---
type: concept
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  contentHash: 951bd0bfd7cd9d116082c0dda15d850c1d1672037b552a0dac229e4cd955a84c
  pageDirectory: concepts
  sources:
    - distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - sparse-data-optimization-for-xgboost-on-spark
    - SDOFXOS
  citations:
    - file: distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md
title: Sparse Data Optimization for XGBoost on Spark
description: Optimization for training on sparse feature datasets using enable_sparse_data_optim=True and missing=0.0 with pyspark.ml.linalg.SparseVector input.
tags:
  - machine-learning
  - optimization
  - sparse-data
  - xgboost
timestamp: "2026-06-18T12:06:34.934Z"
---

# Sparse Data Optimization for XGBoost on Spark

**Sparse Data Optimization for XGBoost on Spark** refers to a performance enhancement feature in the `xgboost.spark` module that improves training efficiency when working with datasets containing a high proportion of zero values (sparse features). When enabled, this optimization leverages the compressed storage format of sparse vectors to reduce memory usage and computation time during distributed XGBoost training on Apache Spark. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Overview

The PySpark estimators defined in the `xgboost.spark` module — `SparkXGBRegressor`, `SparkXGBClassifier`, and `SparkXGBRanker` — support a dedicated optimization mode for training on datasets with sparse features. This mode is particularly beneficial for use cases such as text classification with bag-of-words representations, recommendation systems with user-item interaction matrices, and any domain where feature vectors are predominantly composed of zeros. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Requirements

- Databricks Runtime 12.0 ML and above
- The `xgboost>=1.7` Python package (included in the ML runtime) ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Enabling Sparse Data Optimization

To enable sparse data optimization, you must satisfy two conditions:

1. **Provide a sparse features column**: The dataset passed to the `fit()` method must contain a features column consisting of values of type `pyspark.ml.linalg.SparseVector`.
2. **Set estimator parameters**: Configure the XGBoost estimator with `enable_sparse_data_optim=True` and `missing=0.0`. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

### Code Example

```python
from xgboost.spark import SparkXGBClassifier

classifier = SparkXGBClassifier(
    enable_sparse_data_optim=True,
    missing=0.0
)

classifier.fit(dataset_with_sparse_features_col)
```

^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## How It Works

When sparse data optimization is enabled, XGBoost on Spark processes the feature data using the natural sparsity-aware algorithms built into the XGBoost core. By providing a `SparseVector` features column and setting `missing=0.0`, the framework can:

- **Skip zero-value computations**: The tree-building algorithm can avoid processing empty entries during split finding, focusing only on non-zero feature values.
- **Reduce memory overhead**: The sparse vector format stores only non-zero indices and values, significantly lowering memory consumption for high-dimensional sparse datasets.
- **Improve distributed efficiency**: When combined with `num_workers` for distributed training, each worker processes a smaller effective data volume per partition. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Parameter Details

| Parameter | Value | Description |
|-----------|-------|-------------|
| `enable_sparse_data_optim` | `True` | Activates the sparse data optimization path in the estimator ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md] |
| `missing` | `0.0` | Treats zero values as missing, enabling the sparse processing logic ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md] |

## Combining with Other Features

Sparse data optimization can be combined with other `xgboost.spark` capabilities:

### Distributed Training

You can use `num_workers` alongside sparse optimization for parallel processing:

```python
from xgboost.spark import SparkXGBClassifier

classifier = SparkXGBClassifier(
    enable_sparse_data_optim=True,
    missing=0.0,
    num_workers=sc.defaultParallelism
)
``` ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

### GPU Training

For GPU-accelerated training, set `use_gpu=True`:

```python
classifier = SparkXGBClassifier(
    enable_sparse_data_optim=True,
    missing=0.0,
    use_gpu=True
)
``` ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Converting to Sparse Vectors in PySpark

To prepare a dataset for sparse optimization, convert dense feature columns to `SparseVector` format. The following example demonstrates using PySpark's vector assembler and a conversion step:

```python
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.linalg import Vectors
from pyspark.sql.functions import udf
from pyspark.sql.types import VectorUDT

# Assemble features into a dense vector
assembler = VectorAssembler(inputCols=feature_columns, outputCol="dense_features")
dense_df = assembler.transform(raw_data)

# Convert dense vectors to sparse vectors
to_sparse = udf(lambda v: Vectors.sparse(v.size, v.toArray().nonzero()[0].tolist(), 
                                          v.toArray()[v.toArray() != 0].tolist()), 
                VectorUDT())
sparse_df = dense_df.withColumn("features", to_sparse("dense_features"))
```

*Note: This is an illustrative example; actual conversion logic depends on your data structure.*

## Best Practices

- **Ensure sparse vector type**: Verify that the features column contains `SparseVector` instances, not `DenseVector`, before calling `fit()`. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]
- **Set `missing` correctly**: The `missing=0.0` parameter must be explicitly set alongside `enable_sparse_data_optim=True` to align the sparsity handling with zero-value semantics. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]
- **Combine with appropriate `num_workers`**: For large sparse datasets, consider setting `num_workers = sc.defaultParallelism` to distribute the sparse data partitions across all available task slots. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]
- **Avoid autoscaling**: Distributed XGBoost training cannot be used on clusters with autoscaling enabled, as new worker nodes that start in this elastic scaling paradigm cannot receive new sets of tasks and remain idle. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Limitations

- The `enable_sparse_data_optim` parameter only applies to the `xgboost.spark` estimators, not to the deprecated `sparkdl.xgboost` module.
- Sparse optimization requires a Spark cluster that can handle `SparseVector` objects efficiently within the ML pipeline.
- The `mlflow.xgboost.autolog` function is not compatible with distributed XGBoost training. Use `mlflow.spark.log_model()` instead for model logging. ^[distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md]

## Related Concepts

- [Distributed XGBoost Training](/concepts/distributed-xgboost-training-on-databricks.md) — Scaling XGBoost with `num_workers` parameter
- [XGBoost on GPU with PySpark](/concepts/xgboostspark-module.md) — GPU acceleration for XGBoost Spark estimators
- MLflow Model Logging for Spark Models — Alternative autologging approach for Spark-trained XGBoost models
- PySpark MLlib Vector Types — DenseVector and SparseVector representations in Spark ML
- XGBoost Parameters — General XGBoost parameter reference

## Sources

- distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md

# Citations

1. [distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws.md](/references/distributed-training-of-xgboost-models-using-xgboostspark-databricks-on-aws-49a22c88.md)
