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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2022/09/15 19:43:56 UTC
[GitHub] [hudi] nsivabalan commented on a diff in pull request #6672: [HUDI-4757] Create pyspark examples
nsivabalan commented on code in PR #6672:
URL: https://github.com/apache/hudi/pull/6672#discussion_r972359680
##########
hudi-spark-datasource/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java:
##########
@@ -190,6 +192,30 @@ public List<HoodieRecord> generateUpdates(Integer n) {
}).collect(Collectors.toList());
}
+ /**
+ * Generates new updates, one for each of the keys above
+ * list
+ *
+ * @param n Number of updates (must be no more than number of existing keys)
+ * @return list of hoodie record updates
+ */
+ public List<HoodieRecord> generateUniqueUpdatesForExample(Integer n) {
Review Comment:
what "example" matters? why can't we make it generic to be used even in our tests?
##########
hudi-spark-datasource/hudi-spark/src/main/java/org/apache/hudi/QuickstartUtils.java:
##########
@@ -190,6 +192,30 @@ public List<HoodieRecord> generateUpdates(Integer n) {
}).collect(Collectors.toList());
}
+ /**
+ * Generates new updates, one for each of the keys above
+ * list
+ *
+ * @param n Number of updates (must be no more than number of existing keys)
+ * @return list of hoodie record updates
+ */
+ public List<HoodieRecord> generateUniqueUpdatesForExample(Integer n) {
Review Comment:
oh, this datagenerator is used just for QuickStartUtils. so, you can just name this "generateUniqueUpdates". would suffice.
##########
hudi-examples/hudi-examples-spark/src/test/python/HoodiePySparkQuickstart.py:
##########
@@ -0,0 +1,260 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+import os
+from pyspark import sql
+import random
+from pyspark.sql.functions import lit
+from functools import reduce
+import tempfile
+
+
+
+class ExamplePySpark:
+ def __init__(self, spark: sql.SparkSession, tableName: str, basePath: str):
+ self.spark = spark
+ self.tableName = tableName
+ self.basePath = basePath + "/" + tableName
+ self.hudi_options = {
+ 'hoodie.table.name': tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': tableName,
+ 'hoodie.datasource.write.operation': 'upsert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ self.dataGen = spark._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()
+ self.snapshotQuery = "SELECT begin_lat, begin_lon, driver, end_lat, end_lon, fare, partitionpath, rider, ts, uuid FROM hudi_trips_snapshot"
+ return
+
+ def runQuickstart(self):
+
+ def snap():
+ return self.spark.sql(self.snapshotQuery)
+ insertDf = self.insertData()
+ self.queryData()
+ assert len(insertDf.exceptAll(snap()).collect()) == 0
+
+ snapshotBeforeUpdate = snap()
+ updateDf = self.updateData()
+ self.queryData()
+ assert len(snap().intersect(updateDf).collect()) == len(updateDf.collect())
+ assert len(snap().exceptAll(updateDf).exceptAll(snapshotBeforeUpdate).collect()) == 0
+
+
+ self.timeTravelQuery()
+ self.incrementalQuery()
+ self.pointInTimeQuery()
+
+ self.softDeletes()
+ self.queryData()
+
+ snapshotBeforeDelete = snap()
+ deletesDf = self.hardDeletes()
+ self.queryData()
+ assert len(snap().select(["uuid", "partitionpath", "ts"]).intersect(deletesDf).collect()) == 0
+ assert len(snapshotBeforeDelete.exceptAll(snap()).exceptAll(snapshotBeforeDelete).collect()) == 0
+
+ snapshotBeforeInsertOverwrite = snap()
+ insertOverwriteDf = self.insertOverwrite()
+ self.queryData()
+ withoutSanFran = snapshotBeforeInsertOverwrite.filter("partitionpath != 'americas/united_states/san_francisco'")
+ expectedDf = withoutSanFran.union(insertOverwriteDf)
+ assert len(snap().exceptAll(expectedDf).collect()) == 0
+ return
+
+ def insertData(self):
+ print("Insert Data")
+ inserts = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateInserts(10))
+ df = self.spark.read.json(self.spark.sparkContext.parallelize(inserts, 2))
+ df.write.format("hudi").options(**self.hudi_options).mode("overwrite").save(self.basePath)
+ return df
+
+ def updateData(self):
+ print("Update Data")
+ updates = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateUniqueUpdatesForExample(5))
+ df = self.spark.read.json(spark.sparkContext.parallelize(updates, 2))
+ df.write.format("hudi").options(**self.hudi_options).mode("append").save(self.basePath)
+ return df
+
+ def queryData(self):
+ print("Query Data")
+ tripsSnapshotDF = self.spark.read.format("hudi").load(self.basePath)
+ tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
+ self.spark.sql("SELECT fare, begin_lon, begin_lat, ts FROM hudi_trips_snapshot WHERE fare > 20.0").show()
+ self.spark.sql("SELECT _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare FROM hudi_trips_snapshot").show()
