Minimize Data Loss for Debug Feature Implementation in Google Cloud Storage

D.

In this scenario, the application reliability team has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most 15 MB, and are expected to peak at 3,000 events per second. The goal is to minimize data loss. Let's analyze each process option provided and determine which one is the best approach.

Option A: Append metadata to file body Compress individual files Name files with serverName " Timestamp Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket. Otherwise, save files to existing bucket.

This approach involves appending metadata to the file body, compressing individual files, naming files with a serverName and timestamp, and creating a new bucket if the bucket is older than 1 hour. This method ensures that the data is compressed and that metadata is added to each file, making it easier to analyze later. Creating a new bucket if the bucket is older than 1 hour also ensures that the data is not lost if the bucket becomes corrupted or has issues. Overall, this approach is a good option for minimizing data loss.

Option B: Batch every 10,000 events with a single manifest file for metadata Compress event files and manifest file into a single archive file Name files using serverName " EventSequence Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket. Otherwise, save the single archive file to existing bucket.

This approach involves batching every 10,000 events with a single manifest file for metadata, compressing event files and manifest file into a single archive file, naming files using serverName and EventSequence, and creating a new bucket if the bucket is older than 1 day. This approach ensures that the data is batched and compressed, making it easier to analyze later. However, creating a new bucket if the bucket is older than 1 day may lead to data loss if the bucket is corrupted or has issues.

Option C: Compress individual files Name files with serverName " EventSequence Save files to one bucket Set custom metadata headers for each object after saving.

This approach involves compressing individual files, naming files with a serverName and EventSequence, saving files to one bucket, and setting custom metadata headers for each object after saving. This method ensures that the data is compressed and that metadata is added to each file, making it easier to analyze later. However, setting custom metadata headers after saving may cause data loss if there are issues or if the headers are not properly set.

Option D: Append metadata to file body Compress individual files Name files with a random prefix pattern Save files to one bucket.

This approach involves appending metadata to the file body, compressing individual files, naming files with a random prefix pattern, and saving files to one bucket. This method ensures that the data is compressed and that metadata is added to each file. However, naming files with a random prefix pattern may make it difficult to analyze the data later.

Overall, the best approach for minimizing data loss would be Option A. It involves appending metadata to the file body, compressing individual files, naming files with a serverName and timestamp, and creating a new bucket if the bucket is older than 1 hour. This method ensures that the data is compressed, that metadata is added to each file, and that data is not lost if the bucket becomes corrupted or has issues.