Let us compare and contrast between Managed Tables and External 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/${USER}/warehouse
- When we say EXTERNAL and specify LOCATION or LOCATION alone as part of CREATE TABLE, it makes the table EXTERNAL.
- Rest of the syntax is same as Managed Table.
- However, when we drop Managed Table, it will delete metadata from metastore as well as data from HDFS.
- When we drop External Table, only metadata will be dropped, not the data.
- Typically we use External Table when the same dataset is processed by multiple frameworks such as Hive, Pig, Spark, etc.
- We cannot run TRUNCATE TABLE command against External Tables.
USE itversity_retail
SHOW tables
spark.sql("DESCRIBE FORMATTED orders").show(200, false)
TRUNCATE TABLE orders
spark.sql("DESCRIBE FORMATTED order_items").show(200, false)
TRUNCATE TABLE order_items
DROP TABLE orders
DROP TABLE order_items
import sys.process._
s"hdfs dfs -ls /user/${username}/retail_db/orders" !