Upsert data in postgresql using spark structured streaming - apache-spark

Question

I am trying to run a structured streaming application using (py)spark. My data is read from a Kafka topic and then I am running windowed aggregation on event time.
# I have been able to create data frame pn_data_df after reading data from Kafka
Schema of pn_data_df
|
- id StringType
- source StringType
- source_id StringType
- delivered_time TimeStamp
windowed_report_df = pn_data_df.filter(pn_data_df.source == 'campaign') \
.withWatermark("delivered_time", "24 hours") \
.groupBy('source_id', window('delivered_time', '15 minute')) \
.count()
windowed_report_df = windowed_report_df \
.withColumn('start_ts', unix_timestamp(windowed_report_df.window.start)) \
.withColumn('end_ts', unix_timestamp(windowed_report_df.window.end)) \
.selectExpr('CAST(source_id as LONG)', 'start_ts', 'end_ts', 'count')
I am writing this windowed aggregation to my postgresql database which I have already created.
CREATE TABLE pn_delivery_report(
source_id bigint not null,
start_ts bigint not null,
end_ts bigint not null,
count integer not null,
unique(source_id, start_ts)
);
Writing to postgresql using spark jdbc allows me to either Append or Overwrite. Append mode fails if there is an existing composite key existing in the database, and Overwrite just overwrites entire table with current batch output.
def write_pn_report_to_postgres(df, epoch_id):
df.write \
.mode('append') \
.format('jdbc') \
.option("url", "jdbc:postgresql://db_endpoint/db") \
.option("driver", "org.postgresql.Driver") \
.option("dbtable", "pn_delivery_report") \
.option("user", "postgres") \
.option("password", "PASSWORD") \
.save()
windowed_report_df.writeStream \
.foreachBatch(write_pn_report_to_postgres) \
.option("checkpointLocation", '/home/hadoop/campaign_report_df_windowed_checkpoint') \
.outputMode('update') \
.start()
How can I execute a query like
INSERT INTO pn_delivery_report (source_id, start_ts, end_ts, COUNT)
VALUES (1001, 125000000001, 125000050000, 128),
(1002, 125000000001, 125000050000, 127) ON conflict (source_id, start_ts) DO
UPDATE
SET COUNT = excluded.count;
in foreachBatch.
Spark has a jira feature ticket open for it, but it seems that it has not been prioritised till now.
https://issues.apache.org/jira/browse/SPARK-19335

Answer 1

that's worked for me:
def _write_streaming(self,
df,
epoch_id
) -> None:
df.write \
.mode('append') \
.format("jdbc") \
.option("url", f"jdbc:postgresql://localhost:5432/postgres") \
.option("driver", "org.postgresql.Driver") \
.option("dbtable", 'table_test') \
.option("user", 'user') \
.option("password", 'password') \
.save()
df_stream.writeStream \
.foreachBatch(_write_streaming) \
.start() \
.awaitTermination()
You need to add ".awaitTermination()" at the end.