Install Spark and run master and slaves (workers) in standalone mode.

```
brew install apache-spark
/usr/local/Cellar/apache-spark/2.3.1/bin/spark-class org.apache.spark.deploy.master.Master
/usr/local/Cellar/apache-spark/2.3.1/bin/spark-class org.apache.spark.deploy.worker.Worker  spark://<MASTER_IP>:7077 -c 1 -m 512M
```

In PostgreSQL
```
CREATE TABLE items(
  id VARCHAR (100),
  description VARCHAR (100),
);
```
Load CSV from file
COPY items FROM '/Users/<pathTo>/items.csv' DELIMITER ',' CSV HEADER;

Launch Spark Shell
```
pyspark --conf spark.executor.extraClassPath=/Users/<pathTo>/postgresql-42.2.4.jar  --driver-class-path /Users/<pathTo>/postgresql-42.2.4.jar  --master spark://192.168.1.199:7077 --executor-memory 512m
```

Connect to existing PostgreSQL

```
df = spark.read \
    .format("jdbc") \
    .option("driver", "org.postgresql.Driver") \
    .option("url", "jdbc:postgresql:retailme") \
    .option("dbtable", "items") \
    .option("user", "<postgres_user>") \
    .option("password", "") \
    .load()

df.count() #This fires the query and displayed the count once can check progress in the Spark UI

# Join, shows number of records where ids exist sin df1 but not df2

left_join = df1.join(df2, df1.id == df2.id,how='left') # Could also use 'left_outer'
left_join.filter(col('id').isNull()).count()


# Write data to tables
mode = "overwrite"
url = "jdbc:postgresql:retailme"
properties = {"user": "<postgreUser>","password": "","driver": "org.postgresql.Driver"}
df.write.jdbc(url=url, table="items", mode=mode, properties=properties)

```