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Apache Spark vs Postgres

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Rated
-
Full reviewWebsite
Postgres logo

Postgres

Technology

The world's most advanced open source database

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; Postgres each major version is supported for only 5 years after its initial release, after which it is end-of-life
  • They diverge on capability: Apache Spark covers Unified engine, Postgres covers ACID compliance.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Postgres actually diverge.

Attributes where Apache Spark and Postgres differ
AttributeApache SparkPostgres
PlatformsWebLinux, Windows, Macos, Docker
FoundedUnknown1996

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated), category (Technology).

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

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in Postgres

  • ACID compliance
  • Complex queries
  • Foreign keys
  • Triggers
  • Views
  • Stored procedures
  • JSON/JSONB support
  • Full-text search

What people use each for

The jobs each tool is most often brought in to do.

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Postgres
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Postgres
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Postgres
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Postgres

Postgres

  • Running a general purpose relational database for applicationsnot Apache Spark
  • Self-hosting an open source SQL database with no licence feenot Apache Spark
  • Workloads needing extensions, JSON and full text search in one enginenot Apache Spark

Where each one falls short

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

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

Postgres

  • Each major version is supported for only 5 years after its initial release, after which it is end-of-life
  • Major version upgrades break on-disk compatibility and require a full dump and reload or the pg_upgrade tool
  • New major versions ship about once a year, so staying supported means a disruptive upgrade cycle
  • Minor releases contain only frequently-encountered bug fixes, low-risk fixes, security issues and data corruption fixes, so feature gaps are not addressed within a major version
  • There is no vendor SLA; commercial support must be bought separately from third party professional services listed by the project

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Postgres

Free
  • Community EditionFree
    • Full database features
    • No limitations
    • Community support

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose Postgres if

  • You need acid compliance.
  • You want to start without paying.
  • You work on Linux, Windows, Macos, Docker.
  • You also want complex queries.
Postgres review

Questions people ask

Is Apache Spark or Postgres better?
Neither clearly leads. Apache Spark starts at Free and Postgres at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Postgres?
Apache Spark starts at Free and Postgres at Free.
Does Apache Spark or Postgres run on more platforms?
Apache Spark runs on Web. Postgres runs on Linux, Windows, Macos, Docker.
Can I use Apache Spark for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what Postgres is typically brought in for.
What can Apache Spark do that Postgres cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Postgres covers ACID compliance, Complex queries, Foreign keys, Triggers.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

Postgres: How much does PostgreSQL cost to use?

PostgreSQL is completely free to download, install, and use. No licensing fees, subscription costs, or per-seat charges apply. The database is open source under the PostgreSQL License. Source: https://www.postgresql.org

Source
Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

Postgres: Are there commercial PostgreSQL support options available?

Official PostgreSQL (the project) is free. Commercial PostgreSQL services such as hosting, professional support, training, and managed database services are offered by third-party vendors, not the PostgreSQL project itself. Source: https://www.postgresql.org

Source
Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

Postgres: Do I need a license to use PostgreSQL commercially?

No. PostgreSQL is open source under the PostgreSQL License, which permits free commercial use without royalties, licensing fees, or support obligations. Source: https://www.postgresql.org

Source
Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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