+ return
+
+ def timeTravelQuery(self):
+ query = "SELECT begin_lat, begin_lon, driver, end_lat, end_lon, fare, partitionpath, rider, ts, uuid FROM time_travel_query"
+ print("Time Travel Query")
+ self.spark.read.format("hudi").option("as.of.instant", "20210728141108").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ self.spark.read.format("hudi").option("as.of.instant", "2021-07-28 14:11:08.000").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ self.spark.read.format("hudi").option("as.of.instant", "2021-07-28").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ return
+
+ def incrementalQuery(self):
+ print("Incremental Query")
+ self.spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+ self.commits = list(map(lambda row: row[0], self.spark.sql("SELECT DISTINCT(_hoodie_commit_time) AS commitTime FROM hudi_trips_snapshot ORDER BY commitTime").limit(50).collect()))
+ beginTime = self.commits[len(self.commits) - 2]
+ incremental_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.begin.instanttime': beginTime,
+ }
+ tripsIncrementalDF = self.spark.read.format("hudi").options(**incremental_read_options).load(self.basePath)
+ tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")
+ self.spark.sql("SELECT `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts FROM hudi_trips_incremental WHERE fare > 20.0").show()
+
+ def pointInTimeQuery(self):
+ print("Point-in-time Query")
+ beginTime = "000"
+ endTime = self.commits[len(self.commits) - 2]
+ point_in_time_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.end.instanttime': endTime,
+ 'hoodie.datasource.read.begin.instanttime': beginTime
+ }
+
+ tripsPointInTimeDF = self.spark.read.format("hudi").options(**point_in_time_read_options).load(self.basePath)
+ tripsPointInTimeDF.createOrReplaceTempView("hudi_trips_point_in_time")
+ self.spark.sql("SELECT `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts FROM hudi_trips_point_in_time WHERE fare > 20.0").show()
+
+ def softDeletes(self):
+ print("Soft Deletes")
+ spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+
+ # fetch total records count
+ trip_count = spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ non_null_rider_count = spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot WHERE rider IS NOT null").count()
+ print(f"trip count: {trip_count}, non null rider count: {non_null_rider_count}")
+ # fetch two records for soft deletes
+ soft_delete_ds = spark.sql("SELECT * FROM hudi_trips_snapshot").limit(2)
+ # prepare the soft deletes by ensuring the appropriate fields are nullified
+ meta_columns = ["_hoodie_commit_time", "_hoodie_commit_seqno", "_hoodie_record_key",
+ "_hoodie_partition_path", "_hoodie_file_name"]
+ excluded_columns = meta_columns + ["ts", "uuid", "partitionpath"]
+ nullify_columns = list(filter(lambda field: field[0] not in excluded_columns, \
+ list(map(lambda field: (field.name, field.dataType), soft_delete_ds.schema.fields))))
+
+ hudi_soft_delete_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'upsert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ soft_delete_df = reduce(lambda df,col: df.withColumn(col[0], lit(None).cast(col[1])), \
+ nullify_columns, reduce(lambda df,col: df.drop(col[0]), meta_columns, soft_delete_ds))
+
+ # simply upsert the table after setting these fields to null
+ soft_delete_df.write.format("hudi").options(**hudi_soft_delete_options).mode("append").save(self.basePath)
+
+ # reload data
+ self.spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+
+ # This should return the same total count as before
+ trip_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ # This should return (total - 2) count as two records are updated with nulls
+ non_null_rider_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot WHERE rider IS NOT null").count()
+ print(f"trip count: {trip_count}, non null rider count: {non_null_rider_count}")
+
+ def hardDeletes(self):
+ print("Hard Deletes")
+ # fetch total records count
+ total_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ print(f"total count: {total_count}")
+ # fetch two records to be deleted
+ ds = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").limit(2)
+
+ # issue deletes
+ hudi_hard_delete_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'delete',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ deletes = list(map(lambda row: (row[0], row[1]), ds.collect()))
+ hard_delete_df = self.spark.sparkContext.parallelize(deletes).toDF(['uuid', 'partitionpath']).withColumn('ts', lit(0.0))
+ hard_delete_df.write.format("hudi").options(**hudi_hard_delete_options).mode("append").save(self.basePath)
+
+ # run the same read query as above.
