Performance Analysis of Apache Spark and Presto in Cloud Environment

Today, users have multiple options for big data analytics in terms of open-source and proprietary systems as well as in cloud computing service providers. In order to obtain the best value for their money in a SaaS cloud environment, users need to be aware of the performance of each service as well as its associated costs, while also taking into account aspects such as usability in conjunction with monitoring, interoperability, and administration capabilities.

We present an independent analysis of two mature and well-known data analytics systems, Apache Spark and Presto. Both running on the Amazon EMR platform, but in the case of Apache Spark, we also analyze the Databricks Unified Analytics Platform and its associated runtime and optimization capabilities. Our analysis is based on running the TPC-DS benchmark and thus focuses on SQL performance, which still is indispensable for data scientists and engineers. In our talk we will present quantitative results that we expect to be valuable for end users, accompanied by an in depth look into the advantages and disadvantages of each alternative.

Thus, attendees will be better informed of the current big data analytics landscape and find themselves in a better position to avoid common pitfalls in deploying data analytics at a scale.

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1.WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2.Performance Analysis of Apache Spark and Presto in Cloud Environments Víctor Cuevas-Vicenttín, Barcelona Supercomputing Center #UnifiedDataAnalytics #SparkAISummit

3.About BSC The Barcelona Supercomputing Center (BSC) is the Spanish national supercomputing facility, and a top EU research institution, established in 2005 by the Spanish government, the Catalan government and the UPC/BarcelonaTECH university. The mission of BSC is to be at the service of the international scientific community and of industry in need of HPC resources. BSC's research lines are developed within the framework of European Union research funding programmes, and the centre also does basic and applied research in collaboration with companies like IBM, Microsoft, Intel, Nvidia, Repsol, and Iberdrola. 3

4.13.7 Petaflops 4

5. TPC-DS Benchmark Work The BSC collaborated with Databricks to benchmark comparisons on large-scale analytics computations, using the TPC-DS Toolkit v2.10.1rc3 The Transaction Processing Performance Council (TPC) Benchmark DS (1) has the objective of evaluating decision The TPC is a non-profit support systems, which process large volumes of data in corporation focused on order to provide answers to real-world business questions. developing data-centric Our results are not official TPC Benchmark DS results. benchmark standards and disseminating objective, verifiable performance data to Databricks provided BSC an account and credits, which the industry. BSC then independently used for the benchmarking study with other analytics products on the market. 5

6.Context and motivation • Need to adopt data analytics in a cost-effective manner – SQL still very relevant – Open-source based analytics platforms – On-demand computing resources from the Cloud • Evaluate Cloud-based SQL engines #UnifiedDataAnalytics #SparkAISummit 6

7.Systems Under Test (SUTs) • Databricks Unified Analytics Platform – Based on Apache Spark but with optimized Databricks Runtime – Notebooks for interactive development and production Jobs – JDBC and custom API access – Delta storage layer supporting ACID transactions #UnifiedDataAnalytics #SparkAISummit 7

8.Systems Under Test (SUTs) • AWS EMR Presto – Distributed SQL engine created by Facebook – Connectors non-relational and relational sources – JDBC and CLI access – Based on in-memory, pipelined parallel execution • AWS EMR Spark – Based on open-source Apache Spark #UnifiedDataAnalytics #SparkAISummit 8

9.Plan • TPC Benchmark DS • Hardware and software configuration • Benchmarking infrastructure • Benchmark results and their analysis • Usability and developer productivity • Conclusions #UnifiedDataAnalytics #SparkAISummit 9

10.TPC Benchmark DS • Created around 2006 to evaluate decision support systems • Based on a retailer with several channels of distribution • Process large volumes of data to answer real-world business questions #UnifiedDataAnalytics #SparkAISummit 10

11.TPC Benchmark DS • Snowflake schema: fact tables associated with multiple dimension tables • Data produced by data generator • 99 queries of various types – reporting – ad hoc – iterative – data mining #UnifiedDataAnalytics #SparkAISummit 11

12.#UnifiedDataAnalytics #SparkAISummit 12

13.TPC Benchmark DS ORC, • Load Test (1 TB) .dat parquet • Power Test Query 1 Query 2 ... Query 99 • Data Refresh Query1,1 Query1,2 ... Query1,99 ... • Throughput Test Queryn,1 Queryn,2 ... Queryn,99 #UnifiedDataAnalytics #SparkAISummit 13

