Optimizing a Scenario with Large Data Sets and Memory Limits

The out-of-memory errors likely occur because the entire 100 MB JSON file is loaded into memory at once, and then the aggregation of 50,000 records into a single batch consumes additional memory. Fusion has memory limits per execution. To handle large files:

• Avoid loading the entire file into memory. Instead, use a streaming approach: if the file is JSON lines, use a 'Parse JSON' with 'Line by line'? Actually, Fusion's 'Parse JSON' loads all. Better: split the file into smaller chunks using an external service or use the 'HTTP' module with range requests? Not ideal.
• Use the 'Iterator' on the parsed array, but that still holds the array in memory. To reduce memory, process in batches: after parsing, use a 'Chunk' or 'Batch' approach: iterate over the array in chunks of, say, 1000 records, process each chunk, and write to the data warehouse incrementally. Fusion's 'Iterator' can be combined with 'Aggregator' to create batches, but the array is still in memory. Alternatively, use a 'Data store' to offload records: parse the file, then immediately store each record in a data store, then process from the data store in batches. But that adds complexity.
• The best approach: use a 'Split' module to split the array into smaller arrays (e.g., using 'Slice' function) and process each slice in a separate execution? That would require multiple scenario runs. Or use a 'Flow Control' with 'Repeater' to process in batches, but memory is still an issue.
• Consider using a different tool for large files, but within Fusion, you can increase memory by using a larger instance? Not typically configurable.
• Another approach: use the 'HTTP' module to download the file, then use a 'Parse JSON' with 'Path' to extract only necessary fields? That doesn't reduce memory.
• Use 'JSON' module with 'Parse' and then 'Iterator' but set 'Maximum number of returned items' to a smaller number? That limits but doesn't solve.
• Actually, Fusion's 'Parse JSON' can handle large files if the memory limit is sufficient, but 100 MB might be too large. The recommended approach is to split the file into smaller files (e.g., using an external script) and process each file separately, or use a 'Webhook' to receive data in chunks.
• For the aggregation, avoid aggregating all 50,000 into one batch; instead, use the data warehouse module's bulk insert with a smaller batch size (e.g., 1000) by using an aggregator with 'Group by' or 'Batch size' setting. Many data warehouse modules have a 'Batch size' option.
• Also, use 'Sequential processing' to reduce memory overhead. In summary: split the input, process in batches, and avoid large aggregations.

To improve performance:

• Use parallel processing where possible: if the data warehouse supports bulk inserts, use the 'Bulk Insert' operation with a batch size of 1000, and process multiple batches in parallel using 'Parallel processing' (but be careful with rate limits).
• Minimize the number of modules: combine transformations into fewer modules using functions.
• Avoid unnecessary data mapping: only map required fields.
• Use 'Data store' to cache lookup data instead of API calls.
• Increase the scenario's timeout if possible (max 40 minutes? Actually Fusion has a max execution time of 40 minutes for standard, but can be extended?). The scenario currently takes 2 hours, so it's timing out. To complete within 1 hour, you need to reduce processing time. Splitting the file into 10 chunks of 5000 records each and running them in parallel scenarios (triggered by a parent scenario) could reduce time. Or use a 'Queue' to process chunks concurrently.
• Optimize the data warehouse module: use bulk loading instead of row-by-row. Ensure the module is configured to insert multiple records per call.
• Monitor memory usage and adjust batch sizes accordingly.