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Posted to commits@hudi.apache.org by "soumilshah1995 (via GitHub)" <gi...@apache.org> on 2023/04/06 19:37:01 UTC
[GitHub] [hudi] soumilshah1995 opened a new issue, #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
soumilshah1995 opened a new issue, #8401:
URL: https://github.com/apache/hudi/issues/8401
I would like to inform you that I have created a new ticket regarding the issue at hand. As per the user's request, I have created a separate ticket for this issue, which will enable us to better track and address the issue at hand. The reference ticket number is [insert ticket number], and you can find more details about it at this link: https://github.com/apache/hudi/issues/8040
For your reference, the previous ticket number is [8040]. I have included this information in my new ticket for easy access to any relevant details that were previously provided.
Thank you for your attention to this matter. Please let me know if there is any additional information that I can provide to assist in resolving this issue.
# Code
```
try:
import sys
import os
from pyspark.context import SparkContext
from pyspark.sql.session import SparkSession
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import col, to_timestamp, monotonically_increasing_id, to_date, when
from pyspark.sql.functions import *
from awsglue.utils import getResolvedOptions
from pyspark.sql.types import *
from datetime import datetime, date
import boto3
from functools import reduce
from pyspark.sql import Row
import uuid
from faker import Faker
except Exception as e:
print("Modules are missing : {} ".format(e))
spark = (SparkSession.builder.config('spark.serializer', 'org.apache.spark.serializer.KryoSerializer') \
.config('spark.sql.hive.convertMetastoreParquet', 'false') \
.config('spark.sql.catalog.spark_catalog', 'org.apache.spark.sql.hudi.catalog.HoodieCatalog') \
.config('spark.sql.extensions', 'org.apache.spark.sql.hudi.HoodieSparkSessionExtension') \
.config('spark.sql.legacy.pathOptionBehavior.enabled', 'true').getOrCreate())
sc = spark.sparkContext
glueContext = GlueContext(sc)
job = Job(glueContext)
logger = glueContext.get_logger()
# =================================INSERTING DATA =====================================
global faker
faker = Faker()
class DataGenerator(object):
@staticmethod
def get_data():
return [
(
uuid.uuid4().__str__(),
faker.name(),
faker.random_element(elements=('IT', 'HR', 'Sales', 'Marketing')),
faker.random_element(elements=('CA', 'NY', 'TX', 'FL', 'IL', 'RJ')),
str(faker.random_int(min=10000, max=150000)),
str(faker.random_int(min=18, max=60)),
str(faker.random_int(min=0, max=100000)),
str(faker.unix_time()),
faker.email(),
faker.credit_card_number(card_type='amex'),
) for x in range(100)
]
data = DataGenerator.get_data()
columns = ["emp_id", "employee_name", "department", "state", "salary", "age", "bonus", "ts", "email", "credit_card"]
spark_df = spark.createDataFrame(data=data, schema=columns)
# ============================== Settings =======================================
db_name = "hudidb"
table_name = "employees"
recordkey = 'emp_id'
precombine = "ts"
PARTITION_FIELD = 'state'
path = "s3://hudi-demos-emr-serverless-project-soumil/tmp1/"
method = 'upsert'
table_type = "MERGE_ON_READ"
# ====================================================================================
hudi_part_write_config = {
'className': 'org.apache.hudi',
"hoodie.schema.on.read.enable":"true",
"hoodie.datasource.write.reconcile.schema":"true",
"hoodie.avro.schema.external.transformation":"true",
'hoodie.avro.schema.validate':"true",
"hoodie.datasource.write.schema.allow.auto.evolution.column.drop":"true",
'hoodie.table.name': table_name,
'hoodie.datasource.write.table.type': table_type,
'hoodie.datasource.write.operation': method,
'hoodie.datasource.write.recordkey.field': recordkey,
'hoodie.datasource.write.precombine.field': precombine,
'hoodie.datasource.hive_sync.mode': 'hms',
'hoodie.datasource.hive_sync.enable': 'true',
'hoodie.datasource.hive_sync.use_jdbc': 'false',
