Assign a Stable Group-Level Index in PySpark Without Joins: hash() vs concat_ws() for Massive Datasets

Create a stable per-group index in PySpark without joins using hash() or concat_ws(). Scale datasets, cut shuffles, and keep group identifiers consistent.

When you keep time series or event logs in a long table and need a stable identifier per logical group, row-level IDs won’t cut it. The goal here is simple: assign the same index to every row that shares the same combination of classification, id_1, and id_2, at the scale of hundreds of millions of rows in PySpark—without building and joining a separate mapping table.

Baseline approach that feels heavy

A straightforward way to get a per-group index is to extract distinct keys, assign an ID, and join it back. It works, but on big data it’s a lot of shuffling.

from pyspark.sql import functions as S

# Original dataset
data = spark.table("some_source")

# Build a mapping of unique group keys to an index, then join it back
key_map = (
    data
    .select("classification", "id_1", "id_2")
    .dropDuplicates()
    .withColumn("grp_key", S.monotonically_increasing_id())
)

data_indexed = data.join(key_map, ["classification", "id_1", "id_2"]) 

This achieves the desired result, but the extra distinct + join path can be expensive at the billion-row mark.

What’s the core of the problem?

You need an identical value for all rows that belong to the same key triple, not a unique value per row. That rules out monotonically_increasing_id(), which is row-scoped. The trick is to derive a single value directly from the grouping columns themselves, so every row of the same group computes to the same result without building a separate mapping.

Two native ways to derive a per-group index without a join

The first option is hashing. By hashing classification, id_1, and id_2 together, you get a single integer that consistently represents the group. This uses a built-in Spark function and avoids extra data structures.

from pyspark.sql import functions as S

indexed_by_hash = (
    data
    .withColumn(
        "grp_idx",
        S.hash(S.col("classification"), S.col("id_1"), S.col("id_2"))
    )
) 

The second option is concatenation. If you prefer a human-readable index, concatenate the key columns into a single string, ideally with a delimiter so values don’t blur together.

from pyspark.sql import functions as S

indexed_by_concat = (
    data
    .withColumn(
        "grp_idx",
        S.concat_ws("::", S.col("classification"), S.col("id_1"), S.col("id_2"))
    )
) 

Both approaches compute the index directly from the columns on every row, which removes the need for dropDuplicates() and the subsequent join.

Why this choice matters

Hashing produces a compact, opaque key that serves well when you only need a stable identifier. Concatenation is easier to interpret at a glance but can inflate storage, especially when the original strings are long. If payload size is a concern, hashing is usually the pragmatic default.

Wrap-up

To assign a group-level index in PySpark at scale, derive it from the grouping columns instead of building a lookup and joining it back. Use hash(...) when you want a compact, native integer index computed in place. Use concat_ws(...) when readability is a priority and the string footprint is acceptable. Either route keeps the logic simple and aligns with Spark’s native capabilities.