Import spark

Import spark DEFAULT

Since Spark 2.0 SparkSession has become an entry point to PySpark to work with RDD, DataFrame. Prior to 2.0, SparkContext used to be an entry point. Here, I will mainly focus on explaining what is SparkSession by defining and describing how to create SparkSession and using default SparkSession variable from .

What is SparkSession

SparkSession introduced in version 2.0, It is an entry point to underlying PySpark functionality in order to programmatically create PySpark RDD, DataFrame. It’s object is default available in pyspark-shell and it can be created programmatically using SparkSession.

SparkSession

With Spark 2.0 a new class SparkSession () has been introduced. SparkSession is a combined class for all different contexts we used to have prior to 2.0 relase (SQLContext and HiveContext e.t.c). Since 2.0 SparkSession can be used in replace with SQLContext, HiveContext, and other contexts defined prior to 2.0.

As mentioned in the beginning SparkSession is an entry point to PySpark and creating a SparkSession instance would be the first statement you would write to program with RDD, DataFrame, and Dataset. SparkSession will be created using builder patterns.

Though SparkContext used to be an entry point prior to 2.0, It is not completely replaced with SparkSession, many features of SparkContext are still available and used in Spark 2.0 and later. You should also know that SparkSession internally creates SparkConfig and SparkContext with the configuration provided with SparkSession.

Spark Session also includes all the APIs available in different contexts –

  • Spark Context,
  • SQL Context,
  • Streaming Context,
  • Hive Context.

You can create as many SparkSession objects you want using either or .

SparkSession in PySpark shell

Be default PySpark shell provides “” object; which is an instance of SparkSession class. We can directly use this object where required in spark-shell. Start your “” shell from folder and enter the below statement.

Similar to PySpark shell, In most of the tools, the environment itself creates default SparkSession object for us to use so you don’t have to worry about creating SparkSession object.

Create SparkSession

In order to create SparkSession programmatically( in .py file) in PySpark, you need to use the builder pattern method as explained below. method returns an already existing SparkSession; if not exists, it creates a new SparkSession.

 – If you are running it on the cluster you need to use your master name as an argument to master(). usually, it would be either  or  depends on your cluster setup.

  • Use when running in Standalone mode. x should be an integer value and should be greater than 0; this represents how many partitions it should create when using RDD, DataFrame, and Dataset. Ideally, x value should be the number of CPU cores you have.

 – Used to set your application name.

 – This returns a SparkSession object if already exists, creates new one if not exists.

Note:  SparkSession object “spark” is by default available in PySpark shell.

You can also create a new SparkSession using method.

This always creates a new SparkSession object.

SparkSession Commonly Used Methods

– Returns Spark version where your application is running, probably the Spark version you cluster is configured with.

– This creates a DataFrame from a collection and an RDD

– returns an active Spark session.

– Returns an instance of class, this is used to read records from csv, parquet, avro and more file formats into DataFrame.

– Returns an instance of  class, this is used to read streaming data. that can be used to read streaming data into DataFrame.

 – Returns a SparkContext.

– Returns a DataFrame after executing the SQL mentioned.

– Returns SQLContext.

– Stop the current SparkContext.

– Returns a DataFrame of a table or view.

– Creates a PySpark UDF to use it on DataFrame, Dataset, and SQL.

Conclusion

In this PySpark article, you have learned SparkSession can be created using the builder() method and learned SparkSession is an entry point to PySpark, and creating a SparkSession instance would be the first statement you would write to program and finally have learned some of the commonly used SparkSession methods.

Related Articles

Reference

Happy Learning !!

Tags: SparkSession

NNK

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PySpark – What is SparkSession?
Sours: https://sparkbyexamples.com/pyspark/pyspark-what-is-sparksession/

PySpark and SparkSQL Basics

Python is revealed the Spark programming model to work with structured data by the Spark Python API which is called as PySpark.

This post’s objective is to demonstrate how to run Spark with PySpark and execute common functions.

Python programming language requires an installed IDE. The easiest way to use Python with Anaconda since it installs sufficient IDE’s and crucial packages along with itself.

With the help of this link, you can download Anaconda. After the suitable Anaconda version is downloaded, click on it to proceed with the installation procedure which is explained step by step in the Anaconda Documentation.

When the installation is completed, the Anaconda Navigator Homepage will be opened. In order to use Python, simply click on the “Launch” button of the “Notebook” module.

To be able to use Spark through Anaconda, the following package installation steps shall be followed.

Anaconda Prompt terminal

conda install pyspark

conda install pyarrow

After PySpark and PyArrow package installations are completed, simply close the terminal and go back to Jupyter Notebook and import the required packages at the top of your code.

import pandas as pd
from pyspark.sql import SparkSession
from pyspark.context import SparkContext
from pyspark.sql.functions
import *from pyspark.sql.types
import *from datetime import date, timedelta, datetime
import time

First of all, a Spark session needs to be initialized. With the help of SparkSession, DataFrame can be created and registered as tables. Moreover, SQL tables are executed, tables can be cached, and parquet/JSON/CSV/Avro data formatted files can be read.

sc = SparkSession.builder.appName("PysparkExample")\
.config ("spark.sql.shuffle.partitions", "50")\ .config("spark.driver.maxResultSize","5g")\
.config ("spark.sql.execution.arrow.enabled", "true")\ .getOrCreate()

For detailed explanations for each parameter of SparkSession, kindly visit pyspark.sql.SparkSession.

A DataFrame can be accepted as a distributed and tabulated collection of titled columns which is similar to a table in a relational database. In this post, we will be using DataFrame operations on PySpark API while working with datasets.

You can download the Kaggle dataset from this link.

3.1. From Spark Data Sources

DataFrames can be created by reading text, CSV, JSON, and Parquet file formats. In our example, we will be using a .json formatted file. You can also find and read text, CSV, and Parquet file formats by using the related read functions as shown below.

#Creates a spark data frame called as raw_data.#JSON
dataframe = sc.read.json('dataset/nyt2.json')#TXT FILES#
dataframe_txt = sc.read.text('text_data.txt')#CSV FILES#
dataframe_csv = sc.read.csv('csv_data.csv')#PARQUET FILES#
dataframe_parquet = sc.read.load('parquet_data.parquet')

Duplicate values in a table can be eliminated by using dropDuplicates() function.

dataframe = sc.read.json('dataset/nyt2.json')
dataframe.show(10)

After dropDuplicates() function is applied, we can observe that duplicates are removed from the dataset.

dataframe_dropdup = dataframe.dropDuplicates() dataframe_dropdup.show(10)

Querying operations can be used for various purposes such as subsetting columns with “select”, adding conditions with “when” and filtering column contents with “like”. Below, some of the most commonly used operations are exemplified. For the complete list of query operations, see the Apache Spark doc.

5.1. “Select” Operation

It is possible to obtain columns by attribute (“author”) or by indexing (dataframe[‘author’]).

#Show all entries in title column
dataframe.select("author").show(10)#Show all entries in title, author, rank, price columns
dataframe.select("author", "title", "rank", "price").show(10)

5.2. “When” Operation

In the first example, the “title” column is selected and a condition is added with a “when” condition.

