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Databases · head to head

Memcached vs Apache Spark MLlib

M

Memcached

Databases

Distributed memory object caching system

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: Memcached no persistence at all: restart a node and its cache is gone, which every design must assume; 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: Memcached covers In-memory key-value cache, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Memcached and Apache Spark MLlib differ
AttributeMemcachedApache Spark MLlib
Pricing modelOpen source, no licence fee; managed cloud billed separatelyopen-source
PlatformsLinux, macOS, Windows, Docker, Self-hostedLinux, macOS, Windows
CategoryDatabasesMachine Learning
FoundedUnknown1999

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 Memcached

  • In-memory key-value cache
  • Multithreaded
  • Client-side sharding
  • Predictable memory use

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.

Memcached

  • Caching expensive database query results to cut loadnot Apache Spark MLlib
  • Session storage where losing sessions on restart is acceptablenot Apache Spark MLlib
  • Fronting an API whose responses are costly and change slowlynot 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 Memcached
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Memcached
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Memcached
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Memcached

Where each one falls short

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

Memcached

  • No persistence at all: restart a node and its cache is gone, which every design must assume
  • No replication or failover, so losing a node loses that share of the cache
  • Only simple key-value, with none of the lists, sorted sets or streams Redis offers
  • Values are capped at 1MB by default, which surprises teams caching large documents

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

Memcached

Free
  • MemcachedFree
    • Full functionality
    • Self-hosted
    • No usage limits

Apache Spark MLlib

Free

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

Which should you pick?

Choose Memcached if

  • You need in-memory key-value cache.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Self-hosted.
  • You also want multithreaded.
Memcached 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 Memcached or Apache Spark MLlib better?
Neither clearly leads. Memcached 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, Memcached or Apache Spark MLlib?
Memcached starts at Free and Apache Spark MLlib at Free.
Does Memcached or Apache Spark MLlib run on more platforms?
Memcached runs on Linux, macOS, Windows, Docker, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Memcached for free?
Both have a free tier, so you can try either at no cost before committing.
What is Memcached best used for?
Memcached is most often used for caching expensive database query results to cut load, session storage where losing sessions on restart is acceptable, fronting an api whose responses are costly and change slowly. Of those, caching expensive database query results to cut load and session storage where losing sessions on restart is acceptable are not what Apache Spark MLlib is typically brought in for.
What can Memcached do that Apache Spark MLlib cannot?
Memcached covers In-memory key-value cache, Multithreaded, Client-side sharding, Predictable memory use. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Memcached: Is Memcached free?

Yes, open source with no licence fee.

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.

Memcached: Memcached or Redis?

Memcached is a pure cache: simpler, multithreaded and very predictable. Redis adds persistence, replication and rich data structures, which is why it is the default choice unless you specifically want a cache and nothing more.

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.

Memcached: Does Memcached persist data?

No. Everything is in memory and lost on restart, by design.

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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