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Databases · head to head

Elasticsearch vs Apache Spark MLlib

Elasticsearch logo

Elasticsearch

Databases

The heart of the Elastic Stack for search and analytics

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: Elasticsearch eventual consistency model with 1-second default refresh interval, not suitable for real-time transactional 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.
  • They diverge on capability: Elasticsearch covers Full-text Search, 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 Elasticsearch and Apache Spark MLlib actually diverge.

Attributes where Elasticsearch and Apache Spark MLlib differ
AttributeElasticsearchApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsLinux, Windows, macOS, Docker, KubernetesLinux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20101999

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 Elasticsearch

  • Full-text Search
  • Real-time Analytics
  • Distributed Architecture
  • RESTful API
  • Schema-free JSON
  • Aggregations
  • Machine Learning
  • Kibana

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.

Elasticsearch

  • Real-time applicationsnot Apache Spark MLlib
  • Content managementnot Apache Spark MLlib
  • User profilesnot Apache Spark MLlib
  • Mobile backendsnot Apache Spark MLlib
  • Cachingnot 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 Elasticsearch
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Elasticsearch
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Elasticsearch
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Elasticsearch

Where each one falls short

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

Elasticsearch

  • Eventual consistency model with 1-second default refresh interval, not suitable for real-time transactional requirements
  • No support for ACID transactions or rollbacks; updates delete and re-insert documents
  • JVM-dependent architecture requires careful memory management and monitoring to prevent garbage collection issues at scale

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

Elasticsearch

Free
  • Self-ManagedFree
    • Open source
    • Self-hosted
  • Elasticsearch Cloud$16.4/month
    • Managed service
    • 14-day free trial

Apache Spark MLlib

Free

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

Which should you pick?

Choose Elasticsearch if

  • You need full-text search.
  • You want to start without paying.
  • You work on Linux, Windows, macOS, Docker, Kubernetes.
  • You also want real-time analytics.
Elasticsearch 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 Elasticsearch or Apache Spark MLlib better?
Neither clearly leads. Elasticsearch 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, Elasticsearch or Apache Spark MLlib?
Elasticsearch starts at Free and Apache Spark MLlib at Free.
Does Elasticsearch or Apache Spark MLlib run on more platforms?
Elasticsearch runs on Linux, Windows, macOS, Docker, Kubernetes. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Elasticsearch for free?
Both have a free tier, so you can try either at no cost before committing.
What is Elasticsearch best used for?
Elasticsearch is most often used for real-time applications, content management, user profiles, mobile backends. Of those, real-time applications and content management are not what Apache Spark MLlib is typically brought in for.
What can Elasticsearch do that Apache Spark MLlib cannot?
Elasticsearch covers Full-text Search, Real-time Analytics, Distributed Architecture, RESTful API. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Elasticsearch: Is Elasticsearch free?

Yes, Elasticsearch can be deployed as free and open-source software for self-managed installations. Elastic Cloud managed service starts at $16.40 per month, with a free 14-day trial available.

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.

Elasticsearch: Can I use Elasticsearch without Kibana?

Yes, Elasticsearch is a search engine independent of Kibana. Kibana is a visualization and analytics tool that works with Elasticsearch but is optional. You can use the Elasticsearch API directly for searching.

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.

Elasticsearch: Does Elasticsearch support real-time indexing?

Elasticsearch indexes data with a refresh interval, typically 1 second. Data becomes searchable after the refresh cycle, making it near-real-time but not instantaneous. This can be configured but impacts performance.

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.

Elasticsearch: What are Elasticsearch's scaling limitations?

Elasticsearch requires careful operational management at scale, including shard balancing, heap sizing, and monitoring. Large clusters can suffer from garbage collection issues and become expensive to operate.

Source
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.

Elasticsearch: Does Elasticsearch support transactions and rollbacks?

No, Elasticsearch does not support ACID transactions or rollbacks. Updates are expensive operations that delete and re-insert documents, making it unsuitable for transactional workloads.

Source
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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