We are currently working with a large JSON file (approx. 250 GB total, with individual files around 20 GB each) that contains a list of JSON objects. We’re using the mongoimport command with MongoDB Community Edition and have seen some improvement with the --numInsertionWorkers option. However, we are experiencing data loss and haven’t fully optimized the process.
One challenge we’re facing is chunking the JSON file since it’s an array, which requires loading it into memory for splitting, leading to inefficiencies. We’re exploring the possibility of moving to MongoDB Enterprise Edition, but we’re unsure if it would provide significant performance benefits for this particular use case.
Our goal is to find an optimal way to handle this large data import without data loss.
Could the Enterprise Edition help with this, and what would be the best architecture or approach for handling large JSON array imports? Are there best practices we should follow for parallel processing or chunking in a memory-efficient way?
Laarge JSON files can be a challenge, but there are definitely ways to improve performance and avoid data loss during imports…
A few things to think about:
Chunking Without Loading Entire File into Memory: Since the JSON file is an array of objects, one efficient option could be streaming the file rather than loading the entire thing into memory. You could use something like jq to process the file in chunks. For example, in Python, you could use the ijson library, which allows you to parse large JSON files one object at a time, avoiding memory overload.
Optimize mongoimport Command: awesome that you’re using --numInsertionWorkers to parallelize the insertions, but there are a couple more flags you can try to speed things up and reduce errors:
--maintainInsertionOrder false: Allows inserts to happen out of order, which can improve speed.
--batchSize <number>: changing the batch size to match your system’s capacity can reduce overhead (maybe start with fewer documents per batch).
Data Loss Concerns: Data loss during import can sometimes happen if the mongoimport process fails midway. You might want to check logs for any errors or use --stopOnError to ensure you catch probs/issues during import.
Enterprise Advanced – Is It Worth It? Moving to MongoDB EA could yield advantages, especially in terms of perf and security. Here’s a datasheet with details about the offering… oh and you’d also get access to Ops Manager, which helps with automation and monitoring of large-scale imports.
Use bulkWrite : Instead of relying just on mongoimport, you could write a script to process the file in chunks and use MongoDB’s bulkWrite() operation. This will probably give you greater control over exactly how the data is inserted and give you flexibility with error handling and retries.
Some Best Practice tips:
Indexes: Make sure you create the necessary indexes after importing the data, not during. Probably obvious but this will speed up the import process massively.
Sharding: If you’re planning to distribute the data across multiple servers, consider sharding your collection, especially if you think the data will grow a lot over time.
Resource Monitoring: Keep an eye on your system’s CPU, memory, and disk I/O to ensure you’re not maxing out resources.
If you stick with Community Edition, streaming and parallelizing the import process will probably be your best bet… otherwise, MongoDB Enterprise Advanced will enable scale and monitoring of things more efficiently.
Hope this helps! Let us know if you want to dive deeper or need more info on any of these options…
