Suppose If i am given a maximum of 20 cores to run my data pipeline or ETL framework, i will need to strategically allocate and optimize resources to avoid performance issues, job failures, or SLA breaches.
Hereβs how you can accommodate within a 20-core limit, explained across key areas:
πΉ 1. Optimize Spark Configurations
Set Spark executor/core settings carefully to avoid over-provisioning:
spark.conf.set("spark.executor.cores", "4") # cores per executor
spark.conf.set("spark.executor.instances", "4") # 4 * 4 = 16 cores used
spark.conf.set("spark.driver.cores", "4") # driver gets the remaining 4 cores
β Total = 16 (executors) + 4 (driver) = 20 cores
π‘ Add
spark.dynamicAllocation.enabled = true
if workloads vary during pipeline runs.
πΉ 2. Stagger or Schedule Jobs
Donβt run all pipelines concurrently. Use:
- Sequential execution: Run high-priority pipelines first.
- Window-based scheduling: Batch pipelines into time slots.
- Metadata flags: Add
priority
,is_active
, orgroup_id
columns in your metadata table to control execution order.
πΉ 3. Partition & Repartition Smartly
Avoid unnecessary shuffles or over-parallelism:
- Use
.repartition(n)
only when needed. - Use
.coalesce()
to reduce number of partitions post-join/write. - Ensure data is evenly distributed to prevent skewed execution.
df = df.repartition("customer_id") # if downstream join is on customer_id
πΉ 4. Broadcast Small Tables
Use broadcast joins for small lookup or dimension tables:
from pyspark.sql.functions import broadcast
df = fact_df.join(broadcast(dim_df), "dim_key")
This avoids expensive shuffles and reduces core usage per job.
πΉ 5. Monitor and Throttle
Track usage of each job and restrict heavy jobs during peak hours.
- Use Spark UI, Ganglia, or Databricks Spark metrics to monitor.
- Kill or delay low-value or experimental jobs.
- Set CPU quota limits if using containerized environments.
πΉ 6. Enable Caching Strategically
Cache only reusable datasets and avoid over-caching to free up resources:
df.cache() # or persist(StorageLevel.MEMORY_AND_DISK)
Drop cache after usage:
df.unpersist()
πΉ 7. Use Retry Logic
When using fewer cores, some transient failures may occur. Enable:
- Retry in framework using metadata flags like
max_retries
- Delay-based exponential backoff for re-execution
πΉ 8. Log & Alert Resource Breaches
Your logging module should capture:
- Core usage spikes
- Long-running tasks
- Tasks stuck in
SCHEDULED
orPENDING
πΉ 9. Adaptive Query Execution (Spark 3.x+)
Enable AQE to optimize joins and shuffles dynamically:
spark.conf.set("spark.sql.adaptive.enabled", "true")
This reduces resource usage per stage and adapts based on runtime stats.
β Summary Strategy:
Area | Optimization |
---|---|
Spark Config | Use 4 cores/executor, 4 instances max |
Scheduling | Sequential or grouped jobs |
Partitioning | Repartition only when needed |
Joins | Use broadcast for small tables |
Caching | Only when reused and memory allows |
AQE | Enable to auto-optimize execution |
Monitoring | Use logs & alerts to avoid overload |
Configuring a Spark cluster effectively (driver memory, executor memory, cores, and number of executors) is critical for performance, stability, and resource utilization. Below is a complete guide with rules of thumb, configuration logic, and use case-specific tuning, including memory management best practices.
