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Machine Learning · head to head

Apache Spark MLlib vs Stellarium

Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Rated
-
Full reviewWebsite
Stellarium logo

Stellarium

Education

Free open source desktop planetarium software, distinct from the paid Stellarium Mobile Plus app

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.; Stellarium it is desktop-only software, so it has no native touch-optimised phone interface; the similarly named mobile app is a different paid product from a different company.
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Stellarium covers Photorealistic sky rendering.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Apache Spark MLlib and Stellarium actually diverge.

Attributes where Apache Spark MLlib and Stellarium differ
AttributeApache Spark MLlibStellarium
Pricing modelopen-sourceOpen source, no licence fee
PlatformsLinux, macOS, WindowsWindows, macOS, Linux
CategoryMachine LearningEducation
Founded1999Unknown

Identical on both: starting price (Free), free tier (Yes), 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 MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

Only in Stellarium

  • Photorealistic sky rendering
  • Large object catalogue
  • Telescope control
  • Scripting engine
  • Free and open source
  • Plugin ecosystem

What people use each for

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

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Stellarium
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Stellarium
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Stellarium
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Stellarium

Stellarium

  • A student or educator wanting a serious, scriptable planetarium tool without a budget line itemnot Apache Spark MLlib
  • An amateur astronomer wanting telescope control from desktop software they can inspect and modifynot Apache Spark MLlib
  • Someone running a planetarium display or public astronomy event on a scripted shownot Apache Spark MLlib
  • A hobbyist who wants a large object catalogue without paying a subscription, and does not need a phone appnot Apache Spark MLlib

Where each one falls short

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

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Stellarium

  • It is desktop-only software, so it has no native touch-optimised phone interface; the similarly named mobile app is a different paid product from a different company.
  • As a volunteer-maintained open source project, support is community-based rather than a guaranteed commercial help desk.
  • The interface, while capable, is less immediately intuitive for a first-time user than a polished commercial phone app.
  • Telescope control and some advanced plugins require additional setup that a non-technical user may find fiddly.
  • Because it shares a name with the unrelated paid Stellarium Mobile Plus app, buyers researching pricing online can easily conflate the two products.

Pricing, plan by plan

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Stellarium

Free
  • Stellarium DesktopFree
    • Full software, GPL licensed
    • No account, ads or subscription
    • Community-developed plugins

Which should you pick?

Choose Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Choose Stellarium if

  • You need photorealistic sky rendering.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want large object catalogue.
Stellarium review

Questions people ask

Is Apache Spark MLlib or Stellarium better?
Neither clearly leads. Apache Spark MLlib starts at Free and Stellarium at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Stellarium?
Apache Spark MLlib starts at Free and Stellarium at Free.
Does Apache Spark MLlib or Stellarium run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Stellarium runs on Windows, macOS, Linux.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Stellarium is typically brought in for.
What can Apache Spark MLlib do that Stellarium cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Stellarium covers Photorealistic sky rendering, Large object catalogue, Telescope control, Scripting engine.

Answered from the vendors’ own pages

Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

Stellarium: Is Stellarium really free?

The desktop software is free and open source under the GPL licence. The separate mobile app, Stellarium Mobile Plus, is a different paid product.

Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

Stellarium: Is the mobile app made by the same team?

No. Stellarium Mobile Plus is built by Noctua Software, a different company, and is sold on subscription.

Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

Stellarium: What platforms does the free version run on?

Windows, macOS and Linux desktop.

Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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