemalloc in Koda. Now Koda is 100% Java. Karma is the next (still on jemalloc). Similar to memcached slab allocator. BigBase project started (Summer 2013). 17. BigBase Architecture 18. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache 19. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Disk JVMRAM Bucket cache One level of caching : RAM (L2) 20. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Bucket cache JVMRAM One level of caching : RAM (L2) Or DISK (L3) 21. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Disk JVMRAM Bucket cache BigBase 1.0 Block Cache L3 SSD JVMRAM Row Cache L1 Block Cache L2 22. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Disk JVMRAM Bucket cache BigBase 1.0 JVMRAM Row Cache L1 Block Cache L2 BlockCache L3 Network 23. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Disk JVMRAM Bucket cache BigBase 1.0 JVMRAM Row Cache L1 Block Cache L2 BlockCache L3 memcached 24. MLC Multi-Level Caching HBase 0.94 Disk JVMRAM LRUBlockCache HBase 0.96 Disk JVMRAM Bucket cache BigBase 1.0 JVMRAM Row Cache L1 Block Cache L2 BlockCache L3 DynamoDB 25. BigBase Row Cache (L1) 26. Where is BigTables Scan Cache? Scan Cache caches hot rows data. Complimentary to Block Cache. Still missing in HBase (as of 0.98). Its 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. 27. Row Cache vs. Block Cache HFile Block HFile BlockHFile BlockHFile BlockHFile Block 28. Row Cache vs. Block Cache 29. Row Cache vs. Block Cache BLOCK CACHE ROW CACHE 30. Row Cache vs. Block Cache ROW CACHE BLOCK CACHE 31. Row Cache vs. Block Cache ROW CACHE BLOCK CACHE 32. BigBase Row Cache Off Heap Scan Cache for HBase. Cache size: 100s of GBs to TBs. Eviction policies: LRU, LFU, FIFO, Random. Pure 100% - compatible Java. Sub-millisecond latencies, zero GC. Implemented as RegionObserver coprocessor. Row Cache YAMM Codecs Kryo SerDe KODA 33. BigBase Row Cache Read through cache. It caches rowkey:CF. Invalidates key on every mutation. Can be enabled/disabled per table and per table:CF. New ROWCACHE attribute. Best for small rows (&lt; block size) Row Cache YAMM Codecs Kryo SerDe KODA 34. Performance-Scalability GET (small rows &lt; 100 bytes): 175K operations per sec per one Region Server (from cache). MULTI-GET (small rows &lt; 100 bytes): &gt; 1M records per second (network limited) per one Region Server. LATENCY : 99% &lt; 1ms (for GETs) with 100K ops. Vertical 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. 35. BigBase Block Cache (L2, L3) 36. What is wrong with Bucket Cache? Scalability LIMITED Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (file mode) NOT FRIENDLY Compression NOT SUPPORTED 37. What is wrong with Bucket Cache? Scalability LIMITED Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (file mode) NOT FRIENDLY Compression NOT SUPPORTED 38. What is wrong with Bucket Cache? Scalability LIMITED Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap 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 Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap mode) NOT SUPPORTED Low latency apps ? SSD friendliness (file mode) NOT FRIENDLY Compression NOT SUPPORTED 40. What is wrong with Bucket Cache? Scalability LIMITED Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap 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 Multi-Level Caching (MLC) NOT SUPPORTED Persistence (offheap mode) NOT SUPPORTED Low latency apps NOT SUPPORTED SSD friendliness (file mode) NOT FRIENDLY Compression NOT SUPPORTED 42. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 43. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 44. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 45. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 46. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 47. Here comes BigBase Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE 48. Wait, there are more Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE Non diskbased L3 cache SUPPORTED RAM Cache optimization IBCO 49. Wait, there are more Scalability HIGH Multi-Level Caching (MLC) SUPPORTED Persistence (offheap mode) SUPPORTED Low latency apps SUPPORTED SSD friendliness (file mode) SSD-FRIENDLY Compression SNAPPY, LZ4, LZHC, DEFLATE Non diskbased L3 cache SUPPORTED RAM Cache optimization IBCO 50. 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 (partially 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 optimized YES NO MLC support YES (L1, L2, L3) NO (either L2 or L3) Scalability HIGH MEDIUM (limited by JVM heap) 51. YCSB Benchmark 52. 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 implementation. 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. 53. 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 implementation. 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. 54. Benchmark results (RPS) 11405 6123 5553 6265 4086 3850 15150 3512 28553224 1500 709820 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 55. Average latency (ms) 13 24 2723 36 3910 44 5248 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 56. 95% latency (ms) 51 91 10088 124 138 38 152 197175 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 57. 99% latency (ms) 133 190 213225 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 58. YCSB 100% Read 3621 1308 2281 11111253 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? 59. YCSB 100% Read 3621 1308 2281 11111253 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 60. 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) 61. 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% &lt; 1ms 62. 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 compacting mode . Integration with Hadoop metrics. Row Cache: merge rows on update (good for counters). Block Cache: new eviction policy (LRU-2Q). File read posix_fadvise ( bypass OS page cache). Row Cache: make it available for server-side apps 63. What is next? Release 2.0 (2014 Q3) HBASE-5263: Preserving cache data on compaction Cache data blocks on memstore flush (configurable). HBASE-10648: Pluggable Memstore. Off heap implementation, based on Karma (off heap BTree lib). Release 3.0 (2014 Q4) Real Scan Cache caches results of Scan operations on immutable store files. Scan Cache integration with Phoenix and with other 3rd party libs provided rich query API for HBase. 64. Download/Install/Uninstall Download BigBase 1.0 from www.bigbase.org Installation/upgrade takes 10-20 minutes Beatification operator EM(*) is invertible: HBase = EM-1(BigBase) (the same 10-20 min) 65. Q &amp; A Vladimir Rodionov Hadoop/HBase architect Founder of BigBase.org HBase: Extreme makeover Features &amp; Internal Track </p>