I was trying to connect Google big query using pySpark using the below code :
from pyspark.sql import SparkSession
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName("GCP")
sc = SparkContext(conf=conf)
master = "yarn"
spark = SparkSession.builder \
.master("local")\
.appName("GCP") \
.getOrCreate()
spark._jsc.hadoopConfiguration().set("google.cloud.auth.service.account.json.keyfile","key.json")
df = spark.read.format('bigquery') \
.option("parentProject", "project_name") \
.option('table', 'project_name.table_name') \
.load()
df.show()
my spark version 2.3 and big query jar : spark-bigquery-latest_2.12
Though my service account was having "BigQuery Job User" permission at project level and bigquery data viewer and bigquery user at dataset level , but still I am getting the below error when trying to execute the above code
Traceback (most recent call last):
File "/home/lo815/GCP/gcp.py", line 23, in <module>
df.show()
File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 350, in show
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o93.showString.
: com.google.cloud.spark.bigquery.repackaged.com.google.api.gax.rpc.PermissionDeniedException: com.google.cloud.spark.bigquery.repackaged.io.grpc.StatusRuntimeException: PERMISSION_DENIED: request failed: the user does not have 'bigquery.readsessions.create' permission for 'projects/GCP'
at com.google.cloud.spark.bigquery.repackaged.com.google.api.gax.rpc.ApiExceptionFactory.createException(ApiExceptionFactory.java:53)
Related
I am trying to run spark streaming, but getting this issue. Please help
from pyspark.sql import SparkSession
if __name__ == "__main__":
print("Application started")
spark = SparkSession \
.builder \
.appName("Socker streaming demo") \
.master("local[*]")\
.getOrCreate()
# Steam will return unbounded table
stream_df = spark\
.readStream\
.format("socket")\
.option("host","localhost")\
.option("port","1100")\
.load()
print(stream_df.isStreaming)
stream_df.printSchema()
write_query = stream_df \
.writeStream\
.format("console")\
.start()
# this line of code will turn to streaming application into never ending
write_query.awaitTermination()
print("Application Completed")
Error is getting
22/07/31 00:13:16 WARN ResolveWriteToStream: Temporary checkpoint location created which is deleted normally when the query didn't fail: C:\Users\786000702\AppData\Local\Temp\temporary-9bfc22f8-6f1a-49e5-a3fb-3e4ac2c1de54. If it's required to delete it under any circumstances, please set spark.sql.streaming.forceDeleteTempCheckpointLocation to true. Important to know deleting temp checkpoint folder is best effort.
Traceback (most recent call last):
File "D:\PySparkProject\pySparkStream\socker_streaming.py", line 23, in <module>
write_query = stream_df \
File "D:\PySparkProject\venv\lib\site-packages\pyspark\sql\streaming.py", line 1202, in start
return self._sq(self._jwrite.start())
File "D:\PySparkProject\venv\lib\site-packages\py4j\java_gateway.py", line 1321, in __call__
return_value = get_return_value(
File "D:\PySparkProject\venv\lib\site-packages\pyspark\sql\utils.py", line 111, in deco
return f(*a, **kw)
File "D:\PySparkProject\venv\lib\site-packages\py4j\protocol.py", line 326, in get_return_value
raise Py4JJavaError(
py4j.protocol.Py4JJavaError: An error occurred while calling o36.start.
**: org.apache.hadoop.fs.InvalidPathException: Invalid path name Path part /C:/Users/786000702/AppData/Local/Temp/temporary-9bfc22f8-6f1a-49e5-a3fb-3e4ac2c1de54 from URI hdfs://0.0.0.0:19000/C:/Users/786000702/AppData/Local/Temp/temporary-9bfc22f8-6f1a-49e5-a3fb-3e4ac2c1de54 is not a valid filename.
