Avoiding 500x500 Hash Shuffles In YDB Queries

This article addresses a critical performance issue encountered in YDB (Yandex Database) queries: excessive hash shuffles, specifically 500x500 shuffles, leading to Out-of-Memory (OOM) errors. We will delve into the problem, analyze the root cause, and propose a solution to mitigate this issue. This will involve understanding how YDB plans query execution, the impact of task distribution, and how to optimize task allocation to prevent memory exhaustion.

Understanding the Problem: Excessive Hash Shuffles

The core issue lies in the creation of a large number of channels during the query execution planning phase. In the specific scenario discussed, a query resulted in the creation of 560 early aggregation tasks and 420 final aggregation tasks. This led to a hash shuffle involving 420 multiplied by 560, which equals approximately 250,000 channels. The massive number of channels consumes a significant amount of memory, and since the memory consumption per channel is not directly controllable, the system quickly ran into OOM errors, effectively halting the query execution.

To illustrate the problem, consider a scenario where each channel, even with minimal data, occupies a certain amount of memory. Multiplying this by 250,000 channels results in a substantial memory footprint. When this footprint exceeds the available memory resources, the system's stability is compromised, leading to the dreaded OOM error. This situation highlights the importance of careful query planning and task distribution within the YDB architecture.

Furthermore, the problem isn't just about the sheer number of channels. It's also about the way data is distributed and processed across these channels. A large number of channels can indicate a suboptimal data partitioning strategy, where data is unnecessarily shuffled and redistributed, leading to increased overhead and resource consumption. Efficient data partitioning is crucial for minimizing the number of channels and the overall memory footprint of the query. Therefore, optimizing the query plan to reduce the number of channels directly addresses the memory pressure and enhances the query's performance.

Root Cause Analysis: Task Planning and Memory Consumption

The primary cause of this issue is the way YDB plans the execution of aggregation queries, particularly the number of tasks created for early and final aggregation stages. The planner, without sufficient consideration for the memory impact of channel creation, can generate an excessive number of tasks. This over-provisioning of tasks leads to the creation of a vast number of channels for data transfer between these tasks, resulting in memory exhaustion. The root cause is the lack of memory-aware task planning.

The number of tasks directly influences the number of channels required for shuffling data. If the planner creates a large number of tasks, it inherently increases the potential for a large number of channels, amplifying the risk of memory overflow. This emphasizes the need for a planning mechanism that takes into account the memory overhead associated with each task and channel. A more intelligent planner would strive to minimize the number of tasks while maintaining parallelism and performance.

The investigation revealed that YDB's query planner wasn't adequately accounting for the memory consumed by these channels. The default planning strategy seemed to prioritize parallelism without fully considering the memory implications of the resulting data shuffles. This highlights a critical area for improvement: incorporating memory constraints into the query planning process. This means that the planner should dynamically adjust the number of tasks based on the available memory resources and the estimated memory footprint of each task and channel.

Solution: Memory-Aware Task Planning and Filtering

The proposed solution involves two key strategies: implementing memory-aware task planning and utilizing data filtering techniques. The first strategy focuses on improving the query planner's ability to estimate and control memory consumption. The second strategy aims to reduce the overall data volume processed by the query, thereby reducing the number of channels and the memory footprint.

1. Memory-Aware Task Planning:

The core of the solution lies in enhancing the YDB query planner to consider the memory implications of task creation. The planner should estimate the memory footprint of each task and the channels required for data shuffling. Based on this estimation and the available memory resources, the planner should dynamically adjust the number of tasks to prevent OOM errors. This involves incorporating a cost model that penalizes excessive task creation and prioritizes plans that minimize memory consumption.

This enhancement requires a more sophisticated cost model that takes into account not only CPU and I/O costs but also the memory overhead associated with each task. The planner should be able to predict the number of channels that will be created for a given task distribution and estimate the memory required for these channels. This estimation can be based on factors such as the size of the data being processed, the number of distinct values in the aggregation keys, and the degree of data skew.

