DataFusion: Stop Unnecessary Repartitioning Of Small Datasets
Hey guys! Let's talk about something that can really impact the speed of your data processing: unnecessary repartitioning in DataFusion, especially when dealing with small datasets. This can be a real drag on performance, and in this article, we'll dive into why it happens and how to potentially fix it. We will explore how to stop data being unnecessarily repartitioned in DataFusion for small datasets. This will boost performance! The issue arises when DataFusion, the powerful query engine, decides to split up your data across multiple partitions, even when the dataset is tiny. This adds overhead without providing any real benefit from parallel processing. This leads to slower query execution times.
The Bug: Unnecessary Repartitioning
So, what's the deal? The core issue is that small datasets are sometimes needlessly split across multiple partitions. Imagine a dataset with only five rows. In some queries, DataFusion might still decide to split this tiny dataset, leading to overhead that completely outweighs any potential gains from parallel processing. This is a classic case of optimization gone wrong, where the engine's attempt to speed things up actually slows it down. The current behavior can be observed in various query scenarios. The most common scenario involves aggregation operations on small CSV or Parquet files. DataFusion often inserts RepartitionExec operators into the query plan. These operators shuffle data across different partitions, even when the data volume is minimal. This unnecessary shuffling adds overhead and reduces overall performance. Understanding the source of the problem is the first step towards a solution. The root cause lies in how DataFusion determines when and how to partition data. The engine uses heuristics and statistics to optimize query execution. However, these mechanisms can sometimes be overly aggressive. The heuristics might trigger repartitioning for small datasets. The statistics might not accurately reflect the true data distribution. For example, DataFusion might analyze the size of the input files or the number of distinct values in certain columns to decide whether to repartition data. While these factors are important, they may not be sufficient for small datasets. The engine may lack the ability to accurately gauge the overhead associated with repartitioning tiny datasets.
Impact of the Bug
Why should you even care? Well, unnecessary repartitioning can significantly impact the performance of your queries, especially when dealing with small datasets. This inefficiency translates to longer query execution times and increased resource consumption. Moreover, if you're working in a resource-constrained environment, this can be a serious problem. You could see an unnecessary increase in CPU usage and memory consumption. This can lead to slower overall performance for your applications. The longer it takes to execute these queries, the longer it takes to get results, and the more resources are consumed. Ultimately, this can affect user experience, especially in interactive data exploration scenarios. So, what's the solution? The goal is to make DataFusion smarter about when to repartition data, particularly for small datasets. This requires a more nuanced approach to query optimization. The solution involves improving the heuristics used by DataFusion to determine when repartitioning is necessary. This will reduce unnecessary repartitioning and thus improve overall performance. This means making sure DataFusion understands the characteristics of your data and the implications of partitioning it. We'll explore some specific strategies for improvement. Some potential improvements will focus on refining the logic for determining when to insert RepartitionExec operators into a query plan. Others might involve adding new optimization passes. There will also be other improvements that analyze data statistics more precisely before making partitioning decisions.
Reproducing the Issue: A Practical Example
Let's get down to the nitty-gritty. An example of this issue can be found in the aggregate_repartition.slt file. This test sets up two tables, each with only five rows, in both Parquet and CSV formats. Then, it performs an aggregation on one of the columns. Despite the tiny size of the datasets, the query plan decides to repartition the data. This means the engine is dividing the data into multiple chunks and processing them separately, even though the dataset is small. The setup involves creating two small tables. These tables contain the same data but use different file formats: CSV and Parquet. Both tables have only five rows, making them ideal for demonstrating the issue. After creating the tables, the test runs a simple aggregation query. The query groups the data by a specific column (env) and counts the occurrences of each group. The core of the problem lies in the execution plan generated by DataFusion. Instead of directly processing the data from the source files, DataFusion inserts RepartitionExec operators. These operators introduce unnecessary shuffling of data across partitions. The test demonstrates that the engine performs repartitioning even for very small datasets. This adds overhead and negatively impacts performance. By reproducing this scenario, you can observe the problem firsthand. You can analyze the query plan and confirm that repartitioning is indeed happening. This understanding is key to finding a solution.
Setting Up the Data
Here’s how you can set up the data yourself:
COPY ( SELECT * FROM (VALUES
('prod', 100, 'A'),
('dev', 200, 'B'),
('test', 150, 'A'),
('prod', 300, 'C'),
('dev', 250, 'B') ) AS t(env, value, category) ) TO 'test_files/scratch/aggregate_repartition/dim.csv' STORED AS CSV OPTIONS ('format.has_header' 'true');
COPY ( SELECT * FROM (VALUES
('prod', 100, 'A'),
('dev', 200, 'B'),
('test', 150, 'A'),
('prod', 300, 'C'),
('dev', 250, 'B') ) AS t(env, value, category) ) TO 'test_files/scratch/aggregate_repartition/dim.parquet' STORED AS PARQUET;
CREATE EXTERNAL TABLE dim_csv STORED AS CSV LOCATION 'test_files/scratch/aggregate_repartition/dim.csv' OPTIONS ('format.has_header' 'true');
CREATE EXTERNAL TABLE dim_parquet STORED AS PARQUET LOCATION 'test_files/scratch/aggregate_repartition/dim.parquet';
This SQL code creates two CSV and Parquet files, each containing a small dataset of five rows. This is the foundation for reproducing the bug.