π· 1. Spark Cluster Components
Component | Description |
---|---|
Driver | Coordinates Spark jobs, collects metadata, plans DAGs |
Worker Nodes | Physical machines where tasks (executors) run |
Executor | JVM process running on a worker that performs tasks, stores shuffle data/cache |
Executor Memory | Heap size available for computations (excluding overhead) |
Executor Cores | Number of concurrent tasks per executor |
π· 2. Basic Configuration Formula
Assume:
- Total available cores: 20
- Total available memory per node: 64 GB
- Leave 1 core and ~1 GB for OS and overhead per node
β Thumb Rules
Parameter | Rule |
---|---|
Executor cores | 4 cores (ideal for parallelism without causing GC overhead) |
Executors per node | (Total cores – 1 OS core) / executor_cores |
Executor memory | (Total memory – OS memory – overhead) / executors per node |
Driver memory | Usually same as executor memory or slightly higher |
π· 3. Executor Memory Formula
executor_memory = (total_node_memory - OS_mem - overhead_mem) / executors_per_node
- Overhead is typically ~7% of executor memory, or you can set via:
spark.yarn.executor.memoryOverhead = 512MB or 0.1 * executor_memory
π· 4. Sample Config: 64 GB Node, 20 Cores
# Nodes: 1 node with 64GB RAM, 20 cores
executor_cores = 4
executors_per_node = (20 - 1) / 4 = 4
executor_memory = (64GB - 1GB OS - 1GB overhead) / 4 = ~15.5GB
π§ Spark Submit Configuration:
spark-submit \
--num-executors 4 \
--executor-cores 4 \
--executor-memory 15g \
--driver-memory 16g
π· 5. Cluster Configuration by Use Case
Use Case | Cluster Strategy | Notes |
---|---|---|
ETL/Batch Pipeline | More memory per executor | For large joins, shuffles, aggregations |
Streaming (e.g. Kafka) | Fewer cores, more executors | Prioritize latency and backpressure control |
MLlib Model Training | More executor cores, cache memory | Parallel ML tasks, cache intermediate data |
Data Exploration (notebooks) | Bigger driver memory | Driver handles collection & plotting |
Metadata-Driven Pipeline | Balanced config, retry logic | Memory-efficient caching + safe SQL parsing |
π· 6. Memory Management Strategy
β Heap Memory vs. Storage Memory
- Execution Memory: Used for joins, aggregations
- Storage Memory: Used for caching/persisted datasets
- Unified Memory Model (post Spark 1.6): They share the same pool.
spark.memory.fraction = 0.6 # Total usable memory (default 60% of executor memory)
spark.memory.storageFraction = 0.5 # 50% of that memory for caching
π§ Best Practices
Tip | Reason |
---|---|
Avoid too many small executors | Overhead in shuffle and GC |
Avoid too few large executors | Risk of skew, long GC pauses |
Enable dynamic allocation | Adjust resources at runtime (spark.dynamicAllocation.enabled=true ) |
Tune broadcast timeout | spark.sql.broadcastTimeout=300 to avoid failed joins |
Use caching wisely | persist() only if reused across stages |
Monitor with Spark UI | Check skew, shuffle spill, task duration |
π· 7. Example Scenario-Based Tuning
πΈ Case 1: Daily ETL Load from Hive β Delta
- Heavy joins, aggregations
- Needs higher memory, fewer large executors
--executor-cores 5
--executor-memory 18g
--num-executors 3
Use:
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1) # Force sort-merge joins for large tables
πΈ Case 2: Kafka Streaming Pipeline
- Lower latency, small payloads, stability
--executor-cores 2
--executor-memory 6g
--num-executors 8
Enable:
spark.streaming.backpressure.enabled=true
πΈ Case 3: Metadata-driven Spark Job Runner
- SQL + DataFrame mix, dynamic logic
--executor-cores 3
--executor-memory 10g
--num-executors 5
- Add retries:
max_attempts = 3
in framework - Partition-wise load balance
π· 8. Driver Tuning
Scenario | Driver Memory |
---|---|
Notebook / Exploratory | 8β16 GB |
Cluster scheduler (your framework) | Same as executor |
Broadcast-heavy tasks | More memory (20+ GB) |