# Show title and assign 0 or 1 depending on titledataframe.select("title",when(dataframe.title != 'ODD HOURS',
1).otherwise(0)).show(10)

In the second example, the “isin” operation is applied instead of “when” which can be also used to define some conditions to rows.

# Show rows with specified authors if in the given optionsdataframe [dataframe.author.isin("John Sandford",
"Emily Giffin")].show(5)

5.3. “Like” Operation

In the brackets of the “Like” function, the % character is used to filter out all titles having the “ THE ” word. If the condition we are looking for is the exact match, then no % character shall be used.

# Show author and title is TRUE if title has " THE " word in titlesdataframe.select("author", "title",
dataframe.title.like("% THE %")).show(15)

5.4. “Startswith” — “ Endswith”

StartsWith scans from the beginning of word/content with specified criteria in the brackets. In parallel, EndsWith processes the word/content starting from the end. Both of the functions are case-sensitive.

dataframe.select("author", "title", dataframe.title.startswith("THE")).show(5)dataframe.select("author", "title", dataframe.title.endswith("NT")).show(5)

5.5. “Substring” Operation

Substring functions to extract the text between specified indexes. In the following examples, texts are extracted from the index numbers (1, 3), (3, 6), and (1, 6).

dataframe.select(dataframe.author.substr(1, 3).alias("title")).show(5)dataframe.select(dataframe.author.substr(3, 6).alias("title")).show(5)dataframe.select(dataframe.author.substr(1, 6).alias("title")).show(5)

Data manipulation functions are also available in the DataFrame API. Below, you can find examples to add/update/remove column operations.

6.1. Adding Columns

# Lit() is required while we are creating columns with exact values.dataframe = dataframe.withColumn('new_column',
F.lit('This is a new column'))display(dataframe)

6.2. Updating Columns

For updated operations of DataFrame API, withColumnRenamed() function is used with two parameters.

# Update column 'amazon_product_url' with 'URL'dataframe = dataframe.withColumnRenamed('amazon_product_url', 'URL')dataframe.show(5)

6.3. Removing Columns

Removal of a column can be achieved in two ways: adding the list of column names in the drop() function or specifying columns by pointing in the drop function. Both examples are shown below.

dataframe_remove = dataframe.drop("publisher", "published_date").show(5)dataframe_remove2 = dataframe \ .drop(dataframe.publisher).drop(dataframe.published_date).show(5)

There exist several types of functions to inspect data. Below, you can find some of the commonly used ones. For a deeper look, visit the Apache Spark doc.

# Returns dataframe column names and data types
dataframe.dtypes# Displays the content of dataframe
dataframe.show()# Return first n rows
dataframe.head()# Returns first row
dataframe.first()# Return first n rows
dataframe.take(5)# Computes summary statistics
dataframe.describe().show()# Returns columns of dataframe
dataframe.columns# Counts the number of rows in dataframe
dataframe.count()# Counts the number of distinct rows in dataframe
dataframe.distinct().count()# Prints plans including physical and logical
dataframe.explain(4)

The grouping process is applied with GroupBy() function by adding column name in function.

# Group by author, count the books of the authors in the groupsdataframe.groupBy("author").count().show(10)

Filtering is applied by using the filter() function with a condition parameter added inside of it. This function is case-sensitive.

# Filtering entries of title
# Only keeps records having value 'THE HOST'
dataframe.filter(dataframe["title"] == 'THE HOST').show(5)

For every dataset, there is always a need for replacing, existing values, dropping unnecessary columns, and filling missing values in data preprocessing stages. pyspark.sql.DataFrameNaFunction library helps us to manipulate data in this respect. Some examples are added below.

# Replacing null values
dataframe.na.fill()
dataFrame.fillna()
dataFrameNaFunctions.fill()# Returning new dataframe restricting rows with null valuesdataframe.na.drop()
dataFrame.dropna()
dataFrameNaFunctions.drop()# Return new dataframe replacing one value with another
dataframe.na.replace(5, 15)
dataFrame.replace()
dataFrameNaFunctions.replace()

It is possible to increase or decrease the existing level of partitioning in RDD Increasing can be actualized by using the repartition(self, numPartitions) function which results in a new RDD that obtains the higher number of partitions. Decreasing can be processed with coalesce(self, numPartitions, shuffle=False) function that results in a new RDD with a reduced number of partitions to a specified number. For more info, please visit theApache Spark docs.

# Dataframe with 10 partitions
dataframe.repartition(10).rdd.getNumPartitions()# Dataframe with 1 partition
dataframe.coalesce(1).rdd.getNumPartitions()

Raw SQL queries can also be used by enabling the “sql” operation on our SparkSession to run SQL queries programmatically and return the result sets as DataFrame structures. For more detailed information, kindly visit Apache Spark docs.

# Registering a table
dataframe.registerTempTable("df")sc.sql("select * from df").show(3)sc.sql("select \
CASE WHEN description LIKE '%love%' THEN 'Love_Theme' \ WHEN description LIKE '%hate%' THEN 'Hate_Theme' \ WHEN description LIKE '%happy%' THEN 'Happiness_Theme' \ WHEN description LIKE '%anger%' THEN 'Anger_Theme' \ WHEN description LIKE '%horror%' THEN 'Horror_Theme' \ WHEN description LIKE '%death%' THEN 'Criminal_Theme' \ WHEN description LIKE '%detective%' THEN 'Mystery_Theme' \ ELSE 'Other_Themes' \ END Themes \
from df").groupBy('Themes').count().show()

13.1. Data Structures

DataFrame API uses RDD as a base and it converts SQL queries into low-level RDD functions. By using the .rdd operation, a dataframe can be converted into RDD. It is also possible to convert Spark Dataframe into a string of RDD and Pandas formats.

# Converting dataframe into an RDD
rdd_convert = dataframe.rdd# Converting dataframe into a RDD of string dataframe.toJSON().first()# Obtaining contents of df as Pandas
dataFramedataframe.toPandas()

13.2. Write & Save to Files

Any data source type that is loaded to our code as data frames can easily be converted and saved into other types including .parquet and .json. For more save, load, write function details, please visit Apache Spark doc.

# Write & Save File in .parquet format
dataframe.select("author", "title", "rank", "description") \
.write \
.save("Rankings_Descriptions.parquet")
# Write & Save File in .json format
dataframe.select("author", "title") \
.write \
.save("Authors_Titles.json",format="json")

13.3. Stopping SparkSession

Spark Session can be stopped by running the stop() function as follows.

# End Spark Session
sc.stop()

The code and Jupyter Notebook are available on my GitHub.

Questions and comments are highly appreciated!

Sours: https://towardsdatascience.com/pyspark-and-sparksql-basics-6cb4bf967e53
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How to import the sparksession

As undefined_variable mentioned, you need to run to access the class.

It was also mentioned that you don't need to create your own SparkSession in the Spark console because it's already created for you.

enter image description here

Notice the "Spark session available as 'spark'" message when the console is started.

