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Apache Spark MLlib vs Timeplus

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
Timeplus logo

Timeplus

Databases

Streaming SQL engine built on ClickHouse internals, shipping as one small binary

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.; Timeplus proton, the free version, is single-node by design, so any requirement for high availability or horizontal scale forces the commercial licence; the open source edition is a trial in practical terms.
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Timeplus covers Streaming SQL.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and Timeplus differ
AttributeApache Spark MLlibTimeplus
Pricing modelopen-sourcePer month for cloud, quoted for self-hosted
PlatformsLinux, macOS, WindowsLinux, macOS, Docker, Kubernetes, Web
CategoryMachine LearningDatabases
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 Timeplus

  • Streaming SQL
  • Unified streaming and historical
  • ClickHouse-based engine
  • Single binary deployment
  • External streams
  • Materialised views

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

Timeplus

  • Real-time alerting on Kafka topics where standing up a Flink cluster is more work than the use case justifiesnot Apache Spark MLlib
  • Fraud or anomaly detection that must join a live event stream against recent history in one querynot Apache Spark MLlib
  • Streaming ETL from Kafka or MySQL change data capture into ClickHouse without writing Javanot Apache Spark MLlib
  • A small data team that needs continuous aggregation but has no platform engineers to operate JVM infrastructurenot 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.

Timeplus

  • Proton, the free version, is single-node by design, so any requirement for high availability or horizontal scale forces the commercial licence; the open source edition is a trial in practical terms.
  • It is a young project against Apache Flink’s decade of production history, so the hiring pool, the connector library and the body of known failure modes are all much smaller.
  • Inheriting ClickHouse internals also inherits ClickHouse constraints: memory-hungry queries, awkward updates and a SQL dialect that is not portable to other engines.
  • Exactly-once semantics and state recovery guarantees are less battle-tested than Flink checkpointing, which matters if the pipeline moves money.
  • Cloud pricing is by provisioned instance size rather than usage, so a bursty workload pays for peak capacity around the clock or has to be resized by hand.

Pricing, plan by plan

Apache Spark MLlib

Free

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

Timeplus

Free
  • Timeplus ProtonFree
    • Apache 2.0 licence
    • Single node only
    • Full streaming SQL engine
  • Timeplus Cloud$199/month
    • One to thirty-two CPUs
    • 4 GB to 128 GB memory
    • From 250 GB SSD storage
  • Self-hosted or BYOC$undefined/year
    • Multi-node clustering
    • Kubernetes or bare metal
    • Customisable compute and storage

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 Timeplus if

  • You need streaming sql.
  • You want to start without paying.
  • You work on Linux, macOS, Docker, Kubernetes, Web.
  • You also want unified streaming and historical.
Timeplus review

Questions people ask

Is Apache Spark MLlib or Timeplus better?
Neither clearly leads. Apache Spark MLlib starts at Free and Timeplus at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Timeplus?
Apache Spark MLlib starts at Free and Timeplus at Free.
Does Apache Spark MLlib or Timeplus run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Timeplus runs on Linux, macOS, Docker, Kubernetes, Web.
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 Timeplus is typically brought in for.
What can Apache Spark MLlib do that Timeplus cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Timeplus covers Streaming SQL, Unified streaming and historical, ClickHouse-based engine, Single binary deployment.

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.

Timeplus: Is Timeplus open source?

The core engine, Timeplus Proton, is Apache 2.0. Timeplus Enterprise and Cloud are commercial.

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.

Timeplus: What is the difference from Flink?

Timeplus is one binary with SQL as the only interface; Flink is a JVM cluster with a Java and SQL API and far more operational surface.

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.

Timeplus: Can Proton run in production?

It can, but it is single-node only, so there is no high availability without the commercial edition.

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.

Timeplus: How much is the cloud?

From 199 US dollars a month, sized by CPU and memory, with a fourteen day trial.

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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