Furthermore, the planner should be able to explore alternative query plans with different task distributions and choose the plan that minimizes memory consumption while still meeting performance requirements. This might involve reducing the number of tasks, using alternative aggregation algorithms that are more memory-efficient, or employing techniques like early aggregation to reduce the amount of data being shuffled.

2. Data Filtering:

An immediate workaround, as demonstrated in the provided example, is to apply filters to reduce the amount of data processed by the query. By filtering out irrelevant data early in the query execution pipeline, the number of rows processed by the aggregation stages is reduced, consequently decreasing the number of channels and the memory footprint. Filtering is a crucial optimization technique.

In the specific case, adding a filter successfully reduced the data volume and allowed the query to complete without encountering an OOM error. This illustrates the effectiveness of data filtering as a means of controlling memory consumption. However, relying solely on manual filtering is not a scalable solution. The query planner should be able to automatically identify opportunities for filtering and apply filters intelligently to reduce the data volume without compromising the accuracy of the results.

This involves analyzing the query predicates and identifying filters that can be applied early in the query plan. The planner should also consider the selectivity of the filters and choose filters that are most effective in reducing the data volume. In some cases, it might be beneficial to apply multiple filters in a specific order to maximize the reduction in data volume.

Example of Successful Mitigation:

The provided example highlights the effectiveness of data filtering. By adding a filter to reduce the data volume, the query successfully executed without encountering an OOM error. The query plan of the successful run demonstrates a more manageable task distribution and a significantly reduced memory footprint.

The successful run serves as a proof of concept for the proposed solution. It shows that by reducing the amount of data processed by the query, the memory pressure can be alleviated, and the query can be executed successfully. This underscores the importance of both memory-aware task planning and data filtering in preventing OOM errors in YDB queries.

Implementation Details and Considerations

The implementation of memory-aware task planning involves several key steps. First, the YDB query planner needs to be enhanced with a memory cost model that estimates the memory footprint of each task and channel. This model should take into account factors such as the data volume, the number of distinct values in aggregation keys, and the degree of data skew.

Second, the planner should be able to explore alternative query plans with different task distributions and choose the plan that minimizes memory consumption while still meeting performance requirements. This might involve techniques like dynamic programming or branch-and-bound search to efficiently explore the plan space.

Third, the planner should be able to dynamically adjust the number of tasks based on the available memory resources. This requires a mechanism for monitoring memory usage and adjusting the query plan in real-time. If the planner detects that the memory usage is approaching the limit, it can reduce the number of tasks or switch to a more memory-efficient aggregation algorithm.

Data filtering, on the other hand, can be implemented by analyzing the query predicates and identifying filters that can be applied early in the query plan. The planner should consider the selectivity of the filters and choose filters that are most effective in reducing the data volume. This might involve techniques like predicate pushdown, where filters are applied as early as possible in the query plan.

Conclusion: Towards Robust and Scalable YDB Queries

Avoiding excessive hash shuffles and OOM errors is crucial for ensuring the robustness and scalability of YDB queries. By implementing memory-aware task planning and leveraging data filtering techniques, YDB can effectively mitigate these issues and provide a more reliable and efficient query execution environment. Memory-aware query planning is paramount. This approach not only addresses the immediate problem of OOM errors but also lays the foundation for handling increasingly complex and data-intensive workloads in the future.

The combination of memory-aware task planning and data filtering provides a comprehensive solution to the problem of excessive hash shuffles in YDB queries. By proactively managing memory consumption and reducing the data volume, YDB can ensure that queries execute efficiently and reliably, even under heavy load. This ultimately leads to a better user experience and a more scalable and robust database system.

To further your understanding of database optimization and query planning, consider exploring resources like the Database Systems Concepts website, which offers comprehensive information on database systems and query optimization techniques.