Modern language models require significant computational resources to process and analyze vast amounts of data. Preparing datasets, training models and running inference can take weeks, especially when dealing with terabytes of text. To handle these challenges developers need a reliable, scalable solution that speeds up data processing and optimizes infrastructure workflows.

What is Apache Spark?What is Apache Spark?

Apache Spark is an open-source distributed computing platform. It was originally created as a research project to accelerate big data processing in the AMPLab at the University of California, Berkeley, and quickly gained popularity. The platform is relatively easy to use and its performance outperforms traditional approaches like MapReduce.

At the core of Spark is the idea of parallel data processing, where data is split into smaller chunks and distributed across the nodes of a cluster. This approach allows Spark to process petabytes of data in parallel, significantly reducing the time required to perform complex analytical and machine learning tasks.

The platform uses the resilient distributed dataset (RDD) model — a logically immutable, distributed data collection that enables parallel operations on large datasets, such as filtering or aggregation. In case of failures, Spark can reconstruct lost fragments through a series of transformations (known as lineage) without reloading the entire dataset.

RDD supports two types of operations:

• Transformations: These are operations like mapping, filtering or joining performed on an RDD. The result of a transformation is a new RDD containing the results of the operation.

• Actions: These operations (e.g., reduction, counting, and others) return a value obtained from computations on the RDD.

Transformations in Spark are executed in Lazy Evaluation mode — meaning their results aren’t computed immediately. Instead, Spark “remembers” the operation to be performed and the dataset (e.g., a specific file) on which the operation should be applied. The computation happens only when an action is invoked and the result is returned to the main program.

This design boosts Spark’s efficiency. For instance, if a large file is transformed in various ways and passed to an action, Spark will process and return the result for only the first row, rather than processing the entire file in the same manner.

By default, each RDD transformation can be re-executed every time a new action is performed on it. However, RDDs can be stored in memory (e.g., in different levels of cache), which allows Spark to keep the necessary elements in the cluster and retrieve them much faster when needed.

Although Spark initially relied on RDDs, modern tasks — especially those involving data processing for LLMs — favor the DataFrame and Dataset APIs, as they are more efficient and support optimizations.

Spark architectureSpark architecture

Apache Spark’s architecture consists of three main components: the driver node, worker nodes and the cluster manager. The driver node coordinates the execution of the application, tracks task progress and distributes tasks to the worker nodes. Worker nodes perform the main computational work, each running one or more executors, which handle portions of the data (partitions). The more worker nodes in the cluster, the faster large volumes of data can be processed. The cluster manager, whether Spark’s built-in manager, YARN, Kubernetes or Mesos, is responsible for allocating resources between the driver and workers.

At the core, Spark creates tasks responsible for parallel transformations on data partitions. These tasks are then distributed from the driver node to the worker nodes, which use available CPU cores to perform the transformations. By distributing tasks across potentially many worker nodes, Spark enables horizontal scaling and supports complex data pipelines.

Think of Spark like a grocery store: data partitions are the products, tasks are the shoppers and the CPU cores are the cashiers. Each core (cashier) can serve only one shopper (task) at a time. Therefore, the more cores there are, the more tasks can be executed in parallel — this is horizontal scaling. If tasks (shoppers) are assigned partitions (products) with different numbers of rows, the workload for the cashiers will be uneven — leading to slower execution, overloaded memory and potential data skew.

Key components of the Spark ecosystemKey components of the Spark ecosystem

• Spark SQL: A module for working with data using SQL queries. It allows interaction with heterogeneous data sources (e.g., Parquet, ORC, PostgreSQL) as well as traditional tables.

• MLlib: A machine learning library with built-in algorithms (such as linear regression, decision trees, clustering, collaborative filtering) that simplifies the development of basic models.

• Structured Streaming: A tool for real-time data stream processing with exactly-once delivery semantics. Structured Streaming processes data in micro-batches, which brings it closer to real-time processing, but it is not fully streaming in the traditional sense (like Flink).

• GraphX: A library for working with graphs and performing graph computations. In addition to built-in graph manipulation operations, it also offers a library of common graph algorithms, such as PageRank.

Advantages of SparkAdvantages of Spark

Lazy Evaluation and execution optimizationLazy Evaluation and execution optimization

Spark uses a Lazy Evaluation mechanism: the platform does not execute operations immediately, but constructs a directed acyclic graph (DAG) of tasks. Only when an action is invoked does Spark optimize the graph, minimizing the number of data passes and eliminating unnecessary steps.