π· 9. Monitoring Tools
Tool | Purpose |
---|---|
Spark UI | View stages, tasks, GC, skew |
Ganglia / Prometheus / Datadog | Cluster CPU/mem trends |
Event Logs | Replay and diagnose job failures |
Spark History Server | Review past runs |
β Final Checklist for Configuration:
- Set executor memory & cores to balance performance and GC
- Use dynamic allocation for variable workloads
- Monitor tasks and adapt for skew or shuffle issues
- Cache only when reused across actions
- Tune joins (broadcast, AQE) for SQL-heavy pipelines
- Log and retry when cluster limits are breached
Hereβs a Spark Cluster Tuning Cheat Sheet (inline format) with best practices and config guidance β ideal for quick reference:
β Spark Cluster Tuning Cheat Sheet
πΉ General Thumb Rules
- Executor Cores: 4 (Ideal for parallelism and reduced GC overhead)
- Executor Memory:
(Node Memory - OS - Overhead) / Executors per Node
- Memory Overhead:
spark.yarn.executor.memoryOverhead β 7β10%
of executor memory or set manually - Driver Memory: β₯ executor memory (especially for broadcast-heavy or notebook workloads)
πΉ Config Formula (Example: 64 GB RAM, 20 Cores/node)
- Leave for OS: 1 core + 1 GB RAM
- Executor Cores: 4
- Executors per Node:
(20 - 1) / 4 = 4
- Executor Memory:
(64 - 1 - 1) / 4 = 15.5 GB
- Spark Submit Example:
--executor-cores 4 \
--executor-memory 15g \
--num-executors 4 \
--driver-memory 16g
πΉ Scenario-Based Config
Use Case | Executor Cores | Executor Memory | Notes |
---|---|---|---|
Batch ETL | 4β5 | 15β20 GB | Optimize for shuffle & joins |
Streaming (Kafka) | 2 | 4β6 GB | Enable backpressure |
MLlib / Training | 5β6 | 20β25 GB | More cores for parallelism |
Notebooks / Dev | 2 | 8β12 GB | Larger driver memory |
Metadata Framework | 3β4 | 8β12 GB | Balanced for SQL + PySpark |
πΉ Memory & Storage
spark.memory.fraction = 0.6
β total usable executor memoryspark.memory.storageFraction = 0.5
β for caching- Use
.persist()
only when reused across actions - Avoid unnecessary
.repartition()
unless required
πΉ Join Optimization
- Use
broadcast()
for small dimension tables - Tune with:
spark.sql.autoBroadcastJoinThreshold
- Enable:
spark.sql.adaptive.enabled=true
(for AQE)
πΉ Dynamic Allocation
spark.dynamicAllocation.enabled=true
spark.dynamicAllocation.minExecutors=2
spark.dynamicAllocation.maxExecutors=10
Use when job stages vary in workload.
πΉ Monitoring & Debugging
- Use Spark UI to check:
- Skewed stages
- Task duration
- GC overhead
- Enable Event Logging for replays
- Track executor usage and spill with logs
πΉ Tips
β
Avoid too many small executors (high overhead)
β
Avoid too few large executors (risk GC pauses)
β
Always validate cluster behavior in Spark UI after tuning
β
Cache only reused datasets
β
Log failures and retry intelligently
Great! Letβs walk end-to-end through a 100 GB CSV file processing pipeline in Spark, using the tuning config we built earlier. We’ll explain how the job is submitted, executed, how each Spark component is involved, and how resources (memory/cores) are used.
β Problem Overview
You need to:
- Read a 100 GB CSV
- Cast/transform some columns (e.g., string to int/date)
- Write it back (say, as Parquet/Delta)
πΉ 1. Cluster Configuration (as per our previous config)
Assume:
- Total RAM per node: 64 GB
- Total cores per node: 20
- Executors per node: 4
- Executor memory: 15 GB
- Executor cores: 4
- Driver memory: 16 GB
We use:
spark-submit \
--executor-cores 4 \
--executor-memory 15g \
--num-executors 8 \
--driver-memory 16g \
my_csv_transform.py
β Step-by-Step Execution Flow
π© [1] Job Submission
You run the command via:
spark-submit --executor-cores 4 ...
πΉ What happens:
- Spark launches a Driver program.
- The Driver connects to a Cluster Manager (e.g., YARN, Kubernetes, or Spark Standalone).
- Requests 8 executors with 4 cores and 15 GB RAM each.