You can run this code in the console but it actually doesn't create a new SparkSession:

The portion tells Spark to use an existing SparkSession if it exists and only create a new SparkSession if necessary. In this case, the Spark Shell created a SparkSession, so the existing SparkSession will be used.

See this post for more information on how to manage the SparkSession in production applications.

answered Apr 21 '20 at 15:48

PowersPowers

13.7k77 gold badges6363 silver badges8989 bronze badges

Sours: https://stackoverflow.com/questions/57585301/how-to-import-the-sparksession
Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 1 - Install Spark on Jupyter Notebook

Databricks Connect

Databricks Connect allows you to connect your favorite IDE (Eclipse, IntelliJ, PyCharm, RStudio, Visual Studio Code), notebook server (Jupyter Notebook, Zeppelin), and other custom applications to Databricks clusters.

This article explains how Databricks Connect works, walks you through the steps to get started with Databricks Connect, explains how to troubleshoot issues that may arise when using Databricks Connect, and differences between running using Databricks Connect versus running in a Databricks notebook.

Overview

Databricks Connect is a client library for Databricks Runtime. It allows you to write jobs using Spark APIs and run them remotely on a Databricks cluster instead of in the local Spark session.

For example, when you run the DataFrame command using Databricks Connect, the parsing and planning of the job runs on your local machine. Then, the logical representation of the job is sent to the Spark server running in Databricks for execution in the cluster.

With Databricks Connect, you can:

  • Run large-scale Spark jobs from any Python, Java, Scala, or R application. Anywhere you can , , or , you can now run Spark jobs directly from your application, without needing to install any IDE plugins or use Spark submission scripts.
  • Step through and debug code in your IDE even when working with a remote cluster.
  • Iterate quickly when developing libraries. You do not need to restart the cluster after changing Python or Java library dependencies in Databricks Connect, because each client session is isolated from each other in the cluster.
  • Shut down idle clusters without losing work. Because the client application is decoupled from the cluster, it is unaffected by cluster restarts or upgrades, which would normally cause you to lose all the variables, RDDs, and DataFrame objects defined in a notebook.

Note

For Python development with SQL queries, Databricks recommends that you use the Databricks SQL Connector for Python instead of Databricks Connect. the Databricks SQL Connector for Python is easier to set up than Databricks Connect. Also, Databricks Connect parses and plans jobs runs on your local machine, while jobs run on remote compute resources. This can make it especially difficult to debug runtime errors. The Databricks SQL Connector for Python submits SQL queries directly to remote compute resources and fetches results.

Requirements

  • Only the following Databricks Runtime versions are supported:

    • Databricks Runtime 9.1 LTS ML, Databricks Runtime 9.1 LTS
    • Databricks Runtime 7.3 LTS ML, Databricks Runtime 7.3 LTS
    • Databricks Runtime 6.4 ML, Databricks Runtime 6.4
    • Databricks Runtime 5.5 LTS ML, Databricks Runtime 5.5 LTS
  • The minor version of your client Python installation must be the same as the minor Python version of your Databricks cluster. The table shows the Python version installed with each Databricks Runtime.

    Databricks Runtime versionPython version
    9.1 LTS ML, 9.1 LTS 3.8
    7.3 LTS ML, 7.3 LTS 3.7
    6.4 ML, 6.4 3.7
    5.5 LTS ML 3.6
    5.5 LTS 3.5

    For example, if you’re using Conda on your local development environment and your cluster is running Python 3.5, you must create an environment with that version, for example:

    conda create --name dbconnect python=3.7 conda
  • The Databricks Connect major and minor package version must always match your Databricks Runtime version. Databricks recommends that you always use the most recent package of Databricks Connect that matches your Databricks Runtime version. For example, when using a Databricks Runtime 7.3 LTS cluster, use the package.

  • Java Runtime Environment (JRE) 8. The client has been tested with the OpenJDK 8 JRE. The client does not support Java 11.

Set up the client

Step 1: Install the client

  1. Uninstall PySpark. This is required because the package conflicts with PySpark. For details, see Conflicting PySpark installations.

  2. Install the Databricks Connect client.

    pip install -U "databricks-connect==7.3.*"# or X.Y.* to match your cluster version.

    Note

    Always specify instead of , to make sure that the newest package is installed.

Step 2: Configure connection properties

  1. Collect the following configuration properties:

    • Databricks workspace URL.

    • Databricks personal access token.

    • The ID of the cluster you created. You can obtain the cluster ID from the URL. Here the cluster ID is .

      Cluster ID
    • The port that Databricks Connect connects to. Set to .

  2. Configure the connection. You can use the CLI, SQL configs, or environment variables. The precedence of configuration methods from highest to lowest is: SQL config keys, CLI, and environment variables.

    • CLI

      1. Run .

        databricks-connect configure

        The license displays:

        Copyright(2018)Databricks,Inc.Thislibrary(the"Software")maynotbeusedexceptinconnectionwiththeLicensee's use of the Databricks Platform Services pursuant to an Agreement...
      2. Accept the license and supply configuration values. For Databricks Host and Databricks Token, enter the workspace URL and the personal access token you noted in Step 1.

        Do you accept the above agreement? [y/N] y Set new config values (leave input empty to accept default): Databricks Host [no current value, must start with https://]: <databricks-url> Databricks Token [no current value]: <databricks-token> Cluster ID (e.g., 0921-001415-jelly628) [no current value]: <cluster-id> Org ID (Azure-only, see ?o=orgId in URL) [0]: <org-id> Port [15001]: <port>
    • SQL configs or environment variables. The following table shows the SQL config keys and the environment variables that correspond to the configuration properties you noted in Step 1. To set a SQL config key, use . For example: .

      ParameterSQL config keyEnvironment variable name
      Databricks Hostspark.databricks.service.addressDATABRICKS_ADDRESS
      Databricks Tokenspark.databricks.service.tokenDATABRICKS_API_TOKEN
      Cluster IDspark.databricks.service.clusterIdDATABRICKS_CLUSTER_ID
      Org IDspark.databricks.service.orgIdDATABRICKS_ORG_ID
      Portspark.databricks.service.portDATABRICKS_PORT

      Important

      Databricks does not recommend putting tokens in SQL configurations.

