#SalesforceApexHours #FarmingtonHillsSFDCDug
Agenda :-
• Whats is HyperBatch
• Difference between Old Traditional Apex Batch job and HyperBatch
• Demo
• FAQ
How to Extend Apache Spark with Customized OptimizationsDatabricks
There are a growing set of optimization mechanisms that allow you to achieve competitive SQL performance. Spark has extension points that help third parties to add customizations and optimizations without needing these optimizations to be merged into Apache Spark. This is very powerful and helps extensibility. We have added some enhancements to the existing extension points framework to enable some fine grained control. This talk will be a deep dive at the extension points that is available in Spark today. We will also talk about the enhancements to this API that we developed to help make this API more powerful. This talk will be of benefit to developers who are looking to customize Spark in their deployments.
Accelerating Apache Spark Shuffle for Data Analytics on the Cloud with Remote...Databricks
The increasing challenge to serve ever-growing data driven by AI and analytics workloads makes disaggregated storage and compute more attractive as it enables companies to scale their storage and compute capacity independently to match data & compute growth rate. Cloud based big data services is gaining momentum as it provides simplified management, elasticity, and pay-as-you-go model.
Evening out the uneven: dealing with skew in FlinkFlink Forward
Flink Forward San Francisco 2022.
When running Flink jobs, skew is a common problem that results in wasted resources and limited scalability. In the past years, we have helped our customers and users solve various skew-related issues in their Flink jobs or clusters. In this talk, we will present the different types of skew that users often run into: data skew, key skew, event time skew, state skew, and scheduling skew, and discuss solutions for each of them. We hope this will serve as a guideline to help you reduce skew in your Flink environment.
by
Jun Qin & Karl Friedrich
Kat Grigg, Confluent, Senior Customer Success Architect + Jen Snipes, Confluent, Senior Customer Success Architect
This presentation will cover tips and best practices for Apache Kafka. In this talk, we will be covering the basic internals of Kafka and how these components integrate together including brokers, topics, partitions, consumers and producers, replication, and Zookeeper. We will be talking about the major categories of operations you need to be setting up and monitoring including configuration, deployment, maintenance, monitoring and then debugging.
https://www.meetup.com/KafkaBayArea/events/270915296/
How to Extend Apache Spark with Customized OptimizationsDatabricks
There are a growing set of optimization mechanisms that allow you to achieve competitive SQL performance. Spark has extension points that help third parties to add customizations and optimizations without needing these optimizations to be merged into Apache Spark. This is very powerful and helps extensibility. We have added some enhancements to the existing extension points framework to enable some fine grained control. This talk will be a deep dive at the extension points that is available in Spark today. We will also talk about the enhancements to this API that we developed to help make this API more powerful. This talk will be of benefit to developers who are looking to customize Spark in their deployments.
Accelerating Apache Spark Shuffle for Data Analytics on the Cloud with Remote...Databricks
The increasing challenge to serve ever-growing data driven by AI and analytics workloads makes disaggregated storage and compute more attractive as it enables companies to scale their storage and compute capacity independently to match data & compute growth rate. Cloud based big data services is gaining momentum as it provides simplified management, elasticity, and pay-as-you-go model.
Evening out the uneven: dealing with skew in FlinkFlink Forward
Flink Forward San Francisco 2022.
When running Flink jobs, skew is a common problem that results in wasted resources and limited scalability. In the past years, we have helped our customers and users solve various skew-related issues in their Flink jobs or clusters. In this talk, we will present the different types of skew that users often run into: data skew, key skew, event time skew, state skew, and scheduling skew, and discuss solutions for each of them. We hope this will serve as a guideline to help you reduce skew in your Flink environment.
by
Jun Qin & Karl Friedrich
Kat Grigg, Confluent, Senior Customer Success Architect + Jen Snipes, Confluent, Senior Customer Success Architect
This presentation will cover tips and best practices for Apache Kafka. In this talk, we will be covering the basic internals of Kafka and how these components integrate together including brokers, topics, partitions, consumers and producers, replication, and Zookeeper. We will be talking about the major categories of operations you need to be setting up and monitoring including configuration, deployment, maintenance, monitoring and then debugging.