π‘ Role of Driver:
- Parses your code
- Constructs DAG (Directed Acyclic Graph)
- Divides tasks into stages
- Schedules execution
π© [2] CSV File Read (Distributed Read Stage)
df = spark.read.csv("s3://mybucket/large.csv", header=True, inferSchema=True)
πΉ What Spark Does:
- Divides the 100 GB file into splits (based on HDFS/S3 block size or file size)
- Assigns read tasks (input splits) to executors in parallel
- Each executor reads a portion of the data
- Creates an RDD of internal row objects
π§ Resource Usage:
- This is I/O-bound; each task uses 1 core
- Executor memory used to buffer + parse CSV rows
π© [3] Transformation (Casting Columns)
df = df.withColumn("amount", df["amount"].cast("double")) \
.withColumn("date", df["date"].cast("date"))
πΉ What Happens:
- Lazy transformation β no computation yet
- Spark updates the logical plan
π§ Driver performs:
- Catalyst optimization on the logical plan
- Converts it into a physical plan
π© [4] Action (Trigger Execution)
df.write.mode("overwrite").parquet("s3://mybucket/output/")
πΉ This action triggers execution:
- Spark creates the DAG of stages
- Stages split into tasks and sent to executors
- Tasks execute in parallel (4 per executor)
π§ Resource Usage:
- Executor performs: reading, parsing, casting, serialization
- If caching used: memory pressure increases
π οΈ Memory and Shuffle Management
Phase | Risk / Concern | Mitigation |
---|---|---|
Read | High GC if parsing fails | Use schema instead of inferSchema |
Cast | CPU + memory pressure | Avoid unnecessary transformations |
Write | Shuffle if partitioned | Use .repartition() wisely, or write without partitioning |
Example:
df.repartition(8).write.parquet(...)
- Writing with 8 partitions β 1 task per partition
β Internal Components Involved
Component | Role |
---|---|
Driver | Builds DAG, coordinates execution |
Cluster Manager | Allocates executor containers |
Executors | Run tasks: CSV read, cast, write |
Task Scheduler | Assigns tasks to available executor cores |
DAG Scheduler | Divides job into stages based on transformations |
Catalyst | Optimizes query plan |
Tungsten | Manages physical execution + memory layout |
Shuffle Manager | Manages intermediate data if shuffle happens |
π Example Internal Job Breakdown
- Stage 1: CSV Read
- 100 tasks for 100 file blocks
- 1 task = 1 input split (runs on 1 core of executor)
- Stage 2: Cast Transformation
- No shuffle, tasks remain same
- Stage 3: Write to Parquet
- If
.repartition(8)
: 8 write tasks
- If
Each stage has a task for each partition (parallel units of work).
π Spark UI Debugging
- Stages tab: See execution plan, duration
- SQL tab: View query plan (logical β physical)
- Storage tab: Monitor caching if used
- Executors tab: Monitor memory usage, active tasks
- Jobs tab: Stage failure, retries, GC times
β Recommendations
Step | Optimization |
---|---|
Read CSV | Use schema instead of inferSchema to save time |
Memory | Leave ~10β15% for overhead (spark.yarn.executor.memoryOverhead ) |
Repartition | Use repartition() before writing large files |
AQE | Enable Adaptive Execution for joins, skew: |
spark.conf.set("spark.sql.adaptive.enabled", "true")
| Logging | Use spark.eventLog.enabled
to replay Spark UI offline |
π Summary of Spark Job Lifecycle
spark-submit --> Driver --> Cluster Manager --> Executors --> Tasks
| | |
Build DAG Request cores Execute read β cast β write
Excellent question! The choice between .repartition()
vs .coalesce()
is critical in optimizing Spark jobs, especially in large data pipelines like your 100 GB CSV read-transform-write job.
Let me now extend the earlier response to include:
- π Difference between
repartition()
vscoalesce()
- π¬ When to use which
- π§ What happens deep down (task creation, shuffle, cluster-level resource usage)
- π§ How it fits into Sparkβs distributed architecture
β Updated Section: repartition() vs coalesce()
Feature | repartition(n) | coalesce(n) |
---|---|---|
Does it shuffle? | β Yes β full shuffle across the cluster | π« No shuffle (narrow transformation) |
Use case | Increase partitions (e.g., for write parallelism) | Reduce partitions (e.g., minimize small files) |
Expensive? | πΈ Yes β triggers full data shuffle | π‘ No β much cheaper |
Task distribution | Balanced across all executors | May cause skew β fewer executors used |
π§ Usage
# Increase partition parallelism before writing
df.repartition(8).write.parquet(...)
# Reduce partitions after wide transformations to save write cost
df.coalesce(4).write.parquet(...)
π What Happens Deep Down
1. repartition(8)
- Spark triggers a shuffle.
- All data is re-distributed across 8 new partitions using hash/range partitioning.
- All executors participate β balanced parallelism.
- New tasks = 8 (1 per partition)
- Useful before writes to get desired file count and write speed.
2. coalesce(4)
- Spark collapses existing partitions into fewer, without shuffle.