at org.apache.hadoop.fs.AbstractFileSystem.getUriPath(AbstractFileSystem.java:427)
at org.apache.hadoop.fs.Hdfs.mkdir(Hdfs.java:366)
at org.apache.hadoop.fs.FileContext$4.next(FileContext.java:809)
at org.apache.hadoop.fs.FileContext$4.next(FileContext.java:805)
at**
org.apache.hadoop.fs.FSLinkResolver.resolve(FSLinkResolver.java:90)
at org.apache.hadoop.fs.FileContext.mkdir(FileContext.java:812)
at org.apache.spark.sql.execution.streaming.FileContextBasedCheckpointFileManager.createCheckpointDirectory(CheckpointFileManager.scala:368)
at org.apache.spark.sql.execution.streaming.ResolveWriteToStream$.resolveCheckpointLocation(ResolveWriteToStream.scala:121)
at org.apache.spark.sql.execution.streaming.ResolveWriteToStream$$anonfun$apply$1.applyOrElse(ResolveWriteToStream.scala:42)
at
You can modify the FS path that Spark defaults by editing fs.defaultFS in core-site.xml file located either in your Spark or Hadoop conf directorie
You seem to have set that at hdfs://0.0.0.0:19000/ rather than some file:// URI path, based on the error
I succesfully instaled Spark and Pyspark in my machine, added path variables, etc. but keeps facing import problems.
This is the code:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.config("spark.hadoop.hive.exec.dynamic.partition", "true") \
.config("spark.hadoop.hive.exec.dynamic.partition.mode", "nonstrict") \
.enableHiveSupport() \
.getOrCreate()
And this is the error message:
"C:\...\Desktop\Clube\venv\Scripts\python.exe" "C:.../Desktop/Clube/services/ce_modelo_analise.py"
Traceback (most recent call last):
File "C:\...\Desktop\Clube\services\ce_modelo_analise.py", line 1, in <module>
from pyspark.sql import SparkSession
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\__init__.py", line 51, in <module>
from pyspark.context import SparkContext
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\context.py", line 31, in <module>
from pyspark import accumulators
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\accumulators.py", line 97, in <module>
from pyspark.serializers import read_int, PickleSerializer
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\serializers.py", line 71, in <module>
from pyspark import cloudpickle
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\cloudpickle.py", line 145, in <module>
_cell_set_template_code = _make_cell_set_template_code()
File "C:\Spark\spark-2.4.0-bin-hadoop2.7\python\pyspark\cloudpickle.py", line 126, in _make_cell_set_template_code
return types.CodeType(
TypeError: 'bytes' object cannot be interpreted as an integer
If I remove the import line, those problems disappear. As I said before, my path variables are set:
and
Also, Spark is running correctly in cmd:
Going deeper I found the problem: I'm using Spark in version 2.4, which works with Python 3.7 tops.
As I was using Python 3.10, the problem was happening.
So if you're experiencing the same kind of issue, try to change your versions.
Whenever I run this error shows up:
Traceback (most recent call last):
File "~/test-tung/spark_tf.py", line 69, in <module>
'spark_tf').master('yarn').getOrCreate()
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/sql/session.py", line 186, in getOrCreate
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/context.py", line 371, in getOrCreate
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/context.py", line 131, in __init__
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/context.py", line 193, in _do_init
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/pyspark.zip/pyspark/context.py", line 310, in _initialize_context
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1569, in __call__
File "~/main-projects/spark/spark-3.0.0-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: java.lang.NoClassDefFoundError: org/spark_project/guava/base/Preconditions
A part of my python app spark_tf.py:
spark = SparkSession.builder.appName(
'spark_tf').master('yarn').getOrCreate()
model = tf.keras.models.load_model('./model/kdd_binary.h5')
weights = model.get_weights()
config = model.get_config()
bc_weights = spark.sparkContext.broadcast(weights)
bc_config = spark.sparkContext.broadcast(config)
scheme = StructType().add('#timestamp', StringType()).add('#address', StringType())
stream = spark.readStream.format('kafka') \
.option('kafka.bootstrap.servers', 'my-host:9092') \
.option('subscribe', 'dltest') \
.load() \
.selectExpr("CAST(value AS STRING)") \
.select(from_json('value', scheme).alias('json'),
online_predict('value').alias('result')) \
.select(to_json(struct('result', 'json.#timestamp', 'json.#address'))
.alias('value'))
x = stream.writeStream \
.format('kafka') \
.option("kafka.bootstrap.servers", 'my-host:9092') \
.option('topic', 'dlpred') \
.option('checkpointLocation', './kafka_checkpoint') \
.start()
x.awaitTermination()
My submit line: spark-submit --deploy-mode client --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.0 spark_tf.py
I think it's probably because of improper Spark setup but I don't know what caused that.
