Following: https://docs.delta.io/latest/quick-start.html#python
I have installed delta-spark and run:
from delta import *
builder = pyspark.sql.SparkSession.builder.appName("MyApp") \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")
spark = spark = configure_spark_with_delta_pip(builder).getOrCreate()
However when I run:
data = spark.range(0, 5)
data.write.format("delta").save("/tmp/delta-table")
the error states: delta not recognised
& if I run
DeltaTable.isDeltaTable(spark, "packages/tests/streaming/data")
It states: TypeError: 'JavaPackage' object is not callable
It seemed that I could run these commands locally (such as unit tests) without Maven or running it in a pyspark shell? It would be good to just see if I am missing a dependency?
You can just install delta-spark PyPi package using pip install delta-spark (it will pull pyspark as well), and then refer to it.
Or you can add a configuration option that will fetch Delta package. It's .config("spark.jars.packages", "io.delta:delta-core_2.12:<delta-version>"). For Spark 3.1 Delta versions is 1.0.0 (see releases mapping docs for more information).
I have an example of using Delta tables in unit tests (please note, that import statement is in the function definition because Delta package is loaded dynamically):
import pyspark
import pyspark.sql
import pytest
import shutil
from pyspark.sql import SparkSession
delta_dir_name = "/tmp/delta-table"
#pytest.fixture
def delta_setup(spark_session):
data = spark_session.range(0, 5)
data.write.format("delta").save(delta_dir_name)
yield data
shutil.rmtree(delta_dir_name, ignore_errors=True)
def test_delta(spark_session, delta_setup):
from delta.tables import DeltaTable
deltaTable = DeltaTable.forPath(spark_session, delta_dir_name)
hist = deltaTable.history()
assert hist.count() == 1
environment is initialized via pytest-spark:
[pytest]
filterwarnings =
ignore::DeprecationWarning
spark_options =
spark.sql.extensions: io.delta.sql.DeltaSparkSessionExtension
spark.sql.catalog.spark_catalog: org.apache.spark.sql.delta.catalog.DeltaCatalog
spark.jars.packages: io.delta:delta-core_2.12:1.0.0
spark.sql.catalogImplementation: in-memory
Here I want to use SFTPToGCSOperator in composer enviornment(1.10.6) of GCP. I know there is a limitation because The operator present only in latest version of airflow not in composer latest version 1.10.6.
See the refrence -
https://airflow.readthedocs.io/en/latest/howto/operator/gcp/sftp_to_gcs.html
I found the alternative of operator and I created a plugin class, But again I faced the issue for sftphook class, Now I am using older version of sftphook class.
see the below refrence -
from airflow.contrib.hooks.sftp_hook import SFTPHook
https://airflow.apache.org/docs/stable/_modules/airflow/contrib/hooks/sftp_hook.html
I have created a plugin class, later It's import in my DAG script. It's working fine only when we are moveing one file, In that case we need to pass complete file path with extension.
Please refer below example(It's working fine in this scenrio)
DIR = "/test/sftp_dag_test/source_dir"
OBJECT_SRC_1 = "file.csv"
source_path=os.path.join(DIR, OBJECT_SRC_1),
Except this If we are using wildcard, I mean if we want to move all the files from directory I am getting error for get_tree_map method.
Please see below DAG code
import os
from airflow import models
from airflow.models import Variable
from PluginSFTPToGCSOperator import SFTPToGCSOperator
#from airflow.contrib.operators.sftp_to_gcs import SFTPToGCSOperator
from airflow.utils.dates import days_ago
default_args = {"start_date": days_ago(1)}
DIR_path = "/main_dir/sub_dir/"
BUCKET_SRC = "test-gcp-bucket"
with models.DAG(
"dag_sftp_to_gcs", default_args=default_args, schedule_interval=None
) as dag:
copy_sftp_to_gcs = SFTPToGCSOperator(
task_id="t_sftp_to_gcs",
sftp_conn_id="test_sftp_conn",
gcp_conn_id="google_cloud_default",
source_path=os.path.join(DIR_path, "*.gz"),
destination_bucket=BUCKET_SRC,
)
copy_sftp_to_gcs
Here we are using wildcard * in DAG script, please see below plugin class.