This reduces the load on the cluster and speeds up the processing of large datasets, which is especially beneficial for multi-step text transformations in LLMs.

Memory management and cachingMemory management and caching

Spark is capable of caching intermediate results in the cluster’s memory and executing tasks much faster than traditional frameworks.

For instance, a Java program written for MapReduce would contain around 50 lines of code, whereas on Spark (Scala), it would take only four.

For caching data, Spark offers two main options:

• Memory-only: Stores data only in memory, providing maximum execution speed but with the risk of data loss in case of failure.

• Memory-and-disk: A hybrid option that stores data on disk when memory is insufficient.

Spark also provides an interactive shell (REPL), allowing you to test the result of each line of code without having to program and run the entire task. This enables faster development and situational data analysis.

Data partitioning and shufflingData partitioning and shuffling

Spark divides data into partitions — independent fragments processed in parallel across worker nodes. This allows for scalable computations and speeds up the processing of massive datasets. However, some operations, like grouping or joins, require shuffling data between nodes.

Shuffling is one of the most resource-intensive operations in Spark. During this process, data is transferred across the network, written to disk and reloaded for subsequent steps. Without proper optimization, this can lead to slow task execution and even memory overflow.

To minimize the negative effects of shuffling, it is crucial to set the correct number of partitions. If there are too few partitions, the load on individual nodes increases, while too many partitions lead to overhead for coordination and data transfer. The issue of data skew — when data is unevenly distributed across partitions — should also be considered, as it can slow task execution.

Effective partitioning is especially important when working with LLMs, where the volume of textual data may be enormous and complex transformations require multiple operations on different parts of the corpus.

Distributed execution and fault toleranceDistributed execution and fault tolerance

One of the main advantages of Spark is its reliability in distributed computing. The system is designed with the possibility of failures in individual nodes in mind, meaning that the loss of part of the data or failure of a worker does not cause the entire task to fail.

Spark uses a lineage mechanism to track the chain of transformations applied to the data. Instead of storing intermediate results, Spark keeps a record of the operations that led to the current state of the data. If a worker fails, Spark can recover the lost data by simply re-executing the necessary steps for the relevant partition.

FlexibilityFlexibility

Spark supports several programming languages, including Python, Scala, Java and R. Moreover, it integrates with popular machine learning libraries such as TensorFlow, PyTorch, Hugging Face and others.

Why is Apache Spark useful for working with LLMs?Why is Apache Spark useful for working with LLMs?

Scalable text processingScalable text processing

Preparing text data for training large language models (LLMs) involves a sequence of complex operations — tokenization, lemmatization, text cleaning, n-gram extraction and vectorization. Spark enables these processes to run in parallel by splitting the text into partitions and processing them independently across multiple nodes in a cluster.

For instance, when working with a multi-terabyte text corpus, Spark can divide it into thousands of partitions, process each one separately and then merge the results into a unified dataset.

When parsing data, Spark efficiently extracts text from various formats (JSON, CSV, Parquet), normalizes case, removes extraneous characters and stopwords, performs tokenization and vectorization. Using the MLlib library, it can tokenize text in parallel and generate TF-IDF or Word2Vec vectors.

Seamless integration with Kafka, databases and file systems simplifies data collection and normalization. Data aggregation and filtering are streamlined with Spark SQL, while custom processing tasks can be implemented in Python or Scala using User Defined Functions (UDFs).

Beyond data preprocessing, Spark can also facilitate distributed LLM inference, optimizing query handling across a cluster with load balancing.

Model trainingModel training

While Spark is not a replacement for GPU-optimized deep learning frameworks, it supports integration with TensorFlowOnSpark and Horovod, making it a valuable tool for distributed training of foundational models. Algorithms like logistic regression or GBDT can be parallelized across multiple nodes, significantly accelerating training.

Additionally, Spark serves as a key orchestration component — preparing data, feeding it into TensorFlow or PyTorch for training and then processing inference results efficiently.

Inference and post-processingInference and post-processing

After training a model, Spark enhances inference speed and facilitates result analysis:

• Parallel inference: Spark partitions input data and distributes inference tasks across multiple nodes. This is particularly beneficial for large-scale text processing and real-time data streams.

