HBaseConEast2016: Splice machine open source rdbms
- 1. Splice Machine Open Source RDBMS September 26, 2016 Daniel Gómez Ferro John Leach
- 2. Open Source Stack: Spark, Hadoop and Derby Apache Derby ▪ ANSI SQL-99 RDBMS ▪ Java-based ▪ ODBC/JDBC Compliant Apache HBase/Hadoop ▪ Auto-sharding ▪ High availability ▪ Scalability to 100s of PBs Apache Spark ▪ Analytical engine ▪ Fast, in-memory technology ▪ Memory resilient to node failure 2
- 3. Splice Machine: Query Execution 3
- 4. Splice Machine: Query Execution 4 1. Parse SQL • Generate Abstract Syntax Tree (AST) • Bind AST to Transactional Dictionary
- 5. Splice Machine: Query Execution 5 1. Parse SQL 2. Optimize query plan • Determine join order and storage structure (e.g., base table, index) using table statistics (e.g., cardinality estimates) • Push predicates • Unroll nested subqueries
- 6. Splice Machine: Query Execution 6 3. Generate optimal byte code 1. Parse SQL 2. Optimize query plan
- 7. Splice Machine: Query Execution 7 OLTP Execution on HBase 4a. Execute OLTP query from byte code 5a. Use block cache and bloom filters to optimize data access 6a. Return results 3. Generate optimal byte code 1. Parse SQL 2. Optimize query plan
- 8. Splice Machine: Query Execution 8 OLAP Execution on Spark 4b. Generate Spark execution plan OLTP Execution on HBase 4a. Execute OLTP query from byte code 5a. Use block cache and bloom filters to optimize data access 6a. Return results 3. Generate optimal byte code 1. Parse SQL 2. Optimize query plan OLAP Execution on Spark 4b. Generate Spark execution plan 5b. Submit Spark plan with byte code 6b. Fair scheduling of distributed of tasks 7b. Generate RDD from HFiles and Memstore 8b. Execute query and return results
- 9. Architectural Differences: Don’t we already have SQL on HBase? Transactional System Tephra Centralized SI Two Phase Commit Hierarchical Distributed SI Analytical Engine HBase Coprocessors, JDBC Client HBase Coprocessors, Executor Services Processes Spark on Yarn Import Process Python or MapReduce MapReduce via Hive JDBC Command Spark job Scanning Data Coprocessor Internal Scans, HBase Scans Coprocessor Internal Scans, HBase Scans File Oriented Hybrid Scanner Compaction HBase Compaction HBase Compaction Spark Compaction Resource Management HBase Call Queues Workload Management System Spark Job Scheduling (FAIR)
- 10. TPCH 100 Load Times Tables Row Count LINEITEM 600037902 5:19:27 1:25:46 0:22:34 ORDERS 150000000 0:51:28 0:15:29 0:09:58 PARTSUPP 80000000 0:18:41 0:08:52 0:06:28 PART 20000000 0:07:26 0:02:27 0:02:14 CUSTOMERS 15000000 0:05:37 0:02:03 0:01:42 SUPPLIER 1000000 0:01:48 0:00:26 0:00:18 NATION 25 0:00:41 0:00:07 0:00:01 REGION 5 0:00:43 0:00:05 0:00:01
- 11. TPCH 100 Load Throughput
- 12. Write Pipeline ▪ Features ▪ Batched writes per region server ▪ Congestion control, retries ▪ Asynchronous writes ▪ Constraint checking (PK, FK…) ▪ Index updates ▪ One-for-all pipeline ▪ OLTP queries ▪ Batch data ingestion (Imports, Hadoop OutputFormat, OLAP query inserts...) ▪ Streaming data ingestion (Kafka, Spark streaming…)
- 13. Spark Compactions 13 Spark UI ▪ Out of process compactions ▪ Minor and Major ▪ Decrease Regionserver load ▪ Increase stability ▪ Remote compactions ▪ Prioritized by Spark’s fair scheduler
- 14. TPCH 100 Query Times (seconds) Query 1 395 TRAFODION-2237 99 2 PHOENIX-3322 516 44 3 PHOENIX-3322 TRAFODION-2237 126 4 PHOENIX-3322 TBD 133 5 PHOENIX-3322 TBD 192 6 74 3178 38 7 PHOENIX-3322 4442 220 8 PHOENIX-3322 TRAFODION-2239 620 9 PHOENIX-3322 941 273 10 PHOENIX-3322 TRAFODION-2241 101 11 PHOENIX-3317 463 56
- 15. TPCH 100 Query Times (seconds) Query 12 379 TBD 85 13 PHOENIX-3318 TBD 71 14 PHOENIX-3322 TBD 50 15 PHOENIX-3319 TBD 102 16 PHOENIX-3322 TBD 33 17 PHOENIX-3322 TBD 929 18 PHOENIX-3322 TBD SPLICE-34 19 PHOENIX-3322 TBD 57 20 PHOENIX-3320 TBD SPLICE-410 21 PHOENIX-3321 TBD 479 22 PHOENIX-3322 TBD 219
- 16. Splice Machine: Advanced Spark Integration 16 Innovative, High-Performance RDD Creation ▪ Fast access to HFiles in HDFS ▪ Merged with deltas from Memstore ▪ Avoids slower HBase API ▪ Reduces load in HBase Universal Execution Plan and Byte Code ▪ Optimizer, plan and code shared across Spark or HBase execution ••• HBase Region Server HDFS ••• Region 1 Memstore Spark Worker •••RDD 1 HFile HFile••• PHYSICAL NODE RDD N HFile••• HFile••• Region N Memstore HBase Region Server HDFS ••• Region 1 Memstore Spark Worker •••RDD 1 HFile HFile••• PHYSICAL NODE RDD N HFile••• HFile••• Region N Memstore
- 17. Resources ▪ Do you trust us? Nah... ▪ Give it a shot yourself and let us know what you find... ▪ https://github.com/splicemachine/benchmarks ▪ Want to get involved? ▪ http://community.splicemachine.com/ ▪ Want to code? Yeah, me too... ▪ https://github.com/splicemachine/spliceengine