samredai commented on code in PR #75: URL: https://github.com/apache/iceberg-docs/pull/75#discussion_r890338257
########## 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), + 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 your Existing Spark Environment + +To add Iceberg, use the `--packages` option. + +{{% codetabs "AddIcebergToSpark" %}} +{{% addtab "SparkSQL" checked %}} +{{% addtab "SparkShell" %}} +{{% addtab "PySpark" %}} +{{% tabcontent "SparkSQL" %}} +```sh +spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% tabcontent "SparkShell" %}} +```sh +spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% tabcontent "PySpark" %}} +```sh +pyspark --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% /codetabs %}} + +{{< hint info >}} +If you want to include Iceberg in your Spark installation, add the [`iceberg-spark-runtime-3.2_2.12` Jar](spark-runtime-jar) to Spark's `jars` folder. +{{< /hint >}} + +[spark-runtime-jar]: https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark-runtime-3.2_2.12/{{% icebergVersion %}}/iceberg-spark-runtime-3.2_2.12-{{% icebergVersion %}}.jar + +#### Adding A Catalog to Your Existing Spark Environment + +Iceberg has several catalog back-ends that can be used to track tables, like JDBC, Hive MetaStore and Glue. +Catalogs are configured using properties under `spark.sql.catalog.(catalog_name)`. In this quickstart guide, +we use JDBC, but you can follow these instructions to configure other catalog types. To learn more, check out +the [Catalog](../docs/latest/spark-configuration/#catalogs) page in the Spark section. + +This configuration creates a path-based catalog named `local` for tables under `$PWD/warehouse` and adds support for Iceberg tables to Spark's built-in catalog. + + +{{% codetabs "AddingACatalog" %}} +{{% addtab "SparkSQL" checked %}} +{{% addtab "SparkShell" %}} +{{% addtab "PySpark" %}} +{{% tabcontent "SparkSQL" %}} +```sh +spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}}\ + --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \ + --conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \ + --conf spark.sql.catalog.spark_catalog.type=hive \ + --conf spark.sql.catalog.demo=org.apache.iceberg.spark.SparkCatalog \ + --conf spark.sql.catalog.demo.type=hadoop \ + --conf spark.sql.catalog.demo.warehouse=$PWD/warehouse \ + --conf spark.sql.defaultCatalog=demo Review Comment: Done! Made a `CLI` and a `spark-defaults` tab; ########## 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), + 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 your Existing Spark Environment + +To add Iceberg, use the `--packages` option. + +{{% codetabs "AddIcebergToSpark" %}} +{{% addtab "SparkSQL" checked %}} +{{% addtab "SparkShell" %}} +{{% addtab "PySpark" %}} +{{% tabcontent "SparkSQL" %}} +```sh +spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% tabcontent "SparkShell" %}} +```sh +spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% tabcontent "PySpark" %}} +```sh +pyspark --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}} +``` +{{% /tabcontent %}} +{{% /codetabs %}} + +{{< hint info >}} +If you want to include Iceberg in your Spark installation, add the [`iceberg-spark-runtime-3.2_2.12` Jar](spark-runtime-jar) to Spark's `jars` folder. +{{< /hint >}} + +[spark-runtime-jar]: https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark-runtime-3.2_2.12/{{% icebergVersion %}}/iceberg-spark-runtime-3.2_2.12-{{% icebergVersion %}}.jar + +#### Adding A Catalog to Your Existing Spark Environment + +Iceberg has several catalog back-ends that can be used to track tables, like JDBC, Hive MetaStore and Glue. +Catalogs are configured using properties under `spark.sql.catalog.(catalog_name)`. In this quickstart guide, +we use JDBC, but you can follow these instructions to configure other catalog types. To learn more, check out +the [Catalog](../docs/latest/spark-configuration/#catalogs) page in the Spark section. + +This configuration creates a path-based catalog named `local` for tables under `$PWD/warehouse` and adds support for Iceberg tables to Spark's built-in catalog. + + +{{% codetabs "AddingACatalog" %}} +{{% addtab "SparkSQL" checked %}} +{{% addtab "SparkShell" %}} +{{% addtab "PySpark" %}} +{{% tabcontent "SparkSQL" %}} +```sh +spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:{{% icebergVersion %}}\ + --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \ + --conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \ + --conf spark.sql.catalog.spark_catalog.type=hive \ + --conf spark.sql.catalog.demo=org.apache.iceberg.spark.SparkCatalog \ + --conf spark.sql.catalog.demo.type=hadoop \ + --conf spark.sql.catalog.demo.warehouse=$PWD/warehouse \ + --conf spark.sql.defaultCatalog=demo Review Comment: Done! 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