Let me give you a complete, inline and intuitive breakdown of how Spark processes your pipeline stage by stage — including data size, partition count, shuffle impact, memory usage, time estimates, and executor configuration.

This will simulate a real-world Spark job in Cloudera BDL using Parquet input → Transformations → Joins → Final write to HDFS/S3, operating on ~5.5 crore final customer rows.

💡 CONFIG OVERVIEW (Used for all Estimations)

⚙️ STAGE-WISE ANALYSIS: Memory, Shuffle, Time, Partitions

✅ STAGE 1: Read & Filter + Add Month Column

🔸 Action

txn_df = spark.read.parquet("txn/*.parquet")
txn_df = txn_df.withColumn("month", F.date_format("txn_date", "yyyy-MM"))

🔹 Details

• Input: 4 txn tables × 3B rows → ~1.2 TB compressed Parquet
• Filtered down to relevant columns (say ~30) via schema pruning

🔸 Memory & Execution

✅ STAGE 2: GroupBy + Pivot on Month + txn_type

🔸 Action

pivot_df = txn_df.groupBy("cust_id", "month").pivot("txn_type").agg(F.sum("txn_amount"))

🔹 Details

• Input: 3B rows → grouped to ~6 crore rows
• Each group becomes ~120 columns after pivot (assuming 10 txn types over 12 months)

🔸 Memory & Execution

🔧 Tips

• Pre-partition by cust_id
• Use AQE: spark.sql.adaptive.enabled=true
• Monitor skewed partitions in Spark UI

✅ STAGE 3: Window Function (Rolling Average)

🔸 Action

window_spec = Window.partitionBy("cust_id").orderBy("month").rowsBetween(-2, 0)
pivot_df = pivot_df.withColumn("rolling_avg", F.avg("DEBIT").over(window_spec))

🔹 Details

• Operates on ~6 crore rows, sorted within each cust_id
• Costly if cust_id cardinality is high or unbalanced

🔸 Memory & Execution

✅ STAGE 4: Join with Dimension & Fact Tables

🔸 Action

enriched_df = pivot_df.join(broadcast(dim_df), "cust_id", "left")

🔹 Details

• Join ~6 crore rows with 20 small dim tables (broadcasted)
• Fact tables: 20 × ~6 crore rows → join selectively (e.g., left semi or left join on few tables)

🔸 Memory & Execution

✅ STAGE 5: Final Transform + Filter

🔸 Action

final_df = enriched_df.filter(...).withColumn("flag", ...)

🔹 Details

• Adds 1–2 lightweight columns
• Applies filter on amount fields

🔸 Memory & Execution

✅ STAGE 6: Final Repartition + Write to Parquet

🔸 Action

final_df.repartition(200).write.mode("overwrite").parquet("final_fact/...")

🔹 Details

• Final row count: ~5.5 crore
• Final columns: ~150
• Output file size: ~20 GB/month

🔸 Memory & Execution

🧠 TOTAL PIPELINE SUMMARY

🧪 CLUSTER SIZING RECOMMENDATION (20 Executors)

✅ Final Output Characteristics

Time Estimation–

Here’s how the time was estimated for each job/stage/wave in your Spark pipeline, based on reasonable production-like assumptions and formulas. Let’s break it down:

⚙️ Assumptions for Estimation

⏱ Time Estimation Formula

For each stage:

Time (sec) = Total Input Size / (Number of Executors × Effective Read Speed per Executor)

If there’s shuffle or a wide transformation:

Time += Shuffle Size / (Number of Executors × Network Bandwidth per Executor)

For CPU-heavy jobs (e.g., window, pivot), we factor in CPU usage:

Time += Compute Factor × Number of Partitions / Total Cores

✅ Estimation Example Breakdown

Let’s go inline step-by-step through the transformation pipeline.