- Only data from adjacent partitions is merged.
- Less network IO, but task skew may occur if partitions arenβt evenly sized.
- Useful after filter/joins when many empty/small partitions are left.
π Updated CSV Pipeline Example (100 GB)
df = spark.read.csv("s3://mybucket/large.csv", header=True, schema=mySchema)
df = df.withColumn("amount", df["amount"].cast("double")) \
.withColumn("date", df["date"].cast("date"))
# OPTION 1: Repartition before write for parallelism
df.repartition(8).write.mode("overwrite").parquet("s3://mybucket/output/")
# OPTION 2: Coalesce before write to reduce file count
# df.coalesce(4).write.mode("overwrite").parquet("s3://mybucket/output/")
β Deep Dive into Spark Architecture
Letβs now see how everything plays out step-by-step, deep into Spark internals and cluster resources:
π· Step-by-Step Execution
Step | What Happens |
---|---|
1. spark-submit | Driver launches and connects to Cluster Manager |
2. Driver creates DAG | Parses logic (read β transform β repartition β write) |
3. DAG Scheduler | Breaks into stages β narrow & wide transformations |
4. Task Scheduler | Schedules tasks based on partitions for each stage |
5. Cluster Manager | Allocates executors with 4 cores, 15 GB memory each |
6. Executors Run Tasks | Tasks fetch input splits, perform parsing, cast, write |
7. Shuffle | repartition() stage triggers shuffle read/write |
8. Parquet Write | Tasks write to S3 β 1 file per partition |
π§ Stage Breakdown
- Stage 1: CSV Read
- 100+ tasks (one per file split)
- Tasks read β parse β create DataFrames
- Stage 2: Column Cast
- Narrow transformation β No shuffle
- In-place column type changes
- Stage 3:
repartition(8)
- Wide transformation β full shuffle occurs
- Data is redistributed across 8 partitions
- Stage 4: Write
- 8 tasks (1 per partition)
- Write to 8 files in S3
βοΈ Cluster Component Roles (During Job)
Component | Role |
---|---|
Driver | Parses code, builds DAG, optimizes plans |
DAG Scheduler | Breaks logical plan into stages, manages shuffle boundaries |
Task Scheduler | Sends tasks to available executor cores |
Cluster Manager | Allocates resources (executors) on YARN/K8s/Standalone |
Executors | Run tasks (parse, cast, shuffle, write) using 4 cores, 15 GB each |
Shuffle Manager | Handles reading/writing intermediate results for repartition |
π§ When Should You Use repartition()
or coalesce()
?
Goal | Use | Why |
---|---|---|
Write n output files | repartition(n) | Balanced parallel file writing |
Reduce # of small files | coalesce(n) | Avoid shuffle; fast collapse |
Join optimization | repartition(key) | Improve skewed joins |
Save cost on writes | coalesce(n) | Write fewer files to S3/HDFS |
β Best Practices
Task | Tip |
---|---|
Reading CSV | Always provide schema, avoid inferSchema=true for large files |
Partition Management | Use repartition() before wide writes, coalesce() after filtering |
Memory Mgmt | Leave 10β15% for overhead, cache only reused datasets |
Shuffle Mgmt | Monitor via Spark UI β Stages tab β Shuffle Read/Write size |
Output Files | Use repartition(n) to control file count if needed for downstream systems |
π Final Notes
- Every
repartition()
creates a wide dependency, adding a shuffle stage. - Every
coalesce()
is narrow unless it needs to increase partitions, in which case it acts like repartition. - Choosing the right one affects task count, executor balance, cluster load, and write throughput.
- Monitor partition sizes and execution time from Spark UI β Jobs β Tasks β Executors
Great β now letβs deep dive into your core question:
How do 100 tasks get created when we only have 20 cores? How are they executed? How long does it take? Where to see this in logs or Spark UI?
Weβll walk through:
- πΉ How Spark decides number of tasks
- πΉ How 20 cores are utilized
- πΉ How execution happens (task scheduling, batching)
- πΉ How to estimate total time
- πΉ Where to see this in logs / Spark UI
π· 1. How are β100 tasksβ created?
