Loading Data into Tables - Local¶

Let us understand how to load data into Spark Metastore tables. We can load either from local file system or from HDFS.

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

Data should be in sync with Spark Metastore table structure.
We need to create table with the same file format and delimiters so that we can load the data in files into Spark Metastore tables.
Our data is in text files, line delimiter is new line character and field delimiter is comma.
As our table uses default file format (text file), default line/record delimiter and field delimiter is specified as comma, we should be able to load the data with out any issues.
Here is the script which will create table and then load data into the table.

%%sql

USE itversity_retail

%%sql

DROP TABLE orders

%%sql

CREATE TABLE orders (
  order_id INT COMMENT 'Unique order id',
  order_date STRING COMMENT 'Date on which order is placed',
  order_customer_id INT COMMENT 'Customer id who placed the order',
  order_status STRING COMMENT 'Current status of the order'
) COMMENT 'Table to save order level details'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','

%%sql

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

Using Spark SQL with Python or Scala

spark.sql("USE itversity_retail")

spark.sql("DROP TABLE orders")

spark.sql("""
CREATE TABLE orders (
  order_id INT COMMENT 'Unique order id',
  order_date STRING COMMENT 'Date on which order is placed',
  order_customer_id INT COMMENT 'Customer id who placed the order',
  order_status STRING COMMENT 'Current status of the order'
) COMMENT 'Table to save order level details'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
""")

spark.sql("LOAD DATA LOCAL INPATH '/data/retail_db/orders' INTO TABLE orders")

Once the data is loaded we can run these queries to preview the data.

%%sql

SELECT * FROM orders LIMIT 10

%%sql

SELECT count(1) FROM orders

Apache Spark using SQL

Loading Data into Tables - Local¶