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Inconsistent CDC Query Results With ORDER BY In Apache Hudi

September 30, 2025 by StackCamp Team 60 views

Inconsistent CDC Query Results with ORDER BY in Apache Hudi ## Understanding Change Data Capture (CDC) and Apache Hudi Guys, let's dive deep into an intriguing issue we've encountered with Apache Hudi's Change Data Capture (CDC) functionality. **Change Data Capture (CDC)** is a crucial process for tracking and propagating data changes across different systems. Apache Hudi, a powerful data lake platform, offers CDC capabilities to efficiently manage and track these changes. However, we've identified a scenario where the results of CDC queries become inconsistent when using the `ORDER BY` clause. This article will walk you through the problem, its implications, and potential solutions. When working with data lakes, it's essential to have a reliable way to capture changes made to the data. CDC in Hudi allows us to track these changes, making it easier to maintain data consistency across various systems. **_But what happens when the results we get from CDC queries are not consistent?_** Let's explore this further. ### The Bug: Inconsistent Row Counts in CDC Queries The core issue revolves around the behavior of CDC queries when the `ORDER BY` clause is included. Specifically, the number of rows returned by a CDC query differs depending on whether the `ORDER BY` clause is used. Imagine running a query to fetch all changes in a table. Without ordering, you might get nine rows, but with ordering, you get only six. **_That's a significant discrepancy!_** This inconsistency can lead to serious problems, especially when relying on CDC data for auditing, replication, or other critical tasks. If the data is used for downstream processing, such as updating a reporting dashboard or synchronizing with another database, the missing records can cause data inconsistencies and incorrect results. **_The impact is substantial, right?_** We need to ensure that CDC queries return all the changes, regardless of the ordering. ### Reproducing the Issue To better understand the problem, let's outline the steps to reproduce this bug. By following these steps, you can see the issue firsthand and verify any potential solutions. 1. **Create a Hudi table with CDC enabled:** First, you need to set up a Hudi table and ensure that CDC is enabled. This is a prerequisite for capturing changes in the data. 2. **Perform insert/update/delete operations:** Next, perform a series of data modifications, such as inserting new records, updating existing ones, and deleting some records. These operations will generate the changes that CDC is supposed to capture. 3. **Run CDC query without `ORDER BY`:** Execute a CDC query without the `ORDER BY` clause. For example: ```sql SELECT op, ts_ms, ... FROM hudi_table_changes('table_path', 'cdc', 'earliest'); ``` 4. **Run the same CDC query with `ORDER BY`:** Now, run the same query but include the `ORDER BY` clause. For example: ```sql SELECT op, ts_ms, ... FROM hudi_table_changes('table_path', 'cdc', 'earliest') ORDER BY ts_ms ASC; ``` 5. **Observe different row counts and results:** Compare the results of the two queries. You should notice a difference in the number of rows returned and potentially different records included in the results. The provided example demonstrates this issue clearly. The query without `ORDER BY` returns nine rows, while the query with `ORDER BY ts_ms ASC` returns only six rows. **_This discrepancy is the core of the bug._** ### Example Run Results Let's examine the provided run results to illustrate the problem. The following queries were executed: **Query 1: Without `ORDER BY`** ```sql spark-sql (default)> SELECT op, ts_ms, get_json_object(before, '$.ts') AS before_ts, get_json_object(before, '$.rider') as before_rider, get_json_object(after, '$.rider') AS after_rider FROM hudi_table_changes('file:///tmp/hudi_test_table', 'cdc', 'earliest'); ``` **Result:** ``` i 20250924110448254 NULL NULL rider-E i 20250924110628340 NULL NULL rider-G u 20250924110905831 1695516137 rider-G rider-E i 20250924110448254 NULL NULL rider-C u 20250924110905831 1695091554 rider-C rider-E i 20250924110448254 NULL NULL rider-A i 20250924110448254 NULL NULL rider-F u 20250924110551107 1695516137 rider-F rider-E d 20250924110611165 1695516137 rider-E NULL Time taken: 0.141 seconds, Fetched 9 row(s) ``` This query returns nine rows, showing various insert (`i`), update (`u`), and delete (`d`) operations. **Query 2: With `ORDER BY`** ```sql spark-sql (default)> SELECT op, ts_ms, get_json_object(before, '$.ts') AS before_ts, get_json_object(before, '$.rider') as before_rider, get_json_object(after, '$.rider') AS after_rider FROM hudi_table_changes('file:///tmp/hudi_test_table', 'cdc', 'earliest') order by ts_ms asc; ``` **Result:** ``` i 20250924110448254 NULL NULL rider-A u 20250924110551107 1695516137 rider-F rider-E d 20250924110611165 1695516137 rider-E NULL i 20250924110628340 NULL NULL rider-G u 20250924110905831 1695516137 rider-G rider-E u 20250924110905831 1695091554 rider-C rider-E Time taken: 0.214 seconds, Fetched 6 row(s) ``` This query, with the `ORDER BY` clause, returns only six rows. Notice that some records are missing compared to the first query. **_This discrepancy highlights the inconsistency we're addressing._** ### Environment Details To provide context, the issue was observed in the following environment: * **Hudi version:** 1.1 * **Query