vinothchandar opened a new issue #3323:
URL: https://github.com/apache/hudi/issues/3323
**Describe the problem you faced**
A clear and concise description of the problem.
**To Reproduce**
Steps to reproduce the behavior:
```
from pyspark.sql import SparkSession
spark = (
SparkSession.builder.appName("Hudi_Data_Processing_Framework")
.config("spark.serializer",
"org.apache.spark.serializer.KryoSerializer")
.config("spark.sql.hive.convertMetastoreParquet", "false")
.config(
"spark.jars.packages",
"org.apache.hudi:hudi-spark3-bundle_2.12:0.8.0,org.apache.spark:spark-avro_2.12:3.0.2"
)
.getOrCreate()
)
input_df = spark.createDataFrame(
[
(100, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
(101, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
(102, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
(103, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
(104, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
(105, "2015-01-01", "2015-01-01T01:01:01.010101Z", 10),
],
("acctid", "date", "ts", "deposit"),
)
hudi_options = {'hoodie.table.name': 'compaction',
'hoodie.datasource.write.table.type': 'MERGE_ON_READ',
'hoodie.datasource.write.operation': 'upsert',
'hoodie.datasource.write.recordkey.field': 'acctid',
'hoodie.datasource.write.partitionpath.field': 'date',
'hoodie.datasource.write.precombine.field': 'ts',
'hoodie.datasource.write.hive_style_partitioning': 'true',
'hoodie.upsert.shuffle.parallelism': 2,
'hoodie.insert.shuffle.parallelism': 2,
'hoodie.delete.shuffle.parallelism': 2
}
# INSERT
(
input_df.write.format("org.apache.hudi")
.options(**hudi_options)
.mode("append")
.save("/tmp/hudi_incr1")
)
# UPDATE deposit to **11** for key 100
update_df = spark.createDataFrame(
[(100, "2015-01-01", "2015-01-01T11:01:01.000000Z", 11)], ("acctid",
"date", "ts", "deposit"))
update_df.write.format("org.apache.hudi").options(**hudi_options).mode("append").save("/tmp/hudi_incr1")
# UPDATE deposit to **12** for key 100
update_df = spark.createDataFrame(
[(100, "2015-01-01", "2015-01-01T12:01:01.000000Z", 12)], ("acctid",
"date", "ts", "deposit"))
update_df.write.format("org.apache.hudi").options(**hudi_options).mode("append").save("/tmp/hudi_incr1")
# UPDATE deposit to **13** for key 100
update_df = spark.createDataFrame(
[(100, "2015-01-01", "2015-01-01T13:01:01.000000Z", 13)], ("acctid",
"date", "ts", "deposit"))
update_df.write.format("org.apache.hudi").options(**hudi_options).mode("append").save("/tmp/hudi_incr1")
output_df = spark.read.format("org.apache.hudi").load(
"/tmp/hudi_incr1/"
)
output_df.show()
first_commit = '20210721080013' # As per this particular run
output_df = (spark.read
.option("hoodie.datasource.query.type", "incremental")
.option("hoodie.datasource.read.begin.instanttime",
first_commit)
# .option("hoodie.datasource.read.end.instanttime", last_commit)
.format("org.apache.hudi")
.load("/tmp/hudi_incr1/"))
output_df.show()
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment Description**
* Hudi version :
* Spark version :
* Hive version :
* Hadoop version :
* Storage (HDFS/S3/GCS..) :
* Running on Docker? (yes/no) :
**Additional context**
Add any other context about the problem here.
**Stacktrace**
```Add the stacktrace of the error.```
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