DuckDB & Azure: Resolving Read Issues With Appendable Logs

by Alex Johnson 59 views

Introduction

This article addresses a specific challenge encountered when using DuckDB with Azure Data Lake Storage Gen2 (ADLS Gen2), specifically concerning reading from continuously appendable logs. Many users are leveraging the power of DuckDB to query data stored in various cloud storage solutions, including Azure's ADLS Gen2. The ability to read data directly from these storage locations without the need for intermediate data loading can significantly improve query performance and simplify data workflows. However, when dealing with logs that are continuously being written to, a potential race condition can arise, leading to read errors. In this article, we'll dive deep into the issue, explore the root cause, and discuss potential solutions to ensure seamless reading from appendable logs in DuckDB with Azure.

The Challenge: Reading Continuously Appended Logs in Azure with DuckDB

When working with data in Azure Data Lake Storage Gen2, a common scenario involves writing logs to files in an append-only fashion. This approach is particularly useful for applications that generate a continuous stream of data, such as system logs, application activity logs, or sensor data. DuckDB, with its ability to directly query data in cloud storage, becomes a powerful tool for analyzing these logs. However, a problem arises when you attempt to read from these logs while they are still being written to. The user encountered the following error when trying to query continuously appended logs in ADLS Gen2 using DuckDB:

SELECT count(*) FROM 'abfss://<path>/**.jsonl';
94% ▕███████████████████████████████████▋ ▏ (~10 seconds remaining) IO Error:
AzureBlobStorageFileSystem Read to 'abfss://<path>/<hive-partition>/*.jsonl' failed with ConditionNotMet Reason Phrase: The condition specified using HTTP conditional header(s) is not met.

This error indicates a conflict between the read operation initiated by DuckDB and the write operation appending data to the log file. Let's delve deeper into the technical aspects to understand the root cause of this issue.

Understanding the Root Cause: ETag Validation and Race Conditions

The error message "ConditionNotMet" and the reference to HTTP conditional headers point to the core of the problem: ETag validation. ETags (Entity Tags) are unique identifiers assigned to objects in Azure Blob Storage. They change whenever an object is modified, acting as a versioning mechanism. When DuckDB interacts with Azure Blob Storage, it uses ETags to ensure data consistency. The DuckDB Azure extension, as highlighted in the provided code snippet (https://github.com/duckdb/duckdb-azure/blob/60fec8598707bc9588e4db83ba798bad86d71bf1/src/azure_dfs_filesystem.cpp#L191-L210), likely performs the following steps:

  1. Lists files: DuckDB first lists the files matching the specified pattern (e.g., 'abfss://<path>/**.jsonl'). This listing operation retrieves the ETags of the files at that specific point in time.
  2. Reads files: After listing, DuckDB proceeds to read the contents of the files. During the read operation, the Azure HTTP client used by the extension validates the ETag of the file against the ETag obtained during the listing. This validation is crucial to ensure that the file hasn't been modified between the listing and reading steps.

The Race Condition: The problem arises when a file is appended to (modified) between the listing and reading steps. In this scenario, the ETag of the file changes. When DuckDB's Azure HTTP client attempts to read the file, the ETag validation fails, resulting in the "ConditionNotMet" error. This is a classic race condition: the outcome of the read operation depends on the timing of the write operation.

Proposed Solutions: Addressing the Race Condition

To address this issue and enable seamless reading from continuously appended logs, the following solutions are proposed. These solutions aim to mitigate the race condition by either gracefully handling ETag mismatches or preventing them altogether.

1. Gracefully Read Files, Ignoring Failures

This approach focuses on handling ETag mismatches as exceptions rather than fatal errors. The idea is to allow DuckDB to continue reading from other files even if one or more files fail the ETag validation. This can be achieved by modifying the DuckDB Azure extension to:

  • Catch the "ConditionNotMet" error: Implement exception handling to specifically catch the ETag mismatch error during file reading.
  • Log the error: Log the error for monitoring and debugging purposes, indicating that a file could not be read due to ETag validation failure.
  • Continue reading: Proceed with reading other files that match the query pattern, effectively skipping the files that failed the ETag validation.

