apache/pinot

Última actividad de origen registrada: 13 de septiembre de 2026 a las 20:06

Catálogo de SkillsMP actualizado: 5 de noviembre de 2026 a las 19:57

skills recopiladas: 13

Estrellas en GitHub: 6172
Forks en GitHub: 1532

Instalar con un asistente de IA

Copia este prompt en el asistente de IA que estés usando.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/apache/pinot.

README del repositorio

Archivo de origen del README: README.md

Leer el README original

Mostrar README completo

<div align="center"> <img src="https://imgur.com/wBkyzNm.png" align="center" alt="Apache Pinot"/> --------------------------------------- [![Unit Tests](https://github.com/apache/pinot/actions/workflows/pinot_unit_tests.yml/badge.svg?event=push)](https://github.com/apache/pinot/actions/workflows/pinot_unit_tests.yml) [![Integration Tests](https://github.com/apache/pinot/actions/workflows/pinot_integration_tests.yml/badge.svg?event=push)](https://github.com/apache/pinot/actions/workflows/pinot_integration_tests.yml) [![Quickstart Tests](https://github.com/apache/pinot/actions/workflows/pinot_quickstart_tests.yml/badge.svg?event=push)](https://github.com/apache/pinot/actions/workflows/pinot_quickstart_tests.yml) [![Compatibility Checks](https://github.com/apache/pinot/actions/workflows/pinot_compatibility_checks.yml/badge.svg?event=push)](https://github.com/apache/pinot/actions/workflows/pinot_compatibility_checks.yml) [![Release](https://img.shields.io/github/release/apache/pinot/all.svg)](https://pinot.apache.org/download/) [![codecov.io](https://codecov.io/github/apache/pinot/branch/master/graph/badge.svg)](https://codecov.io/github/apache/pinot) [![Join the chat at https://communityinviter.com/apps/apache-pinot/apache-pinot](https://img.shields.io/badge/slack-apache--pinot-brightgreen?logo=slack)](https://communityinviter.com/apps/apache-pinot/apache-pinot) [![Twitter Follow](https://img.shields.io/twitter/follow/apachepinot.svg?label=Follow&style=social)](https://twitter.com/intent/follow?screen_name=apachepinot) [![License](https://img.shields.io/github/license/apache/pinot.svg)](LICENSE) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/apache/pinot) </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) - [Apache Pinot YouTube Channel](#apache-pinot-youtube-channel) - [Building Pinot](#building-pinot) - [Deploying Pinot to Kubernetes](#deploying-pinot-to-kubernetes) - [Join the Community](#join-the-community) - [Documentation](#documentation) - [License](#license) # What is Apache Pinot? [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 Pinot was originally built at LinkedIn to power rich interactive real-time analytic applications such as [Who Viewed Profile](https://www.linkedin.com/me/profile-views/urn:li:wvmp:summary/), [Company Analytics](https://www.linkedin.com/company/linkedin/insights/), [Talent Insights](https://business.linkedin.com/talent-solutions/talent-insights), and many more. [UberEats Restaurant Manager](https://eng.uber.com/restaurant-manager/) is another example of a customer facing Analytics App. At LinkedIn, Pinot powers 50+ user-facing products, ingesting millions of events per second and serving 100k+ queries per second at millisecond latency. * **Fast Queries**: Filter and aggregate petabyte data sets with P90 latencies in the tens of milliseconds—fast enough to return live results interactively in the UI. * **High Concurrency**: With user-facing applications querying Pinot directly, it can serve hundreds of thousands of concurrent queries per second. * **SQL Query Interface**: The highly standard SQL query interface is accessible through a built-in query editor and a REST API. * **Versatile Joins**: Perform arbitrary fact/dimension and fact/fact joins on petabyte data sets. * **Column-oriented**: a column-oriented database with various compression schemes such as Run Length, Fixed Bit Length. * [**Pluggable indexing**](https://docs.pinot.apache.org/basics/indexing): pluggable indexing technologies including [timestamp](https://docs.pinot.apache.org/basics/indexing/timestamp-index), [inverted](https://docs.pinot.apache.org/basics/indexing/inverted-index), [StarTree](https://docs.pinot.apache.org/basics/indexing/star-tree-index), [Bloom