Jack
1106a37488
### What problem does this PR solve? Issue Number: None Related PR: None Problem Summary: An equality filter reads a posting list of millions of documents through `decode_window_prefix_plan`, so small costs per document, per PFOR run or per window add up. In the stack samples of a filtered phrase prefix query on a 99M-row table: - The window loop cleared its vector, so every window zero-filled it again before decoding wrote over it (4% of the SNII samples). - The sum of the docid deltas checked for a zero delta and for overflow at every document (about 7%, with the sum itself). - Each PFOR run read its exception count, and each exception its two fields, through the out-of-line varint decoders, although they are nearly always one byte (about 5%). The window loop now decodes over the vector, so a resize zero-fills only what it adds. A window's deltas are checked whole, with no branch per document, before they are summed: the same checks, and the same error code when one fails. If a window breaks more than one of them, the zero delta is reported first, whichever came first in the window. `pfor_decode` and `pfor_skip` use `get_varint32_fast`, which handles a one-byte value inline and leaves anything else to the checked decoder. The on-disk format and the write path do not change. | Query (99M rows), new/previous | Hot | Lowest run, new/previous | Cold | |---|---|---|---| | filtered phrase prefix | 0.94 / 0.96 / 0.86 | 0.95 / 0.93 / 0.93 | 0.98 | | phrase with tenant, app, type and status | 0.92 / 0.92 / 0.98 | 0.92 / 0.95 / 0.95 | 0.95 | | phrase with a latency filter | 0.95 / 0.91 / 0.99 | 0.94 / 0.92 / 0.96 | | | MATCH_ALL with the same four filters | 0.94 / 0.92 / 1.10 | 0.94 / 0.93 / 0.93 | | | top-50 with tenant and payload equalities | 0.94 / 0.94 / 0.95 | 0.95 / 0.94 / 0.99 | 1.00 | | the other queries | 0.93-1.03 | | | Hot: both versions interleaved in one BE process on a quiet host, three runs each. Cold: BE restarted and the index files evicted before every run, configuration order alternated, 8 runs for the phrase prefix, 5 for the others. The build also had #69879, #69882, #69887, #69891 and #69895.
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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