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Databases · head to head

Apache Spark vs StarRocks

Apache Spark logo

Apache Spark

Databases

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

From
Free
Free plan
Yes
Visit Apache SparkFull review
StarRocks logo

StarRocks

Databases

Apache 2.0 MPP analytical database built for joins on open table formats

From
Free
Free plan
Yes
Visit StarRocksFull review

The short version

  • Both are free.
  • Only Apache Spark lists DataFrame and SQL APIs; only StarRocks lists primary key tables.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and StarRocks differ
AttributeApache SparkStarRocks
Pricing modelOpen-sourceOpen source, no licence fee
PlatformsWebLinux, Self-hosted

Identical on both: starting price (Free), free tier (Yes), category (Databases).

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 StarRocks

  • Cost-based optimiser
  • Lakehouse query engine
  • Primary key tables
  • Materialised views
  • Shared-data mode
  • MySQL wire protocol

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 StarRocks
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot StarRocks
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot StarRocks
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot StarRocks

StarRocks

  • Customer-facing analytics where queries join a fact table to several dimensions and must return in well under a secondnot Apache Spark
  • Querying an Iceberg lakehouse directly without copying data into a proprietary warehouse formatnot Apache Spark
  • Replacing a ClickHouse deployment that has become unmanageable because every new question needs another denormalised tablenot Apache Spark
  • Real-time analytics fed by change data capture where rows must be updated in place rather than appendednot 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

StarRocks

  • Self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode
  • CelerData is by far the dominant contributor despite Linux Foundation stewardship, so the practical roadmap risk is the same as any single-vendor open source project
  • It inherits a MySQL-flavoured SQL dialect from its Doris ancestry, so queries written for PostgreSQL, Snowflake or Trino need rewriting rather than porting
  • Ecosystem support is thinner than ClickHouse or Trino: fewer client libraries, fewer managed hosting options and a much smaller pool of engineers who have run it in production
  • Memory pressure under concurrent large joins is a common production failure, and the tuning knobs for query memory limits are unforgiving compared with a cloud warehouse that just scales

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

StarRocks

Free
  • StarRocksFree
    • Apache 2.0 licence
    • Linux Foundation governance
    • No usage or node limits
  • CelerData CloudOn request
    • Managed StarRocks from the primary contributor
    • BYOC and serverless deployment options
    • Enterprise support and SLAs

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 StarRocks if

  • You need cost-based optimiser.
  • You want to start without paying.
  • You work on Linux, Self-hosted.
  • You also want lakehouse query engine.
StarRocks review

Questions people ask

Is Apache Spark or StarRocks better?
Neither clearly leads. Apache Spark starts at Free and StarRocks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or StarRocks?
Apache Spark starts at Free and StarRocks at Free.
Does Apache Spark or StarRocks run on more platforms?
Apache Spark runs on Web. StarRocks runs on Linux, Self-hosted.
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 StarRocks is typically brought in for.
What can Apache Spark do that StarRocks cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Materialised views.

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.

StarRocks: Is StarRocks open source?

Yes, Apache 2.0, governed under the Linux Foundation since 2023.

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.

StarRocks: How does it differ from ClickHouse?

StarRocks is built for joins across a star schema with a cost-based optimiser; ClickHouse is fastest on denormalised single tables.

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.

StarRocks: Who maintains it?

CelerData, formerly StarRocks Inc, is the dominant contributor and sells the managed service.

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.

StarRocks: Can it query Iceberg tables directly?

Yes, along with Hudi, Delta Lake, Hive and Paimon, with a local cache for repeat queries.

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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