https://www.meetup.com/KafkaBayArea/events/270915296/
Burp Suite is an integrated platform for performing security testing of web applications. It is designed to support the methodology of a hands-on tester, and gives you complete control over the actions that it performs, and deep analysis of the results. Burp contains several tools that work together to carry out virtually any task you will encounter in your testing. It can automate all kinds of tasks in customizable ways, and lets you combine manual and automated techniques to make your testing faster, more reliable and more fun.
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
With the rise of the cloud, data intensive systems and the Internet of Things the use of distributed systems have become widespread.
The first big player was Hadoop, which provided an integral solution to Big Data storage and computation problems. Its popularity empowered many organizations to adopt this technology. However new challenges appeared, like the need to be able to execute iterative, interactive or in-memory algorithms without the disk-intensive burden of MapReduce. For that very reason Hadoop evolved, decoupling its resources manager from the main computation engine: YARN was born. As a result of its vast adoption, YARN has become the de-facto distributed operating system for Big Data.
Since early releases, Apache Spark provided a way to be executed on YARN-powered clusters. In this talk we will take a look into that technology, and we will learn what it means having Spark running on this kind of infrastructure.
How to Performance-Tune Apache Spark Applications in Large ClustersDatabricks
Omkar Joshi offers an overview on how performance challenges were addressed at Uber while rolling out its newly built flagship ingestion system, Marmaray (open-sourced) for data ingestion from various sources like Kafka, MySQL, Cassandra, and Hadoop.
Dynamic Scaling: How Apache Flink Adapts to Changing Workloads (at FlinkForwa...Till Rohrmann
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
MyRocks is an open source LSM based MySQL database, created by Facebook. This slides introduce MyRocks overview and how we deployed at Facebook, as of 2017.
Why My Streaming Job is Slow - Profiling and Optimizing Kafka Streams Apps (L...confluent
Kafka Streams performance monitoring and tuning is important for many reasons, including identifying bottlenecks, achieving greater throughput, and capacity planning. In this talk we’ll share the techniques we used to achieve greater performance and save on compute, storage, and cost. We’ll cover: Identifying design bottlenecks in by reviewing logs, metrics, and serdes. State store access patterns, design, and optimization Using profiling tools such as JMX, YourKit etc. Performance tuning of Kafka and Kafka Streams configuration and properties. JVM optimization for correct heap size and garbage collection strategies. Functional programming and imperative programming trade offs.
Introduction to Machine Learning in Spark. Presented at Bangalore Apache Spark Meetup by Shashank L and Shashidhar E S on 17/10/2015.
http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/225649429/
Where is my bottleneck? Performance troubleshooting in FlinkFlink Forward
Flinkn Forward San Francisco 2022.
In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times.
by
Piotr Nowojski
Burp Suite is an integrated platform for performing security testing of web applications. It is designed to support the methodology of a hands-on tester, and gives you complete control over the actions that it performs, and deep analysis of the results. Burp contains several tools that work together to carry out virtually any task you will encounter in your testing. It can automate all kinds of tasks in customizable ways, and lets you combine manual and automated techniques to make your testing faster, more reliable and more fun.
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
With the rise of the cloud, data intensive systems and the Internet of Things the use of distributed systems have become widespread.
The first big player was Hadoop, which provided an integral solution to Big Data storage and computation problems. Its popularity empowered many organizations to adopt this technology. However new challenges appeared, like the need to be able to execute iterative, interactive or in-memory algorithms without the disk-intensive burden of MapReduce. For that very reason Hadoop evolved, decoupling its resources manager from the main computation engine: YARN was born. As a result of its vast adoption, YARN has become the de-facto distributed operating system for Big Data.