  3. Test connectivity to Databricks.

    If the cluster you configured is not running, the test starts the cluster which will remain running until its configured autotermination time. The output should be something like:

    * PySpark is installed at /.../3.5.6/lib/python3.5/site-packages/pyspark * Checking java version java version "1.8.0_152" Java(TM) SE Runtime Environment (build 1.8.0_152-b16) Java HotSpot(TM) 64-Bit Server VM (build 25.152-b16, mixed mode) * Testing scala command 18/12/10 16:38:44 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 18/12/10 16:38:50 WARN MetricsSystem: Using default name SparkStatusTracker for source because neither spark.metrics.namespace nor spark.app.id is set. 18/12/10 16:39:53 WARN SparkServiceRPCClient: Now tracking server state for 5abb7c7e-df8e-4290-947c-c9a38601024e, invalidating prev state 18/12/10 16:39:59 WARN SparkServiceRPCClient: Syncing 129 files (176036 bytes) took 3003 ms Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT /_/ Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_152) Type in expressions to have them evaluated. Type :help for more information. scala> spark.range(100).reduce(_ + _) Spark context Web UI available at https://10.8.5.214:4040 Spark context available as 'sc' (master = local[*], app id = local-1544488730553). Spark session available as 'spark'. View job details at <databricks-url>/?o=0#/setting/clusters/<cluster-id>/sparkUi View job details at <databricks-url>?o=0#/setting/clusters/<cluster-id>/sparkUi res0: Long = 4950 scala> :quit * Testing python command 18/12/10 16:40:16 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 18/12/10 16:40:17 WARN MetricsSystem: Using default name SparkStatusTracker for source because neither spark.metrics.namespace nor spark.app.id is set. 18/12/10 16:40:28 WARN SparkServiceRPCClient: Now tracking server state for 5abb7c7e-df8e-4290-947c-c9a38601024e, invalidating prev state View job details at <databricks-url>/?o=0#/setting/clusters/<cluster-id>/sparkUi

Set up your IDE or notebook server

The section describes how to configure your preferred IDE or notebook server to use the Databricks Connect client.

Jupyter notebook

The Databricks Connect configuration script automatically adds the package to your project configuration. To get started in a Python kernel, run:

frompyspark.sqlimportSparkSessionspark=SparkSession.builder.getOrCreate()

To enable the shorthand for running and visualizing SQL queries, use the following snippet:

fromIPython.core.magicimportline_magic,line_cell_magic,Magics,[email protected]_classclassDatabricksConnectMagics(Magics):@line_cell_magicdefsql(self,line,cell=None):ifcellandline:raiseValueError("Line must be empty for cell magic",line)try:fromautovizwidget.widget.utilsimportdisplay_dataframeexceptImportError:print("Please run `pip install autovizwidget` to enable the visualization widget.")display_dataframe=lambdax:xreturndisplay_dataframe(self.get_spark().sql(cellorline).toPandas())defget_spark(self):user_ns=get_ipython().user_nsif"spark"inuser_ns:returnuser_ns["spark"]else:frompyspark.sqlimportSparkSessionuser_ns["spark"]=SparkSession.builder.getOrCreate()returnuser_ns["spark"]ip=get_ipython()ip.register_magics(DatabricksConnectMagics)

PyCharm

The Databricks Connect configuration script automatically adds the package to your project configuration.

Python 3 clusters

  1. When you create a PyCharm project, select Existing Interpreter. From the drop-down menu, select the Conda environment you created (see Requirements).

    Select interpreter
  2. Go to Run > Edit Configurations.

  3. Add as an environment variable.

    Python 3 cluster configuration

SparkR and RStudio Desktop

  1. Download and unpack the open source Spark onto your local machine. Choose the same version as in your Databricks cluster (Hadoop 2.7).

  2. Run . This command returns a path like . Copy the file path of one directory above the JAR directory file path, for example, , which is the directory.

  3. Configure the Spark lib path and Spark home by adding them to the top of your R script. Set to the directory where you unpacked the open source Spark package in step 1. Set to the Databricks Connect directory from step 2.

    # Point to the OSS package path, e.g., /path/to/.../spark-2.4.0-bin-hadoop2.7library(SparkR,lib.loc=.libPaths(c(file.path('<spark-lib-path>','R','lib'),.libPaths())))# Point to the Databricks Connect PySpark installation, e.g., /path/to/.../pysparkSys.setenv(SPARK_HOME="<spark-home-path>")
  4. Initiate a Spark session and start running SparkR commands.

    sparkR.session()df<-as.DataFrame(faithful)head(df)df1<-dapply(df,function(x){x},schema(df))collect(df1)

sparklyr and RStudio Desktop

Note

Before you begin, you must meet the requirements and set up the client for Databricks Connect.

You can copy sparklyr-dependent code that you’ve developed locally using Databricks Connect and run it in a Databricks notebook or hosted RStudio Server in your Databricks workspace with minimal or no code changes.

Requirements

  • sparklyr 1.2 or above.
  • Databricks Runtime 6.4 or above with matching Databricks Connect.

Install, configure, and use sparklyr

  1. In RStudio Desktop, install sparklyr 1.2 or above from CRAN or install the latest master version from GitHub.

    # Install from CRANinstall.packages("sparklyr")# Or install the latest master version from GitHubinstall.packages("devtools")devtools::install_github("sparklyr/sparklyr")
  2. Activate the Python environment with Databricks Connect installed and run the following command in the terminal to get the :

    databricks-connect get-spark-home
  3. Initiate a Spark session and start running sparklyr commands.

    library(sparklyr)sc<-spark_connect(method="databricks",spark_home="<spark-home-path>")iris_tbl<-copy_to(sc,iris,overwrite=TRUE)library(dplyr)src_tbls(sc)iris_tbl%>%count
  4. Close the connection.

IntelliJ (Scala or Java)

  1. Run .

  2. Point the dependencies to the directory returned from the command. Go to File > Project Structure > Modules > Dependencies > ‘+’ sign > JARs or Directories.

    IntelliJ JARs

    To avoid conflicts, we strongly recommend removing any other Spark installations from your classpath. If this is not possible, make sure that the JARs you add are at the front of the classpath. In particular, they must be ahead of any other installed version of Spark (otherwise you will either use one of those other Spark versions and run locally or throw a ).

  3. Check the setting of the breakout option in IntelliJ. The default is All and will cause network timeouts if you set breakpoints for debugging. Set it to Thread to avoid stopping the background network threads.

    IntelliJ Thread

Eclipse

  1. Run .

  2. Point the external JARs configuration to the directory returned from the command. Go to Project menu > Properties > Java Build Path > Libraries > Add External Jars.

    Eclipse external JAR configuration

    To avoid conflicts, we strongly recommend removing any other Spark installations from your classpath. If this is not possible, make sure that the JARs you add are at the front of the classpath. In particular, they must be ahead of any other installed version of Spark (otherwise you will either use one of those other Spark versions and run locally or throw a ).

    Eclipse Spark configuration

Visual Studio Code

  1. Verify that the Python extension is installed.

  2. Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux).

  3. Select a Python interpreter. Go to Code > Preferences > Settings, and choose python settings.

  4. Run .

  5. Add the directory returned from the command to the User Settings JSON under . This should be added to the Python Configuration.

  6. Disable the linter. Click the on the right side and edit json settings. The modified settings are as follows:

    VS Code configuration
  7. If running with a virtual environment, which is the recommended way to develop for Python in VS Code, in the Command Palette type and point to your environment that matches your cluster Python version.

    Select Python interpreter

    For example, if your cluster is Python 3.5, your local environment should be Python 3.5.

