Basic DDL and DMLΒΆ

As part of this section we will primarily focus on basic DDL and DML using Spark Metastore.

  • Create Spark Metastore Tables

  • Overview of Data Types

  • Adding Comments

  • Loading Data into Tables - Local

  • Loading Data into Tables - HDFS

  • Loading Data - Append and Overwrite

  • Creating External Tables

  • Managed Tables vs. External Tables

  • Overview of File Formats

  • Dropping Tables and Databases

  • Truncating Tables

Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

val username = System.getProperty("user.name")

import org.apache.spark.sql.SparkSession

val username = System.getProperty("user.name")
val spark = SparkSession.
    builder.
    config("spark.ui.port", "0").
    config("spark.sql.warehouse.dir", s"/user/${username}/warehouse").
    enableHiveSupport.
    appName(s"${username} | Spark SQL - Managing Tables - Basic DDL and DML").
    master("yarn").
    getOrCreate

If you are going to use CLIs, you can use Spark SQL using one of the 3 approaches.

Using Spark SQL

spark2-sql \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Scala

spark2-shell \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Pyspark

pyspark2 \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse
Exercises - Basic SQL Queries Create Spark Metastore Tables