I want to convert data from Dataframe to RDD, and save it to MongoDB, here is my code:
import pymongo
import pymongo_spark
from pyspark import SparkConf, SparkContext
from pyspark import BasicProfiler
from pyspark.sql import SparkSession
class MyCustomProfiler(BasicProfiler):
def show(self, id):
print("My custom profiles for RDD:%s" % id)
conf = SparkConf().set("spark.python.profile", "true")
spark = SparkSession.builder \
.master("local[*]") \
.appName("Word Count") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
# Important: activate pymongo_spark.
pymongo_spark.activate()
on_time_dataframe = spark.read.parquet(r'\data\on_time_performance.parquet')
on_time_dataframe.show()
# Note we have to convert the row to a dict to avoid https://jira.mongodb.org/browse/HADOOP-276
as_dict = on_time_dataframe.rdd.map(lambda row: row.asDict())
as_dict.saveToMongoDB('mongodb://localhost:27017/agile_data_science.on_time_performance')
some errors occurs:
py4j.protocol.Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile.
: java.lang.ClassNotFoundException: com.mongodb.hadoop.io.BSONWritable
I have installed the Mongo-hadoop file; It seems I don't have a Bsonweitable class. I'm not good at java, So I want someone to help me.
Related
I have two py files
com/demo/DemoMain.py
com/demo/Sample.py
In both of the above files i am recreating the SparkSession object , In Pyspark,how do i create a Sparksession in one file and reuse it in other py files . In Scala it is easily possible by creating in one object and import that it everywhere
DemoMain.py
from pyspark.sql.types import StringType, StructType, StructField
from pyspark.sql import SparkSession
from pyspark.sql import Row
def main():
spark = SparkSession \
.builder \
.appName("Python Spark SQL basic example") \
.getOrCreate()
sc = spark.sparkContext
data=["surender,34","ajay,21"]
lines = sc.parallelize(data)
parts = lines.map(lambda l: l.split(","))
people = parts.map(lambda p: Row(name=p[0], age=int(p[1])))
df=spark.createDataFrame(people)
df.show()
if __name__ == '__main__':
main()
sample.py
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.appName("Python Spark SQL basic example") \
.getOrCreate()
rdd = spark.sparkContext.parallelize(["surender","raja"])
rdd.collect()
My requirement is to call a "Spark Scala" function from an existing PySpark program.
What is the best way to pass sparkSession created in PySpark program to Scala function.
I pass my scala jar to Pyspark as follows.
spark-submit --jars ScalaExample-0.1.jar pyspark_call_scala_example.py iris.data
Scalacode
def getDf(spark: SparkSession, query:String, df: DataFrame, log: Logger): DataFrame = {
import spark.implicits._
val df = spark.sql(query)
df
}
Pysparkcode
if __name__ == '__main__':
query = sys.argv[1]
spark = SparkSession \
.builder \
.appName("PySpark using Scala example") \
.getOrCreate()
log4jLogger = sc._jvm.org.apache.log4j
log = log4jLogger.LogManager.getLogger(__name__)
query_df = DataFrame(sc._jvm.com.crowdstrike.dsci.sparkjobs.PythonHelper.getDf(???, query, ???), sqlContext)
Question
How to pass sparksession and logger to getDf ?
https://www.crowdstrike.com/blog/spark-hot-potato-passing-dataframes-between-scala-spark-and-pyspark/
To pass SparkSession from Python to Scala, use spark._jsparkSession.
I'm trying to connect spark (pyspark) to mongodb as follows:
conf = SparkConf()
conf.set('spark.mongodb.input.uri', default_mongo_uri)
conf.set('spark.mongodb.output.uri', default_mongo_uri)
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
spark = SparkSession \
.builder \
.appName("my-app") \
.config("spark.mongodb.input.uri", default_mongo_uri) \
.config("spark.mongodb.output.uri", default_mongo_uri) \
.getOrCreate()
But when I do the following:
users = spark.read.format("com.mongodb.spark.sql.DefaultSource") \
.option("uri", '{uri}.{col}'.format(uri=mongo_uri, col='users')).load()
I get this error:
java.lang.ClassNotFoundException: Failed to find data source:
com.mongodb.spark.sql.DefaultSource
I did the same thing from pyspark shell and I was able to retrieve data. This is the command I ran:
pyspark --conf "spark.mongodb.input.uri=mongodb_uri" --conf "spark.mongodb.output.uri=mongodburi" --packages org.mongodb.spark:mongo-spark-connector_2.11:2.2.2
But here we have the option to specify the package we need to use. But what about standalone apps and scripts. how can I configure mongo-spark-connector there.
