linrrarity
cc8a24a13e
### What problem does this PR solve?
Issue Number: close #xxx
Related PR: #xxx
Problem Summary:
AI function queries lack visibility into request counts, retries,
failures, latency, and token consumption.
This PR collects shared per-fragment statistics for scalar AI functions,
text/multimodal EMBED, and AI_AGG. Query profiles expose an AISummary
section aggregating the latest reports across backends, fragments, and
query execution attempts, while retaining fragment-level details.
Repeated reports do not double-count statistics.
Token usage includes provider-reported values from all HTTP attempts,
including retries and error responses. Coverage counters distinguish
missing usage from explicitly reported zero usage
```text
AIFunctionSummary:
- AIFunctionCancelledCount: 0
- AIFunctionCompletionTokens: 3.615K (3615)
- AIFunctionCompletionUsageCount: 8
- AIFunctionDnsTime: 20.216ms
- AIFunctionErrorCount: 0
- AIFunctionFirstAttemptCount: 8
- AIFunctionFirstByteTime: 1sec487ms
- AIFunctionHttpTime: 22sec896ms
- AIFunctionInputRows: 10
- AIFunctionLogicalRequestCount: 8
- AIFunctionNewConnectionCount: 8
- AIFunctionParseErrorCount: 0
- AIFunctionParseTime: 1.698ms
- AIFunctionPromptTokens: 1.704K (1704)
- AIFunctionPromptUsageCount: 8
- AIFunctionRequestCount: 8
- AIFunctionRetryCount: 0
- AIFunctionRetryWaitTime: 0ns
- AIFunctionSkippedNullRows: 0
- AIFunctionTcpConnectTime: 13.898ms
- AIFunctionTimeoutCount: 0
- AIFunctionTlsHandshakeTime: 166.740ms
- AIFunctionTotalTokens: 5.319K (5319)
- AIFunctionTotalUsageCount: 8
- AIFunctionUsageParseTime: 903.169us
```
### Release note
None
### Check List (For Author)
- Test <!-- At least one of them must be included. -->
- [ ] Regression test
- [ ] Unit Test
- [ ] Manual test (add detailed scripts or steps below)
- [ ] No need to test or manual test. Explain why:
- [ ] This is a refactor/code format and no logic has been changed.
- [ ] Previous test can cover this change.
- [ ] No code files have been changed.
- [ ] Other reason <!-- Add your reason? -->
- Behavior changed:
- [ ] No.
- [ ] Yes. <!-- Explain the behavior change -->
- Does this need documentation?
- [ ] No.
- [ ] Yes. <!-- Add document PR link here. eg:
https://github.com/apache/doris-website/pull/1214 -->
### Check List (For Reviewer who merge this PR)
- [ ] Confirm the release note
- [ ] Confirm test cases
- [ ] Confirm document
- [ ] Add branch pick label <!-- Add branch pick label that this PR
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Apache Doris is an open-source, real-time analytics and search database built on MPP architecture. It provides fast SQL analytics, lakehouse query acceleration, and hybrid search across structured, text, and vector data.
Explore the official website for the latest product overview, use cases, ecosystem updates, blogs, and user stories. For version updates, see all release notes.
📈 Use Cases
| Use Case | What it provides |
|---|---|
| Customer-Facing Analytics | Ship sub-second interactive analytics to external users. |
| Data Warehousing | Build one real-time warehouse across business domains. |
| Observability | Analyze high-throughput logs, events, and metrics with SQL. |
| Doris for AI | Use vector, text, JSON, and structured search in one SQL engine. |
🚀 Core Capabilities
Apache Doris is built around three core capabilities. The website is the source of truth for detailed product descriptions and examples.
| Capability | What it provides |
|---|---|
| Real-Time Analytics | Streaming ingestion, incremental transformation, and sub-second queries under high concurrency. |
| Lakehouse Analytics | Fast SQL analytics over open table formats such as Iceberg, Delta Lake, and Hudi. |
| Hybrid Search | SQL-native analytics across JSON, full-text, and vector data for AI and search workloads. |
🔌 Ecosystem
Doris sits at the center of the modern data stack. It connects upstream databases, streaming systems, and lakehouse storage with downstream BI, AI, analytics, and observability tools.
For the latest ecosystem coverage, visit the official website and the connection and integration documentation.
👣 Get Started
🧱 Architecture
Apache Doris supports both compute-storage coupled and compute-storage decoupled deployments. In decoupled mode, stateless compute groups run over shared object storage, so you can scale compute on demand and isolate workloads.
Learn more in the deployment guide and deployment mode guide.
📣 Project Updates
| Resource | What it provides |
|---|---|
| Community Report | Weekly updates on community activity, merged PRs, contributors, and feature progress. |
| Roadmap 2026 | The 2026 planning discussion for AI and hybrid search, query engine, storage, and data lake work. |
🧩 Components
Doris provides connectors and tools for common data engineering workflows.
- Doris Flink Connector
- Doris Spark Connector
- Doris Kafka Connector
- Doris Stream Loader
- Doris Kubernetes Operator
👨👩👧👦 Users
Apache Doris is used in production by thousands of companies worldwide across internet services, finance, retail, logistics, manufacturing, energy, telecommunications, AI, and other industries.
🙌 Contributors
Apache Doris graduated from the Apache Incubator and became an Apache Top-Level Project in June 2022. Thanks to all community contributors who help build Doris.
🌈 Community and Support
💬 Contact Us
🧰 Links
- Apache Doris official website: doris.apache.org
- X: @doris_apache
- Medium: @ApacheDoris
📜 License
Note
Some licenses of the third-party dependencies are not compatible with Apache 2.0 License. So you need to disable some Doris features to comply with Apache 2.0 License. For details, refer to the
thirdparty/LICENSE.txt