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Machine Learning · head to head

Apache Spark MLlib vs TiDB

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
TiDB logo

TiDB

Databases

Apache 2.0 distributed SQL database with MySQL wire compatibility and a separate columnar replica for analytical queries.

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.; TiDB a production cluster needs several placement driver, storage and SQL nodes before it is fault tolerant, so the minimum viable footprint is far larger than a MySQL server and TiDB is never the economical choice for a small database.
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, TiDB covers MySQL wire compatibility.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and TiDB differ
AttributeApache Spark MLlibTiDB
Pricing modelopen-sourcefreemium
PlatformsLinux, macOS, WindowsCloud, AWS, Azure, Google Cloud Platform, Self-managed
CategoryMachine LearningDatabases
Founded19992015

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 TiDB

  • MySQL wire compatibility
  • Horizontal write scaling
  • Distributed ACID transactions
  • TiFlash columnar replica
  • Automatic rebalancing
  • Raft replication
  • Apache 2.0 licence
  • Online schema change

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

TiDB

  • A MySQL workload that has hit the write ceiling of a single primary and would otherwise need an application-level sharding layernot Apache Spark MLlib
  • Reporting that must run against current transactional data, where the columnar replica removes the delay and the cost of an ETL pipelinenot Apache Spark MLlib
  • Multi-region deployments needing a single logical database with automatic failover rather than manual primary promotionnot Apache Spark MLlib
  • Migrating off a sharded MySQL estate where the sharding logic in the application has become the main source of bugs and operational toilnot 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.

TiDB

  • A production cluster needs several placement driver, storage and SQL nodes before it is fault tolerant, so the minimum viable footprint is far larger than a MySQL server and TiDB is never the economical choice for a small database.
  • Every transaction takes a timestamp from the placement driver and crosses the network to storage nodes, so simple point queries are slower than on single-node MySQL and latency-sensitive paths need to be measured, not assumed.
  • MySQL compatibility is at the wire and dialect level but not complete; stored procedures, triggers and events are not supported, so an application that pushed logic into the database cannot simply be repointed.
  • The columnar replica is an extra full copy of the data on its own nodes, so hybrid analytics roughly doubles storage and adds hardware that must be sized and paid for separately.
  • Operating it well requires cluster-specific expertise in TiUP or the Kubernetes operator, region hot spots, and rebalancing behaviour, so the licence is free but the running cost includes an engineer who understands distributed storage.

Pricing, plan by plan

Apache Spark MLlib

Free

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

TiDB

Free
  • ServerlessFree
    • 5GB storage
    • 50M request units
    • Free forever tier
  • Dedicated$250/month
    • Dedicated resources
    • SLA guarantees
    • Enterprise support

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 TiDB if

  • You need mysql wire compatibility.
  • You want to start without paying.
  • You work on Cloud, AWS, Azure, Google Cloud Platform, Self-managed.
  • You also want horizontal write scaling.
TiDB review

Questions people ask

Is Apache Spark MLlib or TiDB better?
Neither clearly leads. Apache Spark MLlib starts at Free and TiDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or TiDB?
Apache Spark MLlib starts at Free and TiDB at Free.
Does Apache Spark MLlib or TiDB run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. TiDB runs on Cloud, AWS, Azure, Google Cloud Platform, Self-managed.
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 TiDB is typically brought in for.
What can Apache Spark MLlib do that TiDB cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. TiDB covers MySQL wire compatibility, Horizontal write scaling, Distributed ACID transactions, TiFlash columnar replica.

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.

TiDB: Is TiDB a drop-in replacement for MySQL?

At the protocol and dialect level it is close, and most applications connect unchanged. Stored procedures, triggers and events are not supported, and latency characteristics differ, so it needs testing rather than assumption.

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.

TiDB: What licence is it under?

Apache 2.0, for both TiDB and the underlying TiKV storage engine. TiKV is a graduated CNCF project, which is a meaningful governance signal in a market where several competitors moved to source-available licences.

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.

TiDB: Do I need TiFlash?

Only for analytical queries. It is an optional columnar replica; without it TiDB is a distributed transactional database. With it you get analytics on live data at the cost of an additional full copy.

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.

TiDB: Is the managed cloud the same software?

TiDB Cloud runs the same engine, with the control plane, scaling and operational tooling provided as a service. The entry tier is metered differently from a dedicated cluster, so the cost model rather than the engine is what changes.

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.

TiDB: When is TiDB the wrong choice?

When the database is small enough for one server, when latency on single-row lookups is the primary constraint, or when the application depends on MySQL stored procedures and triggers.

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