Scaling ERPNext business whitepaper

When data and transactions in your ERPNext installation grows, it's important to scale various areas to combat the bottlenecks.

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Scaling ERPNext

In ERPNext, when the database and number of transactions increase to a very large level, various parts become bottlenecks. Be it hardware or software, various scaling methods need to be applied to run a large ERP system smoothly. The optimizations and scaling methods discussed in this document are for ERPNext and cover topics like database scaling, hardware scaling, code optimizations, and other relevant methods.

This business white paper explores ways to scale ERPNext as your organization grows. Using proper configuration to remove bottlenecks is vital for smooth functioning of a growing ERPNext system.

Scaling ERPNext Summary

This paper covers the common challenges that users are faced with when running ERPNext on a massive scale. We explore strategies that can be employed to overcome such challenges by scaling ERPNext. ERPNext is built on a three-tier architecture with a database backend, an application layer built on a front end. Apart from this, in deployments at large volumes, data will be fed by API integrations with payment and IOT platforms while the users will be consumers of data. In this paper, we cover scaling each tier and handling specific bottlenecks that appear with the growth of an ERPNext implementation. For large volumes of accounting or stock transactions, the “ledger” will become the bottleneck and this paper will also discuss strategies for partitioning the ledger.

Scaling ERPNext Whitepaper
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Table of contents

Background    

1. ERPNext Architecture    

1.1 Frappe Framework    

1.2 Services    

1.3 Frappe Bench    

2. Scaling Resources    

2.1 Service Oriented Architecture (SOA)    

2.2 Hardware Scaling    

2.3 Database Scaling    

2.3.1 Configuration    

2.3.2 Index Optimization    

2.3.3 MariaDB / MySQL Scaling    

2.3.4 Data Partitioning    

2.4 Scaling the Application Server (Frappe)    

2.4.1 NGINX Proxy    

2.4.2 Separation of Web and Job Workers    

2.4.3 Separation of Read and Write Queries    

2.4.4 Async Python using GEvent    

2.5 Scaling Redis    

2.6 Scaling Front-end    

3. Application Scaling    

3.1 Performance Monitoring Tools    

3.2 Scaling Reports / MIS    

3.3 Increasing Throughput    

3.4 Refactoring    

3.5 Partitioning Ledgers / Federation    

3.6 Customization and Plugins    

3.7 Custom Front End    

3.8 Load Balancing    

3.9 Migrations at Scale    

4. Testimonial    

Conclusion    

Credits   

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