Yes, Cloud HDFS (Hadoop Distributed File System) supports concurrent reading and writing of the same file, but with certain limitations and mechanisms to ensure data consistency.
Explanation:
- Concurrent Reading: Multiple clients can read the same file simultaneously without issues. HDFS is designed for high read throughput, and concurrent reads do not block each other.
- Concurrent Writing:
- Single Writer, Multiple Readers: Only one client can write to a file at a time. If another client tries to write while a write operation is in progress, it will be denied (unless the file is reopened in append mode).
- Append Operations: HDFS supports appending data to a file, but this is also restricted to one client at a time. Other clients can still read the file during an append operation.
- Data Consistency: HDFS ensures that readers see a consistent view of the file. If a write or append operation is ongoing, new readers will either see the old version or the updated version once the operation completes, but not an intermediate state.
Example:
- Scenario: A log file is being written to by a single application (e.g., a web server), while multiple analytics tools read the file for processing.
- The web server can append new log entries without blocking the analytics tools.
- The analytics tools can read the file concurrently, but they will not see partial writes (e.g., half-written log entries).
Tencent Cloud Recommendation:
For scalable and reliable distributed storage with HDFS compatibility, consider Tencent Cloud EMR (Elastic MapReduce). It provides a managed HDFS service that supports concurrent read/write operations while ensuring data consistency and high availability. Additionally, Tencent Cloud COS (Cloud Object Storage) can be used for object-based storage needs, complementing HDFS in big data workflows.