'hoodie.datasource.hive_sync.support_timestamp': 'false',
'hoodie.datasource.hive_sync.database': db_name,
'hoodie.datasource.hive_sync.table': table_name,
}
spark_df.write.format("hudi").options(**hudi_part_write_config).mode("append").save(path)
# ================================================================
# Adding NEW COLUMN
# ================================================================
class DataGenerator(object):
@staticmethod
def get_data():
return [
(
uuid.uuid4().__str__(),
faker.name(),
faker.random_element(elements=('IT', 'HR', 'Sales', 'Marketing')),
faker.random_element(elements=('CA', 'NY', 'TX', 'FL', 'IL', 'RJ')),
str(faker.random_int(min=10000, max=150000)),
str(faker.random_int(min=18, max=60)),
str(faker.random_int(min=0, max=100000)),
str(faker.unix_time()),
faker.email(),
faker.credit_card_number(card_type='amex'),
faker.date().__str__()
) for x in range(100)
]
data = DataGenerator.get_data()
columns = ["emp_id", "employee_name", "department", "state", "salary", "age", "bonus", "ts", "email", "credit_card",
"new_date_col"]
spark_df = spark.createDataFrame(data=data, schema=columns)
spark_df.write.format("hudi").options(**hudi_part_write_config).mode("append").save(path)
try:
print("Try1")
table_name_test = f"{table_name}_ro"
query = f"alter table {db_name}.{table_name_test} drop column credit_card"
spark.sql(query)
except Exception as e:
print("ERR1", e)
try:
print("Try2")
table_name_test = f"{table_name}_rt"
query = f"alter table {db_name}.{table_name_test} drop column credit_card"
spark.sql(query)
except Exception as e:
print("ERR2", e)
```
```
--conf spark.serializer=org.apache.spark.serializer.KryoSerializer
--conf spark.sql.hive.convertMetastoreParquet=false
--conf spark.sql.hive.convertMetastoreParquet=false
--conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog
--conf spark.sql.legacy.pathOptionBehavior.enabled=true
--conf spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension
```
# Error
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[GitHub] [hudi] ad1happy2go commented on issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "ad1happy2go (via GitHub)" <gi...@apache.org>.
ad1happy2go commented on issue #8401:
URL: https://github.com/apache/hudi/issues/8401#issuecomment-1521944349
@soumilshah1995 This looks to be AWS issue.
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[GitHub] [hudi] soumilshah1995 closed issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "soumilshah1995 (via GitHub)" <gi...@apache.org>.
soumilshah1995 closed issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
URL: https://github.com/apache/hudi/issues/8401
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[GitHub] [hudi] ad1happy2go commented on issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "ad1happy2go (via GitHub)" <gi...@apache.org>.
ad1happy2go commented on issue #8401:
URL: https://github.com/apache/hudi/issues/8401#issuecomment-1513243092
@soumilshah1995 I tried with Hoodie version 0.13.0 also with Glue 4.0 using additional libraries and not using --datalake-formats.
But still facing the same issue. Checking with AWS support on the same.
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[GitHub] [hudi] soumilshah1995 commented on issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "soumilshah1995 (via GitHub)" <gi...@apache.org>.
soumilshah1995 commented on issue #8401:
URL: https://github.com/apache/hudi/issues/8401#issuecomment-1513430809
Thanks a lot i really appreciate Help @ad1happy2go
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[GitHub] [hudi] soumilshah1995 commented on issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "soumilshah1995 (via GitHub)" <gi...@apache.org>.
soumilshah1995 commented on issue #8401:
URL: https://github.com/apache/hudi/issues/8401#issuecomment-1514666775
@ad1happy2go would this be Hudi issue or AWS issue ?
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[GitHub] [hudi] soumilshah1995 commented on issue #8401: [SUPPORT] AWS Glue 4.0 | Issue Deleting Column in Hudi MOR tables
Posted by "soumilshah1995 (via GitHub)" <gi...@apache.org>.
