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Databases · head to head

Apache Doris vs Apache Spark

Apache Doris logo

Apache Doris

Databases

MPP analytical database with a MySQL wire protocol and sub-second aggregation on wide tables

From
Free
Free plan
Yes
Visit Apache DorisFull review
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

The short version

  • Both are free.
  • Only Apache Doris lists MySQL wire protocol; only Apache Spark lists unified engine.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Doris and Apache Spark differ
AttributeApache DorisApache Spark
Pricing modelOpen source, no licence feeOpen-source
PlatformsLinux, Self-hostedWeb

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 Doris

  • MySQL wire protocol
  • Aggregate and unique key models
  • Materialised views
  • Multi-catalogue federation
  • Routine load from Kafka
  • Compute storage separation
  • Inverted indexes
  • Workload groups

Only in Apache Spark

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

What people use each for

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

Apache Doris

  • A team whose MySQL read replica can no longer serve reporting queries and wants an OLAP engine its existing drivers already speaknot Apache Spark
  • A real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent usersnot Apache Spark
  • An ad or ecommerce platform that needs updates and deletes on analytical tables, which append-only OLAP engines handle badlynot Apache Spark
  • A data team that wants one SQL endpoint over both internal tables and existing Hive or Iceberg tables in the lakenot Apache Spark

Apache Spark

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

Where each one falls short

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

Apache Doris

  • Two competing commercial vendors, VeloDB and SelectDB, were founded by overlapping core contributors, which makes the long-term governance and roadmap of the project harder to predict than a single-sponsor project
  • A large share of design discussion, issue reports and documentation detail originates in Chinese, so teams that do not read it get a thinner picture of known problems and workarounds
  • Operating a cluster means managing frontend and backend node roles, tablet balancing and compaction tuning, and compaction backlogs under heavy upsert load are a recurring production complaint
  • The MySQL protocol compatibility is at the wire level, not full MySQL semantics, so queries and functions still need porting and the familiarity can mislead
  • Managed cloud availability outside China and major clouds is limited compared with ClickHouse or Snowflake, so many Western adopters end up self-hosting whether they wanted to or not

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

Pricing, plan by plan

Apache Doris

Free
  • Apache DorisFree
    • Apache 2.0 licence with no usage restrictions
    • All engine features included
    • Community support via mailing list and Slack

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Which should you pick?

Choose Apache Doris if

  • You need mysql wire protocol.
  • You want to start without paying.
  • You work on Linux, Self-hosted.
  • You also want aggregate and unique key models.
Apache Doris review

Choose Apache Spark if

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

Questions people ask

Is Apache Doris or Apache Spark better?
Neither clearly leads. Apache Doris starts at Free and Apache Spark at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Doris or Apache Spark?
Apache Doris starts at Free and Apache Spark at Free.
Does Apache Doris or Apache Spark run on more platforms?
Apache Doris runs on Linux, Self-hosted. Apache Spark runs on Web.
Can I use Apache Doris for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Doris best used for?
Apache Doris is most often used for a team whose mysql read replica can no longer serve reporting queries and wants an olap engine its existing drivers already speak, a real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent users, an ad or ecommerce platform that needs updates and deletes on analytical tables, which append-only olap engines handle badly, a data team that wants one sql endpoint over both internal tables and existing hive or iceberg tables in the lake. Of those, a team whose mysql read replica can no longer serve reporting queries and wants an olap engine its existing drivers already speak and a real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent users are not what Apache Spark is typically brought in for.
What can Apache Doris do that Apache Spark cannot?
Apache Doris covers MySQL wire protocol, Aggregate and unique key models, Materialised views, Multi-catalogue federation. Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.

Answered from the vendors’ own pages

Apache Doris: Is Apache Doris really free?

Yes, it is Apache 2.0 with no usage restrictions. The commercial products are managed services from VeloDB and SelectDB.

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.

Apache Doris: Can I use my MySQL tools with it?

Yes, it implements the MySQL wire protocol, so clients and BI connectors attach without a new driver, though SQL semantics differ.

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.

Apache Doris: How does it compare with ClickHouse?

Doris handles updates and high concurrency more comfortably; ClickHouse is generally faster on raw single-query scan throughput and has far wider Western support.

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.

Apache Doris: Who maintains it?

The Apache Software Foundation project, with most committers employed by VeloDB or SelectDB.

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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