Writing spark DataFrame In Apache Hudi Table - apache-spark

Question

I am new to apace hudi and trying to write my dataframe in my Hudi table using spark shell.
For type first time i am not creating any table and writing in overwrite mode so I am expecting it will create hudi
table.I am Writing below code.
spark-shell \
--packages org.apache.hudi:hudi-spark-bundle_2.12:0.7.0,org.apache.spark:spark-avro_2.12:3.0.1 \
--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
//Initialize a Spark Session for Hudi
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.functions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.hive.MultiPartKeysValueExtractor
import org.apache.spark.sql.SparkSession
val spark1 = SparkSession.builder().appName("hudi-datalake").master("local[*]").config("spark.serializer", "org.apache.spark.serializer.KryoSerializer").config("spark.sql.hive.convertMetastoreParquet", "false").getOrCreat ()
//Write to a Hudi Dataset
val inputDF = Seq(
("100", "2015-01-01", "2015-01-01T13:51:39.340396Z"),
("101", "2015-01-01", "2015-01-01T12:14:58.597216Z"),
("102", "2015-01-01", "2015-01-01T13:51:40.417052Z"),
("103", "2015-01-01", "2015-01-01T13:51:40.519832Z"),
("104", "2015-01-02", "2015-01-01T12:15:00.512679Z"),
("105", "2015-01-02", "2015-01-01T13:51:42.248818Z")
).toDF("id", "creation_date", "last_update_time")
val hudiOptions = Map[String,String](
HoodieWriteConfig.TABLE_NAME -> "work.hudi_test",
DataSourceWriteOptions.TABLE_TYPE_OPT_KEY -> "COPY_ON_WRITE",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "id",
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "creation_date",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "last_update_time",
DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY -> "true",
DataSourceWriteOptions.HIVE_TABLE_OPT_KEY -> "work.hudi_test",
DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY -> "creation_date",
DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY -> classOf[MultiPartKeysValueExtractor].getName)
// Upsert Data
// Create a new DataFrame from the first row of inputDF with a different creation_date value
val updateDF = inputDF.limit(1).withColumn("creation_date", lit("2014-01-01"))
updateDF.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.overwrite).saveAsTable("work.hudi_test")
while writing this write statement i m getting below error message.
java.lang.NoClassDefFoundError: org/apache/spark/sql/execution/datasources/v2/FileDataSourceV2
can somone Kindly Guide me how should I write this statement.

Answer 1

Here is a working sample for your question in pyspark:
from pyspark.sql import SparkSession
from pyspark.sql.functions import lit
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-spark-bundle_2.12:0.7.0,org.apache.spark:spark-avro_2.12:3.0.2"
)
.getOrCreate()
)
input_df = spark.createDataFrame(
[
("100", "2015-01-01", "2015-01-01T13:51:39.340396Z"),
("101", "2015-01-01", "2015-01-01T12:14:58.597216Z"),
("102", "2015-01-01", "2015-01-01T13:51:40.417052Z"),
("103", "2015-01-01", "2015-01-01T13:51:40.519832Z"),
("104", "2015-01-02", "2015-01-01T12:15:00.512679Z"),
("105", "2015-01-02", "2015-01-01T13:51:42.248818Z"),
],
("id", "creation_date", "last_update_time"),
)
hudi_options = {
# ---------------DATA SOURCE WRITE CONFIGS---------------#
"hoodie.table.name": "hudi_test",
"hoodie.datasource.write.recordkey.field": "id",
"hoodie.datasource.write.precombine.field": "last_update_time",
"hoodie.datasource.write.partitionpath.field": "creation_date",
"hoodie.datasource.write.hive_style_partitioning": "true",
"hoodie.upsert.shuffle.parallelism": 1,
"hoodie.insert.shuffle.parallelism": 1,
"hoodie.consistency.check.enabled": True,
"hoodie.index.type": "BLOOM",
"hoodie.index.bloom.num_entries": 60000,
"hoodie.index.bloom.fpp": 0.000000001,
"hoodie.cleaner.commits.retained": 2,
}
# INSERT
(
input_df.write.format("org.apache.hudi")
.options(**hudi_options)
.mode("append")
.save("/tmp/hudi_test")
)
# UPDATE
update_df = input_df.limit(1).withColumn("creation_date", lit("2014-01-01"))
(
update_df.write.format("org.apache.hudi")
.options(**hudi_options)
.mode("append")
.save("/tmp/hudi_test")
)
# REAL UPDATE
update_df = input_df.limit(1).withColumn("last_update_time", lit("2016-01-01T13:51:39.340396Z"))
(
update_df.write.format("org.apache.hudi")
.options(**hudi_options)
.mode("append")
.save("/tmp/hudi_test")
)
output_df = spark.read.format("org.apache.hudi").load(
"/tmp/hudi_test/*/*"
)
output_df.show()
Output:
Hudi table in Filesystem looks as follows:
Note:
Your update operation actually creates a new partition and it does an insert, since you are modifying the partition column (2015-01-01 -> 2014-01-01). You can see that in the output.
And I have provided an update example where it updates the last_update_time to 2016-01-01T13:51:39.340396Z which actually updates the id 100 in partition 2015-01-01 from 2015-01-01T13:51:39.340396Z to 2016-01-01T13:51:39.340396Z
More samples can be found in Hudi Quick Start Guide