Tatsuhiro Chiba, Takeshi Yoshimura, Michihiro Horie and Hiroshi Horii IBM Research
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1 Tatsuhiro Chiba, Takeshi Yoshimura, Michihiro Horie and Hiroshi Horii IBM Research
2 IBM Research 2 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
3 à à Application Dist. Framework JVM OS Hardware 3 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
4 execution time (sec) Performance Improvement History TPC-H Q3 TPC-H Q5 4 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
5 No JVM awareness need to update cost model / heuristics frequently 5 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
6 Choices of Engine and Runtime 1. What about potential gains? observation 3. What data to help building a model? training Empirical Study reasoning 2. What features make the differences? GOAL Run SQL on best engine/runtime Meta Scheduler predicting ML Model 4. How accurately can the model predict? 6 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
7 IBM Research 7 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
8 Catalyst Michael et al., Spark SQL: Relational Data Processing in Spark, SIGMOD 15 8 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
9 IBM Research Ref Apache Tez: A Unifying Framework for Modeling and Building Data Processing Applications, SIGMOD 15 Ref Hadoop Summit IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
10 10 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
11 IBM Research 11 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
12 Choices of Engine and Runtime 1. What about potential gains? observation 3. What data to help building a model? training Empirical Study reasoning 2. What features make the differences? GOAL Run SQL on best engine/runtime Meta Scheduler predicting ML Model 4. How accurately can the model predict? 12 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
13 Machine Description Processor POWER8 3.3 GHz * 2 # Cores 24 cores (2 Sockets * 12 Cores) SMT 8 Memory 1TB Disk Software version Spark Hadoop (HDFS) Tez Hive OpenJDK IBM J9 JVM Flash System (9.3TB) OS Ubuntu (kernel ) 1.8.0_u SR4FP2 (*1) 13 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
14 Configuration Spark / Spark SQL Tez / Hive Executor JVM 1 1 Worker Threads I/O Threads - 12 On Heap Size 192 GB 96 GB Off Heap Size - 96 GB Execution Mode Daemon (Thrift Server) LLAP Daemon Columnar Format Parquet ORC Compression Format gzip (zlib) gzip (zlib) Other JVM Options (Common) GC Threads = 12, -agentpath:libjvmti_oprofile.so echo 3 > /proc/sys/vm/drop_caches 14 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
15 Exectution Time (sec.) Lower is Better OpenJDK is faster IBM J9 OpenJDK ratio Q72 Q15 Q12 Q55 Q63 Q17 Q52 Q13 Q93 Q98 Q46 Q34 Q42 Q68 Q32 Q50 Q60 Q18 Q75 Q31 Q84 Q83 Q22 Q56 Q89 Q26 Q39 Q67 Q94 Q47 Q95 IBM J9 is faster ratio (OpenJDK/IBM J9) 15 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
16 Exectution Time (sec.) Lower is Better OpenJDK is faster IBM J9 OpenJDK ratio Q87 Q80 Q92 Q24 Q22 Q95 Q94 Q85 Q90 Q63 Q89 Q83 Q49 Q96 Q71 Q39 Q98 Q34 Q25 Q55 Q75 Q31 Q19 Q20 Q64 Q3 Q45 Q52 Q7 Q76 Q28 Q70 Q84 IBM J9 is faster ratio (OpenJDK/IBM J9) 16 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
17 Exectution Time (sec.) Lower is Better (Using OpenJDK) Spark Tez Ratio (tez/spark) Tez is faster Spark is faster Q66 Q58 Q17 Q93 Q49 Q71 Q82 Q13 Q6 Q64 Q15 Q32 Q55 Q42 Q7 Q79 Q98 Q3 Q94 Q75 Q34 Q76 Q87 Q91 Q43 Q95 Q97 Q39 Q51 Q ratio (Tez/Spark) IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
18 18 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
19 Lower is Better Q50 Q51 Q58 Q82 19 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
20 Q50 Q51 Q58 Q82 Query # Map Stages # Reduce Stages Input Read Shuffle Output Difference Q GB 1.0 GB Shuffle J9: 11s OpenJDK: 29 s Q GB 5.6 GB Map J9: 66s OpenJDK: 47s 20 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
21 Sun.misc.Unsafe.copyMemory Q50 Q51 Q58 Q82 J9 Advantage: Many Stages, less Shuffling Data OpenJDK Advantage: Few Stages, much Shuffling Data J9 is faster OpenJDK is faster 21 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
22 Q50 Q51 Q58 Q82 Difference Difference Difference Query # Map Stages # Reduce Stages Input Records / GB Shuffle Records / GB Difference Q * 10^9 (5.4 GB) Q * 10^8 (1.3 GB) 5.0 * 10^7 (1.9 GB) 3.5 * 10^8 (3.5 GB) Reduce J9: 106s, OpenJDK: 60s Map J9: 7s, OpenJDK: 21s 22 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
23 Q50 Q51 Q58 Q82 J9 Advantage: Few Vertices, Much Shuffling Data PipelinedSorter (Reduce Vertex) In memory ORC (LLAP) Read java.io.dataoutputstream JOIN / Aggregation Serialize + Spill Serialization/Deserialization 23 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
24 OpenJDK Advantage: Many Vertices, Less Shuffling Data Q50 Q51 Q58 Q82 OpenJDK usr sys wai Map usr sys wai Reduce J IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
25 à à web sales Spark DAG date dim M 1 M 2 R 1 R 2 R 3 R 4 R 5 R 6 store sales date dim 366 rows 687MB Q50 Q51 Q58 Q82 web sales Tez DAG M 1 M 2 M 3 M 4 R 1 R 2 R 5 R 6 R 7 store sales date dim 8116 rows 3.6GB Good query optimizer (Cost Based Optimizer) helps to reduce shuffling data 25 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
26 26 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
27 IBM Research 27 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
28 Choices of Engine and Runtime 1. What about potential gains? observation 3. What data to help building a model? training Empirical Study reasoning 2. What features make the differences? GOAL Run SQL on best engine/runtime Meta Scheduler predicting ML Model 4. How accurately can the model predict? 28 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
29 ( ) ( + 9( ) + ( 29 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
30 ) ( ( 30 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
31 ( ) ( + 9( ) ( 31 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
32 - # of stages makes impact to the model - Using BF or Join types do not affect Features Impact in Random Forest Accuracy Random Forest is better than others 32 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
33 12000 Accumulated Exec Time (sec) baseline ideal(2) predict(2) ideal(4) predict(4) big miss prediction Q21 Q91 Q96 Q3 Q55 Q43 Q73 Q19 Q7 Q66 Q15 Q60 Q71 Q13 Q54 Q97 Q75 Q58 Q93 Q24 Q64 reduced by 35% reduced by 50% 33 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
34 34 IEEE CLOUD 2018 / Towards Selecting Best Combination of SQL-on-Hadoop Systems and JVMs
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