How To Prepare For The Databricks Spark Developer Associate Certification

How To Prepare For The Databricks Spark Developer Associate Certification

A full overview of the course material

So you’re looking to get that Spark Developer Associate certification? As part of my own preparation for the exam, I’ve written a short description for each of the (high-level) topics that are mentioned at the end of the Databricks course, so you don’t have to. 😉

The exam consists of 60 questions, and covers three main topics:

  • Apache Spark Architecture Concepts (17%)
  • Apache Spark Architecture Applications (11%)
  • Apache Spark DataFrame API Applications (72%)

Covering 72% of all questions, the DataFrame API is the most important. Unfortunately, this is also the section that will require the most hand-on experience. So unless you’re like that one lawyer in the series Suits (Michael Ross), and you can memorize the entire documentation word for word, you’ll need to get your hands dirty. Just a fair warning.

There’s a lot of material to cover, so let’s get going!

Apache Spark Architecture Concepts (17%)

Clusters

A Spark cluster is a distributed computing environment that consists of multiple interconnected machines, enabling the parallel processing of large-scale data using Apache Spark, a fast and general-purpose cluster computing system. An overview of its components:

Spark Cluster

Nodes

  • Driver nodes: The driver node is the entry point for a Spark application. It runs the main function and creates the SparkContext, coordinating the distribution of tasks across worker nodes.
  • Worker nodes: Worker nodes are responsible for executing tasks assigned by the driver. They store data and perform computations.

Executors

  • Relationship with worker nodes: Executors are processes launched on worker nodes to run Spark tasks. Each worker node can have multiple executors.
  • Relationship with the JVM: Each executor runs within a Java Virtual Machine (JVM), isolating tasks and providing fault tolerance.

Slots

  • Description: A slot is a basic unit of parallelism in Spark. It represents the computational capacity of an executor to execute a single task.
  • Purpose: Slots allow multiple tasks to run concurrently on a single executor, maximizing parallelism and resource utilization.

Execution Hierarchy

The Spark execution hierarchy consists of a Spark application containing multiple jobs, each job comprising stages, and each stage composed of tasks, representing the fundamental unit of parallel execution in the Spark computing framework.

Spark Execution Hierarchy

Application

  • Description of a Spark application: A Spark application is a self-contained computation, consisting of a driver program and a set of parallelized tasks executed on a cluster.

Jobs

  • Description of Spark Jobs: A job is a Spark application’s high-level unit of work. It consists of stages, and it is initiated by an action triggered in the driver program.

Stages

  • Description of Spark stages: A stage is a set of tasks that can be executed in parallel without data shuffling. Stages are determined by transformations that have narrow dependencies.
  • Identify what separates a Job into multiple stages: A job is divided into stages at the boundaries where data needs to be shuffled between partitions.

Tasks

  • Description of Spark Tasks: A task is the smallest unit of work in Spark, representing the execution of a single operation on a partition of data.

Distribution

Shuffles

  • Description of shuffling: Shuffling is the process of redistributing data across the partitions, often involving data exchange between nodes. It can be a costly operation.
  • Operations that result in a shuffle: Operations like groupByKey, reduceByKey, and join result in shuffling.
  • Configuration prone to a lot of shuffling: Setting a high level of parallelism or having a small number of partitions can lead to increased shuffling.

Partitions

  • Description of partitions: Partitions are basic units of data parallelism in Spark. Each partition holds a subset of the data, and tasks operate on partitions independently.
  • Configurations that affect partitioning: The number of partitions is affected by configurations like spark.default.parallelism and can be controlled during operations like repartition.

Execution Patterns

Lazy Evaluation

  • Description of Lazy Evaluation: Lazy evaluation is a strategy where transformations on RDDs are not immediately executed. Instead, they are recorded, and the execution is deferred until an action is called.
  • Advantages: It minimizes unnecessary computations, optimizes execution plans, and improves performance.
  • Operations that trigger evaluation: Actions like count, collect, and saveAsTextFile trigger the evaluation of transformations.

Transformations

  • Description of transformations: Transformations are operations that create a new RDD from an existing one. They are lazily evaluated.
  • Narrow vs. wide transformations: Narrow transformations (e.g., map) do not require data shuffling, while wide transformations (e.g., groupByKey) do involve shuffling.

Actions

  • Description of actions: Actions are operations that trigger the execution of transformations and return a result to the driver program or write data to an external storage system.
  • Operations that are actions: Examples include count, collect, saveAsTextFile, and reduce.

Apache Spark Architecture Applications (11%)

Clusters

Execution and Deployment Modes

  • Purpose of driver and executor setup: The driver program is responsible for coordinating the Spark application, while executors are responsible for task execution. The setup ensures the distribution and parallel processing of tasks.
  • Types of deployment modes:
    • Cluster mode: Driver and executors run on the cluster.
    • Client mode: Driver runs on the client machine, connecting to executors on the cluster.
    • Local mode: Both driver and executors run on the local machine for development and testing.

Fault Tolerance and Stability

  • Reasons a Spark application will fail: Network issues, hardware failures, and application bugs can lead to failures.
  • Fault-tolerant cluster setups: Spark provides fault tolerance through lineage information and recomputing lost data. Configurations like replication factor and checkpointing enhance fault tolerance.

Storage

Caching

  • How Spark caches data: Spark caches data in memory, reducing recomputation by storing computed results.
  • When to cache DataFrames: Cache DataFrames when they are reused, reducing computation time.

