How to execute .sql file in spark using python

how to execute sql file in spark using scala
how to execute hql file in spark sql
spark sql read query from file
how to execute hive sql file in spark engine?
spark sql tutorial python
spark sql example
spark sql delete rows from table
spark sql syntax
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext

conf = SparkConf().setAppName("Test").set("spark.driver.memory", "1g")
sc = SparkContext(conf = conf)

sqlContext = SQLContext(sc)

results = sqlContext.sql("/home/ubuntu/workload/queryXX.sql")

When I execute this command using: python test.py it gives me an error.

y4j.protocol.Py4JJavaError: An error occurred while calling o20.sql. : java.lang.RuntimeException: [1.1] failure: ``with'' expected but `/' found

/home/ubuntu/workload/queryXX.sql

at scala.sys.package$.error(package.scala:27)

I am very new to Spark and I need help here to move forward.

SqlContext.sql expects a valid SQL query not a path to the file. Try this:

with open("/home/ubuntu/workload/queryXX.sql") as fr:
   query = fr.read()
results = sqlContext.sql(query)

Solved: How to run HQL file in Spark, Solved: I want to read a hql file in spark job. We can use below Scala API to read file: .com/questions/31313361/sparksql-hql-script-in-file-to-be-loaded-on-​python-code This link explains how to execute hive sql using spark-sql shell. In particular, like Shark, Spark SQL supports all existing Hive data formats, user-defined functions (UDF), and the Hive metastore. With features that will be introduced in Apache Spark 1.1.0, Spark SQL beats Shark in TPC-DS performance by almost an order of magnitude.

Run spark-sql --help will give you

CLI options:
 -d,--define <key=value>          Variable subsitution to apply to hive
                                  commands. e.g. -d A=B or --define A=B
    --database <databasename>     Specify the database to use
 -e <quoted-query-string>         SQL from command line
 -f <filename>                    SQL from files
 -H,--help                        Print help information
    --hiveconf <property=value>   Use value for given property
    --hivevar <key=value>         Variable subsitution to apply to hive
                                  commands. e.g. --hivevar A=B
 -i <filename>                    Initialization SQL file
 -S,--silent                      Silent mode in interactive shell
 -v,--verbose                     Verbose mode (echo executed SQL to the
                                  console)

So you can execute your sql script like this:

spark-sql -f <your-script>.sql

Spark SQL and DataFrames, Running SQL Queries Programmatically. Scala; Java; Python; R. The sql function on a SparkSession enables  In this demo, we will be using PySpark which is a Python library for Spark programming to read and write the data into SQL Server using Spark SQL. In order to connect and to read a table from SQL Server, we need to create a JDBC connector which has a common format like driver name, connection string, user name, and password .

I'm not sure will it answer your question. But if you intend to run query on existing table you can use,

spark-sql -i <Filename_with abs path/.sql>

One more thing, if you have pyspark script you can use spark-submit details in here.

Spark SQL and DataFrames, You can connect to a Spark cluster via JDBC using PyHive and then run a script. You should have PyHive installed on the machine where you  Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. Spark SQL can also be used to read data from an existing Hive installation. For more on how to configure this feature, please refer to the Hive Tables section.

How to run SQL queries from Python scripts, Apache Spark has built-in modules for streaming, SQL, machine learning and graph processing. There are various ways to access Spark from  I am using Spark and I would like to know: how to create temporary table named C by executing sql query on tables A and B ? sqlContext .read.json(file_name_A) .createOrReplaceTempView("A")

Execute Pyspark Script from Python and Examples, One use of Spark SQL is to execute SQL queries. Then run the following code which reads sample data from a CSV file, loads it to a DataFrame, queries for people in the people table and To run Spark SQL with Python 3:. How can execute sql script stored in *.sql file using MySQLdb python driver. I was trying. cursor.execute(file(PATH_TO_FILE).read()) but this doesn't work because cursor.execute can run only one sql command at once.

Working with Spark SQL to query data, How to implement Spark with Python Programming This post's objective is to demonstrate how to run Spark with PySpark and execute common DataFrames can be created by reading txt, csv, json and parquet file formats. Load data from JSON file and execute SQL query. Following is a step-by-step process to load data from JSON file and execute SQL query on the loaded data from JSON file : Create a Spark Session Provide application name and set master to local with two threads. Create a Spark Session. SparkSession spark = SparkSession.