    Python version

SBT

To use SBT, you must configure your file to link against the Databricks Connect JARs instead of the usual Spark library dependency. You do this with the directive in the following example build file, which assumes a Scala app that has a main object:

name := "hello-world" version := "1.0" scalaVersion := "2.11.6" // this should be set to the path returned by ``databricks-connect get-jar-dir`` unmanagedBase := new java.io.File("/usr/local/lib/python2.7/dist-packages/pyspark/jars") mainClass := Some("com.example.Test")

Run examples from your IDE

importjava.util.ArrayList;importjava.util.List;importjava.sql.Date;importorg.apache.spark.sql.SparkSession;importorg.apache.spark.sql.types.*;importorg.apache.spark.sql.Row;importorg.apache.spark.sql.RowFactory;importorg.apache.spark.sql.Dataset;publicclassApp{publicstaticvoidmain(String[]args)throwsException{SparkSessionspark=SparkSession.builder().appName("Temps Demo").config("spark.master","local").getOrCreate();// Create a Spark DataFrame consisting of high and low temperatures// by airport code and date.StructTypeschema=newStructType(newStructField[]{newStructField("AirportCode",DataTypes.StringType,false,Metadata.empty()),newStructField("Date",DataTypes.DateType,false,Metadata.empty()),newStructField("TempHighF",DataTypes.IntegerType,false,Metadata.empty()),newStructField("TempLowF",DataTypes.IntegerType,false,Metadata.empty()),});List<Row>dataList=newArrayList<Row>();dataList.add(RowFactory.create("BLI",Date.valueOf("2021-04-03"),52,43));dataList.add(RowFactory.create("BLI",Date.valueOf("2021-04-02"),50,38));dataList.add(RowFactory.create("BLI",Date.valueOf("2021-04-01"),52,41));dataList.add(RowFactory.create("PDX",Date.valueOf("2021-04-03"),64,45));dataList.add(RowFactory.create("PDX",Date.valueOf("2021-04-02"),61,41));dataList.add(RowFactory.create("PDX",Date.valueOf("2021-04-01"),66,39));dataList.add(RowFactory.create("SEA",Date.valueOf("2021-04-03"),57,43));dataList.add(RowFactory.create("SEA",Date.valueOf("2021-04-02"),54,39));dataList.add(RowFactory.create("SEA",Date.valueOf("2021-04-01"),56,41));Dataset<Row>temps=spark.createDataFrame(dataList,schema);// Create a table on the Databricks cluster and then fill// the table with the DataFrame's contents.// If the table already exists from a previous run,// delete it first.spark.sql("USE default");spark.sql("DROP TABLE IF EXISTS demo_temps_table");temps.write().saveAsTable("demo_temps_table");// Query the table on the Databricks cluster, returning rows// where the airport code is not BLI and the date is later// than 2021-04-01. Group the results and order by high// temperature in descending order.Dataset<Row>df_temps=spark.sql("SELECT * FROM demo_temps_table "+"WHERE AirportCode != 'BLI' AND Date > '2021-04-01' "+"GROUP BY AirportCode, Date, TempHighF, TempLowF "+"ORDER BY TempHighF DESC");df_temps.show();// Results://// +-----------+----------+---------+--------+// |AirportCode| Date|TempHighF|TempLowF|// +-----------+----------+---------+--------+// | PDX|2021-04-03| 64| 45|// | PDX|2021-04-02| 61| 41|// | SEA|2021-04-03| 57| 43|// | SEA|2021-04-02| 54| 39|// +-----------+----------+---------+--------+// Clean up by deleting the table from the Databricks cluster.spark.sql("DROP TABLE demo_temps_table");}}
frompyspark.sqlimportSparkSessionfrompyspark.sql.typesimport*fromdatetimeimportdatespark=SparkSession.builder.appName('temps-demo').getOrCreate()# Create a Spark DataFrame consisting of high and low temperatures# by airport code and date.schema=StructType([StructField('AirportCode',StringType(),False),StructField('Date',DateType(),False),StructField('TempHighF',IntegerType(),False),StructField('TempLowF',IntegerType(),False)])data=[['BLI',date(2021,4,3),52,43],['BLI',date(2021,4,2),50,38],['BLI',date(2021,4,1),52,41],['PDX',date(2021,4,3),64,45],['PDX',date(2021,4,2),61,41],['PDX',date(2021,4,1),66,39],['SEA',date(2021,4,3),57,43],['SEA',date(2021,4,2),54,39],['SEA',date(2021,4,1),56,41]]temps=spark.createDataFrame(data,schema)# Create a table on the Databricks cluster and then fill# the table with the DataFrame's contents.# If the table already exists from a previous run,# delete it first.spark.sql('USE default')spark.sql('DROP TABLE IF EXISTS demo_temps_table')temps.write.saveAsTable('demo_temps_table')# Query the table on the Databricks cluster, returning rows# where the airport code is not BLI and the date is later# than 2021-04-01. Group the results and order by high# temperature in descending order.df_temps=spark.sql("SELECT * FROM demo_temps_table " \ "WHERE AirportCode != 'BLI' AND Date > '2021-04-01' " \ "GROUP BY AirportCode, Date, TempHighF, TempLowF " \ "ORDER BY TempHighF DESC")df_temps.show()# Results:## +-----------+----------+---------+--------+# |AirportCode| Date|TempHighF|TempLowF|# +-----------+----------+---------+--------+# | PDX|2021-04-03| 64| 45|# | PDX|2021-04-02| 61| 41|# | SEA|2021-04-03| 57| 43|# | SEA|2021-04-02| 54| 39|# +-----------+----------+---------+--------+# Clean up by deleting the table from the Databricks cluster.spark.sql('DROP TABLE demo_temps_table')
importorg.apache.spark.sql.SparkSessionimportorg.apache.spark.sql.types._importorg.apache.spark.sql.Rowimportjava.sql.DateobjectDemo{defmain(args:Array[String]){valspark=SparkSession.builder.master("local").getOrCreate()// Create a Spark DataFrame consisting of high and low temperatures// by airport code and date.valschema=StructType(Array(StructField("AirportCode",StringType,false),StructField("Date",DateType,false),StructField("TempHighF",IntegerType,false),StructField("TempLowF",IntegerType,false)))valdata=List(Row("BLI",Date.valueOf("2021-04-03"),52,43),Row("BLI",Date.valueOf("2021-04-02"),50,38),Row("BLI",Date.valueOf("2021-04-01"),52,41),Row("PDX",Date.valueOf("2021-04-03"),64,45),Row("PDX",Date.valueOf("2021-04-02"),61,41),Row("PDX",Date.valueOf("2021-04-01"),66,39),Row("SEA",Date.valueOf("2021-04-03"),57,43),Row("SEA",Date.valueOf("2021-04-02"),54,39),Row("SEA",Date.valueOf("2021-04-01"),56,41))valrdd=spark.sparkContext.makeRDD(data)valtemps=spark.createDataFrame(rdd,schema)// Create a table on the Databricks cluster and then fill// the table with the DataFrame's contents.// If the table already exists from a previous run,// delete it first.spark.sql("USE default")spark.sql("DROP TABLE IF EXISTS demo_temps_table")temps.write.saveAsTable("demo_temps_table")// Query the table on the Databricks cluster, returning rows// where the airport code is not BLI and the date is later// than 2021-04-01. Group the results and order by high// temperature in descending order.valdf_temps=spark.sql("SELECT * FROM demo_temps_table "+"WHERE AirportCode != 'BLI' AND Date > '2021-04-01' "+"GROUP BY AirportCode, Date, TempHighF, TempLowF "+"ORDER BY TempHighF DESC")df_temps.show()// Results://// +-----------+----------+---------+--------+// |AirportCode| Date|TempHighF|TempLowF|// +-----------+----------+---------+--------+// | PDX|2021-04-03| 64| 45|// | PDX|2021-04-02| 61| 41|// | SEA|2021-04-03| 57| 43|// | SEA|2021-04-02| 54| 39|// +-----------+----------+---------+--------+// Clean up by deleting the table from the Databricks cluster.spark.sql("DROP TABLE demo_temps_table")}}

