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Apache Spark vs PhpStorm

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Free plan
Yes
Full reviewWebsite
PhpStorm logo

PhpStorm

Developer Tools

JetBrains IDE for PHP web development

From
$24/month
Free plan
No
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; PhpStorm requires an ongoing paid subscription with no free tier for commercial use.
  • They diverge on capability: Apache Spark covers Unified engine, PhpStorm covers PHP-aware coding assistance.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and PhpStorm actually diverge.

Attributes where Apache Spark and PhpStorm differ
AttributeApache SparkPhpStorm
Starting priceFree$24/month
Pricing modelopen-sourcesubscription
Free tierYesNo
PlatformsWebwindows, mac, linux
CategoryTechnologyDeveloper Tools
FoundedUnknown2000

Identical on both: user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in PhpStorm

  • PHP-aware coding assistance
  • Integrated debugger
  • Front-end tooling
  • Database tools
  • Version control integration
  • Docker and remote deployment

What people use each for

The jobs each tool is most often brought in to do.

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot PhpStorm
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot PhpStorm
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot PhpStorm
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot PhpStorm

PhpStorm

  • Building PHP web applications with Laravel or Symfonynot Apache Spark
  • Debugging PHP code with Xdebugnot Apache Spark
  • Working across PHP, HTML, CSS, and JavaScript in one projectnot Apache Spark
  • Managing databases alongside application codenot Apache Spark

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

PhpStorm

  • Requires an ongoing paid subscription with no free tier for commercial use.
  • Heavier and slower to start than lightweight code editors.
  • Some advanced framework-specific tooling requires additional configuration or plugins.
  • Renewal pricing rises for lapsed subscriptions after the grace period.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

PhpStorm

$24/month
  • Individual$24/month
    • Personal use license
    • Access to PhpStorm
    • Free for students and open-source maintainers
  • Organization$34/month
    • Business use license
    • Access to PhpStorm
    • Volume and renewal discounts

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose PhpStorm if

  • You need php-aware coding assistance.
  • You work on windows, mac, linux.
  • You also want integrated debugger.
PhpStorm review

Questions people ask

Is Apache Spark or PhpStorm better?
Neither clearly leads. Apache Spark starts at Free and PhpStorm at $24/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or PhpStorm?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $24/month for PhpStorm.
Does Apache Spark or PhpStorm run on more platforms?
Apache Spark runs on Web. PhpStorm runs on windows, mac, linux.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. PhpStorm starts at $24/month.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what PhpStorm is typically brought in for.
What can Apache Spark do that PhpStorm cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. PhpStorm covers PHP-aware coding assistance, Integrated debugger, Front-end tooling, Database tools.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

PhpStorm: How much does PhpStorm cost?

PhpStorm is sold via JetBrains Toolbox subscription starting around $24/month for individuals and $34/month for organizations, with free licenses for students and open-source maintainers.

Source
Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

PhpStorm: Is there a free trial?

JetBrains offers a free trial of PhpStorm so developers can test it before subscribing.

Source
Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

PhpStorm: What platforms does PhpStorm support?

PhpStorm is a desktop IDE available on Windows, macOS, and Linux via JetBrains Toolbox.

Source
Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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