This solution provides a pragmatic way to read as much data as possible from the logs, even if some data is temporarily unavailable due to concurrent writes. However, it's important to note that this approach might result in incomplete results if the failed files contain crucial information. Application logic should be designed to handle such scenarios appropriately.

2. Lease the File During Read

Another approach to prevent ETag mismatches is to lease the file before reading it. Azure Blob Storage leases provide exclusive write access to a file for a specified duration. By acquiring a lease on the file before reading, DuckDB can ensure that no other process can modify the file during the read operation, thus preventing ETag changes.

Here's how this solution would work:

  1. Acquire a lease: Before reading a file, the DuckDB Azure extension would attempt to acquire a lease on the file. This operation would fail if another process already holds a lease on the file.
  2. Read the file: If the lease is acquired successfully, DuckDB would proceed to read the file. Since the file is leased, no other process can modify it, guaranteeing ETag consistency.
  3. Release the lease: After reading the file, DuckDB would release the lease, allowing other processes to access the file.

This solution guarantees data consistency during the read operation. However, it introduces the overhead of acquiring and releasing leases. Additionally, it might lead to contention if multiple processes are trying to read and write to the same log files simultaneously. Careful consideration needs to be given to the lease duration to balance data consistency with potential performance impacts. The lease duration should be long enough to complete the read operation but short enough to minimize contention.

3. Allow Ignoring ETag Validation During Partial Reads

This solution offers a more fine-grained approach by allowing users to control ETag validation. The idea is to introduce a configuration option that allows DuckDB to bypass ETag validation during specific read operations, particularly when dealing with continuously appended logs. This could be implemented as a connection setting or a query hint.

Here's how this solution could be implemented:

  • Introduce a configuration option: Add a new setting to the DuckDB Azure extension that allows users to disable ETag validation for read operations. This could be a connection-level setting or a query-level hint.
  • Conditional ETag validation: Modify the file reading logic to conditionally perform ETag validation based on the configuration option. If ETag validation is disabled, the read operation would proceed without checking the ETag.

This solution provides flexibility to users who understand the implications of disabling ETag validation. It's particularly useful for scenarios where near real-time data analysis is more important than strict data consistency. However, it's crucial to emphasize the potential risks of disabling ETag validation, as it might lead to reading inconsistent data if the file is modified during the read operation. Users should be made aware of these trade-offs and use this option judiciously.

Conclusion

Reading from continuously appended logs in Azure Data Lake Storage Gen2 with DuckDB presents a challenge due to potential race conditions related to ETag validation. The "ConditionNotMet" error highlights the conflict between read and write operations on the same file. This article has explored three potential solutions to address this issue:

  1. Gracefully reading files and ignoring failures
  2. Leasing the file during the read operation
  3. Allowing users to ignore ETag validation during partial reads

Each solution has its trade-offs, and the best approach depends on the specific requirements of the application. Gracefully ignoring failures provides a pragmatic way to read as much data as possible, while leasing guarantees data consistency at the cost of potential performance overhead. Allowing users to ignore ETag validation offers flexibility but requires careful consideration of data consistency implications.

By understanding the root cause of the issue and the available solutions, users can effectively leverage DuckDB to analyze continuously appended logs in Azure Data Lake Storage Gen2. Further research and experimentation may be required to determine the optimal solution for specific use cases. It's also beneficial to stay updated with the latest developments in DuckDB and its Azure extension, as new features and improvements might be introduced to address this challenge.

For more information on Azure Blob Storage and ETags, you can visit the official Microsoft documentation: https://learn.microsoft.com/en-us/rest/api/storageservices/get-blob-properties