Work with dependencies

Typically your main class or Python file will have other dependency JARs and files. You can add such dependency JARs and files by calling or . You can also add Egg files and zip files with the interface. Every time you run the code in your IDE, the dependency JARs and files are installed on the cluster.

fromlibimportFoofrompyspark.sqlimportSparkSessionspark=SparkSession.builder.getOrCreate()sc=spark.sparkContext#sc.setLogLevel("INFO")print("Testing simple count")print(spark.range(100).count())print("Testing addPyFile isolation")sc.addPyFile("lib.py")print(sc.parallelize(range(10)).map(lambdai:Foo(2)).collect())classFoo(object):def__init__(self,x):self.x=x

Python + Java UDFs

frompyspark.sqlimportSparkSessionfrompyspark.sql.columnimport_to_java_column,_to_seq,Column## In this example, udf.jar contains compiled Java / Scala UDFs:#package com.example##import org.apache.spark.sql._#import org.apache.spark.sql.expressions._#import org.apache.spark.sql.functions.udf##object Test {# val plusOne: UserDefinedFunction = udf((i: Long) => i + 1)#}spark=SparkSession.builder \ .config("spark.jars","/path/to/udf.jar") \ .getOrCreate()sc=spark.sparkContextdefplus_one_udf(col):f=sc._jvm.com.example.Test.plusOne()returnColumn(f.apply(_to_seq(sc,[col],_to_java_column)))sc._jsc.addJar("/path/to/udf.jar")spark.range(100).withColumn("plusOne",plus_one_udf("id")).show()
packagecom.exampleimportorg.apache.spark.sql.SparkSessioncaseclassFoo(x:String)objectTest{defmain(args:Array[String]):Unit={valspark=SparkSession.builder()....getOrCreate();spark.sparkContext.setLogLevel("INFO")println("Running simple show query...")spark.read.parquet("/tmp/x").show()println("Running simple UDF query...")spark.sparkContext.addJar("./target/scala-2.11/hello-world_2.11-1.0.jar")spark.udf.register("f",(x:Int)=>x+1)spark.range(10).selectExpr("f(id)").show()println("Running custom objects query...")valobjs=spark.sparkContext.parallelize(Seq(Foo("bye"),Foo("hi"))).collect()println(objs.toSeq)}}

Access DBUtils

You can use and utilities of the Databricks Utilities module. Supported commands are , , , , , , , , , , . See File system utility (dbutils.fs) or run and Secrets utility (dbutils.secrets) or run .

frompyspark.sqlimportSparkSessionfrompyspark.dbutilsimportDBUtilsspark=SparkSession.builder.getOrCreate()dbutils=DBUtils(spark)print(dbutils.fs.ls("dbfs:/"))print(dbutils.secrets.listScopes())

When using Databricks Runtime 7.3 LTS or above, to access the DBUtils module in a way that works both locally and in Databricks clusters, use the following :

defget_dbutils(spark):frompyspark.dbutilsimportDBUtilsreturnDBUtils(spark)

Otherwise, use the following :

defget_dbutils(spark):ifspark.conf.get("spark.databricks.service.client.enabled")=="true":frompyspark.dbutilsimportDBUtilsreturnDBUtils(spark)else:importIPythonreturnIPython.get_ipython().user_ns["dbutils"]
valdbutils=com.databricks.service.DBUtilsprintln(dbutils.fs.ls("dbfs:/"))println(dbutils.secrets.listScopes())

Copying files between local and remote filesystems

You can use to copy files between your client and remote filesystems. Scheme refers to the local filesystem on the client.

frompyspark.dbutilsimportDBUtilsdbutils=DBUtils(spark)dbutils.fs.cp('file:/home/user/data.csv','dbfs:/uploads')dbutils.fs.cp('dbfs:/output/results.csv','file:/home/user/downloads/')

The maximum file size that can be transferred that way is 250 MB.

Enable

Because of security restrictions, the ability to call is disabled by default. Contact Databricks support to enable this feature for your workspace.

Access the Hadoop filesystem

You can also access DBFS directly using the standard Hadoop filesystem interface:

>importorg.apache.hadoop.fs._// get new DBFS connection>valdbfs=FileSystem.get(spark.sparkContext.hadoopConfiguration)dbfs:org.apache.hado[email protected]2d036335// list files>dbfs.listStatus(newPath("dbfs:/"))res1:Array[org.apache.hadoop.fs.FileStatus]=Array(FileStatus{path=dbfs:/$;isDirectory=true;...})// open file>valstream=dbfs.open(newPath("dbfs:/path/to/your_file"))stream:org.apache.had[email protected]7aa4ef24// get file contents as string>importorg.apache.commons.io._>println(newString(IOUtils.toByteArray(stream)))

Set Hadoop configurations

On the client you can set Hadoop configurations using the API, which applies to SQL and DataFrame operations. Hadoop configurations set on the must be set in the cluster configuration or using a notebook. This is because configurations set on are not tied to user sessions but apply to the entire cluster.

Troubleshooting

Run to check for connectivity issues. This section describes some common issues you may encounter and how to resolve them.

Python version mismatch

Check the Python version you are using locally has at least the same minor release as the version on the cluster (for example, versus is OK, versus is not).

If you have multiple Python versions installed locally, ensure that Databricks Connect is using the right one by setting the environment variable (for example, ).

Server not enabled

Ensure the cluster has the Spark server enabled with . You should see the following lines in the driver log if it is:

18/10/25 21:39:18 INFO SparkConfUtils$: Set spark config: spark.databricks.service.server.enabled -> true ... 18/10/25 21:39:21 INFO SparkContext: Loading Spark Service RPC Server 18/10/25 21:39:21 INFO SparkServiceRPCServer: Starting Spark Service RPC Server 18/10/25 21:39:21 INFO Server: jetty-9.3.20.v20170531 18/10/25 21:39:21 INFO AbstractConnector: Started [email protected] {HTTP/1.1,[http/1.1]}{0.0.0.0:15001} 18/10/25 21:39:21 INFO Server: Started @5879ms

Conflicting PySpark installations

The package conflicts with PySpark. Having both installed will cause errors when initializing the Spark context in Python. This can manifest in several ways, including “stream corrupted” or “class not found” errors. If you have PySpark installed in your Python environment, ensure it is uninstalled before installing databricks-connect. After uninstalling PySpark, make sure to fully re-install the Databricks Connect package:

pip uninstall pyspark pip uninstall databricks-connect pip install -U "databricks-connect==5.5.*"# or X.Y.* to match your cluster version.