Examining the Query Plans
Let's take a look at the query plans generated by DataFusion for the CSV and Parquet files. These plans clearly show the unnecessary repartitioning step.
CSV Physical Plan:
EXPLAIN SELECT env, count(*) FROM dim_csv GROUP BY env;
physical_plan
01)ProjectionExec: expr=[env@0 as env, count(Int64(1))@1 as count(*)]
02)--AggregateExec: mode=FinalPartitioned, gby=[env@0 as env], aggr=[count(Int64(1))]
03)----CoalesceBatchesExec: target_batch_size=8192
04)------RepartitionExec: partitioning=Hash([env@0], 4), input_partitions=4
05)--------AggregateExec: mode=Partial, gby=[env@0 as env], aggr=[count(Int64(1))]
06)----------RepartitionExec: partitioning=RoundRobinBatch(4), input_partitions=1
07)------------DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/aggregate_repartition/dim.csv]]}, projection=[env], file_type=csv, has_header=true
Parquet Physical Plan:
EXPLAIN SELECT env, count(*) FROM dim_parquet GROUP BY env;
physical_plan
01)ProjectionExec: expr=[env@0 as env, count(Int64(1))@1 as count(*)]
02)--AggregateExec: mode=FinalPartitioned, gby=[env@0 as env], aggr=[count(Int64(1))]
03)----CoalesceBatchesExec: target_batch_size=8192
04)------RepartitionExec: partitioning=Hash([env@0], 4), input_partitions=1
05)--------AggregateExec: mode=Partial, gby=[env@0 as env], aggr=[count(Int64(1))]
06)----------DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/aggregate_repartition/dim.parquet]]}, projection=[env], file_type=parquet
Notice the RepartitionExec operators in both plans. This is where the unnecessary data shuffling happens, and it's precisely what we want to eliminate. This is the smoking gun! The RepartitionExec operations are present, even though the datasets are tiny. This indicates that DataFusion is unnecessarily repartitioning the data, which is the problem.
Expected Behavior: No Unnecessary Repartitioning
So, what should happen instead? The ideal scenario is that DataFusion doesn't repartition the data at all for these small datasets. Let's see how the plans should look:
CSV Physical Plan (Expected):
EXPLAIN SELECT env, count(*) FROM dim_csv GROUP BY env;
physical_plan
01)ProjectionExec: expr=[env@0 as env, count(Int64(1))@1 as count(*)]
02)--AggregateExec: mode=FinalPartitioned, gby=[env@0 as env], aggr=[count(Int64(1))]
03)----DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/aggregate_repartition/dim.csv]]}, projection=[env], file_type=csv, has_header=true
Parquet Physical Plan (Expected):
EXPLAIN SELECT env, count(*) FROM dim_parquet GROUP BY env;
physical_plan
01)ProjectionExec: expr=[env@0 as env, count(Int64(1))@1 as count(*)]
02)--AggregateExec: mode=FinalPartitioned, gby=[env@0 as env], aggr=[count(Int64(1))]
03)----DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/aggregate_repartition/dim.parquet]]}, projection=[env], file_type=parquet
In the expected plans, the RepartitionExec operators are gone. This means DataFusion is directly processing the data without any unnecessary shuffling. The difference is the removal of the RepartitionExec operators. This leads to a more efficient query execution for small datasets. The goal is to make the query execution as streamlined as possible by avoiding unnecessary data movement. In these optimized plans, DataFusion directly processes data from the source, leading to faster execution times. The absence of repartitioning demonstrates the desired optimization, resulting in cleaner and more efficient query plans.
Diving into the Code: The Root of the Problem
Let's get a little technical and look at where the problem lies. The core of this issue stems from the enforce_distribution.rs:get_repartition_requirement_status function. This is where DataFusion decides whether to add a repartitioning step. The function evaluates the need for repartitioning based on various factors. It then decides whether to insert a RepartitionExec operator into the query plan. The problem is in the logic it uses to make that decision.
CSV Files: The Optimistic Approach
For CSV files, DataFusion is quite optimistic. Since it doesn't have detailed statistics about the files, it often assumes repartitioning is needed. This is because DataFusion can't determine the data distribution of a CSV file. DataFusion assumes repartitioning is necessary. This assumption is often correct, but not for small datasets. The engine uses a