14.Hardware configuration Type vCPUs Memory Local storage i3.2xlarge 8 (2.3 GHz Intel 61 GiB 1 x 1,900 GB Xeon E5 2686 v4) NVMe SSD 1 master node 8 worker nodes #UnifiedDataAnalytics #SparkAISummit 14

15.Software configuration System Versions Configuration parameters Runtime 5.5, spark.sql.broadcastTimeout: 7200 Spark 2.4.3, spark.sql.crossJoin.enabled: true Scala 2.11 emr-5.26.0, hive.allow-drop-table: true Presto 0.220 hive.compression-codec: SNAPPY hive.s3-file-system-type: PRESTO query.max-memory: 240 GB emr-5.26.0, spark.sql.broadcastTimeout : 7200 Spark 2.4.3 spark.driver.memory: 5692M #UnifiedDataAnalytics #SparkAISummit 15

16. JAR JAR SQL .XLSX client application .log .log cluster execution analysis AWS Glue .dat parquet .dat ORC Metastore #UnifiedDataAnalytics #SparkAISummit 16

17.Benchmark execution time (base) #UnifiedDataAnalytics #SparkAISummit 17

18.Cost-Based Optimizer (CBO) stats • Collect table and column-level statistics to create optimized query evaluation plans – distinct count, min, max, null count #UnifiedDataAnalytics #SparkAISummit 18

19.Benchmark execution time (stats) CBO enabled: ↑ 27.11 #UnifiedDataAnalytics #SparkAISummit 19

20.Speedup with table and column stats CBO enabled: ↓ 0.60 #UnifiedDataAnalytics #SparkAISummit 20

21.TPC-DS Power Test – geom. mean #UnifiedDataAnalytics #SparkAISummit 21

22.TPC-DS Power Test – arith. mean #UnifiedDataAnalytics #SparkAISummit 22

23.Additional configuration for Presto Query modifications (carried on to all systems) 72 manual join re-ordering 95 add distinct clause Session configuration for all queries query_max_stage_count: 102 join_reordering_strategy: AUTOMATIC join_distribution_type: AUTOMATIC Query-specific configuration parameters 5, 75, 78, and 80 join_distribution_type: PARTITIONED 78 and 85 join_reordering_strategy: NONE 67 task_concurrency: 32 18 join_reordering_strategy=ELIMINATE_CROSS_JOINS #UnifiedDataAnalytics #SparkAISummit 23

24.TPC-DS Power Test – Query 72 • Original benchmark join order catalog_sales ⋈ inventory ⋈ warehouse ⋈ item ⋈ customer_demographics ⋈ household_demographics ⋈ date_dim ⋈ date_dim ⋈ date_dim ⟕ promotion ⟕ catalog_returns • Manually modified join order catalog_sales ⋈ date_dim ⋈ date_dim ⋈ inventory ⋈ date_dim ⋈ warehouse ⋈ item ⋈ customer_demographics ⋈ household_demographics ⟕ promotion ⟕ catalog_returns • Databricks optimized join order no stats Same as modified join order + pushed down selections and projections #UnifiedDataAnalytics #SparkAISummit 24

25. TPC-DS Power Test – Query 72 • Databricks optimized join order with stats (((((((catalog_sales ⋈ household_demographics) ⋈ date_dim) ⋈ customer_demographics) ⋈ item) ⋈ (((date_dim ⋈ date_dim) ⋈ inventory) ⋈ warehouse)) ⟕ promotion) ⟕ catalog_returns) +pushed down selections and projections • EMR Spark optimized join order with stats and CBO enabled/disabled Same as modified join order + pushed down selections and projections but different physical plans #UnifiedDataAnalytics #SparkAISummit 25

26. Dynamic data partitioning • Splits a table based on the value of a particular column – Split only 7 largest tables by date surrogate keys – One S3 bucket folder for each value • Databricks and EMR Spark: limit number of files per partition • EMR Presto: out of memory error for largest table – Use Hive with TEZ to load data #UnifiedDataAnalytics #SparkAISummit 26

27.Benchmark exec. time (part + stats) Power Test: 2 failed queries Throughput Test: 6 failed queries #UnifiedDataAnalytics #SparkAISummit 27

28.Speedup with partitioning and stats #UnifiedDataAnalytics #SparkAISummit 28

29.TPC Benchmark total execution time #UnifiedDataAnalytics #SparkAISummit 29