Any ideas?
Here how I did it in Jupyter notebook:
1. Download jars from central or any other repository and put them in directory called "jars":
mongo-spark-connector_2.11-2.4.0
mongo-java-driver-3.9.0
2. Create session and write/read any data
from pyspark import SparkConf
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
working_directory = 'jars/*'
my_spark = SparkSession \
.builder \
.appName("myApp") \
.config("spark.mongodb.input.uri=mongodb://127.0.0.1/test.myCollection") \
.config("spark.mongodb.output.uri=mongodb://127.0.0.1/test.myCollection") \
.config('spark.driver.extraClassPath', working_directory) \
.getOrCreate()
people = my_spark.createDataFrame([("JULIA", 50), ("Gandalf", 1000), ("Thorin", 195), ("Balin", 178), ("Kili", 77),
("Dwalin", 169), ("Oin", 167), ("Gloin", 158), ("Fili", 82), ("Bombur", 22)], ["name", "age"])
people.write.format("com.mongodb.spark.sql.DefaultSource").mode("append").save()
df = my_spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
df.select('*').where(col("name") == "JULIA").show()
As a result you will see this:
If you are using SparkContext & SparkSession, you have mentioned the connector jar packages in SparkConf, check the following Code:
from pyspark import SparkContext,SparkConf
conf = SparkConf().set("spark.jars.packages", "org.mongodb.spark:mongo-spark-connector_2.11:2.3.2")
sc = SparkContext(conf=conf)
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("myApp") \
.config("spark.mongodb.input.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.config("spark.mongodb.output.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.getOrCreate()
df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
df.printSchema()
If you are using only SparkSession then use following code:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("myApp") \
.config("spark.mongodb.input.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.config("spark.mongodb.output.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.config('spark.jars.packages', 'org.mongodb.spark:mongo-spark-connector_2.11:2.3.2') \
.getOrCreate()
df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
df.printSchema()
If you're using the newest version of mongo-spark-connector, i.e. v10.0.1 at the time of writing this, you need to use SparkConf object, as stated by the mongo documentation (https://www.mongodb.com/docs/spark-connector/current/configuration/).
Besides, you don't need to manually download anything, it will do it for you.
Bellow is the solution I came up with, for :
mongo-spark-connector: 10.0.1
mongo server : 5.0.8
spark : 3.2.0
def init_spark():
password = os.environ["MONGODB_PASSWORD"]
user = os.environ["MONGODB_USER"]
host = os.environ["MONGODB_HOST"]
db_auth = os.environ["MONGODB_DB_AUTH"]
mongo_conn = f"mongodb://{user}:{password}#{host}:27017/{db_auth}"
conf = SparkConf()
# Download mongo-spark-connector and its dependencies.
# This will download all the necessary jars and put them in your $HOME/.ivy2/jars, no need to manually download them :
conf.set("spark.jars.packages",
"org.mongodb.spark:mongo-spark-connector:10.0.1")
# Set up read connection :
conf.set("spark.mongodb.read.connection.uri", mongo_conn)
conf.set("spark.mongodb.read.database", "<my-read-database>")
conf.set("spark.mongodb.read.collection", "<my-read-collection>")
# Set up write connection
conf.set("spark.mongodb.write.connection.uri", mongo_conn)
conf.set("spark.mongodb.write.database", "<my-write-database>")
conf.set("spark.mongodb.write.collection", "<my-write-collection>")
# If you need to update instead of inserting :
conf.set("spark.mongodb.write.operationType", "update")
SparkContext(conf=conf)
return SparkSession \
.builder \
.appName('<my-app-name>') \
.getOrCreate()
spark = init_spark()
df = spark.read.format("mongodb").load()
df_grouped = df.groupBy("<some-column>").agg(mean("<some-other-column>"))
df_grouped.write.format("mongodb").mode("append").save()
I was also facing same error "java.lang.ClassNotFoundException: Failed to find data source: com.mongodb.spark.sql.DefaultSource" while trying to connect to MongoDB from Spark (2.3).
I had to download and copy mongo-spark-connector_2.11 JAR file(s) into jars directory of spark installation.
That resolved my issue and I was successfully able to call my spark code via spark-submit.
Hope it helps.
