LOAD vs. INSERT¶

Let us compare and contrast LOAD and INSERT commands. These are the main approaches using which we get data into Spark Metastore 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 - DML and Partitioning").
    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

LOAD will copy the files by dividing them into blocks.
LOAD is the fastest way of getting data into Spark Metastore tables. However, there will be minimal validations at File level.
There will be no transformations or validations at data level.
If it require any transformation while getting data into Spark Metastore table, then we need to use INSERT command.
Here are some of the usage scenarios of insert:
- Changing delimiters in case of text file format
- Changing file format
- Loading data into partitioned or bucketed tables (if bucketing is supported).
- Apply any other transformations at data level (widely used)

%%sql

USE itversity_retail

%%sql

DROP TABLE IF EXISTS order_items

%%sql

CREATE TABLE order_items (
  order_item_id INT,
  order_item_order_id INT,
  order_item_product_id INT,
  order_item_quantity INT,
  order_item_subtotal FLOAT,
  order_item_product_price FLOAT
) STORED AS parquet

%%sql

LOAD DATA LOCAL INPATH '/data/retail_db/order_items'
    INTO TABLE order_items

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

import sys.process._

s"hdfs dfs -ls /user/${username}/warehouse/${username}_retail.db/order_items" !

%%sql

SELECT * FROM order_items LIMIT 10

Apache Spark using SQL

LOAD vs. INSERT¶