Is there a spark-defaults.conf when installed with pip install pyspark - pyspark

I installed pyspark with pip.
I code in jupyter notebooks. Everything works fine but not I got a java heap space error when exporting a large .csv file.
Here someone suggested editing the spark-defaults.config. Also in the spark documentation, it says
"Note: In client mode, this config must not be set through the
SparkConf directly in your application, because the driver JVM has
already started at that point. Instead, please set this through the
--driver-memory command line option or in your default properties file."
But I'm afraid there is no such file when installing pyspark with pip.
I'm I right? How do I solve this?
Thanks!

I recently ran into this as well. If you look at the Spark UI under the Classpath Entries, the first path is probably the configuration directory, something like /.../lib/python3.7/site-packages/pyspark/conf/. When I looked for that directory, it didn't exist; presumably it's not part of the pip installation. However, you can easily create it and add your own configuration files. For example,
mkdir /.../lib/python3.7/site-packages/pyspark/conf
vi /.../lib/python3.7/site-packages/pyspark/conf/spark-defaults.conf

The spark-defaults.conf file should be located in:
$SPARK_HOME/conf
If no file is present, create one (a template should be available in the same directory).
How to find the default configuration folder
Check contents of the folder in Python:
import glob, os
glob.glob(os.path.join(os.environ["SPARK_HOME"], "conf", "spark*"))
# ['/usr/local/spark-3.1.2-bin-hadoop3.2/conf/spark-env.sh.template',
# '/usr/local/spark-3.1.2-bin-hadoop3.2/conf/spark-defaults.conf.template']
When no spark-defaults.conf file is available, built-in values are used
To my surprise, no spark-defaults.conf but just a template file was present!
Still I could look at Spark properties, either in the “Environment” tab of the Web UI http://<driver>:4040 or using getConf().getAll() on the Spark context:
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.appName("myApp") \
.getOrCreate()
spark.sparkContext.getConf().getAll()
# [('spark.driver.port', '55128'),
# ('spark.app.name', 'myApp'),
# ('spark.rdd.compress', 'True'),
# ('spark.sql.warehouse.dir', 'file:/path/spark-warehouse'),
# ('spark.serializer.objectStreamReset', '100'),
# ('spark.master', 'local[*]'),
# ('spark.submit.pyFiles', ''),
# ('spark.app.startTime', '1645484409629'),
# ('spark.executor.id', 'driver'),
# ('spark.submit.deployMode', 'client'),
# ('spark.app.id', 'local-1645484410352'),
# ('spark.ui.showConsoleProgress', 'true'),
# ('spark.driver.host', 'xxx.xxx.xxx.xxx')]
Note that not all properties are listed but:
only values explicitly specified through spark-defaults.conf, SparkConf, or the command line. For all other configuration properties, you can assume the default value is used.
For instance, consider the default parallelism is in my case:
spark._sc.defaultParallelism
8
This is the default for local mode, namely the number of cores on the local machine--see https://spark.apache.org/docs/latest/configuration.html. In my case 8=2x4cores because of hyper-threading.
If passed the property spark.default.parallelism when launching the app
spark = SparkSession \
.builder \
.appName("Set parallelism") \
.config("spark.default.parallelism", 4) \
.getOrCreate()
then the property is shown in the Web UI and in the list
spark.sparkContext.getConf().getAll()
Precedence of configuration settings
Spark will consider given properties in this order (spark-defaults.conf comes last):
SparkConf
flags passed to spark-submit
spark-defaults.conf
From https://spark.apache.org/docs/latest/configuration.html#dynamically-loading-spark-properties:
Properties set directly on the SparkConf take highest precedence, then flags passed to spark-submit or spark-shell, then options in the spark-defaults.conf file. A few configuration keys have been renamed since earlier versions of Spark; in such cases, the older key names are still accepted, but take lower precedence than any instance of the newer key.
Note
Some pyspark Jupyter kernels contain flags for spark-submit in the environment variable $PYSPARK_SUBMIT_ARGS, so one might want to check that too.
Related question: Where to modify spark-defaults.conf if I installed pyspark via pip install pyspark

The spark-defaults.config file is needed when we have to change any of the default configs for spark.
As #niuer suggested, it should be present in the $SPARK_HOME/conf/ directory. But that might not be the case with you. By default, a template config file will be present there. You can just add a new spark-defaults.conf file in $SPARK_HOME/conf/.

Check your spark path. There are configuration files under:
$SPARK_HOME/conf/, e.g.
spark-defaults.conf.