+ roAfterDeleteViewDF = self.spark.read.format("hudi").load(self.basePath)
+ roAfterDeleteViewDF.createOrReplaceTempView("hudi_trips_snapshot")
+ # fetch should return (total - 2) records
+ total_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ print(f"total count: {total_count}")
+ return hard_delete_df
+
+ def insertOverwrite(self):
+ print("Insert Overwrite")
+ self.spark.read.format("hudi").load(self.basePath).select(["uuid","partitionpath"]).sort(["partitionpath", "uuid"]).show(n=100,truncate=False)
+ inserts = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateInserts(10))
+ df = self.spark.read.json(self.spark.sparkContext.parallelize(inserts, 2)).filter("partitionpath = 'americas/united_states/san_francisco'")
+ hudi_insert_overwrite_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'insert_overwrite',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+ df.write.format("hudi").options(**hudi_insert_overwrite_options).mode("append").save(self.basePath)
+ self.spark.read.format("hudi").load(self.basePath).select(["uuid","partitionpath"]).sort(["partitionpath", "uuid"]).show(n=100,truncate=False)
+ return df
+
+if __name__ == "__main__":
+ random.seed(46474747)
+ if len(sys.argv) < 3:
+ print("Usage: python3 HoodiePySparkQuickstart.py <tableName> <jar file path/bundle name>")
Review Comment:
while testing you tested using local jar. but we want to test it w/ `--packages` as well. can we take an argument and add `--packages` or `--jars` based on that.
##########
hudi-examples/hudi-examples-spark/src/test/python/HoodiePySparkQuickstart.py:
##########
@@ -0,0 +1,260 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+import os
+from pyspark import sql
+import random
+from pyspark.sql.functions import lit
+from functools import reduce
+import tempfile
+
+
+
+class ExamplePySpark:
+ def __init__(self, spark: sql.SparkSession, tableName: str, basePath: str):
+ self.spark = spark
+ self.tableName = tableName
+ self.basePath = basePath + "/" + tableName
+ self.hudi_options = {
+ 'hoodie.table.name': tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': tableName,
+ 'hoodie.datasource.write.operation': 'upsert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ self.dataGen = spark._jvm.org.apache.hudi.QuickstartUtils.DataGenerator()
+ self.snapshotQuery = "SELECT begin_lat, begin_lon, driver, end_lat, end_lon, fare, partitionpath, rider, ts, uuid FROM hudi_trips_snapshot"
+ return
+
+ def runQuickstart(self):
+
+ def snap():
+ return self.spark.sql(self.snapshotQuery)
+ insertDf = self.insertData()
+ self.queryData()
+ assert len(insertDf.exceptAll(snap()).collect()) == 0
+
+ snapshotBeforeUpdate = snap()
+ updateDf = self.updateData()
+ self.queryData()
+ assert len(snap().intersect(updateDf).collect()) == len(updateDf.collect())
+ assert len(snap().exceptAll(updateDf).exceptAll(snapshotBeforeUpdate).collect()) == 0
+
+
+ self.timeTravelQuery()
+ self.incrementalQuery()
+ self.pointInTimeQuery()
+
+ self.softDeletes()
+ self.queryData()
+
+ snapshotBeforeDelete = snap()
+ deletesDf = self.hardDeletes()
+ self.queryData()
+ assert len(snap().select(["uuid", "partitionpath", "ts"]).intersect(deletesDf).collect()) == 0
+ assert len(snapshotBeforeDelete.exceptAll(snap()).exceptAll(snapshotBeforeDelete).collect()) == 0
+
+ snapshotBeforeInsertOverwrite = snap()
+ insertOverwriteDf = self.insertOverwrite()
+ self.queryData()
+ withoutSanFran = snapshotBeforeInsertOverwrite.filter("partitionpath != 'americas/united_states/san_francisco'")
+ expectedDf = withoutSanFran.union(insertOverwriteDf)
+ assert len(snap().exceptAll(expectedDf).collect()) == 0
+ return
+
+ def insertData(self):
+ print("Insert Data")
+ inserts = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateInserts(10))
+ df = self.spark.read.json(self.spark.sparkContext.parallelize(inserts, 2))