import os
from tempfile import NamedTemporaryFile
from typing import Optional, Union
from airflow.plugins_manager import AirflowPlugin
from airflow import AirflowException
from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
from airflow.models import BaseOperator
from airflow.contrib.hooks.sftp_hook import SFTPHook
from airflow.utils.decorators import apply_defaults
WILDCARD = "*"
class SFTPToGCSOperator(BaseOperator):
template_fields = ("source_path", "destination_path", "destination_bucket")
#apply_defaults
def __init__(
self,
source_path: str,
destination_bucket: str = "destination_bucket",
destination_path: Optional[str] = None,
gcp_conn_id: str = "google_cloud_default",
sftp_conn_id: str = "sftp_conn_plugin",
delegate_to: Optional[str] = None,
mime_type: str = "application/octet-stream",
gzip: bool = False,
move_object: bool = False,
*args,
**kwargs
) -> None:
super().__init__(*args, **kwargs)
self.source_path = source_path
self.destination_path = self._set_destination_path(destination_path)
print('destination_bucket : ',destination_bucket)
self.destination_bucket = destination_bucket
self.gcp_conn_id = gcp_conn_id
self.mime_type = mime_type
self.delegate_to = delegate_to
self.gzip = gzip
self.sftp_conn_id = sftp_conn_id
self.move_object = move_object
def execute(self, context):
print("inside execute")
gcs_hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to
)
sftp_hook = SFTPHook(self.sftp_conn_id)
if WILDCARD in self.source_path:
total_wildcards = self.source_path.count(WILDCARD)
if total_wildcards > 1:
raise AirflowException(
"Only one wildcard '*' is allowed in source_path parameter. "
"Found {} in {}.".format(total_wildcards, self.source_path)
)
print('self.source_path : ',self.source_path)
prefix, delimiter = self.source_path.split(WILDCARD, 1)
print('prefix : ',prefix)
base_path = os.path.dirname(prefix)
print('base_path : ',base_path)
files, _, _ = sftp_hook.get_tree_map(
base_path, prefix=prefix, delimiter=delimiter
)
for file in files:
destination_path = file.replace(base_path, self.destination_path, 1)
self._copy_single_object(gcs_hook, sftp_hook, file, destination_path)
else:
destination_object = (
self.destination_path
if self.destination_path
else self.source_path.rsplit("/", 1)[1]
)
self._copy_single_object(
gcs_hook, sftp_hook, self.source_path, destination_object
)
def _copy_single_object(
self,
gcs_hook: GoogleCloudStorageHook,
sftp_hook: SFTPHook,
source_path: str,
destination_object: str,
) -> None:
"""
Helper function to copy single object.
"""
self.log.info(
"Executing copy of %s to gs://%s/%s",
source_path,
self.destination_bucket,
destination_object,
)
with NamedTemporaryFile("w") as tmp:
sftp_hook.retrieve_file(source_path, tmp.name)
print('before upload self det object : ',self.destination_bucket)
gcs_hook.upload(
self.destination_bucket,
destination_object,
tmp.name,
self.mime_type,
)
if self.move_object:
self.log.info("Executing delete of %s", source_path)
sftp_hook.delete_file(source_path)
#staticmethod
def _set_destination_path(path: Union[str, None]) -> str:
if path is not None:
return path.lstrip("/") if path.startswith("/") else path
return ""
#staticmethod
def _set_bucket_name(name: str) -> str:
bucket = name if not name.startswith("gs://") else name[5:]
return bucket.strip("/")
class SFTPToGCSOperatorPlugin(AirflowPlugin):
name = "SFTPToGCSOperatorPlugin"
operators = [SFTPToGCSOperator]
So this plugin class I am importing in my DAG script and it's wotking fine when we are using file name, Because code is going inside else condition.
But when we are using wildcard we have cursor inside if condition and I am getting error for get_tree_map method.
see below error -
ERROR - 'SFTPHook' object has no attribute 'get_tree_map'
I found the reason of this error this method itself is not present in composer(airflow 1.10.6)-
https://airflow.apache.org/docs/stable/_modules/airflow/contrib/hooks/sftp_hook.html
This method is present in latest version of airflow
https://airflow.readthedocs.io/en/latest/_modules/airflow/providers/sftp/hooks/sftp.html
Now What should I can try, Is there any alternative of this method or any alternative of this operator class.