💾 Step 1: Read 12-Month Transaction Table (Parquet)

📌 Execution:

• Spark parallel read → uses 20 executors × 5 = 100 tasks in parallel
• Time = 800 GB / (100 exec × 100 MB/s) = 80 seconds

🧹 Step 2: Filter & Cleanse (Light Select + filter)

| Output | ~60% rows retained |
| New Size | 180 Cr rows → ~480 GB compressed |
| Partitions | ~1900 |

📌 Execution:

• Minimal CPU
• Time = 480 GB / (100 × 100 MB/s) = ~48 seconds

🔄 Step 3: Pivot (10 columns × 12 months = 120 new cols)

| Rows After Pivot | 6 Cr |
| New Columns | 120 extra, total ~160 |
| New Size | ~15 GB compressed |

📌 Execution:

• CPU-heavy + wide transformation
• Needs shuffle
• Compute cost: 6 Cr / 100 cores = ~5–8 seconds
• Shuffle size: 15 GB → same as output size
• Shuffle time: 15 GB / (100 × 100 MB/s) = 1.5 seconds
• Total: ~10 seconds

🪟 Step 4: Window Functions (per cust_id over 12-month data)

| Input Rows | 6 Cr |
| Output | 6 Cr |
| Shuffle | Yes, needs full repartition by cust_id |
| Estimated Shuffle Size | ~15 GB |

📌 Execution:

• Repartition 6 Cr rows → 100 partitions
• Time = 15 GB / (100 × 100 MB/s) = 1.5 seconds
• Compute time = ~8 seconds
• Total: ~10 seconds

🔗 Step 5: Join with 4 Big Transactional Tables (each 5–6 Cr rows)

| Method | Broadcast or Shuffle join |
| Join Output | 6 Cr rows |
| Intermediate Join Shuffle Size | ~20–25 GB per join |
| Total Shuffle | ~100 GB |

📌 Execution:

• Time per join = 25 GB / (100 × 100 MB/s) = 2.5 sec
• For 4 joins: 2.5 × 4 = 10 seconds
• CPU = 6–8 seconds extra
• Total: ~18 seconds

📊 Step 6: Join with 20 Small Dim Tables (lookup)

| Type | Broadcast |
| Rows | 10K–50K each |
| Shuffle | No |
| Output | 6 Cr |
| Time | Negligible (~5–10 sec total) |

📦 Final Write: Save 6 Cr rows to Parquet

| Final Rows | 6 Cr |
| Final Size | ~20 GB |
| Time = 20 GB / (100 × 100 MB/s) = 2 seconds |

📉 Summary Table

🕒 Total Time Estimate: ~180–200 seconds (~3 mins) end-to-end.

🧠 Notes on Memory Use

• During wide transformations (pivot, window, join), shuffle memory is critical.
• Each executor will need:
  • 6–8 GB for shuffle buffers
  • 2–3 GB for task computation
  • Remainder for cached broadcast joins or temporary results.

Another Version With Some Additions–

• 12 months of pivoted data (~5–6 Cr rows)
• Multiple aggregation layers: quarterly, 6-month, yearly summaries
• Per-user metrics (cust_id level)
• 30–40 custom metric calculations using Window + Pivot + Filters
• Final write to HDFS/S3 in Parquet (Cloudera BDL)

I’ll now provide a complete estimation:

✅ Spark Job Runtime & Memory Analysis (Inline Estimation)

⚙️ Environment Configuration:

📊 Job Breakdown and Stage Estimation

🧠 Estimated Total Memory Pressure

• Peak In-Memory Footprint per Executor: ~6–10 GB working set
• Shuffle Spill Risk: Medium to High
• Total Shuffle Data across stages: ~70–100 GB
• Temp Storage Needed (Shuffle + Spill): At least 100–150 GB on disk
• Ideal Executor Memory: 16 GB + 3 GB overhead

⏱️ Final Time Estimation Breakdown

⚠️ Note: This aligns with your observed time (~1 hour), especially due to:

• Wide aggregations per cust_id over 12 months × N metrics
• Costly joins and large shuffle
• Memory spill due to heavy window/pivot stages

📌 Optimization Pointers (Realistic for Cloudera BDL)

1. Use .persist(StorageLevel.MEMORY_AND_DISK_SER) on intermediate wide DataFrames to reduce recomputation.
2. Avoid wide shuffles during pivot by doing monthly batches if possible (e.g., process 3 months, union all).
3. Repartition based on cust_id before window/pivot-heavy jobs to localize memory usage.
4. Use broadcast() joins for smaller dims/lookup tables < 500 MB.
5. Enable Spark SQL Adaptive Execution (AQE) if not enabled.
6. Tune shuffle partitions based on cluster: for 100 cores, ~3000 is ideal.

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