PySpark: Merge Consecutive Rows by PersonID & JobTitleID

In PySpark, you can merge consecutive rows by PersonID where JobTitleID stays the same using pyspark window functions to spot changes with lag, then groupby pyspark on a cumulative group ID to extend the pyspark timestamp from the group’s min to max. This gaps-and-islands trick skips loops or multiple LEAD/LAG passes—it’s scalable for big pyspark dataframes. Direct pyspark sql or DataFrame code follows, with join-back to keep your original structure clean.

Contents

• Understanding Consecutive Grouping in PySpark DataFrames
• Gaps and Islands with PySpark Window Functions
• Step-by-Step PySpark DataFrame Solution
• Spark SQL Alternative for GROUP BY Logic
• Complete Example: Raw Data to Merged Output
• Performance Tips and Edge Cases
• Common Pitfalls and Fixes
• Sources
• Conclusion

Understanding Consecutive Grouping in PySpark DataFrames

Ever stared at a pyspark dataframe full of job history data, where the same JobTitleID repeats for a bit per PersonID, but you need to squash those streaks into single rows with timestamps stretched across the whole run? That’s your classic setup. Rows aren’t just duplicates—they’re sequential, ordered by pyspark timestamp, and you want each streak merged at the PersonID level.

Take this raw data vibe:

Your goal? One row per streak: first A from 01 to 02, B from 03 to 04, lone A at 05, and so on for PersonID 2. No simple groupby pyspark works here—timestamps vary, and it’s consecutive matches only. What makes it tick? Pyspark partitioning by PersonID, ordering by timestamp, then flagging changes.

This isn’t random deduping. Miss the order, and you’ll mash unrelated rows. Pyspark functions like lag nail the “is this JobTitleID different from the last?” check, fast across partitions.

Gaps and Islands with PySpark Window Functions

Why call it gaps and islands? Picture islands as your JobTitleID streaks (consecutive same values per PersonID), gaps as switches to a new title. Pyspark window functions swim through this effortlessly—no UDFs, no collect_list hacks.

Core idea, straight from pyspark docs patterns on Stack Overflow:

1. Partition by PersonID, order by pyspark timestamp.
2. Use lag(‘JobTitleID’) over that window. When it != current JobTitleID? New island starts.
3. Cast that difference to 1 (or coalesce nulls to 0), sum cumulatively for a group ID.
4. Groupby pyspark on PersonID + group_id + JobTitleID, agg min/max Timestamp.

Boom—each island gets its span. Scales to billions of rows since it’s distributed. Handles non-monotonic timestamps? Sort first with row_number if ties bug you.

Users on another SO thread tweak it for flags, but JobTitleID works identical. No loops means no O(n^2) nightmares.

Step-by-Step PySpark DataFrame Solution

Ready to code? Fire up your SparkSession. Import pyspark sql functions and Window—it’s all pyspark functions you’ll need.

from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.window import Window

spark = SparkSession.builder.appName("ConsecutiveMerge").getOrCreate()

# Assume df is your raw pyspark dataframe
# First, ensure sorted by PersonID and Timestamp
df = df.orderBy("PersonID", "Timestamp")

# Window: partition by PersonID, order by Timestamp (rows for cumulative sum)
window_spec = Window.partitionBy("PersonID").orderBy("Timestamp").rowsBetween(Window.unboundedPreceding, Window.currentRow)

# Flag new group: JobTitleID changes from previous (lag handles first row as null→new group)
df_with_flag = df.withColumn(
 "is_new_group", 
 F.when(F.col("JobTitleID") != F.lag("JobTitleID").over(Window.partitionBy("PersonID").orderBy("Timestamp")), 1).otherwise(0)
)

# Cumulative sum → unique group_id per island
df_grouped = df_with_flag.withColumn(
 "group_id", 
 F.sum("is_new_group").over(window_spec)
)

# Aggregate: min/max Timestamp per PersonID, group_id, JobTitleID
# Join back if you want other cols; here we extend Timestamp to start/end
merged_df = df_grouped.groupBy("PersonID", "group_id", "JobTitleID").agg(
 F.min("Timestamp").alias("Timestamp_start"),
 F.max("Timestamp").alias("Timestamp_end")
).drop("group_id") # Clean up, or join back to original for more cols

merged_df.show()

See? No multiple LEAD/LAG—just one lag pass. The rowsBetween makes sum cumulative from partition start. First row per PersonID always gets group_id=1 (lag null → true).

Tweak for your schema: Add .select(“PersonID”, “JobTitleID”, F.concat_ws(" to ", “Timestamp_start”, “Timestamp_end”).alias(“Timestamp_span”)) if you want a single extended column.

This mirrors solutions on Giang Black’s blog—they use coalesce for nulls, smart if JobTitleID can be null.

Spark SQL Alternative for GROUP BY Logic

Prefer pyspark sql? Translatable 1:1, great for notebooks or views. Use CTEs for the flag → cumsum → groupby pyspark flow.

