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Databases · head to head

StarRocks vs Trino

StarRocks logo

StarRocks

Databases

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

From
Free
Rated
-
Full reviewWebsite
Trino logo

Trino

Technology

A distributed SQL engine that queries data where it already lives, across object storage, warehouses and operational databases.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Trino trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve.
  • They diverge on capability: StarRocks covers Cost-based optimiser, Trino covers Federated querying.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which StarRocks and Trino actually diverge.

Attributes where StarRocks and Trino differ
AttributeStarRocksTrino
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, Docker, KubernetesWeb
CategoryDatabasesTechnology

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 StarRocks

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

Only in Trino

  • Federated querying
  • Connector architecture
  • Predicate and aggregation pushdown
  • Massively parallel execution
  • Fault-tolerant execution
  • Resource groups
  • Iceberg and Delta table support
  • Standard client protocols

What people use each for

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

StarRocks

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

Trino

  • Ad hoc analysis that spans a data lake and one or more operational databases, without building an ingestion pipeline firstnot StarRocks
  • Serving a BI tool a single SQL endpoint over an estate that is actually several separate storage systemsnot StarRocks
  • Querying Iceberg or Delta tables on object storage interactively, as the compute layer of a lakehousenot StarRocks
  • Investigating whether a dataset is worth ingesting, by querying it in place before committing to a pipeline for itnot StarRocks

Where each one falls short

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

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.

Trino

  • Trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve.
  • Federated queries pull data out of the systems they touch, so a join between a lake table and a production Postgres can put a full table scan onto an OLTP database that other applications depend on, and the person who wrote the query will not see the incident it causes.
  • Releases come roughly every one to two weeks with no community long-term support line, and deprecations arrive quickly, so you either dedicate someone to keeping current or you buy Starburst Enterprise for a supported long-term version.
  • It is memory-based and disk spilling was deprecated in favour of fault-tolerant execution, so a query exceeding cluster memory fails outright rather than degrading; enabling fault-tolerant execution requires an external exchange store on object storage and makes queries measurably slower.
  • It is a query engine and not a warehouse: there is no built-in job scheduling, no incremental materialised view maintenance and no transformation framework, so producing curated tables still needs dbt or an equivalent layer that somebody has to own.
  • The 2020 fork split the ecosystem, so documentation, connectors, Stack Overflow answers and vendor material written before then describe PrestoDB, which is now a different project with different behaviour, and following the wrong one wastes real time.

Pricing, plan by plan

StarRocks

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

Trino

Free

No published plan breakdown. See the Trino review.

Which should you pick?

Choose StarRocks if

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

Choose Trino if

  • You need federated querying.
  • You want to start without paying.
  • You also want connector architecture.
Trino review

Questions people ask

Is StarRocks or Trino better?
Neither clearly leads. StarRocks starts at Free and Trino at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, StarRocks or Trino?
StarRocks starts at Free and Trino at Free.
Does StarRocks or Trino run on more platforms?
StarRocks runs on Linux, Docker, Kubernetes. Trino runs on Web.
Can I use StarRocks for free?
Both have a free tier, so you can try either at no cost before committing.
What is StarRocks best used for?
StarRocks is most often used for customer-facing analytics where queries join a fact table to several dimensions and must return in well under a second, querying an iceberg lakehouse directly without copying data into a proprietary warehouse format, replacing a clickhouse deployment that has become unmanageable because every new question needs another denormalised table, real-time analytics fed by change data capture where rows must be updated in place rather than appended. Of those, customer-facing analytics where queries join a fact table to several dimensions and must return in well under a second and querying an iceberg lakehouse directly without copying data into a proprietary warehouse format are not what Trino is typically brought in for.
What can StarRocks do that Trino cannot?
StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Materialised views. Trino covers Federated querying, Connector architecture, Predicate and aggregation pushdown, Massively parallel execution.

Answered from the vendors’ own pages

StarRocks: Is StarRocks open source?

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

Trino: What is the difference between Trino and Presto?

They share an origin. The original creators left Meta and renamed their fork from PrestoSQL to Trino in December 2020; PrestoDB continues separately under the Linux Foundation. They have diverged in features, connectors and SQL behaviour, so material written for one may not apply to the other.

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.

Trino: Does Trino replace my data warehouse?

Not on its own. It is compute without storage, scheduling or transformation. Paired with Iceberg or Delta on object storage and something like dbt for modelling it can serve as a lakehouse; used alone it is a query layer over what you already have.

StarRocks: Who maintains it?

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

Trino: Why is my federated query slow?

Usually because a connector could not push a filter or aggregation down, so Trino is pulling whole tables across the network to join them itself. The fix is usually better source-side partitioning or statistics, or ingesting that source rather than federating it.

StarRocks: Can it query Iceberg tables directly?

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

Trino: What happens when a query runs out of memory?

It fails. Disk spilling was deprecated in favour of fault-tolerant execution, which checkpoints to an external exchange store such as S3 and lets long queries survive memory pressure and worker loss, at the cost of noticeably slower execution.

Trino: Is there commercial support?

Yes, from Starburst, which offers Starburst Enterprise with long-term supported releases and Starburst Galaxy as a managed service. The open source project itself has no long-term support line.

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