+ df.write.format("hudi").options(**self.hudi_options).mode("overwrite").save(self.basePath)
+ return df
+
+ def updateData(self):
+ print("Update Data")
+ updates = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateUniqueUpdatesForExample(5))
+ df = self.spark.read.json(spark.sparkContext.parallelize(updates, 2))
+ df.write.format("hudi").options(**self.hudi_options).mode("append").save(self.basePath)
+ return df
+
+ def queryData(self):
+ print("Query Data")
+ tripsSnapshotDF = self.spark.read.format("hudi").load(self.basePath)
+ tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
+ self.spark.sql("SELECT fare, begin_lon, begin_lat, ts FROM hudi_trips_snapshot WHERE fare > 20.0").show()
+ self.spark.sql("SELECT _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare FROM hudi_trips_snapshot").show()
+ return
+
+ def timeTravelQuery(self):
+ query = "SELECT begin_lat, begin_lon, driver, end_lat, end_lon, fare, partitionpath, rider, ts, uuid FROM time_travel_query"
+ print("Time Travel Query")
+ self.spark.read.format("hudi").option("as.of.instant", "20210728141108").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ self.spark.read.format("hudi").option("as.of.instant", "2021-07-28 14:11:08.000").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ self.spark.read.format("hudi").option("as.of.instant", "2021-07-28").load(self.basePath).createOrReplaceTempView("time_travel_query")
+ self.spark.sql(query)
+ return
+
+ def incrementalQuery(self):
+ print("Incremental Query")
+ self.spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+ self.commits = list(map(lambda row: row[0], self.spark.sql("SELECT DISTINCT(_hoodie_commit_time) AS commitTime FROM hudi_trips_snapshot ORDER BY commitTime").limit(50).collect()))
+ beginTime = self.commits[len(self.commits) - 2]
+ incremental_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.begin.instanttime': beginTime,
+ }
+ tripsIncrementalDF = self.spark.read.format("hudi").options(**incremental_read_options).load(self.basePath)
+ tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")
+ self.spark.sql("SELECT `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts FROM hudi_trips_incremental WHERE fare > 20.0").show()
+
+ def pointInTimeQuery(self):
+ print("Point-in-time Query")
+ beginTime = "000"
+ endTime = self.commits[len(self.commits) - 2]
+ point_in_time_read_options = {
+ 'hoodie.datasource.query.type': 'incremental',
+ 'hoodie.datasource.read.end.instanttime': endTime,
+ 'hoodie.datasource.read.begin.instanttime': beginTime
+ }
+
+ tripsPointInTimeDF = self.spark.read.format("hudi").options(**point_in_time_read_options).load(self.basePath)
+ tripsPointInTimeDF.createOrReplaceTempView("hudi_trips_point_in_time")
+ self.spark.sql("SELECT `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts FROM hudi_trips_point_in_time WHERE fare > 20.0").show()
+
+ def softDeletes(self):
+ print("Soft Deletes")
+ spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+
+ # fetch total records count
+ trip_count = spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ non_null_rider_count = spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot WHERE rider IS NOT null").count()
+ print(f"trip count: {trip_count}, non null rider count: {non_null_rider_count}")
+ # fetch two records for soft deletes
+ soft_delete_ds = spark.sql("SELECT * FROM hudi_trips_snapshot").limit(2)
+ # prepare the soft deletes by ensuring the appropriate fields are nullified
+ meta_columns = ["_hoodie_commit_time", "_hoodie_commit_seqno", "_hoodie_record_key",
+ "_hoodie_partition_path", "_hoodie_file_name"]
+ excluded_columns = meta_columns + ["ts", "uuid", "partitionpath"]
+ nullify_columns = list(filter(lambda field: field[0] not in excluded_columns, \
+ list(map(lambda field: (field.name, field.dataType), soft_delete_ds.schema.fields))))
