StarRocks vs Apache Doris

StarRocks's primary key model is the option its README points to for upserts and deletes. Rows are identified by a primary key, so a later load for the same key replaces the earlier row, and delete operations remove rows by key. This suits workloads where source systems emit change records for entities that change over time, such as orders, account balances or device states, and where queries should see the latest version of each entity without the application deduplicating rows itself.

• Write pattern: Continuous or batch upserts keyed on a stable identifier, plus explicit deletes.
• When it matters: Your source of truth changes row by row and analysts need current state, not an append-only history.
• Materialized views: The README says views refresh during import and are chosen automatically at query time. Confirm refresh cost and plan selection on your own tables before relying on them.
• Indexing: Confirm which index types apply to primary key tables in your release, since index choice affects point lookups and filters.
• Tenancy: Resource management isolates workloads within one cluster, which matters when several teams share upserted tables.

Before committing, load a representative sample of your change feed, run your heaviest upsert and delete batches, and measure query latency against the latest state of the largest tables in the release you plan to run.

The Doris Unique model is the counterpart for row-level updates. Rows are keyed, and a newer load for the same key supersedes the older row, which gives the same current-state view that primary key tables provide. Doris documents row-level updates in this model specifically, so it fits teams that want the latest version of each entity and already work with MySQL-style update statements against Doris.

• Write pattern: Keyed upserts where each entity has one current row, typical of dimension-like or entity-state data.
• When it matters: Your sources send full or partial row changes and you need current state queryable under MySQL-compatible tooling.
• Update behavior: Confirm how partial column updates, delete handling and merge-on-read or compaction costs behave in your Doris version.
• Indexing: Doris offers inverted and vector indexes. Test which indexes help your filters, since an index that speeds one query can add write cost.
• Comparison: Run the same upsert workload against both engines, because throughput and read latency depend on key cardinality and batch size.

Write a benchmark that mirrors your key distribution, update ratio and query mix, then compare throughput and freshness on both platforms before choosing a table model.

Doris's Duplicate and Aggregate models cover the append and pre-summarized cases. The Duplicate model keeps every loaded row, which suits event, log and metric data where history matters and rows are never corrected in place. The Aggregate model merges rows that share a key by applying aggregation functions during load, which suits rollups such as totals and counts per dimension when you accept that raw detail is not retained.

• Write pattern: Append-only events for Duplicate, and pre-aggregated metrics for Aggregate.
• When it matters: Your data is immutable once written, or you only need summarized results and can accept losing the raw rows.
• Observability and events: Doris lists observability over logs, events and metrics as a main use case, which aligns with the Duplicate model.
• Modeling risk: Aggregate tables are hard to change after load, so confirm how schema changes and reprocessing work before you commit.
• Materialized views: Confirm whether your rollups are better served by materialized views or by the Aggregate model for your query patterns.

For each candidate table, write down whether rows are ever corrected and whether raw detail must survive, then test the chosen model with production-like volumes and schema change scenarios.

The model decision follows from how your data changes after it lands, not from a feature checklist. If rows are corrected or deleted by key, both engines have a keyed model, and the comparison comes down to throughput, query behavior and tooling. If rows are only appended, the Doris Duplicate model maps directly to that pattern. If you only need summaries, the Aggregate model can reduce storage and query work, but only when losing raw detail is acceptable. Mixing models in one platform is possible, so decide per table rather than per platform.

• Mutable entities: Choose a keyed model on either engine and benchmark upsert throughput.
• Immutable events: Choose an append model and confirm retention and partitioning.
• Summaries only: Choose an aggregate design and document what raw data is discarded.
• Mixed workloads: Assign one model per table and record the reason in your data model documentation.
• Governance: Confirm how updates and deletes are audited for regulated data, since your auditors will ask how history is kept.

Before finalizing, map each source table to one model, run both engines on that mapping with your real update and deletion rates, and review the results with the team that owns the data.

Both StarRocks and Apache Doris are licensed under the Apache License 2.0, so the core server code carries the same permissive terms on both sides. For most internal and customer-facing deployments, that means the license itself does not decide between them. Permissive terms allow you to run, modify and redistribute the code, subject to the license's notice and attribution conditions. The license does not cover everything you deploy, though. Your own extensions, connectors, Kubernetes manifests and any third-party images you pull are governed by their own terms, so review them as part of the same evaluation.

• Core engine: Both projects publish their server code under Apache-2.0, as stated in the StarRocks README and the apache/doris repository.
• Operator and tooling: StarRocks maintains an Apache-2.0 Kubernetes operator in its own repository. Confirm the license and maintenance status of any Doris operator or Helm chart you plan to use.
• Bundled components: Check the licenses of drivers, storage connectors and third-party images that your deployment pulls in.
• Redistribution: If you ship either engine inside your own product, have legal review the attribution and notice requirements.

Check the LICENSE and NOTICE files in the exact release tag you plan to deploy, and confirm that no component you add changes those terms.

StarRocks describes itself as a Linux Foundation project, according to its GitHub README. The Linux Foundation hosts many open-source projects and typically provides neutral project infrastructure, such as legal and trademark processes, under its own governance frameworks. What matters for you is how decisions are actually made in the project, which is documented in its governance files and visible in its public contribution history. Read those documents rather than relying on the umbrella name alone.

• Contributor process: Review the project's contribution guidelines, sign-off requirements and how maintainers are added.
• Roadmap: Check whether roadmap items are discussed in public issues, design documents or community meetings, and how far the maintainers who lead those discussions come from one company.
• Vendor dependence: Measure how many active maintainers and contributing organizations sit outside the largest sponsor.
• Release cadence: The supplied release record shows StarRocks 4.1.3 published on September 17, 2026. Track release notes against your upgrade planning.

Ask the project directly how its maintainer base is distributed and what its foundation obligations mean for downstream users.

Apache Doris is an Apache Software Foundation project, which places it under the ASF's incubator and top-level project processes. The ASF operates with a meritocratic model in which committers and project management committee members are elected based on sustained contribution, and decisions are made through public mailing lists and recorded votes. This model is well documented, but the practical outcome for your team depends on how active the Doris committer base is across organizations. Review the project's public mailing list archives and its committer roster before you decide.

• Contributor process: Apache projects accept contributions through public issue trackers and pull requests, with committers granted access through the ASF process.
• Roadmap: Roadmap discussion typically happens on the project's public dev list. Read recent threads to see who proposes and approves changes.
• Vendor dependence: Look at the spread of committers across companies, not only the headline count.
• Release record: The supplied release record shows Apache Doris 4.1.5 published on October 15, 2026.

Confirm the current committer and PMC roster on the project's site, and ask who would sign off on a security fix if your deployment depends on a specific version.

The supplied facts for both projects describe open-source releases only. They do not list a commercial edition, a paid feature tier or a named support vendor for either StarRocks or Apache Doris, so this page does not claim one exists. Support can come from the project community, from a vendor that distributes the software, or from an implementation partner such as DeployOpen, which plans, implements and supports self-hosted platforms in your own infrastructure. Each route carries different contract terms, response commitments and cost.

• Community support: Public issues, mailing lists and forums are free, but response times are not guaranteed.
• Vendor distribution: Ask whether any company sells a packaged or supported distribution, and what it covers.
• Implementation services: Scope, deliverables and timelines are typically agreed in writing before work starts.
• Feature parity: Confirm that any distribution or service includes the same features and versions you will run.

Ask each project or vendor in writing which support options, commercial terms and release commitments apply to the version you plan to deploy.