Since early releases, Apache Spark provided a way to be executed on YARN-powered clusters. In this talk we will take a look into that technology, and we will learn what it means having Spark running on this kind of infrastructure.
How to Performance-Tune Apache Spark Applications in Large ClustersDatabricks
Omkar Joshi offers an overview on how performance challenges were addressed at Uber while rolling out its newly built flagship ingestion system, Marmaray (open-sourced) for data ingestion from various sources like Kafka, MySQL, Cassandra, and Hadoop.
Dynamic Scaling: How Apache Flink Adapts to Changing Workloads (at FlinkForwa...Till Rohrmann
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
MyRocks is an open source LSM based MySQL database, created by Facebook. This slides introduce MyRocks overview and how we deployed at Facebook, as of 2017.
Why My Streaming Job is Slow - Profiling and Optimizing Kafka Streams Apps (L...confluent
Kafka Streams performance monitoring and tuning is important for many reasons, including identifying bottlenecks, achieving greater throughput, and capacity planning. In this talk we’ll share the techniques we used to achieve greater performance and save on compute, storage, and cost. We’ll cover: Identifying design bottlenecks in by reviewing logs, metrics, and serdes. State store access patterns, design, and optimization Using profiling tools such as JMX, YourKit etc. Performance tuning of Kafka and Kafka Streams configuration and properties. JVM optimization for correct heap size and garbage collection strategies. Functional programming and imperative programming trade offs.
Introduction to Machine Learning in Spark. Presented at Bangalore Apache Spark Meetup by Shashank L and Shashidhar E S on 17/10/2015.
http://www.meetup.com/Bangalore-Apache-Spark-Meetup/events/225649429/
Where is my bottleneck? Performance troubleshooting in FlinkFlink Forward
Flinkn Forward San Francisco 2022.
In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times.
by
Piotr Nowojski
This is my latest version of my client side performance presentations. This has been presented at TechEd NZ 2009 & to a couple of .NET user groups around NZ. This presentation focuses on the basics of client-side performance tuning.
Basic performance application optimization techniques that can be applied to any application, from web to desktop or mobile, but with focus on php/mysql stack. How to identify bottlenecks and resolve them and what strategies to choose to avoid them upfront.
Live presentation:
https://www.youtube.com/watch?v=aas8oM7CLjk
Web applications are becoming increasingly data intensive and complex. Yet, users demand a great user experience, including blazingly fast speeds, across many device types. In this talk, we will show you how you can dramatically improve the performance of your web applications by using Sencha Ext JS and Ext Speeder. You will learn how to: accelerate your back-end data requests up to 10x by leveraging sophisticated in-memory, object-oriented techniques, significantly improve application responsiveness without making any modifications to your client Ext JS application, and quickly get started with database acceleration in standard J2EE environments.
AWS re:Invent 2016: How Fulfillment by Amazon (FBA) and Scopely Improved Resu...Amazon Web Services
We’ll share an overview of leveraging serverless architectures to support high performance data intensive applications. Fulfillment by Amazon (FBA) built the Seller Inventory Authority Platform (IAP) using Amazon DynamoDB Streams, AWS Lambda functions, Amazon Elasticsearch Service, and Amazon Redshift to improve results and reduce costs. Scopely will share how they used a flexible logging system built on Kinesis, Lambda, and Amazon Elasticsearch to provide high-fidelity reporting on hotkeys in Memcached and DynamoDB, and drastically reduce the incidence of hotkeys. Both of these customers are using managed services and serverless architecture to build scalable systems that can meet the projected business growth without a corresponding increase in operational costs.
JavaOne2016 - Microservices: Terabytes in Microseconds [CON4516]Malin Weiss
By leveraging memory-mapped files, Speedment and the Chronicle Engine supports large Java maps that easily can exceed the size of your server’s RAM.Because the Java maps are mapped onto files, these maps can be shared instantly between several microservice JVMs and new microservice instances can be added, removed, or restarted very quickly. Data can be retrieved with predictable ultralow latency for a wide range of operations. The solution can be synchronized with an underlying database so that your in-memory maps will be consistently “alive.” The mapped files can be tens of terabytes, which has been done in real-world deployment cases, and a large number of micro services can share these maps simultaneously. Learn more in this session.