filter](https://docs.pinot.apache.org/basics/indexing/bloom-filter), [range](https://docs.pinot.apache.org/basics/indexing/range-index), [text](https://docs.pinot.apache.org/basics/indexing/text-search-support), [JSON](https://docs.pinot.apache.org/basics/indexing/json-index), and [geospatial](https://docs.pinot.apache.org/basics/indexing/geospatial-support) options. * **Stream and batch ingest**: Ingest from [Apache Kafka](https://kafka.apache.org/), [Apache Pulsar](https://pulsar.apache.org/), and [AWS Kinesis](https://aws.amazon.com/kinesis/) in real time. Batch ingest from Hadoop, Spark, AWS S3, and more. Combine batch and streaming sources into a single table for querying. * **Upsert during real-time ingestion**: update the data at-scale with consistency * **Built-in Multitenancy**: Manage and secure data in isolated logical namespaces for cloud-friendly resource management. * **Built for Scale**: Pinot is horizontally scalable and fault-tolerant, adaptable to workloads across the storage and throughput spectrum. * **Cloud-native on Kubernetes**: Helm chart provides a horizontally scalable and fault-tolerant clustered deployment that is easy to manage using Kubernetes. <a href="https://docs.pinot.apache.org/basics/getting-started"><img src="https://gblobscdn.gitbook.com/assets%2F-LtH6nl58DdnZnelPdTc%2F-MKaPf2qveUt5cg0dMbM%2F-MKaPmS1fuBs2CHnx9-Z%2Fpinot-ui-width-1000.gif?alt=media&token=53e4c5a8-a9cd-4610-a338-d54ea036c090" align="center" alt="Apache Pinot query console"/></a> ## When should I use Pinot? Pinot is designed to execute real-time OLAP queries with low latency on massive amounts of data and events. In addition to real-time stream ingestion, Pinot also supports batch use cases with the same low latency guarantees. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion. Pinot works very well for querying time series data with lots of dimensions and metrics. Example query: ```SQL SELECT sum(clicks), sum(impressions) FROM AdAnalyticsTable WHERE ((daysSinceEpoch >= 17849 AND daysSinceEpoch <= 17856)) AND accountId IN (123456789) GROUP BY daysSinceEpoch TOP 100 ``` ## Contributing to Pinot Want to contribute to Apache Pinot? 👋🍷 Want to join the ranks of open source committers to Apache Pinot? Then check out the [Contribution Guide](https://docs.pinot.apache.org/developers/developers-and-contributors/contribution-guidelines) for how you can get involved in the code. If you have a bug or an idea for a new feature, browse the [open issues](https://github.com/apache/pinot/issues) to see what we’re already working on before opening a new one. We also tagged some [beginner issues](https://github.com/apache/pinot/issues?q=is%3Aopen+is%3Aissue+label%3Abeginner-task) new contributors can tackle. ## Apache Pinot YouTube Channel Share Your Pinot Videos with the Community! Have a Pinot use case, tutorial, or conference/meetup recording to share? We’d love to feature it on the [Pinot OSS YouTube channel](https://www.youtube.com/@Apache_Pinot/videos)! Drop your video or a link to your session in the [#pinot-youtube-channel](https://apache-pinot.slack.com/archives/C08GH2MAVT4) on Pinot Slack, and we’ll showcase it for the community! ## Building Pinot ``` # Clone a repo $ git clone https://github.com/apache/pinot.git $ cd pinot # Pinot services require JDK 25+ to build and run # Java/JDBC clients and SPI artifacts continue to target Java 11 bytecode # Build Pinot # -Pbin-dist is required to build the binary distribution # -Pbuild-shaded-jar is required to build the shaded jar, which is necessary for some features like spark connectors $ ./mvnw clean install -DskipTests -Pbin-dist -Pbuild-shaded-jar # Run the Quick Demo $ cd build/ $ bin/quick-start-batch.sh ``` For UI development setup refer this [doc](https://github.com/apache/pinot/blob/master/pinot-controller/src/main/resources/Readme.md). Normal Pinot builds are done using the `./mvnw clean install` command. However this command can take a long time to run. For faster builds it is recommended to use `./mvnw verify -Ppinot-fastdev`, which disables some plugins that are not actually needed for development. More detailed instructions can be found at [Quick Demo](https://docs.pinot.apache.org/basics/getting-started/quick-start) section in the documentation. ### macOS Build Requirements If you're building Pinot on macOS and encounter