upsert (merge) delta with spark structured streaming - apache-spark

Question

I need to upsert data in real time (with spark structured streaming) in python
This data is read in realtime (format csv) and then is written as a delta table (here we want to update the data that's why we use merge into from delta)
I am using delta engine with databricks
I coded this:
from delta.tables import *
spark = SparkSession.builder \
.config("spark.sql.streaming.schemaInference", "true")\
.appName("SparkTest") \
.getOrCreate()
sourcedf= spark.readStream.format("csv") \
.option("header", True) \
.load("/mnt/user/raw/test_input") #csv data that we read in real time
spark.conf.set("spark.sql.shuffle.partitions", "1")
spark.createDataFrame([], sourcedf.schema) \
.write.format("delta") \
.mode("overwrite") \
.saveAsTable("deltaTable")
def upsertToDelta(microBatchOutputDF, batchId):
microBatchOutputDF.createOrReplaceTempView("updates")
microBatchOutputDF._jdf.sparkSession().sql("""
MERGE INTO deltaTable t
USING updates s
ON s.Id = t.Id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
""")
sourcedf.writeStream \
.format("delta") \
.foreachBatch(upsertToDelta) \
.outputMode("update") \
.option("checkpointLocation", "/mnt/user/raw/checkpoints/output")\
.option("path", "/mnt/user/raw/PARQUET/output") \
.start() \
.awaitTermination()
but nothing gets written as expected in the output path , the checkpoint path gets filled in as expected , a display in the delta table gives me results too
display(table("deltaTable"))
in the spark UI I see the writestream step :
sourcedf.writeStream \ .format("delta") \ ....
first at Snapshot.scala:156+details
RDD: Delta Table State #1 - dbfs:/user/hive/warehouse/deltatable/_delta_log
any idea how to fix this so I can upsert csv data into delta tables in S3 in real time with spark
Best regards

Answer 1

Apologies for a late reply, but just in case anyone else has the same problem. I have found the below worked for me, I wonder is it because you didn't use "cloudFiles" on your readstream to make use of autoloader?:
%python
sourcedf= spark.readStream.format("cloudFiles") \
.option("cloudFiles.format", "csv") \
.option("cloudFiles.includeExistingFiles","true") \
.schema(csvSchema) \
.load("/mnt/user/raw/test_input")
%sql
CREATE TABLE IF NOT EXISTS deltaTable(
col1 int NOT NULL,
col2 string NOT NULL,
col3 bigint,
col4 int
)
USING DELTA
LOCATION '/mnt/user/raw/PARQUET/output'
%python
def upsertToDelta(microBatchOutputDF, batchId):
microBatchOutputDF.createOrReplaceTempView("updates")
microBatchOutputDF._jdf.sparkSession().sql("""
MERGE INTO deltaTable t
USING updates s
ON s.Id = t.Id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
""")
%python
sourcedf.writeStream \
.format("delta") \
.foreachBatch(upsertToDelta) \
.outputMode("update") \
.option("checkpointLocation", "/mnt/user/raw/checkpoints/output") \
.start("/mnt/user/raw/PARQUET/output")