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MariaDB vs Apache Spark MLlib

MariaDB logo

MariaDB

Databases

The open source relational database for the enterprise

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: MariaDB jSON support using text fields rather than native binary type; lacks MySQL's JSON syntax and functions; 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.
  • They diverge on capability: MariaDB covers MySQL Compatibility, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where MariaDB and Apache Spark MLlib differ
AttributeMariaDBApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsLinux, Unix, Windows, macOSLinux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20091999

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 MariaDB

  • MySQL Compatibility
  • Aria Storage Engine
  • ColumnStore
  • Galera Cluster
  • MaxScale
  • Spider Engine
  • Temporal Tables
  • phpMyAdmin

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

What people use each for

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

MariaDB

  • Transaction processingnot Apache Spark MLlib
  • Data storagenot Apache Spark MLlib
  • Application backendnot Apache Spark MLlib
  • Reportingnot Apache Spark MLlib
  • Data analyticsnot Apache Spark MLlib

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 MariaDB
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot MariaDB
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot MariaDB
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot MariaDB

Where each one falls short

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

MariaDB

  • JSON support using text fields rather than native binary type; lacks MySQL's JSON syntax and functions
  • Galera Cluster maximum performance limited to the slowest node in cluster
  • InnoDB tables limited to 1,017 columns and 64 secondary indexes
  • Transaction size limits in Galera (128K rows and 2GB by default)
  • Less strict SQL type checking than PostgreSQL; allows implicit conversions

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.

Pricing, plan by plan

MariaDB

Free

No published plan breakdown. See the MariaDB review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose MariaDB if

  • You need mysql compatibility.
  • You want to start without paying.
  • You work on Linux, Unix, Windows, macOS.
  • You also want aria storage engine.
MariaDB review

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

Questions people ask

Is MariaDB or Apache Spark MLlib better?
Neither clearly leads. MariaDB starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, MariaDB or Apache Spark MLlib?
MariaDB starts at Free and Apache Spark MLlib at Free.
Does MariaDB or Apache Spark MLlib run on more platforms?
MariaDB runs on Linux, Unix, Windows, macOS. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use MariaDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is MariaDB best used for?
MariaDB is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what Apache Spark MLlib is typically brought in for.
What can MariaDB do that Apache Spark MLlib cannot?
MariaDB covers MySQL Compatibility, Aria Storage Engine, ColumnStore, Galera Cluster. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

MariaDB: Is MariaDB completely free and open source?

Yes. MariaDB Server is licensed under GPLv2 and guaranteed to remain perpetually free and open source, independent of any commercial entities.

Source
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.

MariaDB: Is MariaDB backward compatible with MySQL?

Yes. MariaDB was designed as a drop-in replacement for MySQL. Every application, driver, and configuration that worked with MySQL works with MariaDB without code changes.

Source
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.

MariaDB: What are the storage engine options in MariaDB?

MariaDB supports multiple storage engines including InnoDB (transactional, default), Aria (crash-safe, good for read-heavy workloads), and MyISAM. The Aria engine is faster than InnoDB for certain read-heavy queries and full table scans.

Source
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.

MariaDB: How much does MariaDB cost?

MariaDB Community Server is completely free to download and use. MariaDB offers paid enterprise support and managed cloud services for organizations needing professional support.

Source
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.

MariaDB: Does MariaDB support native JSON storage?

MariaDB stores JSON using text fields (the JSON type is an alias for LONGTEXT), not as a native binary type like MySQL does. JSON support exists but is less sophisticated than MySQL's JSON functions and syntax.

Source
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.

MariaDB: What scaling options does MariaDB provide?

MariaDB supports both scaling up (more cores, memory, storage) and scaling out (read replication, Galera Cluster with multi-node replication). However, Galera Cluster performance cannot exceed the slowest node in the cluster.

Source
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