Conflicting

If you have previously used Spark on your machine, your IDE may be configured to use one of those other versions of Spark rather than the Databricks Connect Spark. This can manifest in several ways, including “stream corrupted” or “class not found” errors. You can see which version of Spark is being used by checking the value of the environment variable:

System.out.println(System.getenv("SPARK_HOME"));
importosprint(os.environ['SPARK_HOME'])
println(sys.env.get("SPARK_HOME"))

Resolution

If is set to a version of Spark other than the one in the client, you should unset the variable and try again.

Check your IDE environment variable settings, your , , or file, and anywhere else environment variables might be set. You will most likely have to quit and restart your IDE to purge the old state, and you may even need to create a new project if the problem persists.

You should not need to set to a new value; unsetting it should be sufficient.

Conflicting or Missing entry for binaries

It is possible your PATH is configured so that commands like will be running some other previously installed binary instead of the one provided with Databricks Connect. This can cause to fail. You should make sure either the Databricks Connect binaries take precedence, or remove the previously installed ones.

If you can’t run commands like , it is also possible your PATH was not automatically set up by and you’ll need to add the installation dir to your PATH manually. It’s possible to use Databricks Connect with IDEs even if this isn’t set up. However, the command will not work.

Conflicting serialization settings on the cluster

If you see “stream corrupted” errors when running , this may be due to incompatible cluster serialization configs. For example, setting the config can cause this issue. To resolve this issue, consider removing these configs from the cluster settings, or setting the configuration in the Databricks Connect client.

Cannot find on Windows

If you are using Databricks Connect on Windows and see:

ERRORShell:Failedtolocatethewinutilsbinaryinthehadoopbinarypathjava.io.IOException:Couldnotlocateexecutablenull\bin\winutils.exeintheHadoopbinaries.

Follow the instructions to configure the Hadoop path on Windows.

The filename, directory name, or volume label syntax is incorrect on Windows

If you are using Databricks Connect on Windows and see:

Thefilename,directoryname,orvolumelabelsyntaxisincorrect.

Either Java or Databricks Connect was installed into a directory with a space in your path. You can work around this by either installing into a directory path without spaces, or configuring your path using the short name form.

Limitations

The following Databricks features and third-party platforms are unsupported:

  • Structured Streaming.
  • Running arbitrary code that is not a part of a Spark job on the remote cluster.
  • Native Scala, Python, and R APIs for Delta table operations (for example, ) are not supported. However, the SQL API () with Delta Lake operations and the Spark API (for example, ) on Delta tables are both supported.
  • Apache Zeppelin 0.7.x and below.
  • Connecting to clusters with table access control.
  • Connecting to clusters with process isolation enabled (in other words, where is set to ).
  • Delta SQL command.
  • Global temporary views.
  • Koalas.

Sours: https://docs.databricks.com/dev-tools/databricks-connect.html

Spark import

Fork me on GitHub Spark Framework is a free and open source Java Web Framework, released under the Apache 2 License  |  Contact  |  Team
Sours: https://sparkjava.com/documentation
Spark Tutorial - Spark Tutorial for Beginners - Apache Spark Full Course - Learn Apache Spark 2020

Getting Started

Starting Point: SparkSession

The entry point into all functionality in Spark is the class. To create a basic , just use :

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

The entry point into all functionality in Spark is the class. To create a basic , just use :

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

The entry point into all functionality in Spark is the class. To create a basic , just use :

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

The entry point into all functionality in Spark is the class. To initialize a basic , just call :

Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Note that when invoked for the first time, initializes a global singleton instance, and always returns a reference to this instance for successive invocations. In this way, users only need to initialize the once, then SparkR functions like will be able to access this global instance implicitly, and users don’t need to pass the instance around.

in Spark 2.0 provides builtin support for Hive features including the ability to write queries using HiveQL, access to Hive UDFs, and the ability to read data from Hive tables. To use these features, you do not need to have an existing Hive setup.

Creating DataFrames

With a , applications can create DataFrames from an existing , from a Hive table, or from Spark data sources.

As an example, the following creates a DataFrame based on the content of a JSON file:

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

With a , applications can create DataFrames from an existing , from a Hive table, or from Spark data sources.

As an example, the following creates a DataFrame based on the content of a JSON file:

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

With a , applications can create DataFrames from an existing , from a Hive table, or from Spark data sources.

As an example, the following creates a DataFrame based on the content of a JSON file:

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

With a , applications can create DataFrames from a local R data.frame, from a Hive table, or from Spark data sources.

As an example, the following creates a DataFrame based on the content of a JSON file:

Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Untyped Dataset Operations (aka DataFrame Operations)

DataFrames provide a domain-specific language for structured data manipulation in Scala, Java, Python and R.

As mentioned above, in Spark 2.0, DataFrames are just Dataset of s in Scala and Java API. These operations are also referred as “untyped transformations” in contrast to “typed transformations” come with strongly typed Scala/Java Datasets.

Here we include some basic examples of structured data processing using Datasets:

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

For a complete list of the types of operations that can be performed on a Dataset, refer to the API Documentation.

In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

For a complete list of the types of operations that can be performed on a Dataset refer to the API Documentation.

In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

In Python, it’s possible to access a DataFrame’s columns either by attribute () or by indexing (). While the former is convenient for interactive data exploration, users are highly encouraged to use the latter form, which is future proof and won’t break with column names that are also attributes on the DataFrame class.

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

For a complete list of the types of operations that can be performed on a DataFrame refer to the API Documentation.

In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

For a complete list of the types of operations that can be performed on a DataFrame refer to the API Documentation.

In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the DataFrame Function Reference.

Running SQL Queries Programmatically

The function on a enables applications to run SQL queries programmatically and returns the result as a .

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

The function on a enables applications to run SQL queries programmatically and returns the result as a .

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

The function on a enables applications to run SQL queries programmatically and returns the result as a .

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

The function enables applications to run SQL queries programmatically and returns the result as a .

Find full example code at "examples/src/main/r/RSparkSQLExample.R" in the Spark repo.

Global Temporary View

Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. If you want to have a temporary view that is shared among all sessions and keep alive until the Spark application terminates, you can create a global temporary view. Global temporary view is tied to a system preserved database , and we must use the qualified name to refer it, e.g. .

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.
Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

Creating Datasets

Datasets are similar to RDDs, however, instead of using Java serialization or Kryo they use a specialized Encoder to serialize the objects for processing or transmitting over the network. While both encoders and standard serialization are responsible for turning an object into bytes, encoders are code generated dynamically and use a format that allows Spark to perform many operations like filtering, sorting and hashing without deserializing the bytes back into an object.

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.
Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

Interoperating with RDDs

Spark SQL supports two different methods for converting existing RDDs into Datasets. The first method uses reflection to infer the schema of an RDD that contains specific types of objects. This reflection-based approach leads to more concise code and works well when you already know the schema while writing your Spark application.

