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Databases · head to head

ClickHouse vs Apache Spark MLlib

ClickHouse logo

ClickHouse

Databases

Fast open-source column-oriented database for real-time analytics

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

Scalable machine learning on Apache Spark

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: ClickHouse limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: ClickHouse covers Column-oriented Storage, Apache Spark MLlib covers Classification.

Where they differ

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

Attributes where ClickHouse and Apache Spark MLlib differ
AttributeClickHouseApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsLinux, macOS, Windows (via Docker)Linux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20211999

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 ClickHouse

  • Column-oriented Storage
  • Real-time Analytics
  • SQL Support
  • Linear Scalability
  • Data Compression
  • Vectorized Query Execution
  • Approximate Calculations
  • S3

Only in Apache Spark MLlib

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Databricks

Both cover

  • Kafka
  • Linux support
  • Mac support

What people use each for

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

ClickHouse

  • Business intelligencenot Apache Spark MLlib
  • Data warehousingnot Apache Spark MLlib
  • Real-time analyticsnot Apache Spark MLlib
  • Reportingnot Apache Spark MLlib
  • Machine learning

Apache Spark MLlib

  • Machine learning
  • Data sciencenot ClickHouse
  • Distributed computingnot ClickHouse

Both are used for machine learning, on those jobs the choice comes down to price and fit rather than capability.

Where each one falls short

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

ClickHouse

  • Limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems
  • Requires upfront schema design discipline with MergeTree engine choices and sort/partition keys
  • Experimental vector search support, not production-ready for vector operations
  • Different query syntax from standard SQL requiring migration planning
  • Limited JOIN capabilities compared to traditional relational databases
  • Migration complexity with 2-4 weeks estimated for data type mapping and query translation

Apache Spark MLlib

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

Pricing, plan by plan

ClickHouse

Free

No published plan breakdown. See the ClickHouse review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose ClickHouse if

  • You need column-oriented storage.
  • You want to start without paying.
  • You work on Linux, macOS, Windows (via Docker).
  • You also want real-time analytics.
ClickHouse review

Choose Apache Spark MLlib if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
Apache Spark MLlib review

Questions people ask

Is ClickHouse or Apache Spark MLlib better?
Neither clearly leads. ClickHouse 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, ClickHouse or Apache Spark MLlib?
ClickHouse starts at Free and Apache Spark MLlib at Free.
Does ClickHouse or Apache Spark MLlib run on more platforms?
ClickHouse runs on Linux, macOS, Windows (via Docker). Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use ClickHouse for free?
Both have a free tier, so you can try either at no cost before committing.
What is ClickHouse best used for?
ClickHouse is most often used for business intelligence, data warehousing, real-time analytics, reporting. Of those, business intelligence and data warehousing are not what Apache Spark MLlib is typically brought in for.
What can ClickHouse do that Apache Spark MLlib cannot?
ClickHouse covers Column-oriented Storage, Real-time Analytics, SQL Support, Linear Scalability. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Kafka, Linux support, Mac support.

Answered from the vendors’ own pages

ClickHouse: What is ClickHouse best used for?

ClickHouse is optimized for analytical workloads on large datasets. It excels at fast aggregations and queries, being 10-100x faster than PostgreSQL on large aggregations.

Source
Apache Spark MLlib: How much does Apache Spark MLlib cost?

MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.

Source
ClickHouse: Does ClickHouse support transactions?

ClickHouse has limited transaction support and expensive UPDATE/DELETE operations. It is not suitable for transactional workloads requiring strict ACID guarantees.

Source
Apache Spark MLlib: What licensing does MLlib use?

MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.

Source
ClickHouse: How does ClickHouse compare to PostgreSQL?

ClickHouse is 10-100x faster for analytics but PostgreSQL is better for transactional workloads. Many teams use both: PostgreSQL for writes via MaterializedPostgreSQL replication to ClickHouse for analytics.

Source
Apache Spark MLlib: How do I use MLlib?

MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.

Source
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