issues with the gRPC Java plugin during the build process, you may need to configure the protobuf Maven plugin to use a specific executable path. This is a known issue on macOS ARM (Apple Silicon) systems. #### Automatic Profile Activation (macOS ARM64) Pinot's Maven build now includes dedicated profiles for Apple Silicon (ARM64) Macs to ensure reliable protobuf compilation with Homebrew-installed binaries: - **Primary profile:** Activates automatically if `/opt/homebrew/bin/protoc-gen-grpc-java` exists (default for Apple Silicon Macs). - **Fallback profile:** Activates if `/usr/local/bin/protoc-gen-grpc-java` exists and the primary path does not (for Intel Macs or custom Homebrew setups). You do **not** need to manually edit the `pom.xml` or set the plugin executable path. The correct profile will be selected based on your system and Homebrew installation. ##### To install the required tools: ```bash brew install protobuf brew install protoc-gen-grpc-java ``` If you installed Homebrew to a non-default location, ensure the `protoc-gen-grpc-java` binary is available in either `/opt/homebrew/bin/` or `/usr/local/bin/`. To verify which profile is active, run: ```bash ./mvnw help:active-profiles ``` If you encounter issues, check that the `protoc-gen-grpc-java` binary is present in one of the expected locations and is executable. ### Lucene index recovery during loading Normal segment preprocessing automatically rebuilds text and HNSW indexes whose Lucene format cannot be read by the running version. Single-column text, multi-column text, and directory/packed HNSW indexes are rebuilt using the segment's original forward-index values and existing table configuration; no recovery flag is needed. Compatible indexes are reused, and subsequent loads do not rebuild the recovered indexes again. Interrupted builds restart from the retained source values before the segment is loaded. Install this recovery release on the intended rollback target before upgrading Lucene. After a downgrade, loading segments containing newer Lucene indexes rebuilds those indexes with the older writer. Retain the original forward indexes: dictionary/inverted-index reconstruction can lose multi-value ordering and duplicate values. When original values are unavailable, recovery fails before replacing the incompatible index; restore a compatible segment or refresh it from retained source data. Rebuilding adds CPU/I/O and segment-loading time. Existing preprocessing opt-outs remain in effect. Corruption and ordinary I/O errors fail loading instead of triggering format recovery. ## Deploying Pinot to Kubernetes Please refer to [Running Pinot on Kubernetes](https://docs.pinot.apache.org/basics/getting-started/kubernetes-quickstart) in our project documentation. Pinot also provides Kubernetes integrations with the interactive query engine, [Trino](https://docs.pinot.apache.org/integrations/trino), and the data visualization tool, [Apache Superset](helm/superset.yaml). ## Join the Community - Ask questions on [Apache Pinot Slack](https://join.slack.com/t/apache-pinot/shared_invite/zt-5z7pav2f-yYtjZdVA~EDmrGkho87Vzw) - Please join Apache Pinot mailing lists dev-subscribe@pinot.apache.org (subscribe to pinot-dev mailing list) dev@pinot.apache.org (posting to pinot-dev mailing list) users-subscribe@pinot.apache.org (subscribe to pinot-user mailing list) users@pinot.apache.org (posting to pinot-user mailing list) - Apache Pinot Meetup Group: https://www.meetup.com/apache-pinot/ ## Documentation Check out [Pinot documentation](https://docs.pinot.apache.org/) for a complete description of Pinot's features. - [Quick Demo](https://docs.pinot.apache.org/getting-started/running-pinot-locally) - [Pinot Architecture](https://docs.pinot.apache.org/basics/architecture) - [Pinot Query Language](https://docs.pinot.apache.org/users/user-guide-query/pinot-query-language) - [Release policy](RELEASE_POLICY.md) ## License Apache Pinot is under [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)

Cargando el README del repositorio…

Skills en este repositorio

Analistas de garantía de calidad de software y probadores Desarrolladores de software Administradores de redes y sistemas informáticos

3 categorías ocupacionales · 100% clasificado

skill

ocupación

descripción

actualizado