Softwr

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

Scalable machine learning on Apache Spark

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 apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
  • They diverge on capability: Elasticsearch covers Full-text Search, Apache Spark MLlib covers Classification.

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

  • Classification
  • Regression
  • Clustering
  • Collaborative filtering
  • Feature engineering
  • Apache Spark
  • Hadoop
  • Kafka

Both cover

  • Linux support
  • Windows support
  • Mac support

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

  • Machine learningnot Elasticsearch
  • Data sciencenot Elasticsearch
  • Distributed computingnot 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

  • Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.

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 classification.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want regression.
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 Classification, Regression, Clustering, Collaborative filtering. Both handle Linux support, Windows support, Mac support.

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: How much does Apache Spark MLlib cost?

MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.

Source
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: What licensing does MLlib use?

MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.

Source
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: How do I use MLlib?

MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.

Source
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
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
Share

Related pages

More on Elasticsearch

More on Apache Spark MLlib

Keep looking

Other head to heads