Here is how this error got resolved by downloading the jar files below. (Used the solution of this question.)
1.Downloaded the jar files below.
mongo-spark-connector_2.11-2.4.1 from here
mongo-java-driver-3.9.0 from here
copy and paste both these jar files into 'jars' location in spark directory.
Pyspark Code in jupiter notebook:
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("mongo").\
config("spark.mongodb.input.uri","mongodb://127.0.0.1:27017/$database.$table_name").\
config("spark.mongodb.output.uri","mongodb://127.0.0.1:27017/$database.$table_name").\
getOrCreate()
df=spark.read.format('com.mongodb.spark.sql.DefaultSource')\
.option( "uri", "mongodb://127.0.0.1:27017/$database.$table_name") \
.load()
df.printSchema()
#create Temp view of df to view the data
table = df.createOrReplaceTempView("df")
#to read table present in mongodb
query1 = spark.sql("SELECT * FROM df ")
query1.show(10)
You are not using sc to create the SparkSession. Maybe this code can help you:
conf.set('spark.mongodb.input.uri', mongodb_input_uri)
conf.set('spark.mongodb.input.collection', 'collection_name')
conf.set('spark.mongodb.output.uri', mongodb_output_uri)
sc = SparkContext(conf=conf)
spark = SparkSession(sc) # Using the context (conf) to create the session
intellij(spark)--->Hive(Remote)---storage on S3(orc format)
Not able to read remote Hive table through spark/scala.
was able to read table schema but not able to read table.
Error -Exception in thread "main" java.lang.IllegalArgumentException:
AWS Access Key ID and Secret Access Key must be specified as the
username or password (respectively) of a s3 URL, or by setting the
fs.s3.awsAccessKeyId or fs.s3.awsSecretAccessKey properties
(respectively).
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.{Encoders, SparkSession}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.hive.orc._
import org.apache.spark.sql.types.StructType
object mainclas {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder
.master("local[*]")
.appName("hivetable")
.config("hive.metastore.uris", "thrift://10.20.30.40:9083")
.config("access-key","PQHFFDEGGDDVDVV")
.config("secret-key","FFGSGHhjhhhdjhJHJHHJGJHGjHH")
.config("format", "orc")
.enableHiveSupport()
.getOrCreate()
val res = spark.sqlContext.sql("show tables").show()
val res1 =spark.sql("select *from ace.visit limit 5").show()
}
}`
Try this:
val spark = SparkSession.builder
.master("local[*]")
.appName("hivetable")
.config("hive.metastore.uris", "thrift://10.20.30.40:9083")
.config("fs.s3n.awsAccessKeyId","PQHFFDEGGDDVDVV")
.config("fs.s3n.awsSecretAccessKey","FFGSGHhjhhhdjhJHJHHJGJHGjHH")
.config("format", "orc")
.enableHiveSupport()
.getOrCreate()
you need to prefix all the fs. options with spark.hadoop if you are setting them in the spark config. And as noted: use s3a over s3n if you can.
I want to create a decision tree model using spark submit.
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
from pyspark import SparkConf, SparkContext
from numpy import array
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.appName("myApp") \
.config("spark.mongodb.input.uri", "mongodb://127.0.0.1/newumc.classification_data") \
.config("spark.mongodb.output.uri", "mongodb://127.0.0.1/newumc.classification_data") \
.getOrCreate()
df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
dt = df.rdd.map(createLabeledPoints)
model_dt = DecisionTree.trainClassifier(dt, numClasses=467, categoricalFeaturesInfo={0:2,1:2, 2:2, 3:2, 4:2, 5:2, 6:2, 7:2, 8:2, 9:2, 10:2, 11:2, 12:2, 13:2, 14:2, 15:2, 16:2, 17:2, 18:2, 19:2, 20:2, 21:2, 22:2, 23:2, 24:2, 25:2, 26:2, 27:2, 28:2, 29:2, 30:2, 31:2, 32:2, 33:2, 34:2, 35:2, 36:2, 37:2, 38:2}, impurity='gini', maxDepth=30, maxBins=32)
where createLabeledPoints is a function that return to me a labeledpoint
I have no issue when I execute this code using pyspark in the spark-shell
but I want to use spark-submit, when I do that its gives me this error
pickle.PicklingError: Could not serialize object: TypeError: can't pickle thread.lock objects
I think the problem is because I create another sparkSession inside spark-submit (I think) or because pysparksataframe cannot be pickled!
Can anyone please help me !