Services Additional Tips

Introduction

This guide is designed to help you make the most of your service connections and enhance the functionality of your applications through external system integration.

Data Storage and Management

Local Table vs. SQLite for Local Data Storage

Choose the right storage method based on your application's complexity:

• Local Table:

1. Speed: Data structure designed for fast data storage and retrieval needs where complex querying isn't required.

2. Size of Dataset: Suitable for smaller datasets.

3. Memory: Persistent cache memory, so the data in memory will still exist even after app is closed. Data in this memory will be cleared when cache is cleared.

• SQLite:

1. Speed: Local database engine for complex applications with structured data and querying needs. Slower data storage and retrieval process, and advisable to be processed synchronously.

2. Size of Dataset: Useful for efficient storage and retrieval of large datasets.

3. Memory: Persistent memory that is stored separately, and more permanent than local table. Data in this memory will not be cleared on cache clear. Use actions provided to modify or delete data entries.

Additional note on synchronicity:

Read and write operations to SQLite Table are not instantaneous and not synchronous (non-blocking) by default. It is not advisable to execute such operations concurrently with other actions that depend on them. Continue building dependent logic with the appropriate yesCallback or noCallback instead, to guarantee that the values in the SQLite table are ready for use.

Read and write operations to local tables are blocking by default, thus the operation is guaranteed to execute to completion before the next concurrent action.

Optimizing Configurations

Making Configurations Dynamic

Empower your application with dynamic configurations that can adapt to changing scenarios. Avoid hardcoding values whenever possible. Connector configurations can also be accessed and modified at runtime with componentAttr and setComponentAttr.

Efficient Data Processing

Integrating with ERP and External Systems

When integrating with external systems like ERPs, follow these steps for efficient data processing:

1. Data Mapping: Map your application's data fields to the corresponding fields in the external system.

2. Batch Processing: Whenever possible, process data in batches to reduce the number of API calls. You can make use of the limit field inside the ERP table configuration and loadNext action to retrieve data in batches.

3. Scheduled Sync: Implement scheduled synchronization to avoid real-time data overload.

Minimizing API Calls

Minimize API calls to enhance performance:

• Caching: Store frequently accessed data locally using caching mechanisms like local table.

• Throttling: Adhere to API rate limits to prevent service disruptions.

Error Handling and Debugging

Implement robust error handling:

• Meaningful Messages: Provide clear and user-friendly error messages.

• Testing: Test service connections in staging environments before deployment.

Consistent Naming Conventions

Adopt a consistent naming convention for services, actions, and variables:

• Clarity: Clear names aid understanding and organization.

• Maintainability: Consistency simplifies maintenance and collaboration.

Data Validation and Transformation

Ensure data accuracy and compatibility:

• Validation: Validate user inputs and API responses to prevent errors.

• Transformation: Transform data formats to match the requirements of your application.

Conclusion

By following these best practices, you'll create more efficient, reliable, and seamlessly integrated applications using the 'services' section of our no-code platform. Your understanding of data structures, efficient data processing, error handling, and other key aspects will empower you to harness the full potential of external system integration.