The second method for creating Datasets is through a programmatic interface that allows you to construct a schema and then apply it to an existing RDD. While this method is more verbose, it allows you to construct Datasets when the columns and their types are not known until runtime.

Inferring the Schema Using Reflection

The Scala interface for Spark SQL supports automatically converting an RDD containing case classes to a DataFrame. The case class defines the schema of the table. The names of the arguments to the case class are read using reflection and become the names of the columns. Case classes can also be nested or contain complex types such as s or s. This RDD can be implicitly converted to a DataFrame and then be registered as a table. Tables can be used in subsequent SQL statements.

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

Spark SQL supports automatically converting an RDD of JavaBeans into a DataFrame. The , obtained using reflection, defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain field(s). Nested JavaBeans and or fields are supported though. You can create a JavaBean by creating a class that implements Serializable and has getters and setters for all of its fields.

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes. Rows are constructed by passing a list of key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table, and the types are inferred by sampling the whole dataset, similar to the inference that is performed on JSON files.

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

Programmatically Specifying the Schema

When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a can be created programmatically with three steps.

  1. Create an RDD of s from the original RDD;
  2. Create the schema represented by a matching the structure of s in the RDD created in Step 1.
  3. Apply the schema to the RDD of s via method provided by .

For example:

Find full example code at "examples/src/main/scala/org/apache/spark/examples/sql/SparkSQLExample.scala" in the Spark repo.

When JavaBean classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a can be created programmatically with three steps.

  1. Create an RDD of s from the original RDD;
  2. Create the schema represented by a matching the structure of s in the RDD created in Step 1.
  3. Apply the schema to the RDD of s via method provided by .

For example:

Find full example code at "examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQLExample.java" in the Spark repo.

When a dictionary of kwargs cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a can be created programmatically with three steps.

  1. Create an RDD of tuples or lists from the original RDD;
  2. Create the schema represented by a matching the structure of tuples or lists in the RDD created in the step 1.
  3. Apply the schema to the RDD via method provided by .

For example:

Find full example code at "examples/src/main/python/sql/basic.py" in the Spark repo.

Scalar Functions

Scalar functions are functions that return a single value per row, as opposed to aggregation functions, which return a value for a group of rows. Spark SQL supports a variety of Built-in Scalar Functions. It also supports User Defined Scalar Functions.

Aggregate Functions

Aggregate functions are functions that return a single value on a group of rows. The Built-in Aggregation Functions provide common aggregations such as , , , , , etc. Users are not limited to the predefined aggregate functions and can create their own. For more details about user defined aggregate functions, please refer to the documentation of User Defined Aggregate Functions.

Sours: https://spark.apache.org/docs/latest/sql-getting-started.html

Now discussing:

This blog post explains how to import core Spark and Scala libraries like spark-daria into your projects.

It’s important for library developers to organize package namespaces so it’s easy for users to import their code.

Library users should import code so it’s easy for teammates to identify the source of functions when they’re invoked.

I wrote a book called Beautiful Spark that teaches you the easiest way to build Spark applications and manage complex logic. The book will teach you the most important aspects of Spark development and will supercharge your career.

Let’s start with a simple example that illustrates why wilcard imports can generate code that’s hard to follow.

Simple example

Let’s look at a little code snippet that uses the Spark function and the spark-daria function.

import org.apache.spark.sql.functions._ import com.github.mrpowers.spark.daria.sql._ df.withColumn("clean_text", removeAllWhitespace(col("text")))

Wildcard imports (imports with underscores) create code that’s difficult to follow. It’s hard to tell where the function is defined.

Curly brace import

We can use the curly brace import style to make it easy for other programmers to search the codebase and find where is defined.

import org.apache.spark.sql.functions._ import com.github.mrpowers.spark.daria.sql.{removeAllWhitespace}

Per the Databricks Scala style guide:

Avoid using wildcard imports, unless you are importing more than 6 entities, or implicit methods. Wildcard imports make the code less robust to external changes.

In other words, use curly brace imports unless you’re going to use more than 6 methods from the object that’s being imported.

That’s not the best advice because will still leave users confused about where is defined, regardless of how extensively the spark-daria functions are used.

Named import

We can name imports so all function invocations make it clear where the functions are defined.

import org.apache.spark.sql.functions._ import com.github.mrpowers.spark.daria.sql.{functions => dariaFunctions} df.withColumn("clean_text", dariaFunctions.removeAllWhitespace(col("text")))

This allows users to search for and figure out that is defined in spark-daria.

This approach is potentially confusing if the same import isn’t consistently named throughout the codebase. If some developers import the daria functions as and other developers import them as , it could get confusing.

Named imports help here because the package name is so ridiculously long. If the package name was shorter, we could do and invoke the function with . This gives us the best of both worlds – easy imports and consistent function invocation.

Complicated package names are the root issue

Scala package names typically follow the verbose Java conventions. Instead of we have .

Most Spark libraries follow the same trend. We have instead of .

Some of the great libraries created by Li Haoyi allow for short imports:

    It’s arguable that Li Haoyi’s import statments are too short because they could cause some namespace collisions (with another library named for example).

    These imports would strike a good balance of being short and having a low probability of name collisions.

      The internet has mixed feelings on if the import statements should be short or if they should follow the verbose Java style.

      Wildcard Imports are OK for Spark core classes

      Wildcard imports should be avoided in general, but they’re OK for core Spark classes.

      The following code is completely fine, even though you’re importing a ton of functions to the global namespace.

      import org.apache.spark.sql.functions._

      Spark programmers are familiar with the Spark core functions and will know that functions like and are defined in Spark.

      Implicit imports

      You have to use the wildcard syntax to import objects that wrap implicit classes. Here’s a snippet of code that extends the Spark class:

      package com.github.mrpowers.spark.daria.sql import org.apache.spark.sql.Column import org.apache.spark.sql.functions._ object ColumnExt { implicit class ColumnMethods(col: Column) { def chain(colMethod: (Column => Column)): Column = { colMethod(col) } } }

      You can import the column extensions as follows: .

      Staying away from implicits is generally best, so feel free to avoid this language feature.

      Spark implicits

      Spark implicits come in handy, expecially in the notebook enviroment. They can be imported with .

      The Spark shell and Databricks notebooks both import implicits automatically after creating the SparkSession. See here for best practices on managing the SparkSession in Scala projects.

      If you’d like to access Spark implicits other environments (e.g. code in an IntelliJ text editor), you’ll need to import them youself.

      Importing Spark implicits is a little tricky because the SparkSession needs to be instantiated first.

      Conclusion

      There are a variety of ways to import Spark code.

      It’s hard to maintain code that has lots of wildcard imports. The namespace gets cluttered with functions from a variety of objects and it can be hard to tell which methods belong to which library.

      I recommend ignoring the Java conventions of having deeply nested packages, so you don’t force users to write really long import statements. Also give your projects one word names so import statements are shorter too 😉

      Library developers are responsible for providing users with a great user experience. The package structure of your project is a key part of your public interface. Choose wisely!

      Check out Beautiful Spark to learn more Spark best practices.

      Sours: https://mungingdata.com/apache-spark/scala-packages-imports/


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