Principle:Recommenders team Recommenders Spark Random Data Splitting
| Knowledge Sources | |
|---|---|
| Domains | Data Engineering, Distributed Computing, Model Evaluation |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Random data splitting in distributed computing involves splitting Spark DataFrames into train/test sets while handling data distribution across cluster nodes.
Description
Before training a recommendation model, the interaction dataset must be divided into training and test subsets. In a distributed Spark environment, this splitting must account for the fact that data is partitioned across cluster nodes. Naive approaches (e.g., collecting to the driver and splitting with numpy) would defeat the purpose of distributed computing by creating a single-node bottleneck.
Spark's randomSplit method provides a distributed splitting mechanism that:
- Operates in place: Each partition independently assigns rows to splits based on a hash of the row and the provided seed, avoiding any data shuffling.
- Supports arbitrary ratios: A single float produces a two-way split (train/test); a list of floats produces a multi-way split (e.g., train/validation/test).
- Normalizes ratios: If the provided ratios do not sum to 1.0, they are treated as relative weights and normalized automatically.
- Ensures reproducibility: A fixed seed guarantees the same split across runs, which is essential for experiment reproducibility.
The key trade-off in random splitting for recommendation data is that it does not respect user boundaries. Some users may have all their interactions in the training set and none in the test set, or vice versa. For use cases where this matters, stratified splitting (per-user splitting) should be used instead.
Usage
Use random splitting after loading data and before model training. It is appropriate for general-purpose evaluation where per-user stratification is not required. For the ALS workflow, random splitting is the standard approach since ALS can handle users appearing in only one split via its coldStartStrategy="drop" parameter.
Theoretical Basis
Random splitting assigns each row to a split independently with probability proportional to the specified ratio. For a two-way split with ratio r:
P(row in train) = r
P(row in test) = 1 - r
For a multi-way split with ratios [r1, r2, ..., rk]:
Normalized: r'_i = r_i / sum(r1, ..., rk)
P(row in split i) = r'_i
Spark implements this using a pseudo-random number generator seeded with the provided seed value. For each row, a random number is generated and the row is assigned to the split whose cumulative probability range contains that number:
Algorithm: Distributed Random Split
Input: DataFrame D, ratios [r1, r2, ..., rk], seed s
Output: [D1, D2, ..., Dk]
1. Normalize ratios: r'_i = r_i / sum(r1, ..., rk)
2. Compute cumulative boundaries: b_0=0, b_i = b_{i-1} + r'_i
3. For each row x in D (executed per-partition):
a. h = hash(x, seed=s) -> uniform random in [0, 1)
b. Assign x to split i where b_{i-1} <= h < b_i
4. Return [D1, D2, ..., Dk]
The expected sizes of the resulting splits are proportional to the ratios, but exact sizes may vary slightly because the assignment is probabilistic. For large datasets (>100K rows), the actual ratios closely approximate the requested ratios.