Requirements Document for Verifying Data Consistency Between HDFS and OSS
Introduction
The purpose of this document is to provide a clear set of requirements for verifying data consistency between HDFS and OSS (Object Storage Service) using Python. This process is important to ensure that there are no discrepancies between the data stored in HDFS and the data stored in OSS, which may cause issues in data analysis or processing.
Functional Requirements
1. The solution should perform a binary comparison of the files in HDFS and OSS to verify if they are identical.
2. The solution should compare the number of files in HDFS and OSS to ensure that they are equal.
3. The solution should compare the size of files in HDFS and OSS to ensure that they are equal.
4. The solution should handle errors and edge cases gracefully and log them in a file.
5. The solution should be configurable to allow for customization of parameters such as the directories to compare and the log file location.
Non-functional Requirements
1. The solution should be implemented in Python3.
2. The solution should use the OSS SDK for Python and the HDFS Python API.
3. The solution should be optimized to minimize the time and resources required for the comparison process.
4. The solution should be easy to deploy and run on different environments.
5. The solution should be documented and maintainable.
Assumptions
1. The solution assumes that the HDFS and OSS directories to compare are accessible.
2. The solution assumes that the HDFS and OSS directories have the same file structure.
Implementation Plan
1. Install the OSS SDK for Python and the HDFS Python API.
2. Define variables for the HDFS and OSS directories to compare.
3. Read the files in the HDFS and OSS directories, and verify that the number of files in each directory is equal.
4. For each file, compare the file size and binary content in HDFS and OSS to ensure they are equal.
5. Log any errors or discrepancies in a file.
6. Allow for customization of parameters such as directories to compare and the log file location.
7. Test the solution in different environments to ensure its compatibility and functionality.
8. Document the solution and make it maintainable.
Code Implementation in Python3
```python
import pyhdfs
from oss2 import Auth, Bucket
# Define the HDFS and OSS directories to compare
hdfs_path = "/hdfs/path/"
oss_endpoint = "oss_endpoint"
oss_bucket = "oss_bucket"
oss_path = "oss_path/"
# Initialize the HDFS client and retrieve the file list in the directory
client = pyhdfs.HdfsClient(hosts='localhost:50070')
hdfs_files = client.listdir(hdfs_path)
# Initialize the OSS client and retrieve the file list in the directory
auth = Auth('oss_accessKeyId', 'oss_accessKeySecret')
bucket = Bucket(auth, oss_endpoint, oss_bucket)
oss_files = [obj.key for obj in oss2.ObjectIterator(bucket, prefix=oss_path)]
# Verify that the number of files in HDFS and OSS directories are equal
if len(hdfs_files) != len(oss_files):
print("Error: Number of files in HDFS and OSS directories are different")
else:
# Iterate over each file in the HDFS directory
for file in hdfs_files:
# Retrieve the file in HDFS and OSS
hdfs_file = client.open(hdfs_path + file)
oss_file = bucket.get_object(oss_path + file)
# Verify that the size and content of the files are equal
if (hdfs_file.get_content_summary().length != oss_file.content_length) or (hdfs_file.read() != oss_file.read()):
print("Error: Content of files in HDFS and OSS directories are different")
# Close the files
hdfs_file.close()
oss_file.close()
```
Conclusion
In conclusion, verifying data consistency between HDFS and OSS is necessary to ensure accurate and reliable data processing. The requirements document outlines the key functional and non-functional requirements for implementing a solution in Python3. The code implementation demonstrates the steps required to compare the directories and files in HDFS and OSS, and logs any errors or discrepancies in a file. The solution can be customized to suit different environments and is easy to maintain.