WITH flagged AS (
 SELECT 
 PersonID, JobTitleID, Timestamp,
 CASE WHEN JobTitleID != LAG(JobTitleID) OVER (PARTITION BY PersonID ORDER BY Timestamp) 
 THEN 1 ELSE 0 END as is_new_group
 FROM your_table
),
grouped AS (
 SELECT 
 PersonID, JobTitleID, Timestamp,
 SUM(is_new_group) OVER (PARTITION BY PersonID ORDER BY Timestamp 
 ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as group_id
 FROM flagged
)
SELECT 
 PersonID, JobTitleID,
 MIN(Timestamp) as Timestamp_start,
 MAX(Timestamp) as Timestamp_end
FROM grouped
GROUP BY PersonID, group_id, JobTitleID;

Register as temp view (df.createOrReplaceTempView(“your_table”)), then spark.sql(). Drops group_id automatically. Matches this SO pattern for date spans—just swap dates for timestamps.

Why SQL? Easier auditing, and pyspark sql optimizes under the hood same as DataFrames.

Complete Example: Raw Data to Merged Output

Let’s run it live. Paste your raw data:

data = [
 (1, "A", "2023-01-01"),
 (1, "A", "2023-01-02"),
 (1, "B", "2023-01-03"),
 (1, "B", "2023-01-04"),
 (1, "A", "2023-01-05"),
 (2, "C", "2023-01-01"),
 (2, "C", "2023-01-02")
]
df = spark.createDataFrame(data, ["PersonID", "JobTitleID", "Timestamp"])

# Apply the DataFrame solution above...
merged_df.show(truncate=False)

Output nails it:

Pyspark timestamp as strings? Cast to timestamp type first: F.to_timestamp(“Timestamp”, “yyyy-MM-dd”). Handles gaps perfectly.

Performance Tips and Edge Cases

Big data? Repartition by PersonID before windows—pyspark partitioning aligns with your groupby pyspark. Like: df.repartition(“PersonID”).orderBy(“PersonID”, “Timestamp”).

Null JobTitleID? Coalesce lag: F.coalesce(F.lag(“JobTitleID”), F.lit(“NULL”)).over(…). Nulls start new groups usually.

Ties in Timestamp? Add row_number: window.orderBy(“Timestamp”, “row_id”) where row_id = monotonically_increasing_id().

Skewed PersonID? Broadcast small ones, or use AQE (spark.conf.set(“spark.sql.adaptive.enabled”, “true”)).

From consecutiveness checks on SO, lag shines for validation post-merge.

Common Pitfalls and Fixes

Don’t orderBy globally—always partition/order inside window, or islands cross PersonID.

Simple groupby(“PersonID”, “JobTitleID”) ignores consecutiveness: mashes all A’s together.

Extra cols? After agg, join back: merged_df.join(df_grouped.select(“PersonID”, “group_id”, “other_col”).dropDuplicates(), on=[“PersonID”, “group_id”]).

Versus monotonically_increasing_id hacks from older SO? Window wins—distributed, no zip-index mess.

Gaps theory from Binh Hoang translates seamlessly to PySpark.

Sources

1. Grouping consecutive rows in pyspark dataframe — Core lag over partitionBy for change detection: https://stackoverflow.com/questions/51309693/grouping-consecutive-rows-in-pyspark-dataframe
2. Consecutive grouping in Apache Spark — PySpark code with is_new_group flag and cumsum: https://giangblackk.hashnode.dev/consecutive-grouping-in-apache-spark
3. How to aggregate PySpark based on values in consecutive rows — Window cumsum for grouping consecutive flags: https://stackoverflow.com/questions/76203631/how-to-aggregate-pyspark-based-on-values-in-consecutive-rows
4. Pyspark merge consecutive duplicate rows but maintain start and end dates — Group flag with sum over rowsBetween for spans: https://stackoverflow.com/questions/58172107/pyspark-merge-consecutive-duplicate-rows-but-maintain-start-and-end-dates
5. Check if a column is consecutive with groupby in pyspark — Lag for consecutiveness and null handling: https://stackoverflow.com/questions/66970867/check-if-a-column-is-consecutive-with-groupby-in-pyspark
6. How to merge consecutive duplicate rows in pyspark — Alternative segments approach vs window efficiency: https://stackoverflow.com/questions/50338026/how-to-merge-consecutive-duplicate-rows-in-pyspark
7. Gaps and Islands — Theory of dense_rank and partitioning for islands: https://binhhoang.io/blog/gaps-and-islands/

Conclusion

Pyspark window functions plus groupby pyspark make merging consecutive JobTitleID rows per PersonID a breeze—lag flags the islands, cumsum IDs them, agg stretches your pyspark timestamp end-to-end. Test the DataFrame or pyspark sql version on your data; it’ll handle scale where loops choke. Tweak for nulls or extras, and you’re set for cleaner job history views.