[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_r915354890


##########
landing-page/content/common/spark-quickstart.md:
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@@ -0,0 +1,323 @@
+---
+title: "Spark and Iceberg Quickstart"
+weight: 100
+url: spark-quickstart
+aliases:
+    - "quickstart"
+    - "quickstarts"
+    - "getting-started"
+disableSidebar: true
+disableToc: true
+---
+<!--
+ - 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.
+ -->
+
+{{% quickstarts %}}
+
+## Spark and Iceberg Quickstart
+
+This guide will get you up and running with an Iceberg and Spark environment, including sample code to
+highlight some powerful features. You can learn more about Iceberg's Spark runtime by checking out the [Spark](../docs/latest/spark-ddl/) section.
+
+- [Docker-Compose](#docker-compose)
+- [Creating a table](#creating-a-table)
+- [Writing Data to a Table](#writing-data-to-a-table)
+- [Reading Data from a Table](#reading-data-from-a-table)
+- [Adding Iceberg to Spark](#adding-iceberg-to-spark)
+- [Adding A Catalog](#adding-a-catalog)
+- [Next Steps](#next-steps)
+
+### Docker-Compose
+
+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. To use this, you'll need to install the [Docker CLI](https://docs.docker.com/get-docker/) as well as the [Docker Compose CLI](https://github.com/docker/compose-cli/blob/main/INSTALL.md).
+
+Once you have those, save the yaml below 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, start up the docker containers with this command:
+```sh
+docker-compose up
+```
+
+You can then run any of the following commands to start a Spark session.
+
+{{% codetabs "LaunchSparkClient" %}}
+{{% addtab "SparkSQL" checked %}}
+{{% addtab "SparkShell" %}}
+{{% addtab "PySpark" %}}
+{{% tabcontent "SparkSQL"  %}}
+```sh
+docker exec -it spark-iceberg spark-sql
+```
+{{% /tabcontent %}}
+{{% tabcontent "SparkShell" %}}
+```sh
+docker exec -it spark-iceberg spark-shell
+```
+{{% /tabcontent %}}
+{{% tabcontent "PySpark" %}}
+```sh
+docker exec -it spark-iceberg pyspark
+```
+{{% /tabcontent %}}
+{{% /codetabs %}}
+{{< hint info >}}
+You can also launch a notebook server by running `docker exec -it spark-iceberg notebook`.
+The notebook server will be available at [http://localhost:8888](http://localhost:8888)
+{{< /hint >}}
+
+### Creating a table
+
+To create your first Iceberg table in Spark, run a [`CREATE TABLE`](../spark-ddl#create-table) command. Let's create a table
+using `demo.nyc.taxis` where `demo` is the catalog name, `nyc` is the database name, and `taxis` is the table name.
+
+
+{{% codetabs "CreateATable" %}}
+{{% addtab "SparkSQL" checked %}}
+{{% addtab "SparkShell" %}}
+{{% addtab "PySpark" %}}
+{{% tabcontent "SparkSQL"  %}}
+```sql
+CREATE TABLE demo.nyc.taxis
+(
+  vendor_id bigint,
+  trip_id bigint,
+  trip_distance float,
+  fare_amount double,
+  store_and_fwd_flag string
+)
+PARTITIONED BY (vendor_id);
+```
+{{% /tabcontent %}}
+{{% tabcontent "SparkShell" %}}
+```scala
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.Row
+val schema = StructType( Array(
+    StructField("vendor_id", LongType,true),
+    StructField("trip_id", LongType,true),
+    StructField("trip_distance", FloatType,true),
+    StructField("fare_amount", DoubleType,true),
