[GitHub] [iceberg-docs] samredai commented on a diff in pull request #75: Spark quickstart page

[GitBox <gi...@apache.org>]

samredai commented on code in PR #75:
URL: https://github.com/apache/iceberg-docs/pull/75#discussion_r890317656


##########
landing-page/content/common/spark-quickstart.md:
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@@ -0,0 +1,379 @@
+---
+url: spark-quickstart
+toc: true
+aliases:
+    - "spark-quickstart"
+---
+<!--
+ - Licensed to the Apache Software Foundation (ASF) under one or more
+ - contributor license agreements.  See the NOTICE file distributed with
+ - this work for additional information regarding copyright ownership.
+ - The ASF licenses this file to You under the Apache License, Version 2.0
+ - (the "License"); you may not use this file except in compliance with
+ - the License.  You may obtain a copy of the License at
+ -
+ -   http://www.apache.org/licenses/LICENSE-2.0
+ -
+ - Unless required by applicable law or agreed to in writing, software
+ - distributed under the License is distributed on an "AS IS" BASIS,
+ - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ - See the License for the specific language governing permissions and
+ - limitations under the License.
+ -->
+
+# Getting Started With Spark and Iceberg
+
+Spark is currently the most feature-rich compute engine for Iceberg operations. 
+We recommend you to get started with Spark to understand Iceberg concepts and features with examples.
+You can also view documentations of using Iceberg with other compute engine under the
+[**Compute Frameworks**](/compute-frameworks) tab.
+
+## Docker Quickstart (Spark)
+
+The fastest way to get started is to use a docker-compose file that uses the the [tabulario/spark-iceberg](https://hub.docker.com/r/tabulario/spark-iceberg) image
+which contains a local Spark cluster with a configured Iceberg catalog. Save the following into a file named `docker-compose.yml`.
+
+```yaml
+version: "3"
+
+services:
+  spark-iceberg:
+    image: tabulario/spark-iceberg
+    depends_on:
+      - postgres
+    container_name: spark-iceberg
+    environment:
+      - SPARK_HOME=/opt/spark
+      - PYSPARK_PYTON=/usr/bin/python3.9
+      - PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/spark/bin
+    volumes:
+      - ./warehouse:/home/iceberg/warehouse
+      - ./notebooks:/home/iceberg/notebooks/notebooks
+    ports:
+      - 8888:8888
+      - 8080:8080
+      - 18080:18080
+  postgres:
+    image: postgres:13.4-bullseye
+    container_name: postgres
+    environment:
+      - POSTGRES_USER=admin
+      - POSTGRES_PASSWORD=password
+      - POSTGRES_DB=demo_catalog
+    volumes:
+      - ./postgres/data:/var/lib/postgresql/data
+```
+
+Next, run the following to start up the docker containers.
+```sh
+docker-compose up
+```
+
+You can then run any of the following to start a Spark session.
+
+{{% codetabs "LaunchSparkClient" %}}
+{{% addtab "SparkSQL" checked %}}
+{{% addtab "SparkShell" %}}
+{{% addtab "PySpark" %}}
+{{% addtab "Notebook" %}}

Review Comment:
   We could but the thing is, they could use a notebook but still run spark-sql with `%%sql` or spark-shell with a Scala/Java notebook. To prevent any confusion I could remove the `Notebook` tab and just have an info box that mentions launching a notebook server.



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