β€ Spark divides input data into partitions (default 1 partition per file block or ~128 MB):
- 100 GB CSV / 128 MB block size = ~800 partitions
- If you’re reading from S3/CSV with many files or large files, Spark will generate 1 task per input partition
So:
df = spark.read.csv("s3://bucket/large.csv")
π Creates 800 partitions β 800 read tasks
You can control this with:
spark.conf.set("spark.sql.files.maxPartitionBytes", 134217728) # 128 MB
π· 2. How Spark Executes These Tasks with Only 20 Cores?
Letβs say:
- You requested:
--executor-cores 4
--num-executors 4
β 4 executors Γ 4 cores = 16 tasks can run in parallel - Spark Driver uses 1β2 cores, rest for coordination.
π‘ All tasks do not run at once. They are broken into waves (batches):
Term | Meaning |
---|---|
Stage | Logical group of tasks |
Task | Unit of work per partition |
Wave | Parallel execution batch |
β€ Example:
Value | Result |
---|---|
Total Tasks | 800 |
Parallel Slots | 16 tasks at once |
Waves | ceil(800 / 16) = 50 waves |
So Spark will run 50 batches of 16 tasks sequentially.
π· 3. Execution Flow Timeline
β€ Per task duration: suppose ~8 seconds
Then:
Total Time = Waves Γ Max Task Time per Wave
= 50 Γ 8s = 400 seconds β 6.7 minutes
But in real-life:
- Tasks vary in time due to skew
- Later tasks may wait for slow executors
- Some executors might finish early
π§ Use Spark UI β Stage tab to see “Duration” and “Task Summary”
π 4. Where to See This in Spark UI?
β€ Open Spark Web UI (driver:4040
)
Check tabs:
Tab | Insights |
---|---|
Jobs | Shows all jobs and their stages |
Stages | Youβll see 800 tasks, how many succeeded, how long |
Executors | Number of active executors, task time, shuffle size |
SQL | Logical plan, physical plan, stage breakdown |
β€ In Logs (Driver logs):
Look for logs like:
INFO DAGScheduler: Submitting 800 tasks for stage 0 (CSV read)
INFO TaskSchedulerImpl: Adding task set 0.0 with 800 tasks
INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 1, executor 1)
...
π§ Tools to Estimate & Tune
β€ Estimate Task Parallelism
df.rdd.getNumPartitions() # See how many tasks will be triggered
β€ Control it
df = df.repartition(100) # Will create 100 partitions β 100 tasks
df = df.coalesce(20) # Will collapse to 20 tasks
π§ Summary
Concept | Value |
---|---|
Task per partition | 1 task per 128 MB (default) |
Total cores | 20 (16 usable for tasks) |
Execution mode | In waves: ceil(tasks / parallel slots) |
Parallel tasks per wave | 16 (executor-cores Γ num-executors) |
Monitoring | Spark UI β Jobs β Stages β Executors |
Great β letβs clarify when and how to use repartition()
vs coalesce()
, especially in your case where you’re handling 100 GB CSV, doing transformations (like .cast()
), and then writing the result.
π Letβs Recap the Two Options
df = df.repartition(100) # Full shuffle; creates 100 balanced partitions
df = df.coalesce(20) # Narrow dependency; merges adjacent partitions
Method | Shuffle? | When to Use | Cost | Risk |
---|---|---|---|---|
repartition() | β Yes | To increase parallelism (e.g., write to 100 files) | High (shuffles all data) | Slower |
coalesce() | π« No | To decrease partition count (e.g., from 200 to 20) | Low (no shuffle) | May cause skew (unbalanced tasks) |
β Key Decision Factors
πΉ Use repartition(n)
when:
Situation | Reason |
---|---|
Output has too few partitions/files | Improve parallelism while writing |
Large data needs to be evenly distributed | Avoid skew in downstream |
You filtered a lot of data and partitions are empty | Rebalance partitions |
Writing to S3/HDFS and want exactly n output files | Avoid too many or too few files |
π§ Repartition is often better before writing large output files.
πΉ Use coalesce(n)
when:
Situation | Reason |
---|---|
You’ve just filtered down data and want to reduce number of files | Save on small file problem |
You want to minimize shuffle cost | coalesce() does not move data |
You donβt need even partition sizes | Great for writing small data efficiently |
π§ Coalesce is better for compacting files after heavy filtering or if shuffle would be too expensive.
