Scaling Apache Spark at Facebook

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Spark started at Facebook as an experiment when the project was still in its early phases. Spark’s appeal stemmed from its ease of use and an integrated environment to run SQL, MLlib, and custom applications. At that time the system was used by a handful of people to process small amounts of data. However, we’ve come a long way since then. Currently, Spark is one of the primary SQL engines at Facebook in addition to being the primary system for writing custom batch applications. This talk will cover the story of how we optimized, tuned and scaled Apache Spark at Facebook to run on 10s of thousands of machines, processing 100s of petabytes of data, and used by 1000s of data scientists, engineers and product analysts every day. In this talk, we’ll focus on three areas: * *Scaling Compute*: How Facebook runs Spark efficiently and reliably on tens of thousands of heterogenous machines in disaggregated (shared-storage) clusters. * *Optimizing Core Engine*: How we continuously tune, optimize and add features to the core engine in order to maximize the useful work done per second. * *Scaling Users:* How we make Spark easy to use, and faster to debug to seamlessly onboard new users.

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1 .Scaling Apache Spark at Facebook Sameer Agarwal & Ankit Agarwal Spark Summit | San Francisco | 24 th April 2019

2 .About Us Sameer Agarwal - Software Engineer at Facebook (Data Warehouse Team) - Apache Spark Committer (Spark Core/SQL) - Previously at Databricks and UC Berkeley Ankit Agarwal - Production Engineering Manager at Facebook (Data Warehouse Team) - Data Infrastructure Team at Facebook since 2012 - Previously worked on the search team at Yahoo!

3 .Agenda 1. Spark at Facebook 2. Hardware Trends: A tale of two bottlenecks 3. Evolving the Core Engine - History Based Tuning - Join Optimizations 4. Our Users and their Use-cases 5. The Road Ahead

4 .Agenda 1. Spark at Facebook 2. Hardware Trends: A tale of two bottlenecks 3. Evolving the Core Engine - History Based Tuning - Join Optimizations 4. Our Users and their Use-cases 5. The Road Ahead

5 .Data at Facebook

6 .2.7 Billion MAU 2 Billion DAU Source: Facebook Q4 2018 earnings call transcript

7 .The Journey 2019 Scaling Spark 2018 Full-production Largest Compute 2017 deployment Engine at Facebook by CPU Running 60TB+ 2016 shuffle pipelines Successor to Apache Hive at Facebook Few Pipelines in 2015 Production Small Scale Experiments

8 .Agenda 1. Spark at Facebook 2. Hardware Trends: A tale of two bottlenecks 3. Evolving the Core Engine - History Based Tuning - Join Optimizations 4. Our Users and their Use-cases 5. The Road Ahead

9 .Hardware Trends CPU, DRAM, and Disk

10 .Hardware Trends CPU, DRAM, and Disk 1. The industry is optimizing for throughput by adding more cores 2. To optimize performance/watt, next generation processors will have more cores that run at lower frequency

11 .Hardware Trends CPU, DRAM, and Disk 1. The price of DRAM continued to rise throughout 2016-2018 and has started fluctuating this year 2. Need to reduce our over- dependence on DRAM

12 .Hardware Trends CPU, DRAM, and Disk 1. Disk sizes continue to increase but the number of random accesses per second aren’t increasing 2. IOPS becomes a bottleneck

13 .What does this mean for Spark? 1. Optimize Spark for increasing core-memory ratio 2. Run Spark on disaggregated compute/storage clusters - Use server types optimized for compute and storage - Scale/upgrade clusters independently over time depending on whether CPU or IOPS was a bottleneck 3. Scale extremely diverse workloads (SQL, ML etc.) on Spark over clusters of tens of thousands of heterogenous machines

14 .Spark Architecture at Facebook Compute Cluster Storage Cluster Executor #1 Distributed FS instance #1 Executors #2 Distributed FS instance #2 Distributed FS instance #3

15 .Spark Architecture at Facebook Spill, Compute Cluster Storage Cluster Cache, Shuffle Executor #1 Distributed FS instance #1 Executors #2 Distributed FS instance #2 Distributed FS instance #3