Related

sequence files from sqoop import

I have imported a table using sqoop and saved it as a sequence file.
How do I read this file into an RDD or Dataframe?
I have tried sc.sequenceFile() but I'm not sure what to pass as keyClass and value Class. I tried tried using org.apache.hadoop.io.Text, org.apache.hadoop.io.LongWritable for keyClass and valueClass
but it did not work. I am using pyspark for reading the files.
in python its not working however in SCALA it works:
You need to do following steps:
step1:
If you are importing as sequence file from sqoop, there is a jar file generated, you need to use that as ValueClass while reading sequencefile. This jar file is generally placed in /tmp folder, but you can redirect it to a specific folder (i.e. to local folder not hdfs) using --bindir option.
example:
sqoop import --connect jdbc:mysql://ms.itversity.com/retail_export --
username retail_user --password itversity --table customers -m 1 --target-dir '/user/srikarthik/udemy/practice4/problem2/outputseq' --as-sequencefile --delete-target-dir --bindir /home/srikarthik/sqoopjars/
step2:
Also, you need to download the jar file from below link:
http://www.java2s.com/Code/Jar/s/Downloadsqoop144hadoop200jar.htm
step3:
Suppose, customers table is imported using sqoop as sequence file.
Run spark-shell --jars path-to-customers.jar,sqoop-1.4.4-hadoop200.jar
example:
spark-shell --master yarn --jars /home/srikarthik/sqoopjars/customers.jar,/home/srikarthik/tejdata/kjar/sqoop-1.4.4-hadoop200.jar
step4: Now run below commands inside the spark-shell
scala> import org.apache.hadoop.io.LongWritable
scala> val data = sc.sequenceFile[LongWritable,customers]("/user/srikarthik/udemy/practice4/problem2/outputseq")
scala> data.map(tup => (tup._1.get(), tup._2.toString())).collect.foreach(println)
You can use SeqDataSourceV2 package to read the sequence file with the DataFrame API without any prior knowledge of the schema (aka keyClass and valueClass).
Please note that the current version is only compatible with Spark 2.4
$ pyspark --packages seq-datasource-v2-0.2.0.jar
df = spark.read.format("seq").load("data.seq")
df.show()

Merging configurations for spark using typesafe library and extraJavaOptions

I'm trying to merge 2 config file (or create a config file based on a single reference file) using
lazy val finalConfig:
Option(System.getProperty("user.resource"))
.map(ConfigFactory.load)
.map(_.withFallback(ConfigFactory.load(System.getProperty("config.resource"))).resolve())
.getOrElse(ConfigFactory.load(System.getProperty("config.resource")))
I'm defining my java variable inside spark using spark-submit ....... --conf spark.driver.extraJavaOptions=-Dconfig.resource=./reference.conf,-Duser.resource=./user.conf ...
My goal is to be able to point a file that is not inside my jar to be used by System.getProperty("..") in my code. I changed the folder for testing (cd ..) and keep getting the same error so I guess spark doesn't care about my java arguments..?
Is there a way to point to a file (or even 2 files in my case) so that they can be merged?
I also tried to include the reference.conf file but not the user.conf file: it recognizes the reference.conf but not the user.conf that i gave with --conf spark.driver.extraJavaOptions=-Duser.resource=./user.conf .
Is there a way to do that? Thanks if you can help
I don't see you doing ConfigFactory.parseFile to loaded a file containing properties.
Typesafe automatically read any .properties file in the class path, all -D parameters passed in to the JVM and then merges them.
I am reading an external property file which is not part of the jar as following. The file "application.conf" is placed on the same directory where the jar is kept.
val applicationRootPath = System.getProperty("user.dir")
val config = Try {
ConfigFactory.parseFile(new File(applicationRootPath + "/" + "application.conf"))
}.getOrElse(ConfigFactory.empty())
appConfig = config.withFallback(ConfigFactory.load()).resolve
ConfigFactory.load() already contains all the properties present on the properties files in the class path and -d parameters. I am giving priority to my external "application.conf" and falling back on default values. For matching keys "application.conf" take precedence over other sources.

