Affects Version/s: 2.7.2
Release Note:WASB has added an optional capability to execute certain FileSystem operations in parallel on multiple threads for improved performance. Please refer to the Azure Blob Storage documentation page for more information on how to enable and control the feature.
WASB Performance Improvements
Azure Native File system operations like rename/delete which has large number of directories and/or files in the source directory are experiencing performance issues. Here are possible reasons
a) We first list all files under source directory hierarchically. This is a serial operation.
b) After collecting the entire list of files under a folder, we delete or rename files one by one serially.
c) There is no logging information available for these costly operations even in DEBUG mode leading to difficulty in understanding wasb performance issues.
Step 1: Rename and delete operations will generate a list all files under the source folder. We need to use azure flat listing option to get list with single request to azure store. We have introduced config fs.azure.flatlist.enable to enable this option. The default value is 'false' which means flat listing is disabled.
Step 2: Create thread pool and threads dynamically based on user configuration. These thread pools will be deleted after operation is over. We are introducing introducing two new configs
a) fs.azure.rename.threads : Config to set number of rename threads. Default value is 0 which means no threading.
b) fs.azure.delete.threads: Config to set number of delete threads. Default value is 0 which means no threading.
We have provided debug log information on number of threads not used for the operation which can be useful .
If we fail to create thread pool due to ANY reason (for example trying create with thread count with large value such as 1000000), we fall back to serialization operation.
Step 3: Bob operations can be done in parallel using multiple threads executing following snippet
while ((currentIndex = fileIndex.getAndIncrement()) < files.length)
The above strategy depends on the fact that all files are stored in a final array and each thread has to determine synchronized next index to do the job. The advantage of this strategy is that even if user configures large number of unusable threads, we always ensure that work doesn’t get serialized due to lagging threads.
We are logging following information which can be useful for tuning number of threads
a) Number of unusable threads
b) Time taken by each thread
c) Number of files processed by each thread
d) Total time taken for the operation
Failure to queue a thread execute request shouldn’t be an issue if we can ensure at least one thread has completed execution successfully. If we couldn't schedule one thread then we should take serialization path. Exceptions raised while executing threads are still considered regular exceptions and returned to client as operation failed. Exceptions raised while stopping threads and deleting thread pool shouldn't can be ignored if operation all files are done with out any issue.