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new bb054a49afe Add a README Docker quickstart and improve GitHub metadata
(#20164)
bb054a49afe is described below
commit bb054a49afebd53069e5f9a54f6f459a363eb689
Author: Xiang Fu <[email protected]>
AuthorDate: Wed Nov 4 22:41:02 2026 -0800
Add a README Docker quickstart and improve GitHub metadata (#20164)
---
.asf.yaml | 12 +++++++++++-
README.md | 36 ++++++++++++++++++++++++++++++++++++
2 files changed, 47 insertions(+), 1 deletion(-)
diff --git a/.asf.yaml b/.asf.yaml
index 08cbe162538..6f09ecbac30 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -20,10 +20,20 @@
# Please refer
https://cwiki.apache.org/confluence/display/INFRA/Git+-+.asf.yaml+features
github:
- description: "Apache Pinot - A realtime distributed OLAP datastore"
+ description: "Apache Pinot: a real-time analytics database for low-latency
SQL and vector search, serving user-facing and agent-facing applications"
homepage: https://pinot.apache.org/
labels:
- java
+ - database
+ - sql
+ - olap
+ - analytics
+ - real-time-analytics
+ - kafka
+ - distributed-database
+ - vector-search
+ - vector-database
+ - ai-agents
del_branch_on_merge: true
enabled_merge_buttons:
squash: true
diff --git a/README.md b/README.md
index c27335478b2..8de5b2cf0c0 100644
--- a/README.md
+++ b/README.md
@@ -38,6 +38,7 @@
</div>
- [What is Apache Pinot?](#what-is-apache-pinot)
+ - [Docker quickstart](#docker-quickstart)
- [Features](#features)
- [When should I use Pinot?](#when-should-i-use-pinot)
- [Contributing to Pinot](#contributing-to-pinot)
@@ -52,10 +53,45 @@
[Apache Pinot](https://pinot.apache.org) is a real-time distributed OLAP
datastore, built to deliver scalable real-time analytics with low latency. It
can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, Azure ADLS,
Google Cloud Storage) as well as stream data sources (such as Apache Kafka).
+Pinot also supports [vector similarity
search](https://docs.pinot.apache.org/build-with-pinot/indexing/vector-index)
for vector database use cases such as semantic search, recommendations, and
retrieval-augmented generation (RAG). Its low-latency SQL queries over fresh
data make it well suited to [agent-facing
analytics](https://pinot.apache.org/agent-facing-analytics/), including AI
assistants and automated decision systems.
+
Pinot was built by engineers at LinkedIn and Uber and is designed to scale up
and out with no upper bound. Performance always remains constant based on the
size of your cluster and an expected query per second (QPS) threshold.
For getting started guides, deployment recipes, tutorials, and more, please
visit our project documentation at
[https://docs.pinot.apache.org](https://docs.pinot.apache.org).
+## Docker quickstart
+
+With Docker running and at least 6 GB of memory allocated to it, start Pinot
with a bundled sample dataset:
+
+```bash
+docker run --rm --name pinot-quickstart \
+ -p 127.0.0.1:9000:9000 \
+ apachepinot/pinot:1.6.0 QuickStart -type batch
+```
+
+The first run downloads the image and loads sample tables, which can take a
few minutes.
+Wait for `***** Quick start setup complete *****` in the logs, then open the
+[query console](http://localhost:9000/#/query) and run this query to find the
players with the most recorded home runs:
+
+```sql
+SELECT playerID, SUM(homeRuns) AS totalHomeRuns
+FROM baseballStats
+GROUP BY playerID
+ORDER BY totalHomeRuns DESC
+LIMIT 3;
+```
+
+Expected result for the bundled dataset:
+
+| playerID | totalHomeRuns |
+| --- | ---: |
+| bondsba01 | 762 |
+| aaronha01 | 755 |
+| ruthba01 | 714 |
+
+To stop the demo, run `docker stop pinot-quickstart` in another terminal. The
container and its demo data are removed.
+For streaming examples and other ways to run Pinot, see the [quickstart
guide](https://docs.pinot.apache.org/basics/getting-started/quick-start).
+
<img src="https://pinot.apache.org/static/images/hero_diagram.svg";
align="center" alt="Apache Pinot"/>
## Features
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