Scaling Data Analytics Workloads on Databricks

Imagine an organization with thousands of users who want to run data analytics workloads. These users shouldn’t have to worry about provisioning instances from a cloud provider, deploying a runtime processing engine, scaling resources based on utilization, or ensuring their data is secure. Nor should the organization’s system administrators.

In this talk we will highlight some of the exciting problems we’re working on at Databricks in order to meet the demands of organizations that are analyzing data at scale. In particular, data engineers attending this session will walk away with learning how we:

Manage a typical query lifetime through the Databricks software stack
Dynamically allocate resources to satisfy the elastic demands of a single cluster
Isolate the data and the generated state within a large organization with multiple clusters

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1. Scaling Data Analytics Workloads on Databricks Spark + AI Summit, Amsterdam Chris Stevens and Bogdan Ghit October 17, 2019 1

2.Chris Stevens Bogdan Ghit • Software Engineer @ Databricks - Serverless Team • Software Engineer @ Databricks - BI Team • Spent ~10 years doing kernel development • PhD in datacenter scheduling @ TU Delft 2

3.A day in the life ... ● Diverse array of tools ● Interactive queries ● Minimize costs ● Simplify maintenance ● Stable system Alice Alice Bob Bob 3

4.Reality ... Alice Alice Bob 4

5.This Talk Control Plane Resource management Connectors Provisioning 5

6.BI Connectors Control Plane 6

7.Tableau on Databricks Client machine Shard Databricks Runtime Webapp Thrift Proxy layer BI tools ODBC/JDBC to/from Spark ODBC/JDBC Spark driver clusters server 7

8.Event Flow Get Query Fetch schema execution data Webapp Read Get Query metadata schema execution construct destruct protocol protocol BI tools ODBC/JDBC Read ODBC/JDBC Spark driver Query execution server metadata Tableau session 8

9.Behind the Scenes Login to Tableau First cold run of Multiple warm runs of a TPC-DS query a TPC-DS query 9

10. The Databricks BI Stack Single-tenant shard MySQL Metastore CM RDS W Catalyst Scheduler W SQLProxy Thrift LB Spark Driver Webapp W sql/protocolv1/o/0/clusterI d Control Plane Data Plane 10

11.Tableau Connector SDK Plugin Connector connectionBuilder SQL Dialect Custom Dialog File Manifest File connectionProperties connectionMatcher connectionRequired Connection Resolver Connection String 11

12.Simplified Connection Dialog 12

13.Controlling the Dialect <function group='string' name='SPLIT' return-type='str'> <formula> CASE WHEN (%1 IS NULL) THEN CAST(NULL AS STRING) Missing operators: IN_SET, ATAN, CHAR, RSPLIT WHEN NOT (%3 IS NULL) THEN COALESCE( Hive dialect wraps DATENAME in COALESCE operators (CASE WHEN %3 &gt; 0 THEN SPLIT(%1, '%2')[%3-1] ELSE SPLIT( REVERSE(%1), Strategy to determine if two values are distinct REVERSE('%2'))[ABS(%3)-1] END), '') ELSE NULL END The datasource supports booleans natively </formula> <argument type='str' /> <argument type='localstr' /> CASE-WHEN statements should be of boolean type <argument type='localint' /> </function> Achieved 100% compliance with TDVT standard testing 13

14. Polling for Query Results Async Poll Thrift Sends async Blocks for 5 sec if polls for result query is not finished First poll after 100 ms causing high-latency for short-lived metadata queries Cuts in half latency by lowering the polling interval

15.Metadata Queries are Expensive SHOW SCHEMAS SHOW TABLES DESC table DESC table Thrift Each query triggers 6 round-trips from Tableau to Thrift We optimize the sequence of metadata queries by retrieving all needed metadata in one go

16.Data Source Connection Latency New connector delivers 1.7-5x lower latency 16

17.Resource Management Control Plane 17

18.Execution Time Optimized TPCDS q80-v2.4 every 20 minutes Fixed, 20 Worker Cluster executors Cluster Start Execution Time: 315 seconds Cost: $14.95 per hour 18

19.Cost Optimized TPCDS q80-v2.4 every 20 minutes Auto-terminating, 20 Worker Cluster executors Cluster Starts Execution Time: 613 seconds Cost: $7.66 per hour 19

20.Cost vs Execution Time 20

21.Cluster Start Path 3. Launch Containers (30 secs) Cluster Manager 4. Init Spark (15 secs) 1. Cloud Instance Allocation Requests Cloud Provider 2. Instance allocation and setup (55 seconds) 21

22.Node Start Time ● Up to 55 seconds to acquire an instance from the cloud provider and initialize it. ● Up to 30 seconds to launch a container (includes downloading DBR) ● ~15 seconds to start master/worker ● Median for cluster starts is 2 mins and 22 seconds. 22

23.Up-scaling 1. Scheduler Info Cluster Manager 4. Laun ch Con tainers 5. Init S (30 sec park (1 s) 5 secs) 2. Cloud Instance Allocation Requests Cloud Provider 3. Cluster Expansion (55 seconds) 23

24.Down-scaling Cluster 1. Scheduler Info Manager 2. Remove Instance From Cluster Cloud 3. Idle Instance Reclaimed Provider 24

25.Basic Autoscaling TPCDS q80-v2.4 every 20 minutes Standard Autoscaling, 20 Worker Cluster executors executors Cluster Start Execution Time: 318 seconds Cost: $14.24 per hour 25

26.Optimized Autoscaling TPCDS q80-v2.4 every 20 minutes executors Optimized Autoscaling, 20 Worker Cluster Cluster Start Execution Time: 703 seconds “spark.databricks.aggressiveWindowDownS” -> “40” Cost: $7.27 per hour 26

27.Cost vs Execution Time 27

28.Up-scaling with Instance Pools Cluster 1. Scheduler Info Manager 5. Launch Containers and 2. Pool Instance start Spark (25 secs) Allocation Requests 4. Cluster Expansion (~7ms) 3. Pool Instance Allocation Idle Instances Pool Manager Cloud Instance Cloud Allocation Requests Provider Pool Expansion (55 seconds) (background) (background) 28

29.Down-scaling with Instance Pools Cluster 1. Scheduler Info Manager 2. Remove Instance From Cluster Cloud Instance 3. Idle instance to pool Termination Requests (background) Idle Instances Pool Manager Cloud Provider Pool Retraction (background) 29