EDIT: This code I think apparently runs on client instead of Hadoop cluster but running it on the cluster yields the same error.
How can I read data from Druid using spark and Avatica JDBC Driver?
This is avatica JDBC document
Reading data from Druid using python and Jaydebeapi module, I succeed like below code.
$ python
import jaydebeapi
conn = jaydebeapi.connect("org.apache.calcite.avatica.remote.Driver",
"jdbc:avatica:remote:url=http://0.0.0.0:8082/druid/v2/sql/avatica/",
{"user": "druid", "password":"druid"},
"/root/avatica-1.17.0.jar",
)
cur = conn.cursor()
cur.execute("SELECT * FROM INFORMATION_SCHEMA.TABLES")
cur.fetchall()
output is:
[('druid', 'druid', 'wikipedia', 'TABLE'),
('druid', 'INFORMATION_SCHEMA', 'COLUMNS', 'SYSTEM_TABLE'),
('druid', 'INFORMATION_SCHEMA', 'SCHEMATA', 'SYSTEM_TABLE'),
('druid', 'INFORMATION_SCHEMA', 'TABLES', 'SYSTEM_TABLE'),
('druid', 'sys', 'segments', 'SYSTEM_TABLE'),
('druid', 'sys', 'server_segments', 'SYSTEM_TABLE'),
('druid', 'sys', 'servers', 'SYSTEM_TABLE'),
('druid', 'sys', 'supervisors', 'SYSTEM_TABLE'),
('druid', 'sys', 'tasks', 'SYSTEM_TABLE')] -> default tables
But I want to read using spark and JDBC.
I tried it but there is a problem using spark like below code.
$ pyspark --jars /root/avatica-1.17.0.jar
df = spark.read.format('jdbc') \
.option('url', 'jdbc:avatica:remote:url=http://0.0.0.0:8082/druid/v2/sql/avatica/') \
.option("dbtable", 'INFORMATION_SCHEMA.TABLES') \
.option('user', 'druid') \
.option('password', 'druid') \
.option('driver', 'org.apache.calcite.avatica.remote.Driver') \
.load()
output is:
Traceback (most recent call last):
File "<stdin>", line 8, in <module>
File "/root/spark-2.4.4-bin-hadoop2.7/python/pyspark/sql/readwriter.py", line 172, in load
return self._df(self._jreader.load())
File "/root/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/root/spark-2.4.4-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/root/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o2999.load.
: java.sql.SQLException: While closing connection
...
Caused by: java.lang.RuntimeException: com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException: Unrecognized field "rpcMetadata" (class org.apache.calcite.avatica.remote.Service$CloseConnectionResponse), not marked as ignorable (0 known properties: ])
at [Source: {"response":"closeConnection","rpcMetadata":{"response":"rpcMetadata","serverAddress":"172.18.0.7:8082"}}
; line: 1, column: 46]
...
Caused by: com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException: Unrecognized field "rpcMetadata" (class org.apache.calcite.avatica.remote.Service$CloseConnectionResponse), not marked as ignorable (0 known properties: ])
at [Source: {"response":"closeConnection","rpcMetadata":{"response":"rpcMetadata","serverAddress":"172.18.0.7:8082"}}
; line: 1, column: 46]
...
Note:
I downloaded Avatica jar file(avatica-1.17.0.jar) from maven-repository
I installed Druid server using docker-compose and default setting values.