+    StructField("store_and_fwd_flag", StringType,true)
+))
+val df = spark.createDataFrame(spark.sparkContext.emptyRDD[Row],schema)
+df.writeTo("demo.nyc.taxis").create()
+```
+{{% /tabcontent %}}
+{{% tabcontent "PySpark" %}}
+```py
+from pyspark.sql.types import DoubleType, FloatType, LongType, StructType,StructField, StringType
+schema = StructType([
+  StructField("vendor_id", LongType(), True),
+  StructField("trip_id", LongType(), True),
+  StructField("trip_distance", FloatType(), True),
+  StructField("fare_amount', DoubleType(), True),
+  StructField("store_and_fwd_flag', StringType(), True)
+])
+
+df = spark.createDataFrame([], schema)
+df.writeTo("demo.nyc.taxis").create()
+```
+{{% /tabcontent %}}
+{{% /codetabs %}}
+
+Iceberg catalogs support the full range of SQL DDL commands, including:
+
+* [`CREATE TABLE ... PARTITIONED BY`](../spark-ddl#create-table)
+* [`CREATE TABLE ... AS SELECT`](../spark-ddl#create-table--as-select)
+* [`ALTER TABLE`](../spark-ddl#alter-table)
+* [`DROP TABLE`](../spark-ddl#drop-table)
+
+### Writing Data to a Table
+
+Once your table is created, you can insert records.
+
+{{% codetabs "InsertData" %}}
+{{% addtab "SparkSQL" checked %}}
+{{% addtab "SparkShell" %}}
+{{% addtab "PySpark" %}}
+{{% tabcontent "SparkSQL"  %}}
+```sql
+INSERT INTO demo.nyc.taxis
+VALUES (1, 1000371, 1.8, 15.32, 'N'), (2, 1000372, 2.5, 22.15, 'N'), (2, 1000373, 0.9, 9.01, 'N'), (1, 1000374, 8.4, 42.13, 'Y');
+```
+{{% /tabcontent %}}
+{{% tabcontent "SparkShell" %}}
+```scala
+import org.apache.spark.sql.Row
+
+val schema = spark.table("demo.nyc.taxis").schema
+val data = Seq(
+    Row(1: Long, 1000371: Long, 1.8f: Float, 15.32: Double, "N": String),
+    Row(2: Long, 1000372: Long, 2.5f: Float, 22.15: Double, "N": String),
+    Row(2: Long, 1000373: Long, 0.9f: Float, 9.01: Double, "N": String),
+    Row(1: Long, 1000374: Long, 8.4f: Float, 42.13: Double, "Y": String)
+)
+val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
+df.writeTo("demo.nyc.taxis").append()
+```
+{{% /tabcontent %}}
+{{% tabcontent "PySpark" %}}
+```py
+schema = spark.table("demo.nyc.taxis").schema
+data = [
+    (1, 1000371, 1.8, 15.32, "N"),
+    (2, 1000372, 2.5, 22.15, "N"),
+    (2, 1000373, 0.9, 9.01, "N"),
+    (1, 1000374, 8.4, 42.13, "Y")
+  ]
+df = spark.createDataFrame(data, schema)
+df.writeTo("demo.nyc.taxis").append()
+```
+{{% /tabcontent %}}
+{{% /codetabs %}}
+
+### Reading Data from a Table
+
+To read a table, simply use the Iceberg table's name.
+
+{{% codetabs "SelectData" %}}
+{{% addtab "SparkSQL" checked %}}
+{{% addtab "SparkShell" %}}
+{{% addtab "PySpark" %}}
+{{% tabcontent "SparkSQL"  %}}
+```sql
+SELECT * FROM demo.nyc.taxis;
+```
+{{% /tabcontent %}}
+{{% tabcontent "SparkShell" %}}
+```scala
+val df = spark.table("demo.nyc.taxis").show()
+```
+{{% /tabcontent %}}
+{{% tabcontent "PySpark" %}}
+```py
+df = spark.table("demo.nyc.taxis").show()
+```
+{{% /tabcontent %}}
+{{% /codetabs %}}
+
+### Adding Iceberg to Spark

Review Comment:
   Agreed, the section definitely breaks the flow of the guide. I moved it to the end and now the order looks like:
   - Docker-Compose
   - Creating a table
   - Writing Data to a Table
   - Reading Data from a Table
   - Adding A Catalog
   - Next Steps



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