Analysis and Recommendations for Hardware Configuration and PySpark Setup

---

In the Indian numbering system:

1 crore = 10 million
1 crore = 0.01 billion
Example Conversions:
1 crore = 10,000,000 (10 million)
10 crore = 100,000,000 (100 million, which is 0.1 billion)
100 crore = 1,000,000,000 (1 billion)

Estimated Data Sizes

Category	Records (crores)	Monthly Size	12-Month Size
TablesA	80	~8 GB	~96 GB
TablesB	80	~8 GB	~96 GB
Transaction Tables	320	~32 GB	~384 GB
Special Transaction	100–200	~10–20 GB	~120–240 GB
Agency Score	150–450	~15–45 GB	~180–540 GB

Total Estimated Data Volume:

• Monthly: ~73–113 GB.
• 12-Month Total: 876–1456 GB (~1–1.5 TB).

---

Requirements Breakdown
Data Characteristics
TablesA: Unique_ID1-Based Tables

~6M unique IDs → ~80M records/month (10 tables).
Total for 12 months: 960M records across 10 tables.
Approx Size: Assuming 100 bytes per row:
80M records * 100 bytes = 8 GB/month → 96 GB/year.
TablesB: Unique_ID2-Based Tables

~80M records/month (10 tables).
Total for 12 months: 960M records across 10 tables.
Approx Size: Same as TablesA, 96 GB/year.
Transaction Tables

~80M accounts × 10 transactions/month = ~800M transactions/month.
Total for 12 months: ~9.6B records across 4–5 tables.
Approx Size:
800M records * 100 bytes = 80 GB/month → 960 GB/year.
Special Transaction Tables

~50–100M records/month for 2 tables = 100M–200M records/month.
Total for 12 months: ~1.2–2.4B records.
Approx Size:
200M records * 100 bytes = 20 GB/month → 240 GB/year.
Agency Score Tables

~150M records/month for 3 tables.
Total for 12 months: ~1.8B records.
Approx Size:
150M records * 100 bytes = 15 GB/month → 180 GB/year.
Total Approximate Data Size
Monthly Data: ~123 GB/month.
Yearly Data: ~1.5 TB (including intermediate processing).


Cluster and Hardware Recommendations

Cluster Size
Number of Nodes:
Assuming 512 GB/node memory and 16–32 cores/node:

Total memory required: ~700 GB for executors + driver memory.
Number of nodes: ceil(700 GB / 512 GB) = 2 nodes minimum.


Node Specifications:

CPU: 16–32 cores/node (Intel Xeon/AMD EPYC).
Memory: 256–512 GB/node.
Storage: 20 TB/node SSD for faster I/O.
Network: 10 GbE or faster.


YARN Configuration:

Use Dynamic Resource Allocation to adjust executors based on load.
Total cores: 400.
Reserve ~10% cores for OS and YARN (40 cores).
Cores available for Spark jobs: ~360 cores.

PySpark Configuration
Executor Configuration:

Number of Executors: 25 executors.
Executor Cores: 6.
Executor Memory: 25 GB (with 2.5 GB overhead).
Total executor memory = 25 executors * 27.5 GB = 687.5 GB.
Total cores used = 25 executors * 6 cores = 150 cores.

Driver Configuration:

Driver Memory: 50 GB.
Spark Submit Command

spark-submit 
  --master yarn 
  --deploy-mode cluster 
  --num-executors 25 
  --executor-cores 6 
  --executor-memory 25g 
  --driver-memory 50g 
  --conf spark.yarn.executor.memoryOverhead=2500 
  --conf spark.dynamicAllocation.enabled=true 
  --conf spark.sql.shuffle.partitions=1000 
  --conf spark.network.timeout=600 
  --conf spark.executor.heartbeatInterval=100s 
  your_etl_script.py
Table Partitioning and Storage Optimization
Partitioning Strategy:

TablesA & TablesB: Partition by month_year, unique_id or account_id.
Transaction Tables: Partition by month_year and account_id.
Agency Score Tables: Partition by month_year and agency_id.
Example for Hive:


CREATE TABLE transactions (
    account_id STRING,
    transaction_id STRING,
    amount DOUBLE,
    ...
)
PARTITIONED BY (month_year STRING);


Storage Format:

Use Parquet or ORC for efficient columnar storage and compression.
Enable Snappy compression.
Broadcast Joins:

Use broadcast joins for smaller reference datasets:
small_table = spark.table("small_table")
large_table = spark.table("large_table")
result = large_table.join(broadcast(small_table), "key")


ETL Optimizations
Repartitioning:

Repartition DataFrames for efficient joins and aggregations.
Example:
df = df.repartition(1000, "key_column")
Caching:
Cache intermediate results if reused multiple times.
df.cache()
Skew Handling:

Use salting for skewed keys:

df = df.withColumn("salt", expr("floor(rand() * 10)"))
Dynamic Partition Pruning:

Enable dynamic partition pruning for efficient queries:

SET spark.sql.optimizer.dynamicPartitionPruning.enabled = true;





Hardware: 20-node cluster, 16–32 cores/node, 256–512 GB RAM, 10 GbE.
Cluster Resources:
25 executors, 6 cores each, 25 GB memory.
Total cores used: 150 (out of 400).
Total memory used: ~687.5 GB.
Table Sizes: ~1.5 TB/year.
Optimizations: Partitioning, caching, broadcasting, and dynamic resource allocation.
This setup ensures efficient ETL performance for your monthly and yearly data processing requirements.