JavaOne2016 - Microservices: Terabytes in Microseconds [CON4516]Speedment, Inc.
By leveraging memory-mapped files, Speedment and the Chronicle Engine supports large Java maps that easily can exceed the size of your server’s RAM.Because the Java maps are mapped onto files, these maps can be shared instantly between several microservice JVMs and new microservice instances can be added, removed, or restarted very quickly. Data can be retrieved with predictable ultralow latency for a wide range of operations. The solution can be synchronized with an underlying database so that your in-memory maps will be consistently “alive.” The mapped files can be tens of terabytes, which has been done in real-world deployment cases, and a large number of micro services can share these maps simultaneously. Learn more in this session.
Salesforce Apex Hours : Node red for salesforceAmit Chaudhary
NodeRED, a top level OpenSource project of the JavaScript foundation, too follows the philosophy "Clicks, not code" we all got to love in Salesforce. The talk will introduce NodeRED, what it is, what it can do and how to use it in conjunction with use cases for Salesforce.
Agenda:
- Introduction to NodeRED: nodes, flows, runtime
- Talking to Salesforce: SOSL, SOQL, DML, Streaming & Chatter
- Use cases with Salesforce
- Q&A
Introduction to Lightning Web Components (LWC)
Why Lightning Web Component (LWC)
Part 1 - Vanilla HTML5 Web Components
Benefits of Web Component
Pillars of Web Component
Demo of Custom Element and Templating
Part 2 - Lightning Web Component
Hello World
Demo of Lifecycle methods
Call Apex Class
Access Custom Label
Lightning Data Services
Communication between Lightning Component and LWC
Third party Callout from LWC
Introduction to Lightning Web Components (LWC)
Why Lightning Web Component (LWC)
Part 1 - Vanilla HTML5 Web Components
Benefits of Web Component
Pillars of Web Component
Demo of Custom Element and Templating
Part 2 - Lightning Web Component
Hello World
Demo of Lifecycle methods
Call Apex Class
Access Custom Label
Lightning Data Services
Communication between Lightning Component and LWC
Third party Callout from LWC
1 What is Lightning Locker Service?
2 Why there is a need for Lightning Locker?
-- Why do we say that browsers can be insecure?
-- What is malicious JavaScript?
-- What is cross-site scripting(XSS)?
3 How it impacts your Lightning Components?
4 How you can enable/disable Lightning Locker
5 Demo
Agenda
1) Scratch Org VS Sandbox Development
2) SalesforceDX source code format from traditional format
3) Winter 19 Salesforce Source Commands (Retrieve, Deploy and Delete)
4) Using Salesforce Extensions For VSCode against Non-scratch orgs
5) Auto Generating package.xml from changesets and unmanaged package
6) Setting Developer Workflows for Salesforce application development and debugging (ISV apps and changeset based development)
7) Deploying metadata from one org to another using Vscode extensions
Salesforce apex hours Einstein platform servicesAmit Chaudhary
Einstein platform services.
Agenda :-
1.What is AI, Machine Learning and Deep Learning
2.Key concepts of ML
3.Overview of Einstein Platform Services
4.Demo
5.Key Takeaways
Salesforce Apex Hours : How Lightning Platform Query Optimizer works for LDVAmit Chaudhary
How Lightning Platform Query Optimizer works for LDV
--Multi Tenant Architecture
--Skinny Table
--Indexing Table & Index Statistics
--Upper Limit on
--Standard & Custom Indexed Field
--Limit on AND , OR and LIKE operator
--SystemModstamp vs LastModifiedDate
--Query Plan Tool
--Q&A
"Salesforce Apex Hours" is a recurring event to talk about salesforce ! This time we are planning one online session on "Einstein Bots".
