Salesforce API Series: Fast Parallel Data Loading with the Bulk API Webinar

transcript

Salesforce API Series Fast Parallel Data Loading with the Bulk API February 26, 2014

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Safe Harbor

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This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any such uncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, including any projections of product or service availability, subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans of management for future operations, statements of belief, any statements concerning new, planned, or upgraded services or technology developments and customer contracts or use of our services.

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Speakers

Steve Bobrowski Architect Evangelist @sbob909

Sean Regan Architect Evangelist @sfdcsregan

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Developer Force – Force.com Community

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Developer Force

Developer Force Group

How fast can you load data into Salesforce?

How many records can you load into Salesforce in 1 hour?

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Data load throughput

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

OK Fast Faster

Records/Hour

Parallel processing

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A parallel processing analogy: digging a ditch

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Serial processing

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Parallel processing

The number of processes or threads associated with an operation.

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Optimal parallel processing

Serial

Parallel

20M records

5M records

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Sub-optimal parallel processing

Serial

Parallel

5M records

20M records

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Locks, exceptions, triggers, relationships, …

Serial

Parallel

5M records

20M records

Throughput inhibitors

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Data load case studies

§  Get hands on with the Salesforce Bulk API §  Contrast serial data loads vs. parallel data loads §  Measure degrees of parallelism and throughput

§  Identify and avoid throughput inhibitors §  Achieve maximum throughput

Prep work

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Salesforce Bulk API

§  Asynchronous data loading

§  Optimized for large data sets §  REST API

§  Powers many tools

§  Use to build custom tools with any programming language (Java, etc.)

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Demo schema

Bulk API Loads that …

ealize, nvestigate, and lan

Case Studies

Serial Data Load

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Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread

Serial load: Expected plan

•  One job •  100 batches •  10,000 records/batch •  1M total records

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Serial load: Job con!guration

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Serial load: Batch creation

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Serial load: Batch run

Demo Serial load

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Serial load summary

Concurrency Mode Serial Records Loaded 1 million Records Failed 0 Run Time 52 minutes Work Completed 48 minutes Throughput 19,500 records per minute Degree of Parallelism 0.94 Key Problem Degree of parallelism explicitly limited to ~1. Solution Explore parallel load for increased throughput.

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Parallelism vs. Throughput of a Single Job

100000

150000

200000

250000

300000

350000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Serial

Serial Run •  Low degree of parallelism

Degree of Parallelism

Parallel data loads

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Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread

Parallel load: Expected plan

•  One job •  100 batches •  10,000 records/batch •  1M total records

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Parallel load: Job con!guration

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Things to watch for

§  Locks can signi!cantly affect parallel loads –  Wasted processing capacity

–  Reduced throughput

–  Failures

§  Retry logic is not all its cracked up to be

Demo Parallel 1

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Parallel load 1 summary

Concurrency Mode Parallel Records Loaded 125,000 Records Failed 875,000 Run Time 10 minutes Work Completed 2 hours and 30 minutes Throughput 20,000 records per minute Degree of Parallelism 15.79 Key Problem Lock Exceptions. Server worked significantly harder but no increase in throughput. Solution Run the load in serial mode or manage locks.

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Parallelism vs. throughput of a single job

100000

150000

200000

250000

300000

350000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Serial

Parallel Run 1 •  High degree of parallelism •  Low throughput due to locks

Parallel 1

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Time to optimize

§  Let’s make your data load

§  ealize –  Locks inhibit parallelism and throughput

§  nvestigate –  What is causing the locks

§  lan –  Manage the locks

Demo Parallel load 2

Eliminate Locks by Modifying Schema

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Parallel load: Sample results

Concurrency Mode Parallel Records Loaded 1 million Records Failed 0 Run Time 3 minutes and 30 seconds Work Completed 1 hour Throughput 320,000 records per minute Degree of Parallelism 19 Key Problem None Solution n/a

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100000

150000

200000

250000

300000

350000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Serial

Parallel Run 2 •  High degree of parallelism •  High throughput

Parallel 1

Parallel 2

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Locks can be managed by

§  Elimination

§  Ordering load !le

Demo Parallel load 3

Avoid Locks with Ordered Data

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Managing locks … a discussion while we load

§  Master-detail relationships

§  Lookup relationships

§  Roll-up summary !elds

§  Triggers

§  Work"ow rules

§  Group membership locks*

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Parallel load: Sample results

Concurrency Mode Parallel Records Loaded 1 million Records Failed 0 Run Time 4 minutes Work Completed 1 hour Throughput 250,000 records per minute Degree of Parallelism 16.5 Key Problem Minimal overhead due to locks Solution Remove all unnecessary locks

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100000

150000

200000

250000

300000

350000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Serial

Parallel Run 3 •  High degree of parallelism •  High throughput

Parallel 1

Parallel 2

Parallel 3

Controlled feed/parallel

data loads

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Controlled feed load methodology

§  Explicit throttling on parallelism and throughput –  Parallel extraction and loading

–  Prioritization of asynchronous processing capacity

§  Manage inhibitors in complex jobs –  Data Skews

–  Multiple Locks

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100000

150000

200000

250000

300000

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Serial

Controlled Feed Run •  Reduced parallelism •  Expected throughput

Parallel 1

Parallel 2

Parallel 3

Controlled Feed

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Related wiki article and Architect Core Resources

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§  Make your parallel data loads

§  ealize –  Locks inhibit parallelism and throughput

§  nvestigate –  What is causing the locks

§  lan –  Manage the locks

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Steve Bobrowski Architect Evangelist @sbob909

Sean Regan Architect Evangelist @sfdcsregan

Salesforce API Series: Fast Parallel Data Loading with the Bulk API Webinar

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