Hardware Configuration and PySpark Setup for ETL Jobs

Step 1: Understanding the Data Volume

TablesA (unique_id1-based):
– 6 crores of unique_id1.
– ~8 crores of records/month × 10 tables = 80 crores of records/month.
– 12 months data: 80 × 12 = 960 crores of records.

TablesB (unique_id2-based):
– 6 crores of unique_id2.
– ~8 crores of records/month × 10 tables = 80 crores of records/month.
– 12 months data: 80 × 12 = 960 crores of records.

Transaction Tables:
– 8 crores accounts × 10 transactions/month = 80 crores of transactions/month × 4 tables.
– 12 months data: 80 × 12 × 4 = 3840 crores of records.

Special Transaction Tables:
– 2 tables × 50–100 crores of records/month = 100–200 crores/month.
– 12 months data: 1200–2400 crores of records.

Agency Score Table:
– 3 tables × 50–150 crores of records/month = 150–450 crores/month.
– 12 months data: 1800–5400 crores of records.

Step 2: Estimated Data Sizes

Assume 100 bytes per record on average:
| Category                 | Records (crores) | Monthly Size | 12-Month Size |
|————————–|——————|————–|—————|
| TablesA                 | 80               | ~8 GB        | ~96 GB        |
| TablesB                 | 80               | ~8 GB        | ~96 GB        |
| Transaction Tables      | 320              | ~32 GB       | ~384 GB       |
| Special Transaction     | 100–200          | ~10–20 GB    | ~120–240 GB   |
| Agency Score            | 150–450          | ~15–45 GB    | ~180–540 GB   |

Step 3: Hardware Configuration for Hadoop-Based Data Lake

Cluster Nodes:
– Total Data Size: ~1–1.5 TB/year.
– Replication factor of 3 for HDFS → Total storage required: ~3–4.5 TB/year.
– Recommended Cluster Size: 15–20 nodes.
– CPU: 16–32 cores/node.
– RAM: 256–512 GB/node.
– Storage: 10–20 TB/node (preferably SSD for faster I/O).

Step 4: PySpark Configuration

Executor and Driver Configuration:
– Total cores: 400 cores.
– Maximum executor cores: 6.
– Executors allowed: ⌊400 cores / 6 cores per executor⌋ = 66.
– Memory per executor: 25 GB.
– Recommended Configuration:
  – Number of Executors: 25.
  – Executor Cores: 6 cores/executor.
  – Executor Memory: 25 GB/executor.
  – Memory Overhead: max(25 × 0.10, 384 MB) = 2.5 GB.

Spark Submit Command

spark-submit
  –master yarn
  –deploy-mode cluster
  –num-executors 25
  –executor-cores 6
  –executor-memory 25g
  –driver-memory 50g
  –conf spark.yarn.executor.memoryOverhead=2500
  –conf spark.sql.shuffle.partitions=800
  –conf spark.sql.files.maxPartitionBytes=128MB
  –conf spark.sql.broadcastTimeout=1200
  –conf spark.sql.autoBroadcastJoinThreshold=-1
  –conf spark.network.timeout=600
  –conf spark.executor.heartbeatInterval=100s
  your_etl_job.py

Step 5: Optimizations

Partitioning and Bucketing:
– Partition data by logical keys such as month_year, unique_id, or account_id.
– Bucket large tables on join keys for reducing shuffle.

Avoid Skew:
– Use salting for skewed keys in joins and aggregations.

Broadcast Joins:
– Enable for smaller tables.
  Example:
  small_table = spark.table(“small_table”)
  large_table = spark.table(“large_table”)
  result = large_table.join(broadcast(small_table), “key”)

Shuffle Partitions:
– Set spark.sql.shuffle.partitions to a reasonable number based on data volume and executor count (e.g., 800–1000).

Caching:
– Cache intermediate DataFrames reused across transformations.

Final Recommendations

Cluster Size:
– 15–20 nodes with 16–32 cores/node, 256–512 GB RAM, and 10–20 TB storage/node.

Spark Configuration:
– 25 executors, 6 cores/executor, 25 GB memory/executor.
– Total: 150 cores, 687.5 GB memory used.

ETL Job Optimizations:
– Partitioning, bucketing, skew handling, broadcast joins, and caching.

Estimated Data Processing Time:
– With the above setup, processing ~1.5 TB of yearly data should be efficient with Spark’s distributed capabilities.

---

A standard cluster size for a team of 30 developers running PySpark jobs can vary depending on several factors, such as:

• Job complexity and size
• Data volume and velocity
• Required processing power and memory
• Available budget and resources

That being said, here are some general guidelines for cluster sizing:

Small Cluster

• 10-20 nodes (machines)
• 100-400 cores
• 1-4 TB RAM
• Suitable for small to medium-sized teams, simple jobs, and smaller datasets

Medium Cluster

• 20-50 nodes
• 400-1000 cores
• 4-10 TB RAM
• Suitable for medium-sized teams, moderately complex jobs, and medium-sized datasets

Large Cluster

• 50-100 nodes
• 1000-2000 cores
• 10-20 TB RAM
• Suitable for large teams, complex jobs, and large datasets

Based on requirement of 400 cores, One would recommend a medium-sized cluster with:

• 20-30 nodes
• 400-600 cores
• 8-12 TB RAM

This cluster size should provide sufficient resources for your team of 30 developers to run moderately complex PySpark jobs.

Keep in mind that these are rough estimates, and the actual cluster size required may vary depending on specific use case and requirements. It’s always a good idea to consult with a cloud provider or a big data expert to get a more accurate assessment of Cluster / Application needs.