Agenda :-
1) What’s all the fuss about Einstein Bots ?
2) What are my pre-requisites ?
3) What is it made up of - Dialog, Slots and Entities
4) Let’s Build One!
5) Things to Remember
6) Q & A
Recording :-https://www.youtube.com/watch?v=Z2v0Dv6nWaI&t=21s
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
1. Farmington Hills Salesforce Developer User Group
Salesforce Apex Hours :- HyperBatch
#SalesforceApexHours
#FarmingtonHillsSFDCDug
2. Who am I ?
Amit Chaudhary
• Active on Salesforce Developer Community
• Blogging at http://amitsalesforce.blogspot.in/
• Co-Organizer of :- FarmingtonHillsSFDCDug
• @amit_sfdc
• Amit.salesforce21@gmail.com
3. Our Speaker
Daniel Peter
• Salesforce MVP
• 22x Certified
• Bay Area Salesforce Dev Group Leader
• Lead Application Engineer, Kenandy
• dan@danpeter.com
• @danieljpeter
5. Why HyperBatch? Speed!
Case study: Account / Contact Batches
• Prerequisite: 121K Accounts already in the system
• CreateContactsBatch: Creates 3 Contacts for each Account with a random “probability” field for
each. 363k Contacts total.
• UpdateAccountsBatch: For each Account, update the highest and lowest probability on the
Account by querying the child Contacts. Get the overall highest and lowest probability across all
the Accounts.
• DeleteContactsBatch: Delete all of the Contacts in the system. Keep a running total of how many
get deleted.
13. Why HyperBatch? Speed!
Summary
▶ Running all 3 example batch jobs takes only 6 mins instead of 88 mins.
▶ You save 82 mins.
▶ It only takes 6.7% of the time!
17. Why HyperBatch? Concurrency
Row lock behavior
▶ Apex Batch: default is a failed batch execution. Retry logic can be built, but it will likely exceed
the transaction limits.
▶ HyperBatch: row locks retry automatically until the transaction succeeds. Each re-attempt gets a
new context!
18. How it Works - Summary
• HyperBatch interface that mimics the Database.Batchable interface.
• Browser orchestration for selecting jobs and running them on-demand.
• Lightning Design System, Visualforce (Lightning Components would be a data bottleneck).
• AJAX toolkit for PK chunking the query locator.
• Wrapping requests in unique identifiers for closed loop execution – JavaScript function binding.
• Each execute can return some state of type Object, it can be anything.
• Async
• Parallel remote actions fire the qeueables for the batch executions methods. (Not serial!)
• JavaScript polls for the status of the qeueables, waiting for them to complete.
• Execute state is stored in a custom object, and a list of them is returned to the finish() method, then they are deleted.
• Sync
• Parallel remote actions fire the synchronous transactions. (Not serial!)
• Execute state is returned synchronously and stored in JavaScript in the browser until they are all complete.
26. Roadmap
• Throttle requests (x number of open connections)
• Enhance the user interface
• Support custom iterators instead of just query locator
• Support simple data operations like update a field or delete records without having to write Apex
• Chunk in 2 dimensions: (Parent Id, then Record Id) to avoid row lock errors
• Sequence batches in series (run a batch after a batch)
27. Resources
• Salesforce Developer Blogs
• “Data Chunking Techniques for Massive Orgs“ by Daniel Peter
(https://developer.salesforce.com/blogs/developer-relations/2015/11/pk-chunking-techniques-massive-
orgs.html)
• “French Touch Dreamin’ – The first community-led event in France” by Philippe Ozil
(https://developer.salesforce.com/blogs/developer-relations/2016/11/french-touch-dreamin-first-
community-led-event-france.html)
• Presentation from Forcelandia 2016: “PK Chunking – Divide and conquer massive objects in
Salesforce” (http://www.slideshare.net/danieljpeter/forcelandia-2016-pk-chunking)
• GitHub repo: HyperBatch (https://github.com/danieljpeter/HyperBatch)
