Apache Spark is an open-source distributed computing system that provides an easy-to-use interface for programming entire clusters with fault tolerance and parallel processing capabilities. Spark is designed to cover a wide range of workloads that previously required separate distributed systems, including batch applications, iterative algorithms, interactive queries, and streaming. Under the hood, Spark uses a sophisticated execution engine that allows it to efficiently translate code written in high-level languages like Scala, Java, Python, or R into a series of complex operations executed across a distributed cluster.
One of the key features for developers working with Spark is the ability to understand and optimize Spark execution plan. Execution plans are generated by Spark’s catalyst optimizer and provide insight into how Spark understands and executes your code. Profiling these plans can be critical to diagnosing performance bottlenecks and understanding the most efficient ways to structure Spark jobs. In this deep dive, we will explore every aspect of Spark execution plans — from their creation to their optimization — so developers can harness their full potential.
Understanding Logical Plans
When you write a Spark job using DataFrames or Datasets API, your high-level code statements are first translated into a logical plan. The logical plan represents a tree of logical operators that model the computation without deciding on the particulars of how it will be carried out on the cluster. This transformation of your code into a logical plan happens lazily; that is, it doesn’t actually execute any transformations or actions until it is required. To see the logical plan for a DataFrame, one can call the explain(true) method on it.
For example, consider the simple DataFrame transformation:
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder()
.appName("Spark Execution Plan Example")
.master("local[*]")
.getOrCreate()
import spark.implicits._
val data = Seq(("James", "Sales", 3000),
("Michael", "Sales", 4600),
("Robert", "Sales", 4100),
("Maria", "Finance", 3000)
)
val df = data.toDF("employee_name", "department", "salary")
df.createOrReplaceTempView("employee")
val resultDF = spark.sql("SELECT department, sum(salary) as total_salary FROM employee GROUP BY department")
resultDF.explain(true)
This block of code will produce a logical plan as part of its output. The logical plan gives you an idea of the different logical steps that Spark plans to take when executing your query.
Analyzing Logical Plan
The logical plan breaks down the user’s high-level instructions into more granular operations that Spark understands, such as scanning a data source, filtering, aggregating, and more. These operations correspond to the DataFrame transformations or SQL operations used in the code. The plan is logical because it doesn’t include specifics about how the data is partitioned or distributed across the cluster, or how the tasks are scheduled to run on different nodes.
Understanding Physical Plans
The logical plan is then converted by Spark’s catalyst optimizer into one or more physical plans. These plans outline exactly how the logical operations will be executed on the cluster, including which executors will be used, how data will be shuffled across the cluster, and which algorithms will be used to perform aggregations, joins, sorts, and other complex operations. The optimizer evaluates the cost of various physical plans and selects the most efficient one for execution.
The explain() method on a DataFrame or Dataset can also show the physical plan chosen for your query:
resultDF.explain() // by default, this shows the physical plan
A snippet of the output from the above call might look like this:
== Physical Plan ==
*(2) HashAggregate(keys=[department#2], functions=[sum(salary#3)])
+- Exchange hashpartitioning(department#2, 200)
+- *(1) HashAggregate(keys=[department#2], functions=[partial_sum(salary#3)])
+- *(1) FileScan parquet [...]
The physical plan uses different operators that are more execution-specific. For instance, “HashAggregate” indicates that Spark will use a hash-based aggregation strategy, and “Exchange” represents a shuffle of data between executors, which can be a costly operation in terms of network I/O and can affect the performance of your Spark job.
Understanding Spark’s Catalyst Optimizer
The process of transforming a logical plan into one or more physical plans is orchestrated by Spark’s catalyst optimizer. The optimizer uses a set of rules, which are strategies for optimizing queries, to produce the most efficient execution plan. These rules include predicate pushdown, constant folding, join reordering, and many others.
Rule-based Optimization
Predicate pushdown is a technique where Spark tries to move filter conditions closer to the data source so that fewer data are read into memory. For instance, if you have a filter operation after a join, Spark might reorder them to filter the data before the join, reducing the amount of data being joined.
Cost-based Optimization
Cost-based optimization is a more advanced feature where Spark will use statistics about the data to decide on the best execution plan. For example, if Spark knows the size of the data or the distribution of values in a column, it might choose a different join strategy that is more appropriate for the data size or shape.
Inspecting Execution Plan Costs
While rule-based optimizations are applied universally, cost-based optimizations require collecting statistics on the data. You can enable this optimization by gathering table statistics using the ANALYZE TABLE command:
ANALYZE TABLE employee COMPUTE STATISTICS FOR COLUMNS department, salary
With the relevant statistics, Spark’s optimizer makes better choices on how to execute your query, which can lead to significant performance improvements.
Tuning and Optimizing Execution Plans
Understanding Spark’s execution plans is the first step in tuning and optimizing your Spark jobs to run efficiently. Here are some common strategies respondents use based on what they observe in execution plans:
Minimizing Shuffle Operations
Shuffles are one of the most expensive operations in Spark because they involve disk I/O, data serialization, and network I/O. By restructuring your job to minimize shuffles — for example, through careful repartitioning or by performing operations before a shuffle that reduce data volume — you can greatly improve job performance.
Selecting the Right Operator
Some DataFrame operations can be executed using different physical operators. For example, joins can be performed with broadcast hash joins, sort merge joins, or shuffled hash joins. Based on the size and distribution of your data, you can hint Spark to choose the most efficient operator.
Using Cache Wisely
Caching data in memory can speed up access for data that is accessed multiple times. When you observe through the execution plan that certain parts of your data are being recomputed often, caching those parts can help improve speed. However, caching is not free and can result in excessive memory usage, so use it judiciously.
Adjusting Parallelism
The “spark.sql.shuffle.partitions” configuration parameter controls the number of tasks that run in parallel for shuffle operations. The default might not be ideal for your workload; if you have too many small tasks, you might see overhead from task scheduling. If you have too few tasks, you might not be fully utilizing your cluster’s resources. Tuning this parameter can lead to better cluster utilization and performance.
In summary, Spark’s execution plans are critical tools for understanding and optimizing Spark applications. By becoming familiar with execution plans and learning how to influence the physical execution plan with optimizations and configurations, you can develop more efficient Spark applications that make the best use of underlying cluster resources.
However, it’s worth noting that constantly evolving nature of Apache Spark may introduce new optimizations and changes to the execution engine with each release. Thus, developers should always be on the lookout for updates in the documentation and release notes to stay informed of the best practices for tuning and optimization.