• Result analysis: Spark SQL and the DataFrame API simplify inference result analysis, metric aggregation and error filtering.

• Visualization and debugging: Integration with tools like Jupyter and Databricks enables real-time visualization of inference processes and intermediate results within notebooks.

Processing data from multiple sourcesProcessing data from multiple sources

In real-world projects, LLM training data is often scattered across various systems — log files in S3, transactions in databases and user-generated content in Kafka. Spark provides connectors for dozens of popular data sources, streamlining data integration and transformation.

Through the platform, users can unify data from cloud storage services, SQL and NoSQL databases (PostgreSQL, Cassandra, MongoDB) and streaming platforms (e.g., Apache Kafka).

Time and resource optimizationTime and resource optimization

Spark not only accelerates data processing, but also optimizes resource usage. For instance, caching intermediate results in memory prevents redundant computations across multiple iterations. This is especially useful when fine-tuning model hyperparameters or testing different datasets.

Challenges and limitations of SparkChallenges and limitations of Spark

Network overload during data transfersNetwork overload during data transfers

Operations requiring data shuffling — such as joins and groupings — can lead to network congestion, especially when handling large text corpora or embeddings. The sheer volume of data moving between nodes can significantly impact performance.

To reduce network load, it’s best to optimize data structures in advance, using more compact storage formats like Parquet or minimizing the amount of transferred data. Thoughtful partitioning also helps — by logically grouping data beforehand, Spark can reduce inter-node communication.

Limited machine learning libraryLimited machine learning library

While Spark MLlib is useful for basic tasks, it lacks support for computationally intensive models like transformers. Since Spark is optimized for CPU-based processing, its performance lags behind GPU-accelerated frameworks.

Typically, Spark is used for data preprocessing and transformation, while actual model training happens in specialized frameworks like TensorFlow or PyTorch. This hybrid approach leverages Spark’s ability to process vast datasets efficiently while benefiting from the speed of GPU-optimized training.

Memory constraints with large datasetsMemory constraints with large datasets

Processing text or embeddings can lead to memory overflow, especially when caching intermediate results. While Spark’s in-memory caching speeds up computations, it may cause failures if resources are insufficient.

To prevent memory errors, careful resource allocation is essential. Limiting partition sizes and reducing concurrent tasks per executor can help. For large datasets, breaking them into smaller batches and processing incrementally is a more scalable solution.

Slow execution of iterative algorithmsSlow execution of iterative algorithms

Many machine learning algorithms require multiple passes over the data. In Memory-and-Disk storage mode, reloading data from disk during each iteration can slow down processing.

Switching to Memory-Only mode helps mitigate this by storing intermediate results in RAM, reducing redundant data reads. However, for extremely large models, a staged approach — using Spark for data preparation and a specialized framework for training — often delivers better performance.

Complex setup and monitoringComplex setup and monitoring

Deploying and maintaining a Spark cluster requires deep technical expertise, from configuring partitions and load balancing to optimizing memory and network parameters. Without continuous monitoring, performance bottlenecks can occur, making optimization more challenging.

To simplify operations, integrating monitoring tools like Prometheus for metric collection and Grafana for performance visualization can help. This enables real-time tracking of system bottlenecks and faster issue resolution.

ConclusionConclusion

Apache Spark enables developers to handle complex data preparation tasks and accelerate experimentation with large language models through parallel processing and flexible scaling. It also supports the creation of high-performance ML pipelines that can manage growing workloads and data volumes.

With automatic resource scaling and the reliability of distributed computing, teams can test hypotheses more quickly, optimize models more efficiently and speed up production deployment. In the fast-paced field of generative AI, where iteration speed is a competitive advantage, these capabilities are essential.

Apache Spark is a powerful distributed computing platform, but its complexity can create challenges in deployment, configuration and resource management. To address these issues, Nebius has launched Managed Service for Apache Spark, a fully managed data processing engine that simplifies and accelerates data engineering and machine learning workloads. By eliminating the need for manual server setup and maintenance, Managed Spark allows teams to focus on data processing without being burdened by infrastructure concerns.

With built-in autoscaling, the service automatically adjusts computing resources based on workload demands, ensuring efficient execution even for extensive datasets. By leveraging Managed Spark, organizations can streamline their data processing pipelines, minimize downtime and optimize resource allocation, ultimately reducing the overall costs of working with Spark at scale.