engine:** Spark We need to consider these details when investigating the bug and developing a solution. Different versions of Hudi or query engines might behave differently, so it's essential to be specific about the environment. **_Knowing the environment helps us narrow down the potential causes of the bug._** ### Potential Causes and Solutions Understanding why this inconsistency occurs is crucial for fixing it. Here are some potential causes and solutions we can explore. #### 1. **Incorrect Partitioning or Indexing** One potential cause is an issue with how Hudi partitions or indexes the data. If the data is not properly partitioned or indexed, the `ORDER BY` clause might cause the query engine to skip certain partitions or indexes, leading to missing records. * **Solution:** Review the partitioning and indexing strategy for the Hudi table. Ensure that the data is properly partitioned based on the timestamp or other relevant fields. Consider using appropriate indexing techniques to optimize query performance. #### 2. **Bug in Hudi's CDC Implementation** It's possible that there is a bug in Hudi's CDC implementation itself. The bug might be related to how Hudi handles ordering when fetching changes. * **Solution:** Check Hudi's issue tracker and release notes for any known bugs related to CDC and ordering. If a bug is identified, consider upgrading to a newer version of Hudi that includes a fix. You can also contribute to the Hudi project by reporting the bug and providing detailed information. #### 3. **Query Engine Optimization Issues** The query engine (in this case, Spark) might have optimization issues that cause it to incorrectly handle the `ORDER BY` clause in CDC queries. * **Solution:** Investigate Spark's query execution plan for the CDC queries. Look for any optimization steps that might be causing the missing records. Try different Spark configurations or query optimization techniques to see if they resolve the issue. #### 4. **Concurrency and Transaction Issues** If multiple operations are happening concurrently on the Hudi table, there might be transaction-related issues that cause inconsistencies in the CDC results. * **Solution:** Ensure that Hudi's transaction management is properly configured. Investigate any concurrent operations that might be interfering with the CDC queries. Consider using Hudi's optimistic concurrency control features to manage concurrent writes. #### 5. **Data Skew** Data skew, where certain timestamp values have a disproportionately large number of records, can also lead to issues with ordering and query performance. * **Solution:** Analyze the data distribution and identify any skew. If skew is present, consider using techniques such as salting or bucketing to distribute the data more evenly. ### Debugging and Troubleshooting To effectively resolve this issue, we need to employ a systematic approach to debugging and troubleshooting. Here are some steps we can take: 1. **Examine Hudi Logs:** Check the Hudi logs for any error messages or warnings related to CDC or query execution. Logs can provide valuable insights into what's happening behind the scenes. 2. **Review Spark Execution Plans:** As mentioned earlier, reviewing Spark execution plans can help identify optimization issues. Use Spark's UI or explain command to analyze the query plan. 3. **Simplify the Query:** Try simplifying the CDC query to isolate the issue. For example, remove some of the columns or filters to see if the problem persists. 4. **Test with Different Data:** Test the CDC query with different data sets to see if the issue is specific to certain data patterns. 5. **Engage the Hudi Community:** If you're unable to resolve the issue on your own, consider reaching out to the Hudi community for help. The community can provide valuable insights and guidance. ### Best Practices for Using CDC in Hudi To avoid inconsistencies and other issues with CDC in Hudi, it's essential to follow best practices. Here are some recommendations: * **Properly Configure Hudi:** Ensure that Hudi is properly configured for CDC, including setting the appropriate configurations for capturing changes. * **Regularly Monitor CDC Queries:** Regularly monitor CDC queries to ensure that they are returning the expected results. Set up alerts for any unexpected behavior. * **Optimize Query Performance:** Optimize CDC query performance by using appropriate indexing and partitioning strategies. * **Stay Updated with Hudi Releases:** Keep up with the latest Hudi releases and apply any necessary patches or upgrades to address known issues. * **Understand Data Consistency:** Understand the data consistency guarantees provided by Hudi's CDC implementation. Be aware of any potential trade-offs between consistency and performance. ### Conclusion Inconsistent CDC query results when using `ORDER BY` in Apache Hudi is a significant issue that can lead to data discrepancies and incorrect results. By understanding the problem, reproducing it, and exploring potential causes and solutions, we can effectively address this bug. Remember, **_a systematic approach to debugging, combined with best practices for using CDC in Hudi, is crucial for maintaining data integrity and reliability_**. Guys, let's work together to ensure our data pipelines are robust and accurate. By following the steps outlined in this article, you'll be well-equipped to tackle this issue and others that may arise. Happy data engineering!

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