16 .Spark Architecture at Facebook Spill, Compute Cluster Storage Cluster Cache, Shuffle Executor #1 Distributed FS instance #1 Executors #2 Distributed FS instance #2 Distributed FS instance #3 Tangram Scheduler Heterogenous Hardware (purchased over 0-5 years)

17 .Spark Architecture at Facebook Spill, Compute Brian Cho andCluster Dmitry Borovsky, Cosco: An Efficient Storage Cluster Shuffle Service Facebook-Scale Cache, Shuffle Today at 4:30PM (Developer Track) Executor #1 Distributed FS instance #1 Executors #2 Distributed FS instance #2 Rui Jian and Hao Lin, Tangram: Distributed Scheduling for Spark at Facebook Distributed FS instance #3 Tangram Scheduler Tomorrow at 11:50AM (Developer Track) Heterogenous Hardware (purchased over 0-5 years)

18 .Agenda 1. Spark at Facebook 2. Hardware Trends: A tale of two bottlenecks 3. Evolving the Core Engine Contributed 100+ - History Based Tuning patches upstream - Join Optimizations 4. Our Users and their Use-cases 5. The Road Ahead

19 .History-Based Tuning: Motivation max (80-100%) Cluster Memory Utilization p95 (55-70%) p50 (10-60%) 1 week

20 .History-Based Tuning: Motivation max (80-100%) Cluster Memory Utilization p95 (55-70%) p50 (10-60%) One-size-fits-all configs results in under-utilization of resources 1 week

21 .History-Based Tuning: Motivation Percentage of Spark Tasks (CDF) 75% of Spark tasks use less than 600 MB of peak execution memory Peak Execution Memory Bytes

22 .History-Based Tuning: Motivation Percentage of Spark Tasks (CDF) 75% of Spark tasks use less than 600 MB of peak execution memory Individual resource requirements for each Spark task has a huge variance Peak Execution Memory Bytes

23 . History-Based Tuning 1. Need to tune Spark on a per-job or a per-stage basis 2. Leverage historical characteristics of the job to tune resources: • Peak executor memory and spill sizes to tune executor off-heap memory • Shuffle size to optionally not insert partial aggregates in the query plan • Predicting the number of shuffle partitions (job level and stage level)

24 .History-Based Tuning InsertIntoHiveTable [partitions: ds,country] +- *Project [cast(key as int) AS key, value] Query Plan +- *HiveTableScan (db.test) [col: key,value] [part: ds] Template New Query

25 .History-Based Tuning Query Plan Regressions/Failures Template since past N days Apply Conservative Defaults Apply Config New Historical No Regressions/Failures Overrides Query Job Runs since past N days Config Override Rules

26 .Joins in Spark 1. Broadcast Join: Broadcast small table to all nodes, stream the larger table; skew resistant 2. Shuffle-Hash Join: Shuffle both tables, create a hashmap with smaller table and stream the larger table 3. Sort-Merge Join: Shuffle and sort both tables, buffer one side and stream the other side

27 .Sort-Merge-Bucket (SMB) Join 1. Bucketing is a way to shuffle (and optionally sort) output data based on certain columns of table 2. Ideal for write-once, read-many datasets 3. Variant of Sort Merge Join in Spark; overrides outputPartitioning and outputOrdering for HiveTableScanExec and stitches partitioning/ ordering metadata throughout the query plan SPARK-19256

28 .Dynamic Join A hybrid join algorithm where-in each task starts off by executing a shuffle-hash join. In the process of execution, should the hash table exceed a certain size (and OOM), it automatically reconstructs/sorts the iterators and falls back to a sort merge join SPARK- 21505

29 .Skew Join A hybrid join algorithm that processes skewed keys via a broadcast join and non-skewed keys via a shuffle-hash or sort-merge join SELECT /*+ SKEWED_ON(a.userid='10001') */ a.userid FROM table_A a INNER JOIN table_B b ON a.userid = b.userid

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