Spark Shell Add Multiple Drivers/Jars to Classpath using spark-defaults.conf

We are using Spark-Shell REPL Mode to test various use-cases and connecting to multiple sources/sinks
We need to add custom drivers/jars in spark-defaults.conf file, I have tried to add multiple jars separated by comma
like
spark.driver.extraClassPath = /home/sandeep/mysql-connector-java-5.1.36.jar
spark.executor.extraClassPath = /home/sandeep/mysql-connector-java-5.1.36.jar
But its not working, Can anyone please provide details for correct syntax
Note: Verified in Linux Mint and Spark 3.0.1
If you are setting properties in spark-defaults.conf, spark will take those settings only when you submit your job using spark-submit.
Note: spark-shell and pyspark need to verify.
file: spark-defaults.conf
spark.driver.extraJavaOptions -Dlog4j.configuration=file:log4j.properties -Dspark.yarn.app.container.log.dir=app-logs -Dlogfile.name=hello-spark
spark.jars.packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1,org.apache.spark:spark-avro_2.12:3.0.1
In the terminal run your job say wordcount.py
spark-submit /path-to-file/wordcount.py
If you want to run your job in development mode from an IDE then you should use config() method. Here we will set Kafka jar packages and avro package. Also if you want to include log4j.properties, then use extraJavaOptions.
AppName and master can be provided in 2 way.
use .appName() and .master()
use .conf file
file: hellospark.py
from logger import Log4j
from util import get_spark_app_config
from pyspark.sql import SparkSession
# first approach.
spark = SparkSession.builder \
.appName('Hello Spark') \
.master('local[3]') \
.config("spark.streaming.stopGracefullyOnShutdown", "true") \
.config("spark.jars.packages",
"org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1,
org.apache.spark:spark-avro_2.12:3.0.1") \
.config("spark.driver.extraJavaOptions",
"-Dlog4j.configuration=file:log4j.properties "
"-Dspark.yarn.app.container.log.dir=app-logs "
"-Dlogfile.name=hello-spark") \
.getOrCreate()
# second approach.
conf = get_spark_app_config()
spark = SparkSession.builder \
.config(conf=conf)
.config("spark.jars.packages",
"org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1") \
.getOrCreate()
logger = Log4j(spark)
file: logger.py
from pyspark.sql import SparkSession
class Log4j(object):
def __init__(self, spark: SparkSession):
conf = spark.sparkContext.getConf()
app_name = conf.get("spark.app.name")
log4j = spark._jvm.org.apache.log4j
self.logger = log4j.LogManager.getLogger(app_name)
def warn(self, message):
self.logger.warn(message)
def info(self, message):
self.logger.info(message)
def error(self, message):
self.logger.error(message)
def debug(self, message):
self.logger.debug(message)
file: util.py
import configparser
from pyspark import SparkConf
def get_spark_app_config(enable_delta_lake=False):
"""
It will read configuration from spark.conf file to create
an instance of SparkConf(). Can be used to create
SparkSession.builder.config(conf=conf).getOrCreate()
:return: instance of SparkConf()
"""
spark_conf = SparkConf()
config = configparser.ConfigParser()
config.read("spark.conf")
for (key, value) in config.items("SPARK_APP_CONFIGS"):
spark_conf.set(key, value))
if enable_delta_lake:
for (key, value) in config.items("DELTA_LAKE_CONFIGS"):
spark_conf.set(key, value)
return spark_conf
file: spark.conf
[SPARK_APP_CONFIGS]
spark.app.name = Hello Spark
spark.master = local[3]
spark.sql.shuffle.partitions = 3
[DELTA_LAKE_CONFIGS]
spark.jars.packages = io.delta:delta-core_2.12:0.7.0
spark.sql.extensions = io.delta.sql.DeltaSparkSessionExtension
spark.sql.catalog.spark_catalog = org.apache.spark.sql.delta.catalog.DeltaCatalog
As an example in addition to Prateek's answer, I have had some success by adding the following to the spark-defaults.conf file to be loaded when starting a spark-shell session in client mode.
spark.jars jars_added/aws-java-sdk-1.7.4.jar,jars_added/hadoop-aws-2.7.3.jar,jars_added/sqljdbc42.jar,jars_added/jtds-1.3.1.jar
Adding the exact line to the spark-defaults.conf file will load the three jar files as long as they are stored in the jars_added folder when spark-shell is run from the specific directory (doing this for me seems to mitigate the need to have the jar files loaded onto the slaves in the specified locations as well). I created the folder 'jars_added' in my $SPARK_HOME directory so whenever I run spark-shell I must run it from this directory (I have not yet worked out how to change the location the spark.jars setting uses as the initial path, it seems to default to the current directory when launching spark-shell). As hinted at by Prateek the jar files need to be comma separated.
I also had to set SPARK_CONF_DIR to $SPARK_HOME/conf (export SPARK_CONF_DIR = "${SPARK_HOME}/conf") for spark-shell to recognise the location of my config file (i.e. spark-defaults.conf). I'm using PuTTY to ssh onto the master.
Just to clarify once I have added the spark.jars jar1, jar2, jar3 to my spark-defaults.conf file I type the following to start my spark-shell session:
cd $SPARK_HOME //navigate to the spark home directory which contains the jars_added folder
spark-shell
On start up the spark-shell then loads the specified jar files from the jars_added folder