π In your specific case (100 GB CSV β transform β write)
πΈ If you want to increase partition count before writing for parallelism:
df = df.repartition(100)
df.write.parquet(...)
- Good for:
- Balancing partitions
- Parallel file writes (100 tasks writing 100 output files)
β Recommended if you’re writing a large dataset (~100 GB) to S3/Parquet and want control over parallel writes.
πΈ If you had already many partitions (say, 200) and want to reduce file count:
df = df.coalesce(20)
df.write.parquet(...)
- Use if:
- You’ve filtered down to smaller data
- You want to avoid creating hundreds of tiny files
β Recommended after a filtering or aggregation step that reduces data volume.
π Summary Decision Table
Goal | Method | Why |
---|---|---|
Control parallel write & balance | repartition(100) | Better task distribution |
Reduce small files | coalesce(20) | Cheap, avoids shuffle |
Write exactly n output files | repartition(n) | Forces balanced partitions |
Avoid shuffle after filter | coalesce(n) | Preserves narrow lineage |
β Final Recommendation for Your Case
You are:
- Reading 100 GB CSV
- Casting columns
- Writing to Parquet
β‘οΈ Use:
df = df.repartition(100)
df.write.mode("overwrite").parquet("s3://bucket/output/")
Reason: You want 100 balanced tasks writing 100 files in parallel to S3 β this improves write performance and avoids small file issues without skew.
Great follow-up! Yes, deciding how many worker nodes to use is relevant and essential β especially when dealing with large-scale data like your 100 GB CSV, and a fixed number of cores and memory per node.
β Key Concepts to Understand First
πΉ A “worker node” (in YARN, Kubernetes, or Standalone):
- Is a physical or virtual machine
- Hosts one or more executors
- Each executor runs tasks using cores and memory
So the question becomes:
Given my data size and desired performance, how many executors do I need, and how many workers do I need to host them?
π§ Step-by-Step Formula to Estimate Worker Nodes
Step 1: Estimate Total Resources Needed
Letβs say you want to run 100 partitions in parallel for a 100 GB file:
- 1 partition β 1 task
- 1 task needs 1 core + ~2β4 GB RAM (rule of thumb)
β€ Total needed to run all 100 tasks in parallel:
- 100 cores
- 200β400 GB executor memory
Step 2: Know Your Node Specs
Assume:
- 1 worker node = 20 cores, 64 GB memory
(Keep ~1 core + 4 GB for OS and overhead)
Effective per node:
- 16 usable cores
- 60 GB usable memory
Step 3: Decide Number of Worker Nodes
You now calculate:
β€ For 100 tasks in 1 wave:
Cores per task = 1
Total required cores = 100
Usable cores per node = 16
β Need at least 100 / 16 = 6.25 β 7 nodes
β€ For 2 waves (50 tasks per wave):
- Need only 50 cores β 50 / 16 = ~3.2 β 4 nodes
So,
Tasks You Want in Parallel | Nodes Needed (20 cores each) |
---|---|
100 (single wave) | 6β7 |
50 (2 waves) | 3β4 |
20 (5 waves, slow but cheaper) | 2 |
π Quick Decision Strategy
Goal | How many nodes? |
---|---|
Speed (finish job faster) | Add more nodes to run all tasks in fewer waves |
Cost saving (slow is OK) | Use fewer nodes, run tasks in more waves |
High SLA (1-hour batch) | Tune to finish all waves within SLA |
Streaming / Real-time | Ensure enough cores to process within batch interval |
β Cluster Size Estimator (Example Table)
Data Size | Desired Parallel Tasks | Estimated Nodes (20-core) |
---|---|---|
10 GB | 16β32 | 1 |
100 GB | 64β100 | 4β7 |
500 GB | 200β400 | 10β25 |
1 TB | 500+ | 25+ |
π§ Rule: 1 task per 128 MB block, 1 task needs 1 core + ~2β4 GB memory
β Summary
Question | Answer |
---|---|
Is # of worker nodes relevant? | β Absolutely |
How to decide? | Based on cores & memory needed for desired task parallelism |
What if I have fewer nodes? | Spark runs tasks in waves (batches), takes longer |
More nodes = faster job? | β Yes, up to a point (diminishing returns beyond that) |
Perfect β letβs now calculate how many nodes will be used in your original scenario, where:
β Given:
- You have a maximum of 20 cores available
- You want to:
- Read and transform a 100 GB CSV
- Possibly
repartition(100)
for parallel writing
- Youβre using:
--executor-cores 4 --num-executors 4
β€ Thatβs 4 executors Γ 4 cores = 16 cores used in parallel
(The remaining 4 cores are possibly reserved for driver, overhead, or standby)
π· Let’s Break It Down:
β€ 1. Total Available: 20 cores
β€ 2. Your executor plan:
- Each executor needs 4 cores
- So you can fit 4 executors
β Total: 4 Γ 4 cores = 16 usable executor cores
π Now, How Many Nodes Will Be Used?