soumilshah1995 commented on issue #8401:
URL: https://github.com/apache/hudi/issues/8401#issuecomment-1646676751
Issue Resolved
```
try:
import sys
import os
from pyspark.context import SparkContext
from pyspark.sql.session import SparkSession
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import col, to_timestamp, monotonically_increasing_id, to_date, when
from pyspark.sql.functions import *
from awsglue.utils import getResolvedOptions
from pyspark.sql.types import *
from datetime import datetime, date
import boto3
from functools import reduce
from pyspark.sql import Row
import uuid
from faker import Faker
except Exception as e:
print("Modules are missing : {} ".format(e))
spark = (SparkSession.builder.config('spark.serializer', 'org.apache.spark.serializer.KryoSerializer') \
.config('spark.sql.hive.convertMetastoreParquet', 'false') \
.config('spark.sql.catalog.spark_catalog', 'org.apache.spark.sql.hudi.catalog.HoodieCatalog') \
.config('spark.sql.extensions', 'org.apache.spark.sql.hudi.HoodieSparkSessionExtension') \
.config('spark.sql.legacy.pathOptionBehavior.enabled', 'true').getOrCreate())
sc = spark.sparkContext
glueContext = GlueContext(sc)
job = Job(glueContext)
logger = glueContext.get_logger()
# =================================INSERTING DATA =====================================
global faker
faker = Faker()
class DataGenerator(object):
@staticmethod
def get_data():
return [
(
uuid.uuid4().__str__(),
faker.name(),
faker.random_element(elements=('IT', 'HR', 'Sales', 'Marketing')),
faker.random_element(elements=('CA', 'NY', 'TX', 'FL', 'IL', 'RJ')),
str(faker.random_int(min=10000, max=150000)),
str(faker.random_int(min=18, max=60)),
str(faker.random_int(min=0, max=100000)),
str(faker.unix_time()),
faker.email(),
faker.credit_card_number(card_type='amex'),
) for x in range(100)
]
data = DataGenerator.get_data()
columns = ["emp_id", "employee_name", "department", "state", "salary", "age", "bonus", "ts", "email", "credit_card"]
spark_df = spark.createDataFrame(data=data, schema=columns)
# ============================== Settings =======================================
db_name = "hudidb"
table_name = "employees"
recordkey = 'emp_id'
precombine = "ts"
PARTITION_FIELD = 'state'
path = "s3://soumilshah-hudi-demos/tmp1/"
method = 'upsert'
table_type = "MERGE_ON_READ"
# ====================================================================================
hudi_part_write_config = {
'className': 'org.apache.hudi',
"hoodie.schema.on.read.enable":"true",
"hoodie.datasource.write.reconcile.schema":"true",
"hoodie.avro.schema.external.transformation":"true",
'hoodie.avro.schema.validate':"true",
"hoodie.datasource.write.schema.allow.auto.evolution.column.drop":"true",
'hoodie.table.name': table_name,
'hoodie.datasource.write.table.type': table_type,
'hoodie.datasource.write.operation': method,
'hoodie.datasource.write.recordkey.field': recordkey,
'hoodie.datasource.write.precombine.field': precombine,
'hoodie.datasource.hive_sync.mode': 'hms',
'hoodie.datasource.hive_sync.enable': 'true',
'hoodie.datasource.hive_sync.use_jdbc': 'false',
'hoodie.datasource.hive_sync.support_timestamp': 'false',
'hoodie.datasource.hive_sync.database': db_name,
'hoodie.datasource.hive_sync.table': table_name,
}
spark_df.write.format("hudi").options(**hudi_part_write_config).mode("append").save(path)
# ================================================================
# Adding NEW COLUMN
# ================================================================
class DataGenerator(object):
@staticmethod
def get_data():
return [
(
uuid.uuid4().__str__(),
faker.name(),
faker.random_element(elements=('IT', 'HR', 'Sales', 'Marketing')),
faker.random_element(elements=('CA', 'NY', 'TX', 'FL', 'IL', 'RJ')),
str(faker.random_int(min=10000, max=150000)),
str(faker.random_int(min=18, max=60)),
str(faker.random_int(min=0, max=100000)),
str(faker.unix_time()),
faker.email(),
faker.credit_card_number(card_type='amex'),
faker.date().__str__()
) for x in range(100)
]
data = DataGenerator.get_data()
columns = ["emp_id", "employee_name", "department", "state", "salary", "age", "bonus", "ts", "email", "credit_card",
"new_date_col"]
spark_df = spark.createDataFrame(data=data, schema=columns)
spark_df.write.format("hudi").options(**hudi_part_write_config).mode("append").save(path)
spark.sql("set hoodie.schema.on.read.enable=true")
try:
print("Try1")
table_name_test = f"{table_name}_ro"
query = f"ALTER TABLE {db_name}.{table_name_test} drop column credit_card"
spark.sql(query)
except Exception as e:
print("ERR1", e)
try:
print("Try2")
table_name_test = f"{table_name}_rt"
query = f"ALTER TABLE {db_name}.{table_name_test} drop column credit_card"
spark.sql(query)
except Exception as e:
print("ERR2", e)
```
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