Does anyone know if there is a solution for this?
Thanks in Advance.
Please ignore Typo or indentation error in stackoverflow. In my code there is no Indentation error.
"providers" packages are only available from Airflow 2.0, which is not yet available in Cloud Composer (as I write this post, the latest available Airflow image is 1.10.14, released this morning).
BUT you can import backport packages which let you enjoy these new packages in earlier versions 1.10.*.
My requirements.txt:
apache-airflow-backport-providers-ssh==2020.10.29
apache-airflow-backport-providers-sftp==2020.10.29
pysftp>=0.2.9
paramiko>=2.6.0
sshtunnel<0.2,>=0.1.4
You can import PyPi packages directly in your Composer environment from the console.
With these dependencies, I could use the newest airflow.providers.ssh.operators.ssh.SSHOperator (formerly airflow.contrib.operators.ssh_operator.SSHOperator) and the new airflow.providers.google.cloud.transfers.gcs_to_sftp.GCSToSFTPOperator (which had no equivalent in contrib operators).
Enjoy!
To use SFTPToGCSOperator in Google Cloud Composer on Airflow version 1.10.6 we need to create a plugin and somehow "hack" Airflow by copying operator/hook codes into one file to enable SFTPToGCSOperator use code from Airflow 1.10.10 version.
The latest Airflow version has a new airflow.providers directory, which does not exist in earlier versions. This is why you saw following error: No module named airflow.providers. All the changes I made are described here:
I prepared working plugin, which you can download here. Before using it, we have to install following PyPI libraries on the Cloud Composer environment: pysftp, paramiko, sshtunnel.
I copied full SFTPToGCSOperator code, which starts in 792nd line. You can see that this operator uses GCSHook:
from airflow.providers.google.cloud.hooks.gcs import GCSHook
which also need to be copied to the plugin - starts in 193rd line.
Then, GCSHook inherits from GoogleBaseHook class, which we can change for GoogleCloudBaseHook accessible in Airflow 1.10.6 version, and import it:
from airflow.contrib.hooks.gcp_api_base_hook import GoogleCloudBaseHook
Finally, there is a need to import SFTPHook code into the plugin - starts in 39th line, which inherits from SSHHook class, we can use one from Airflow 1.10.6 version by changing import statement:
from airflow.contrib.hooks.ssh_hook import SSHHook
At the end of file, you can find the definition of the plugin:
class SFTPToGCSOperatorPlugin(AirflowPlugin):
name = "SFTPToGCSOperatorPlugin"
operators = [SFTPToGCSOperator]
Plugin creation is needed, as an Airflow built-in operator is not currently available in Airflow 1.10.6 version (the latest in Cloud Composer). You can keep an eye on Cloud Composer version lists in order to see when the newest version of Airflow will be available to use.
I hope you find the above pieces of information useful.
I tried to connect my couchBase server to EMR Spark 1.4.1, while encountered the
val airlines = sqlContext.read.couchbase(schemaFilter = org.apache.spark.sql.sources.EqualTo("type", "airline"))
<console>:24: error: value couchbase is not a member of org.apache.spark.sql.DataFrameReader
Those are all commands executed successfully before that error command:
spark-shell --packages com.couchbase.client:spark-connector_2.10:1.0.0
import org.apache.spark.{SparkContext, SparkConf}
val sc = new SparkContext(new SparkConf().setAppName("test").set("com.couchbase.bucket.travel-sample", ""))
val cfg = new SparkConf().setAppName("keyValueExample").setMaster("local[*]").set("com.couchbase.bucket.travel-sample", "")
import org.apache.spark.sql.SQLContext
val sql = new SQLContext(sc)
import com.couchbase.spark._
Do I need to configure anything more? Since I'm using AWS EMR, I assumed that I don't have to modify the .sbt file? I think I have already imported the package either while specifying when connecting to spark-shell, or in line(command) 7?
