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PySparkSnowflakeDatabricks

Must-Know Databricks Interview Questions & Answers (Real Company Scenarios) – Part 3

26. How do you monitor cluster performance?​

Story-Driven​

Think of monitoring a cluster like keeping an eye on a team of chefs. You check who’s busy, who’s idle, and whether any chef is struggling. This ensures the feast (your job) finishes on time.

Professional / Hands-On​

  • Cluster performance monitoring in Databricks includes:
    • CPU, memory, and disk usage.
    • Spark UI: stages, tasks, and DAG visualization.
    • Ganglia metrics and logs.
  • Steps:
    1. Go to the Clusters page.
    2. Click on a cluster → Metrics/Driver Logs/Worker Logs.
    3. Use Spark UI for detailed job-level performance.

27. How do you handle large datasets in Databricks?​

Story-Driven​

Handling large datasets is like managing a giant library—you can’t read every book at once. You organize, categorize, and fetch only what you need.

Professional / Hands-On​

Strategies:

  • Partition data for faster reads.
  • Use Delta Lake for ACID and efficient storage.
  • Caching frequently accessed datasets.
  • Use spark.read.format(...).option(...).load(...) to stream or load in chunks.
  • Leverage Auto-scaling clusters to dynamically adjust resources.

28. Difference between spark.read and spark.readStream​

Story-Driven​

  • spark.read is like reading a whole book in one go.
  • spark.readStream is like watching a live news feed—you get data as it arrives.

Professional / Hands-On​

Featurespark.readspark.readStream
TypeBatchStreaming
DataStatic snapshotContinuous flow
Use CaseHistorical data analysisReal-time ingestion and analytics

29. Explain structured streaming in Databricks​

Story-Driven​

Structured streaming is like a smart conveyor belt: it continuously processes incoming data in mini-batches, so you always have fresh results without rerunning everything.

Professional / Hands-On​

  • Structured Streaming is Spark’s high-level streaming API.
  • Supports incremental processing with DataFrames/Datasets.
  • Handles late data, checkpoints, and exactly-once semantics.
df = (spark.readStream.format("csv")
      .option("header", "true")
      .load("/stream/input"))
df.writeStream.format("delta").option("checkpointLocation","/checkpoint").start("/delta/output")

30. What are MLflow experiments?​

Story-Driven​

Think of MLflow experiments like a lab notebook for your ML models. Each experiment records parameters, metrics, and results, so you can track progress and compare models.

Professional / Hands-On​

  • MLflow Experiments track:

    • Parameters: Hyperparameters used for training.
    • Metrics: Accuracy, loss, etc.
    • Artifacts: Model files, plots, or logs.

  • Can create multiple runs within an experiment to compare models.

import mlflow
mlflow.start_run()
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.end_run()

31. How does Databricks implement lazy evaluation?​

Story-Driven​

Lazy evaluation is like writing your shopping list but not going to the store until you really need the items. Spark builds a plan but executes it only when necessary.

Professional / Hands-On​

  • Transformations (like map, filter) are lazy.
  • Actions (like count, collect) trigger execution.
  • Benefits: Optimized query plans, reduced unnecessary computations.

32. What is checkpointing in Spark Structured Streaming?​

Story-Driven​

Checkpointing is like saving your progress in a video game. If something crashes, you can resume from where you left off, instead of starting from scratch.

Professional / Hands-On​

  • Checkpointing stores:

    • Offsets of streaming data
    • Metadata for recovery

  • Enables fault-tolerant streaming

df.writeStream.format("delta").option("checkpointLocation","/checkpoint").start("/delta/output")

33. Explain shuffling and how to minimize it in Databricks​

Story-Driven​

Shuffling is like rearranging all the books in a library to sort them—expensive and time-consuming. Minimizing shuffle saves time and resources.

Professional / Hands-On​

  • Shuffling: Redistributing data across partitions for joins or aggregations.

  • Ways to minimize:

    • Partition by join keys.
    • Use broadcast joins for small tables.
    • Avoid unnecessary wide transformations.
from pyspark.sql.functions import broadcast
df.join(broadcast(small_df), "id")

34. What is skew handling in Spark?​

Story-Driven​

Skew is like one chef getting 90% of the work while others are idle. Skew handling balances the load across all workers.

Professional / Hands-On​

  • Data skew: Uneven data distribution causing some tasks to take longer.

  • Solutions:

    • Salting keys.
    • Repartitioning before joins.
    • Skew join hints in Spark SQL.
df.repartition(100, "key")  # Redistribute data evenly

35. How do you optimize joins in large datasets in Spark?​

Story-Driven​

Optimizing joins is like pairing cooks efficiently: match big tasks with helpers and avoid everyone crowding one station.

Professional / Hands-On​

  • Broadcast small tables: Use broadcast() to avoid shuffles.
  • Partition tables by join keys to co-locate data.
  • Avoid multiple wide transformations.
  • Use Delta tables and Z-ordering for faster joins.
from pyspark.sql.functions import broadcast
df1.join(broadcast(df2), "id")