A technical diagram showing the relationship between Driver, Workers, and Executors in an Apache Spark cluster.
Optimization in Apache Spark is the process of adjusting data distribution and memory usage to reduce execution time and operational costs of data pipelines. To achieve maximum efficiency, tasks must be processed in balanced parallelism while avoiding excessive data movement across the cluster network.
Apache Spark operates as a distributed processing platform based on in-memory computing. Unlike traditional systems, most transformations occur directly in the memory of the nodes, which accelerates the analysis of large data volumes. However, performance can be affected by factors such as inefficient memory usage, poor task distribution, and inadequate resources for the workload.
Understand distributed and in-memory processing
Spark’s efficiency comes from its ability to keep data in memory (RAM) during processing. When memory limits are reached, the system uses disk storage, which slows operations.
In-memory computing: Operations occur on cluster nodes to avoid disk I/O.
Task distribution: Work is divided into smaller parts to run simultaneously.
Attention points: Performance decreases when excessive data movement (shuffle) occurs or when CPU resources are insufficient for the processed volume.
Spark architecture in Databricks
When configuring a cluster in Databricks, we define the structure that will execute the code. This architecture is divided into two main roles:
Driver: The central coordinator. It creates the execution plan, manages job state information, and distributes tasks to workers.
Workers: Nodes that perform processing. Each worker runs a JVM (Java Virtual Machine).
Executors: Processes inside workers that execute tasks and store cached data.
Slots: Represent the number of CPU cores available for parallel execution within an executor.
Total cluster parallelism is determined by the sum of all available slots across workers. If there are few slots for large amounts of data, processing becomes sequential and slow.
Execution hierarchy: jobs, stages, and tasks
To optimize a pipeline, you must understand how Spark organizes work internally. There is a clear execution hierarchy:
Job: Initiated whenever an action is triggered (such as write, count, or display).
Stage: The job is divided into stages. A new stage is created whenever data redistribution (shuffle) is required.
Task: The smallest unit of work, executed in a single CPU slot over a data partition.
Shuffle is a costly operation because it consumes network, CPU, and memory resources to move data between nodes. Reducing the number of shuffles is one of the best ways to save resources.
RDDs and data partitions
RDDs (Resilient Distributed Datasets) are the foundation of Spark and have three core characteristics:
Immutability: Once created, they cannot be modified, only transformed into new RDDs.
Distribution: Data is divided into logical partitions processed in parallel.
Fault tolerance: Spark can recompute lost partitions if a node fails.
It is important to differentiate execution partitions (Spark partitions) from storage partitions (Hive partitions). Execution partitions define in-memory parallelism, while storage partitions organize how files are physically stored in the data lake.
The concept of lazy evaluation
In Spark, code is not executed line by line immediately after it is written. Instead, it uses lazy evaluation, dividing operations into two types:
Transformations: Define what to do with the data (such as filter, select, and join). They only create an execution plan.
Actions: Commands that force execution of the plan (such as write, show, or count).
Inserting unnecessary actions such as display() between transformations can cause reprocessing and increase execution time.
Difference between narrow and wide transformations
Narrow transformations: One input partition generates only one output partition (e.g., filter, select). They do not require shuffle and are highly efficient.
Wide transformations: One input partition may contribute to multiple output partitions (e.g., join, groupBy, distinct). They require shuffle, increasing cost and time.
Monitoring with Spark UI
Spark UI is the main observability tool in Databricks. It allows you to identify performance bottlenecks in your code.
Jobs tab: Shows overall progress and total execution time.
Stages tab: Details each stage, displaying task duration and shuffle metrics.
Executors tab: Shows memory and CPU usage per node, helping identify whether the cluster is properly sized.
Task metrics: Helps observe whether tasks are unbalanced (some very slow and others fast).
Read optimization with Data Skipping and Z-Order
One of the largest costs in Spark is file reading. The goal should be to read only what is necessary using Data Skipping.
Data Skipping: Spark and Delta Lake use statistics (minimum and maximum values) to ignore files that do not meet query filters.
OPTIMIZE: Compacts small files into larger ones to improve reading efficiency.
Z-Order: Physically reorganizes data to group related information, dramatically enhancing Data Skipping.
In BIX Tech projects, applying Z-ORDER reduced file reads from hundreds of files to just one, decreasing query time from 14 to 4 seconds. To maintain performance, use the VACUUM command periodically to remove old files and reduce storage costs.
Managing spill and data skew
Memory issues are common in large-scale processing. The two main ones are spill and skew:
Spill: Occurs when data does not fit in memory and Spark must temporarily write it to disk, significantly increasing processing time.
Data Skew: Happens when data is poorly distributed and a single task becomes much larger than the others, delaying stage completion.
To mitigate these issues, adjust the number of spark.sql.shuffle.partitions, use AQE (Adaptive Query Execution) to optimize joins automatically, or apply salting techniques to redistribute problematic keys.
Cache and data persistence
Using cache helps avoid reprocessing costly transformations used multiple times within the same pipeline.
Spark Cache: Stores DataFrames in memory using cache() or persist(). Remember that cache is lazy and only filled after the first action.
Databricks Disk Cache: Uses node SSDs to automatically store data read from storage. It is highly recommended for clusters that perform repeated reads.
If your company faces slow pipelines in Databricks, spill issues, or high processing costs, our specialists can help optimize your Spark architecture. Contact our team to ensure data efficiency.
FAQ — Frequently asked questions about Apache Spark optimization
What is shuffle in Spark?
It is the redistribution of data between cluster nodes, occurring in operations such as joins and aggregations, and is one of the slowest stages in the process.
How does Spark UI help identify problems?
It visually shows whether tasks are balanced, whether there is excessive disk usage (spill), and which stages consume the most time.
What is the difference between OPTIMIZE and Z-Order?
OPTIMIZE groups small files to reduce reading overhead, while Z-Order organizes data within those files to accelerate filtering.
What is Adaptive Query Execution (AQE)?
A Spark 3 feature that adjusts the execution plan in real time based on statistics collected during execution, improving join performance.
When should I increase cluster memory?
When code optimizations and partition tuning do not eliminate disk spill, indicating the workload requires more hardware resources.