Storage Levels

  • Types of storage level: a link to the documentation would be easiest 😉
  • Default storage level: MEMORY_ONLY When to use each storage level: Choose based on memory constraints and computation needs.

Out-of-memory Errors

  • Why out-of-memory errors occur: Large datasets or inefficient transformations can exceed available memory.
  • Strategies for reducing out-of-memory errors: Increase cluster size, optimize code, or use storage levels to spill to disk.

Garbage Collection

  • Purpose: To reclaim memory occupied by objects that are no longer in use.
  • Efficient garbage collection configurations: Configure garbage collection settings based on memory requirements, considering options like G1GC.

Partitioning

Repartitioning

  • How to repartition a DataFrame: Use the repartition method to increase or decrease the number of partitions.

Coalescing

  • How to coalesce a DataFrame: Use the coalesce method to reduce the number of partitions without shuffling.
  • Relationship with repartitioning a DataFrame: Coalescing is a more efficient operation when reducing partitions, as it avoids full shuffling.

Data Skew

  • Describe data skew: Data skew occurs when certain partitions have significantly more data than others, leading to uneven processing.
  • General strategies for avoiding data skew: Preprocess data to distribute keys evenly, use salting techniques, or leverage specific join strategies.

AQE Skew Handling

  • Configurations needed for automatic skew handling: Adaptive Query Execution (AQE) automatically handles skew. No specific configurations are required.

Structures (DataFrame)

Basics

  • Description of Spark DataFrames: DataFrames are distributed collections of data organized into named columns, providing a higher-level abstraction than RDDs.
  • Spark class used as the base for DataFrames: org.apache.spark.sql.DataFrame

Types and Execution

  • Possible column types: many exist, see the documentation.
  • Parallel execution: DataFrames support parallel execution, enabling distributed data processing.

Broadcasting

Variables

  • How to broadcast a variable: Use the broadcast function to mark a variable for broadcasting.
  • What it means to broadcast a variable: Broadcasting a variable means sharing it efficiently across all nodes to reduce data transfer overhead.

Joins

  • Configurations for automatic broadcast joins: Set spark.sql.autoBroadcastJoinThreshold to determine the size threshold for automatic broadcast joins.
  • When broadcast joins are advantageous: Broadcast joins are beneficial when one side of the join is small enough to fit in memory.

Adaptive Query Execution (AQE)

  • Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan.
  • How to set up Spark to automatically handle broadcast joins using AQE: AQE automatically handles broadcast joins without specific setup, improving performance.

Apache Spark DataFrame API Applications (72%)

Columns

Subsetting

  • Select columns from a DataFrame: Use the select method to choose specific columns.
  • Drop columns from a DataFrame: Use the drop method to remove unwanted columns.

Renaming

  • Rename existing columns: Utilize the withColumnRenamed method to rename columns.

Manipulating

  • Cast column types: Use the cast method to convert a column to a different data type.
  • Create a constant column: Use the lit function to add a new column with a constant value.
  • Split an existing string column: Employ the split function to divide a string column into multiple columns.
  • Explode an array column: Use the explode function to transform array elements into separate rows.
  • Date manipulations: Use functions like date_add, datediff, and trunc for date manipulations.

Rows

Filtering

  • Single-condition filtering: Use the filter or where method for basic filtering.
  • Multiple-condition filtering: Combine multiple conditions using logical operators (&, |, ~).

Dropping

  • Dropping duplicates: Use the dropDuplicates (or drop_duplicates) method to remove duplicate rows.
  • Sampling: Utilize the sample method to create a random sample of the DataFrame.

Sorting

  • Order by one column: Use the orderBy or sort methods to sort the DataFrame by one or more columns.
  • Change column order: Select columns in a different order to change the column arrangement.

Aggregating

  • Summary descriptions: Use functions like describe or summary for summary statistics.
  • Single-/multi-column aggregations: Utilize functions like sum, avg, and max for single or multiple column aggregations.
  • Grouped aggregations: Use the groupBy method to perform aggregations on specific groups.

Miscellaneous

Combining data

  • Joins: Use methods like join to combine DataFrames based on common columns. For more details on the types of joins, see the documentation.
  • Broadcast joins: Optimize small-table joins using broadcast joins.
  • Unions: Use union or unionByName to combine DataFrames vertically.

I/O

  • Reading and writing: Use read and write methods to read from and write to various data sources.
  • Write by partitions: Improve write performance by specifying the number of partitions.
  • Read schemas: Provide schema information when reading data.
  • Persist/caching: Use persist or cache to persist DataFrames in memory for faster access.

Partitioning

  • Coalescing: Use the coalesce method to reduce (only reduce!) the number of partitions without shuffling.
  • Repartitioning: Use the repartition method to either increase or decrease the number of partitions.

Custom Functions

Python UDFs

  • Create Python UDFs: Use the udf function to define a Python UDF.
  • Create Pandas UDFs: Use the pandas_udf decorator to create Pandas UDFs.
  • Execution of Python and Pandas UDFs: Apply UDFs using the withColumn method.

Scala UDFs

  • Create Scala UDFs: Register Scala UDFs using the udf function.
  • Execute Scala UDFs: Apply Scala UDFs using the withColumn method.

Spark SQL Execution

  • Execute a SQL query: Use the spark.sql method to execute SQL queries on DataFrames.

© 2024. All rights reserved.

Powered by Hydejack v9.1.6