I found another way to solve this problem. I used spark-druid-connector to connect druid with spark.
But I changed some codes like this to use this code for my environment.
This is my environment:
spark: 2.4.4
scala: 2.11.12
python: python 3.6.8
druid:
zookeeper: 3.5
druid: 0.17.0
However, it has a problem.
If you use spark-druid-connector at least once, all sql queries like spark.sql("select * from tmep_view") used from the following will be entered into this planner.
but, if you use dataframe's api like df.distinct().count(), then there are no problems. I didn't solve yet.
I tried with spark-shell:
./bin/spark-shell --driver-class-path avatica-1.17.0.jar --jars avatica-1.17.0.jar
val jdbcDF = spark.read.format("jdbc")
.option("url", "jdbc:avatica:remote:url=http://0.0.0.0:8082/druid/v2/sql/avatica/")
.option("dbtable", "INFORMATION_SCHEMA.TABLES")
.option("user", "druid")
.option("password", "druid")
.load()
I am trying to read a json file from a google bucket into a pyspark dataframe on a local spark machine. Here's the code:
import pandas as pd
import numpy as np
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession, SQLContext
conf = SparkConf().setAll([('spark.executor.memory', '16g'),
('spark.executor.cores','4'),
('spark.cores.max','4')]).setMaster('local[*]')
spark = (SparkSession.
builder.
config(conf=conf).
getOrCreate())
sc = spark.sparkContext
import glob
import bz2
import json
import pickle
bucket_path = "gs://<SOME_PATH>/"
client = storage.Client(project='<SOME_PROJECT>')
bucket = client.get_bucket ('<SOME_PATH>')
blobs = bucket.list_blobs()
theframes = []
for blob in blobs:
print(blob.name)
testspark = spark.read.json(bucket_path + blob.name).cache()
theframes.append(testspark)
It's reading files from the bucket fine (I can see the print out from blob.name), but then crashes like this:
Traceback (most recent call last):
File "test_code.py", line 66, in <module>
testspark = spark.read.json(bucket_path + blob.name).cache()
File "/home/anaconda3/envs/py37base/lib/python3.6/site-packages/pyspark/sql/readwriter.py", line 274, in json
return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
File "/home/anaconda3/envs/py37base/lib/python3.6/site-packages/py4j/java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/anaconda3/envs/py37base/lib/python3.6/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/anaconda3/envs/py37base/lib/python3.6/site-packages/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o51.json.
: java.io.IOException: No FileSystem for scheme: gs
I've seen this type of error discussed on stackoverflow, but most solutions seem to be in Scala while I have pyspark, and/or involve messing with core-site.xml, which I've done to no effect.
I am using spark 2.4.1 and python 3.6.7.
Help would be much appreciated!
Some config params are required to recognize "gs" as a distributed filesystem.
Use this setting for google cloud storage connector, gcs-connector-hadoop2-latest.jar
spark = SparkSession \
.builder \
.config("spark.jars", "/path/to/gcs-connector-hadoop2-latest.jar") \
.getOrCreate()
Other configs that can be set from pyspark
spark._jsc.hadoopConfiguration().set('fs.gs.impl', 'com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem')
# This is required if you are using service account and set true,
spark._jsc.hadoopConfiguration().set('fs.gs.auth.service.account.enable', 'true')
spark._jsc.hadoopConfiguration().set('google.cloud.auth.service.account.json.keyfile', "/path/to/keyfile")
# Following are required if you are using oAuth
spark._jsc.hadoopConfiguration().set('fs.gs.auth.client.id', 'YOUR_OAUTH_CLIENT_ID')
spark._jsc.hadoopConfiguration().set('fs.gs.auth.client.secret', 'OAUTH_SECRET')
Alternatively you can set up these configs in core-site.xml or spark-defaults.conf.
Hadoop Configuration on Command Line
You can also use spark.hadoop-prefixed configuration properties to set things up when pyspark (or spark-submit in general), e.g.
--conf spark.hadoop.fs.gs.impl=com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem