apache/spark-docker
apache/spark-docker has 176 GitHub stars and 67 forks. GitHot measured 0 stars in 24 hours. Last checked 2026-11-12 18:18 UTC. Last push 2026-11-05.
So werden diese Zahlen gemessen
Official Dockerfile for Apache Spark
Gemessener Sternzuwachs
- in 24h
- 0
- in 7 days
- Verlauf wird gesammelt
- in 15 days
- Verlauf wird gesammelt
- in 1 month
- Verlauf wird gesammelt
Nettoänderungen in überlappenden Zeiträumen, keine Tagessummen. Fehlende Messwerte bleiben leer. Sterne zeigen Interesse, nicht Qualität.
README.md
OriginalApache Spark Official Dockerfiles
What is Apache Spark?
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Create a new version
Step 1 Add dockerfiles for a new version.
You can see 3.4.0 PR as reference.
1.1 Add gpg key to tools/template.py
This gpg key will be used by Dockerfiles (such as 3.4.0) to verify the signature of the Apache Spark tarball.
1.2 Add image build workflow (such as 3.4.0 yaml)
This file will be used by GitHub Actions to build the Docker image when you submit the PR to make sure dockerfiles are correct and pass all tests (build/standalone/kubernetes).
1.3 Using
./add-dockerfiles.sh [version]to add Dockerfiles.You will get a new directory with the Dockerfiles for the specified version.
1.4 Add version and tag info to versions.json, publish.yml and test.yml.
This version file will be used by image build workflow (such as 3.4.0 reference) and docker official image.
Step 2. Publish apache/spark Images.
Click Publish (Java 25 only), Publish (Java 21 only), Publish (Java 17 only) or Publish (such as 3.x) to publish images.
After this, the apache/spark docker images will be published.
Step 3. Publish spark Docker Official Images.
Submit the PR to docker-library/official-images, see (link)[https://github.com/docker-library/official-images/pull/15363] as reference.
You can type tools/manifest.py manifest to generate the content.
After this, the spark docker images will be published.
About images
| Apache Spark Image | Spark Docker Official Image | |
|---|---|---|
| Name | apache/spark | spark |
| Maintenance | Reviewed, published by Apache Spark community | Reviewed, published and maintained by Docker community |
| Update policy | Only build and push once when specific version release | Actively rebuild for updates and security fixes |
| Link | https://hub.docker.com/r/apache/spark | https://hub.docker.com/_/spark |
| source | apache/spark-docker | apache/spark-docker and docker-library/official-images |
We advise using official Apache Spark images to maintain architectural uniformity across related projects, including the Apache Spark K8s Operator and Apache Spark Connect for Swift.
About this repository
This repository contains the Dockerfiles used to build the Apache Spark Docker Image.
See more in SPARK-40513: SPIP: Support Docker Official Image for Spark.
Ähnliche Projekte entdecken
Asabeneh/30-Days-Of-Python
Die Programmier-Challenge 30 Days of Python ist eine Schritt-für-Schritt-Anleitung, um die Programmiersprache Python in 30 Tagen zu lernen. Diese Challenge kann mehr als 100 Tage dauern. Gehe in deinem eigenen Tempo vor. Diese Videos können ebenfalls helfen: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
78,618 Sternegrafana/grafana
Offene und kombinierbare Plattform für Observability und Datenvisualisierung. Visualisiert Metriken, Logs und Traces aus mehreren Quellen wie Prometheus, Loki, Elasticsearch, InfluxDB, Postgres und vielen weiteren.
77,859 Sterneredis/redis
Für Entwickler, die echtzeitfähige, datengesteuerte Anwendungen erstellen, ist Redis der bevorzugte, schnellste und funktionsreichste Cache, Datenstrukturserver und die entsprechende Abfrageengine für Dokumente und Vektoren.
77,472 Sterneapache/superset
Apache Superset ist eine Plattform für Datenvisualisierung und Datenexploration.
75,495 Sterne