+
+ hudi_soft_delete_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'upsert',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ soft_delete_df = reduce(lambda df,col: df.withColumn(col[0], lit(None).cast(col[1])), \
+ nullify_columns, reduce(lambda df,col: df.drop(col[0]), meta_columns, soft_delete_ds))
+
+ # simply upsert the table after setting these fields to null
+ soft_delete_df.write.format("hudi").options(**hudi_soft_delete_options).mode("append").save(self.basePath)
+
+ # reload data
+ self.spark.read.format("hudi").load(self.basePath).createOrReplaceTempView("hudi_trips_snapshot")
+
+ # This should return the same total count as before
+ trip_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ # This should return (total - 2) count as two records are updated with nulls
+ non_null_rider_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot WHERE rider IS NOT null").count()
+ print(f"trip count: {trip_count}, non null rider count: {non_null_rider_count}")
+
+ def hardDeletes(self):
+ print("Hard Deletes")
+ # fetch total records count
+ total_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ print(f"total count: {total_count}")
+ # fetch two records to be deleted
+ ds = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").limit(2)
+
+ # issue deletes
+ hudi_hard_delete_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'delete',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+
+ deletes = list(map(lambda row: (row[0], row[1]), ds.collect()))
+ hard_delete_df = self.spark.sparkContext.parallelize(deletes).toDF(['uuid', 'partitionpath']).withColumn('ts', lit(0.0))
+ hard_delete_df.write.format("hudi").options(**hudi_hard_delete_options).mode("append").save(self.basePath)
+
+ # run the same read query as above.
+ roAfterDeleteViewDF = self.spark.read.format("hudi").load(self.basePath)
+ roAfterDeleteViewDF.createOrReplaceTempView("hudi_trips_snapshot")
+ # fetch should return (total - 2) records
+ total_count = self.spark.sql("SELECT uuid, partitionpath FROM hudi_trips_snapshot").count()
+ print(f"total count: {total_count}")
+ return hard_delete_df
+
+ def insertOverwrite(self):
+ print("Insert Overwrite")
+ self.spark.read.format("hudi").load(self.basePath).select(["uuid","partitionpath"]).sort(["partitionpath", "uuid"]).show(n=100,truncate=False)
+ inserts = self.spark._jvm.org.apache.hudi.QuickstartUtils.convertToStringList(self.dataGen.generateInserts(10))
+ df = self.spark.read.json(self.spark.sparkContext.parallelize(inserts, 2)).filter("partitionpath = 'americas/united_states/san_francisco'")
+ hudi_insert_overwrite_options = {
+ 'hoodie.table.name': self.tableName,
+ 'hoodie.datasource.write.recordkey.field': 'uuid',
+ 'hoodie.datasource.write.partitionpath.field': 'partitionpath',
+ 'hoodie.datasource.write.table.name': self.tableName,
+ 'hoodie.datasource.write.operation': 'insert_overwrite',
+ 'hoodie.datasource.write.precombine.field': 'ts',
+ 'hoodie.upsert.shuffle.parallelism': 2,
+ 'hoodie.insert.shuffle.parallelism': 2
+ }
+ df.write.format("hudi").options(**hudi_insert_overwrite_options).mode("append").save(self.basePath)
+ self.spark.read.format("hudi").load(self.basePath).select(["uuid","partitionpath"]).sort(["partitionpath", "uuid"]).show(n=100,truncate=False)
+ return df
+
+if __name__ == "__main__":
+ random.seed(46474747)
+ if len(sys.argv) < 3:
+ print("Usage: python3 HoodiePySparkQuickstart.py <tableName> <jar file path/bundle name>")
+ quit(-1)
+ #Example jar filepath: /Users/jon/.m2/repository/org/apache/hudi/hudi-spark3.3-bundle_2.12/0.13.0-SNAPSHOT/hudi-spark3.3-bundle_2.12-0.13.0-SNAPSHOT.jar
Review Comment:
Example jar path: [HUDI_BASE_PATH]/packaging/hudi-spark-bundle/target/hudi-spark-bundle[VERSION].jar
do not add any absolute paths in general.
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