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QuestDB vs Apache Spark MLlib

QuestDB logo

QuestDB

Databases

Fast open source time-series database for high throughput ingestion

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: QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features; 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: QuestDB covers High Throughput Ingestion, 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 QuestDB and Apache Spark MLlib actually diverge.

Attributes where QuestDB and Apache Spark MLlib differ
AttributeQuestDBApache Spark MLlib
PlatformsDocker, Kubernetes, Cloud (AWS, Azure, GCP)Linux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20141999

Identical on both: starting price (Free), pricing model (open-source), 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 QuestDB

  • High Throughput Ingestion
  • SQL Support
  • Time-series Optimization
  • SIMD Vectorization
  • Column-oriented Storage
  • Built-in Web Console
  • InfluxDB Line Protocol
  • PostgreSQL

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.

QuestDB

  • Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot Apache Spark MLlib
  • Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot Apache Spark MLlib
  • Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not 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 QuestDB
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot QuestDB
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot QuestDB
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot QuestDB

Where each one falls short

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

QuestDB

  • Open-source edition lacks high-availability, distributed architecture, and enterprise security features
  • Enterprise edition pricing not published; requires contacting sales for custom quote
  • Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements

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

QuestDB

Free

No published plan breakdown. See the QuestDB review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose QuestDB if

  • You need high throughput ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
  • You also want sql support.
QuestDB 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 QuestDB or Apache Spark MLlib better?
Neither clearly leads. QuestDB 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, QuestDB or Apache Spark MLlib?
QuestDB starts at Free and Apache Spark MLlib at Free.
Does QuestDB or Apache Spark MLlib run on more platforms?
QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP). Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use QuestDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is QuestDB best used for?
QuestDB is most often used for time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds, real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics, applications requiring multi-tier storage (hot ingest, real-time sql, cold parquet archive). Of those, time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds and real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics are not what Apache Spark MLlib is typically brought in for.
What can QuestDB do that Apache Spark MLlib cannot?
QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

QuestDB: How much does QuestDB Enterprise cost?

QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.

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.

QuestDB: Does QuestDB offer a free version?

Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.

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.

QuestDB: What deployment options does QuestDB offer?

QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.

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.

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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