Introduction

Spring Batch and Apache Spark can both run ETL pipelines, but they are optimized for different shapes of work. A useful benchmark does not ask which framework is "faster" in the abstract. It asks which one is faster for a specific data volume, transformation pattern, operational model, and infrastructure budget.

What you are really comparing

Spring Batch is a batch-processing framework for JVM applications. It shines when ETL is part of an application stack, when chunked database reads and writes matter, and when restartability and transaction control are first-class requirements.

Spark is a distributed data engine. It shines when datasets are large enough to benefit from parallel execution across cores or machines, or when transformations are computationally heavy enough to offset cluster overhead.

That distinction matters because a benchmark between them is often also a benchmark between:

• single-node versus distributed execution
• database-centric jobs versus file- and analytics-centric jobs
• low startup overhead versus higher startup overhead with more parallelism

A minimal Spring Batch ETL step

A typical Spring Batch job reads items, processes them, and writes them in chunks. The framework handles retries, restarts, and transaction boundaries well.

1@Bean
2public Step importUsersStep(JobRepository jobRepository,
3                            PlatformTransactionManager txManager) {
4    return new StepBuilder("importUsers", jobRepository)
5        .<String, String>chunk(1000, txManager)
6        .reader(() -> "[email protected]")
7        .processor(item -> item.toUpperCase())
8        .writer(items -> {
9            for (String item : items) {
10                System.out.println(item);
11            }
12        })
13        .build();
14}

That is simplified, but it shows the operational model clearly: stream records through managed chunks and commit work in controlled units.

A minimal Spark ETL job

Spark favors dataset-style transformations that can be parallelized across partitions.

1from pyspark.sql import SparkSession
2from pyspark.sql.functions import upper
3
4spark = SparkSession.builder.appName("etl-demo").getOrCreate()
5
6df = spark.createDataFrame(
7    [("[email protected]",), ("[email protected]",)],
8    ["email"]
9)
10
11result = df.select(upper("email").alias("email"))
12result.show()

This example is tiny, but the important point is the execution model. Spark builds a plan, optimizes it, and then runs that plan across partitions. For large data and expensive transforms, that can be a major advantage.

Benchmark dimensions that actually matter

If you want a meaningful comparison, define the workload before choosing the winner. Good benchmark dimensions include:

• input size, such as thousands, millions, or billions of rows
• transform complexity, such as simple field mapping versus joins and aggregations
• source and sink type, such as relational databases, object storage, or parquet files
• startup cost, especially important for short Spark jobs
• failure handling, including restart behavior and partial progress

A 500 MB nightly import from one database table to another often favors Spring Batch because startup is cheap and database-oriented chunk processing fits naturally. A multi-gigabyte enrichment pipeline with joins and wide scans often favors Spark because parallel execution changes the throughput ceiling.

Read benchmark results carefully

A lot of benchmark writeups accidentally measure the wrong thing. If Spark is tested on a cluster and Spring Batch on one laptop core, the result is mostly about hardware. If the Spring Batch job writes every row through an ORM and the Spark job writes parquet files, the result is mostly about sink behavior.

Useful benchmark reporting includes:

• machine or cluster size
• dataset shape and row counts
• serialization format
• warm versus cold runs
• total wall-clock time and resource cost

Without those details, the benchmark is difficult to trust.

Choosing between them

Spring Batch is often the better fit when ETL is operational business logic running inside a traditional application environment. Spark is often the better fit when data volume or transformation complexity justifies distributed execution.

That means the best answer is frequently hybrid. Many teams use Spring Batch for dependable application-side jobs and Spark for heavy analytical pipelines rather than forcing a single tool into both roles.

Common Pitfalls

• Declaring one framework faster without defining the workload.
• Ignoring Spark startup overhead on small jobs.
• Ignoring transaction, restart, and operational requirements when comparing raw throughput.
• Benchmarking different hardware or storage systems and calling it a framework comparison.
• Measuring only runtime and not the operational complexity of running the pipeline in production.

Summary

• Spring Batch and Spark solve overlapping ETL problems with very different execution models.
• Spring Batch is strong for chunked, transactional, application-integrated batch jobs.
• Spark is strong for large-scale or computation-heavy distributed data processing.
• A fair benchmark must control for data size, transform type, hardware, and sink behavior.
• The correct choice depends more on workload shape than on brand-level speed claims.