Documentation says you have to import the following:
scala> import com.couchbase.spark._
import com.couchbase.spark._
scala> import com.couchbase.spark.sql._
import com.couchbase.spark.sql._
Full doc is available here: http://developer.couchbase.com/documentation/server/current/connectors/spark-1.0/spark-shell.html
I've two files.
test.py
from pyspark import SparkContext
from pyspark import SparkConf
from pyspark import SQLContext
class Connection():
conf = SparkConf()
conf.setMaster("local")
conf.setAppName("Remote_Spark_Program - Leschi Plans")
conf.set('spark.executor.instances', 1)
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
print ('all done.')
con = Connection()
test_test.py
from test import Connection
sparkConnect = Connection()
when I run test.py the connection is made successfully but with test_test.py it gives
raise KeyError(key)
KeyError: 'SPARK_HOME'
KEY_ERROR arises if the SPARK_HOME is not found or invalid. So it's better to add it to your bashrc and check and reload in your code. So add this at the top of your test.py
import os
import sys
import pyspark
from pyspark import SparkContext, SparkConf, SQLContext
# Create a variable for our root path
SPARK_HOME = os.environ.get('SPARK_HOME',None)
# Add the PySpark/py4j to the Python Path
sys.path.insert(0, os.path.join(SPARK_HOME, "python", "lib"))
sys.path.insert(0, os.path.join(SPARK_HOME, "python"))
pyspark_submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "")
if not "pyspark-shell" in pyspark_submit_args: pyspark_submit_args += " pyspark-shell"
os.environ["PYSPARK_SUBMIT_ARGS"] = pyspark_submit_args
Also add this at the end of your ~/.bashrc file
COMMAND: vim ~/.bashrc if you are using any Linux based OS
# needed for Apache Spark
export SPARK_HOME="/opt/spark"
export IPYTHON="1"
export PYSPARK_PYTHON="/usr/bin/python3"
export PYSPARK_DRIVER_PYTHON="ipython3"
export PYSPARK_DRIVER_PYTHON_OPTS="notebook"
export PYTHONPATH="$SPARK_HOME/python/:$PYTHONPATH"
export PYTHONPATH="$SPARK_HOME/python/lib/py4j-0.9-src.zip:$PYTHONPATH"
export PYSPARK_SUBMIT_ARGS="--master local[2] pyspark-shell"
export CLASSPATH="$CLASSPATH:/opt/spark/lib/spark-assembly-1.6.1-hadoop2.6.0.jar
Note:
In the above bashrc code, I have given my SPARK_HOME value as /opt/spark you can give the location where you keep your spark folder(the downloaded one from the website).
Also I'm using python3 you can change it to python in the bashrc if you are using python 2.+ versions
I was using Ipython, for easy testing during runtime, like load the data once and test your code many times. If you are using plain old text editor, let me know I will update the bashrc accordingly.
I am using spark 1.3.0.
I have a problem running the python program in spark python shell.
This is how I submit the job :
/bin/spark-submit progname.py
the error I found is,
NameError: name 'sc' is not defined
on that line.
Any idea?
Thanks in advance
## Imports
from pyspark import SparkConf, SparkContext
## CONSTANTS
APP_NAME = "My Spark Application"
##OTHER FUNCTIONS/CLASSES
## Main functionality
def main(sc):
rdd = sc.parallelize(range(1000), 10)
print rdd.mean()
if __name__ == "__main__":
# Configure OPTIONS
conf = SparkConf().setAppName(APP_NAME)
conf = conf.setMaster("local[*]")
#in cluster this will be like
#"spark://ec2-0-17-03-078.compute-#1.amazonaws.com:7077"
sc = SparkContext(conf=conf)
# Execute Main functionality
main(sc)
conf = pyspark.SparkConf()
This is how you should create SparkConf object.
Further you can use chaining to do thins like set application name etc
conf = pyspark.SparkConf().setAppName("My_App_Name")
Then pass this config var to create spark context.
The first thing a Spark program must do is to create a SparkContext object, which tells Spark how to access a cluster. To create a SparkContext you first need to build a SparkConf object that contains information about your application.
conf = SparkConf().setAppName(appName).setMaster(master)
sc = SparkContext(conf=conf)