Delta-rs Includes Pending Versions Written By Spark

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Introduction

Delta-rs is a Rust library that provides a high-performance, columnar storage format for large-scale data processing. It is designed to work seamlessly with Apache Spark, a popular open-source data processing engine. However, a recent issue has been reported by Callum Edwards, highlighting a critical problem with the way Delta-rs handles pending versions written by Spark. In this article, we will delve into the details of this issue, explore the root cause, and discuss potential solutions.

Environment

Delta-rs Version

The version of Delta-rs used in this analysis is 0.25.5.

Binding

The binding used is Rust.

Environment

The environment used is a Network file store.

Bug

Issue Reported by Callum Edwards

The issue reported by Callum Edwards is as follows:

The spark delta.io writer seems to write failed (+pending?) versions to _delta_log/.tmp. However, the LocalFileSystem implementation of the ObjectStore in delta-rs does a recursive walk of the _delta_log directory for a list operation. This in turn means that failed (and maybe uncommitted) files are returned on the list operation used in the load_with_datetime method of the DeltaTable. This causes the library to crash if the failed version (in the .tmp dir) has since been pruned from the parent _delta_log dir.

Investigation

To understand the root cause of this issue, we need to investigate the Delta protocol and the Java code for Hadoop. According to the Delta protocol:

Delta files are stored as JSON in a directory at the root of the table named _delta_log, and together with checkpoints make up the log of all changes that have occurred to a table.

This suggests that pending changes are first written to a temp directory and then later renamed. Looking at the Java code for Hadoop, we can see that this is indeed the case:

In this LogStore implementation, a temp file is created containing a copy of the metadata to be committed into the Delta log. Once that commit to the Delta log is complete, and after the corresponding DynamoDB entry has been removed, it is safe to delete this temp file. In practice, only the latest temp file will ever be used during recovery of a failed commit.

Proposed Solution

Based on the investigation, it seems that the issue can be resolved by changing the load_with_datetime method to only look at the root level of the _delta_log path when listing files in the ObjectStore. This will prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir.

Code Changes

The code changes required to implement this solution are as follows:

// Before
fn load_with_datetime(&self, datetime: DateTime<Utc>) -> Result<Vec<Datum>, Error> {
    let files = self.object_store.list(_delta_log_path())?;
    // ...
}

// After
fn load_with_datetime(&self, datetime: DateTime<Utc>) -> Result<Vec<Datum>, Error> {
    let files = self.object_store.list(_delta_log_path().join("))?; // Only look at the root level
    // ...
}

ConclusionIn conclusion, the issue reported by Callum Edwards highlights a critical problem with the way Delta-rs handles pending versions written by Spark. By investigating the Delta protocol and the Java code for Hadoop, we have identified the root cause of the issue and proposed a solution. The solution involves changing the load_with_datetime method to only look at the root level of the _delta_log path when listing files in the ObjectStore. This will prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir.

Recommendations

Based on the analysis, we recommend the following:

  • Implement the proposed solution to prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir.
  • Review the Delta protocol and the Java code for Hadoop to ensure that the implementation is consistent with the specification.
  • Test the library thoroughly to ensure that the solution does not introduce any new issues.

Future Work

Future work includes:

  • Investigating other potential issues with the way Delta-rs handles pending versions written by Spark.
  • Exploring ways to improve the performance and scalability of the library.
  • Collaborating with the Apache Spark community to ensure that the library is compatible with the latest versions of Spark.

References

Introduction

In our previous article, we discussed a critical issue with the way Delta-rs handles pending versions written by Spark. We investigated the root cause of the issue and proposed a solution to prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir. In this article, we will answer some frequently asked questions (FAQs) related to this issue.

Q&A

Q: What is the root cause of the issue?

A: The root cause of the issue is that the LocalFileSystem implementation of the ObjectStore in delta-rs does a recursive walk of the _delta_log directory for a list operation. This means that failed (and maybe uncommitted) files are returned on the list operation used in the load_with_datetime method of the DeltaTable.

Q: Why is this a problem?

A: This is a problem because it causes the library to crash if the failed version (in the .tmp dir) has since been pruned from the parent _delta_log dir. This can lead to data loss and other issues.

Q: What is the proposed solution?

A: The proposed solution is to change the load_with_datetime method to only look at the root level of the _delta_log path when listing files in the ObjectStore. This will prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir.

Q: How do I implement the proposed solution?

A: To implement the proposed solution, you need to modify the load_with_datetime method to only look at the root level of the _delta_log path when listing files in the ObjectStore. You can do this by changing the line let files = self.object_store.list(_delta_log_path())?; to let files = self.object_store.list(_delta_log_path().join("))?;.

Q: What are the benefits of implementing the proposed solution?

A: The benefits of implementing the proposed solution are that it will prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir. This will ensure that data is not lost and that the library is more stable.

Q: Are there any potential issues with implementing the proposed solution?

A: Yes, there are potential issues with implementing the proposed solution. For example, it may not work correctly if the _delta_log path is not a directory. Additionally, it may not work correctly if the _delta_log path contains subdirectories.

Q: How do I test the proposed solution?

A: To test the proposed solution, you need to create a test case that simulates the scenario where a failed version has been pruned from the parent _delta_log dir. You can then run the test case and verify that the library does not crash.

Q: What are the next steps?

A: The next steps are to implement the proposed solution and test it thoroughly. You should also review the Delta protocol and the Java code for Hadoop to ensure that the implementation is consistent with the specification.

Conclusion

In conclusion, the issue reported by Callum Edwards highlights a critical problem with the way Delta-rs handles pending versions written by Spark. By investigating the Delta protocol and the Java code for Hadoop, we identified the root cause of the issue and proposed a solution. We hope that this Q&A article has provided valuable information to help you understand the issue and implement the proposed solution.

Recommendations

Based on the analysis, we recommend the following:

  • Implement the proposed solution to prevent the library from crashing when failed versions have been pruned from the parent _delta_log dir.
  • Review the Delta protocol and the Java code for Hadoop to ensure that the implementation is consistent with the specification.
  • Test the library thoroughly to ensure that the solution does not introduce any new issues.

Future Work

Future work includes:

  • Investigating other potential issues with the way Delta-rs handles pending versions written by Spark.
  • Exploring ways to improve the performance and scalability of the library.
  • Collaborating with the Apache Spark community to ensure that the library is compatible with the latest versions of Spark.

References