You didnβt specify node specs, so letβs assume:
- Each worker node has 20 cores
Then:
- You can fit all 4 executors in 1 worker node, because:
- 4 executors Γ 4 cores = 16 cores (fits in 20)
- Leaves 4 cores for OS/driver/overhead (ideal)
π Final Answer:
Parameter | Value |
---|---|
Max Cores Allowed | 20 |
Executor Plan | 4 executors Γ 4 cores each |
Total Executor Cores | 16 |
Worker Node Cores | 20 |
Worker Nodes Used | β 1 Node |
Execution Style | β Tasks run in waves if more than 16 tasks |
π§ What If You Needed to Run 100 Tasks?
- You still only have 16 executor cores at once
- Spark will run tasks in waves:
100 tasks / 16 = ~6.25 β 7 waves
Even though only 1 node is involved, Spark can handle any number of tasks, just more slowly if fewer cores.
β Key Insight
- Youβre constrained to 1 node and 16 executor cores at once
- But Spark can handle large input by executing tasks in waves
- If performance or SLA becomes a concern, then add more nodes (and cores)
Excellent question β now you’re correctly considering the driver configuration along with executors, under YARN as the cluster manager. Letβs answer this precisely with architecture-level clarity.
π― Goal:
You’re configuring Spark like this:
spark.conf.set("spark.executor.cores", "4") # 4 cores per executor
spark.conf.set("spark.executor.instances", "4") # 4 executors = 16 cores total
spark.conf.set("spark.driver.cores", "4") # driver will request 4 cores
So, total requested cores =
4 (driver) + 4*4 (executors) = 20 cores
β Can This Fit on a Single Node?
π Assume:
- Each YARN worker node (NodeManager) has 20 cores total
- You’re using YARN client or cluster mode
Breakdown:
Component | Cores Required | Where It Runs |
---|---|---|
Driver | 4 | On client (Client mode) or worker (Cluster mode) |
Executors | 16 (4 Γ 4) | On worker nodes |
πΉ Case 1: YARN Client Mode
- Driver runs on your submitting machine (outside YARN cluster)
- Only executors are launched on the worker nodes
- Total worker usage: only 16 cores
- β Fits easily on 1 node
πΉ Case 2: YARN Cluster Mode
- Driver runs within the YARN cluster, as a separate container
- Driver container needs 4 cores too
Now your total demand = 16 (executors) + 4 (driver) = 20 cores
If 1 node = 20 cores, then:
β Yes, it just fits in 1 node.
β οΈ However, this is tight β no headroom for YARN overhead, OS, shuffle service, AM container, etc.
π§ Best Practice:
Leave 1 core for YARN/OS overhead, so allocate:
- driver.cores = 2
- Or run driver outside the cluster (YARN client mode)
π Final Verdict:
Mode | Will 1 node work? | Notes |
---|---|---|
YARN Client Mode | β Yes | Driver runs outside cluster, only 16 cores needed |
YARN Cluster Mode | β οΈ Risky but β Possible | Will consume full 20 cores. Tight. Not scalable |
Best Practice | β Use 2 nodes OR reduce driver cores | Leaves buffer for OS/YARN overhead |
β Bonus Tip: Check These in YARN Resource Manager
- Use
yarn top
or Resource Manager UI to see:- Cores & memory used per container
- How driver and executor containers are placed
π Summary
Configuration | Value |
---|---|
Executor cores | 4 |
Executor instances | 4 |
Driver cores | 4 |
Total cores requested | 20 |
Worker node cores | 20 |
Nodes used (max) | β 1 Node (just fits) |
Caution | No buffer left for OS/YARN overhead if in cluster mode |
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