Importing PySpark packages

I have downloaded the graphframes package (from here) and saved it on my local disk. Now, I would like to use it. So, I use the following command:
IPYTHON_OPTS="notebook --no-browser" pyspark --num-executors=4 --name gorelikboris_notebook_1 --py-files ~/temp/graphframes-0.1.0-spark1.5.jar --jars ~/temp/graphframes-0.1.0-spark1.5.jar --packages graphframes:graphframes:0.1.0-spark1.5
All the pyspark functionality works as expected, except for the new graphframes package: whenever I try to import graphframes, I get an ImportError. When I examine sys.path, I can see the following two paths:
/tmp/spark-1eXXX/userFiles-9XXX/graphframes_graphframes-0.1.0-spark1.5.jar and /tmp/spark-1eXXX/userFiles-9XXX/graphframes-0.1.0-spark1.5.jar, however these files don't exist. Moreover, the /tmp/spark-1eXXX/userFiles-9XXX/ directory is empty.
What am I missing?
in my case:
1、cd /home/zh/.ivy2/jars
2、jar xf graphframes_graphframes-0.3.0-spark2.0-s_2.11.jar
3、add /home/zh/.ivy2/jar to PYTHONPATH in spark-env.sh like code above:
export PYTHONPATH=$PYTHONPATH:/home/zh/.ivy2/jars:.
This might be an issue in Spark packages with Python in general. Someone else was asking about it too earlier on the Spark user discussion alias.
My workaround is to unpackage the jar to find the python code embedded, and then move the python code into a subdirectory called graphframes.
For instance, I run pyspark from my home directory
~$ ls -lart
drwxr-xr-x 2 user user 4096 Feb 24 19:55 graphframes
~$ ls graphframes/
__init__.pyc examples.pyc graphframe.pyc tests.pyc
You would not need the py-files or jars parameters, though, something like
IPYTHON_OPTS="notebook --no-browser" pyspark --num-executors=4 --name gorelikboris_notebook_1 --packages graphframes:graphframes:0.1.0-spark1.5
and having the python code in the graphframes directory should work.
Add these lines to your $SPARK_HOME/conf/spark-defaults.conf :
spark.executor.extraClassPath file_path/jar1:file_path/jar2
spark.driver.extraClassPath file_path/jar1:file_path/jar2
In the more general case of importing 'orphan' python file (outside of current folder, not part of properly installed package) - use addPyFile, e.g.:
sc.addPyFile('somefolder/graphframe.zip')
addPyFile(path): Add a .py or .zip dependency for all tasks to be executed on this SparkContext in the future. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

How to start a Spark Shell using pyspark in Windows?

I am a beginner in Spark and trying to follow instructions from here on how to initialize Spark shell from Python using cmd: http://spark.apache.org/docs/latest/quick-start.html
But when I run in cmd the following:
C:\Users\Alex\Desktop\spark-1.4.1-bin-hadoop2.4\>c:\Python27\python bin\pyspark
then I receive the following error message:
File "bin\pyspark", line 21
export SPARK_HOME="$(cd ="$(cd "`dirname "$0"`"/..; pwd)"
SyntaxError: invalid syntax
What am I doing wrong here?
P.S. When in cmd I try just C:\Users\Alex\Desktop\spark-1.4.1-bin-hadoop2.4>bin\pyspark
then I receive ""python" is not recognized as internal or external command, operable program or batch file".
You need to have Python available in the system path, you can add it with setx:
setx path "%path%;C:\Python27"
I'm a fairly new Spark user (as of today, really). I am using spark 1.6.0 on Windows 10 and 7 machines. The following worked for me:
import os
import sys
spark_home = os.environ.get('SPARK_HOME', None)
if not spark_home:
raise ValueError('SPARK_HOME environment variable is not set')
sys.path.insert(0, os.path.join(spark_home, 'python'))
sys.path.insert(0, os.path.join(spark_home, 'C:/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip'))
execfile(os.path.join(spark_home, 'python/pyspark/shell.py'))
Using the code above, I was able to launch Spark in an IPython notebook and my Enthought Canopy Python IDE. Before, this, I was only able to launch pyspark through a cmd prompt. The code above will only work if you have your Environment Variables set correctly for Python and Spark (pyspark).
I run these set of path settings whenever I start pyspark in ipython:
import os
import sys
# Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages" "com.databricks:spark-csv_2.10:1.0.3" "sparkr-shell"') for R
### MANNN restart spart using ipython notebook --profile=pyspark --packages com.databricks:spark-csv_2.10:1.0.3
os.environ['SPARK_HOME']="G:/Spark/spark-1.5.1-bin-hadoop2.6"
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/bin")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/sql")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip")
from pyspark import SparkContext
from pyspark import SparkConf
from pyspark import SQLContext
##sc.stop() # IF you wish to stop the context
sc = SparkContext("local", "Simple App")
With the reference and help of the user "maxymoo" I was able to find a way to set a PERMANENT path is Windows 7 as well. The instructions are here:
http://geekswithblogs.net/renso/archive/2009/10/21/how-to-set-the-windows-path-in-windows-7.aspx
Simply set path in System -> Environment Variables -> Path
R Path in my system C:\Program Files\R\R-3.2.3\bin
Python Path in my system c:\python27
Spark Path in my system c:\spark-2
The path must be separated by ";" and there must be no space between paths