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It defines what “large data volume” means in practical terms and maps the main processes impacted—loading/updating, data extraction through reports, queries, and views—then provides strategies such as deferring or bypassing business rule and sharing processing, and selecting the most efficient operations. It also introduces related implementation concepts.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/best-practices-for-deployments-with-large-data-volumes/574163/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/best-practices-for-deployments-with-large-data-volumes/574163.png","ImageObject",442,249,{"name":88,"@type":89},"นรินทร์","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-15","2026-10-14",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What qualifies as a “large data volume” in this paper?","Question",{"text":108,"@type":109},"It refers to deployments with tens of thousands of users, tens of millions of records, or hundreds of gigabytes of total record storage. 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All rights reserved. Salesforce is a registered trademark of Salesforce, Inc., as are other names and marks. Other marks appearing herein may be trademarks oftheir respective owners.  \nCONTENTS  \nBEST PRACTICES FOR DEPLOYMENTS WITH LARGE DATA VOLUMES .... 1  \nIntroduction .......................................................... 1  \nUnderlying Concepts ................................................... 2  \nMultitenancy and Metadata Overview .................................... 2  \nSearch Architecture ................................................. 3  \nInfrastructure for Systems with Large Data Volumes ............................... 4  \nLightning Platform Query Optimizer ...................................... 4  \nDatabase Statistics ................................................. 6  \nSkinny Tables ..................................................... 7  \nIndexes ......................................................... 8  \nDivisions ........................................................ 11  \nTechniques for Optimizing Performance ...................................... 11  \nUsing Mashups .................................................. 12  \nDefer Sharing Calculation ............................................ 12  \nUsing SOQL and SOSL .............................................. 13  \nDeleting Data .................................................... 15  \nSearch ......................................................... 15  \nBest Practices ........................................................ 15  \nReporting ....................................................... 16  \nLoading Data from the API ........................................... 16  \nExtracting Data from the API .......................................... 18  \nSearching ....................................................... 18  \nSOQL and SOSL ................................................... 19  \nDeleting Data .................................................... 20  \nGeneral ........................................................ 20  \nLarge Data Volumes Case Studies .......................................... 21  \nData Aggregation ................................................. 21  \nCustom Search Functionality .......................................... 22  \nIndexing with Nulls ................................................ 22  \nRendering Related Lists with Large Data Volumes ........................... 23  \nAPI Performance .................................................. 23  \nSort Optimization on a Query ......................................... 24  \nMulti-Join Report Performance ........................................ 24  \nSummary .......................................................... 25  \nBEST PRACTICES FOR DEPLOYMENTS WITH LARGE DATA VOLUMES  \nIntroduction  \nWho Should Read This  \nThis paper is for experienced application architects who work with Salesforce deployments that contain large data volumes.  \nA “large data volume” is an imprecise, elastic term. If your deployment has tens of thousands of users, tens of millions of records, or hundreds of gigabytes of total record storage, you have a large data volume. Even if you work with smaller deployments, you can still learn something from these best practices.  \nTo understand the parts of this paper that deal with details of Salesforce implementation, read [https://developer.salesforce.com/page/Multi_Tenant_Architecture](https://developer.salesforce.com/page/Multi_Tenant_Architecture).  \nOverview  \nSalesforce enables customers to easily scale up their applications from small to large amounts of data. This scaling usually happens automatically, but as data sets get larger, the time required for certain operations grows. The ways in which architects design and configure data structures and operations can increase or decrease those operation t","cbCaiuxvgK1oVnrp","https://ap.wps.com/l/cbCaiuxvgK1oVnrp","pdf",1020937,29,"English","# Introduction\n## Who Should Read This\n## Overview\n## What’s in This Paper\n# Underlying Concepts\n## Multitenancy and Metadata Overview\n## Search Architecture\n## Infrastructure for Systems with Large Data Volumes\n## Techniques for Optimizing Performance\n## Using SOQL and SOSL\n## Deleting Data\n# Case Studies and Advanced Topics\n## Large Data Volumes Case Studies\n## Rendering Related Lists with Large Data Volumes\n## API Performance\n## Summary","[{\"question\":\"What qualifies as a “large data volume” in this paper?\",\"answer\":\"It refers to deployments with tens of thousands of users, tens of millions of records, or hundreds of gigabytes of total record storage. The paper notes the term is imprecise and elastic, and smaller deployments can still benefit.\"},{\"question\":\"Which operations are most affected as data volume increases?\",\"answer\":\"The paper highlights loading or updating large numbers of records (including via integrations) and extracting data through reports and queries or through views. Architecture and configuration choices can change operation times by orders of magnitude.\"},{\"question\":\"What optimization strategies does the paper emphasize?\",\"answer\":\"It emphasizes following industry-standard practices for schema changes, deferring or bypassing business rule and sharing processing, and choosing the most efficient operation to accomplish a task. These strategies target the core performance bottlenecks.\"}]","Best Practices for Deployments with Large Data Volumes | PDF",1791985488,10]