samredai commented on code in PR #75: URL: https://github.com/apache/iceberg-docs/pull/75#discussion_r890339356
########## landing-page/content/common/quickstarts.md: ########## @@ -0,0 +1,335 @@ +--- +url: quickstarts +toc: true +aliases: + - "quickstarts" + - "getting-started" +--- +<!-- + - 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. + --> + +## <img src="../img/spark-menu-logo.png"> Spark and Iceberg Quickstart + +This quickstart 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 your Existing Spark Environment](#adding-iceberg-to-your-existing-spark-environment) +- [Adding A Catalog to Your Existing Spark Environment](#adding-a-catalog-to-your-existing-spark-environment) +- [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 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" %}} +{{% 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 %}} +{{% tabcontent "Notebook" %}} +```sh +docker exec -it spark-iceberg notebook +``` +{{< hint warning >}} +The notebook server will be available at [http://localhost:8888](http://localhost:8888) +{{< /hint >}} +{{% /tabcontent %}} +{{% /codetabs %}} + + +#### Creating a table + +To create your first Iceberg table in Spark, run a [`CREATE TABLE`](../spark-ddl#create-table) command. In the following example, we'll create a table +using `demo.nyc.taxis` where `demo` is the catalog name, `nyc` is the schema 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.{DoubleType, FloatType, LongType, StringType, StructField, StructType} +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), Review Comment: Fixed! -- This is an automated message from the Apache Git Service. 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