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Sink is not written into delta table in Spark structured streaming

New Contributor

I want to create a streaming job, that reads messages from a folder within TXT files, does the parsing, some processing, and appends the result into one of 3 possible delta tables depending on the parse result. There is a parse_failed table, an unknwon_msgs table, and a parsed_msgs table.

Reading is done with


sdf = spark.readStream.text(path=path_input, lineSep="\n\n", pathGlobFilter="*.txt", recursiveFileLookup=True)


and writing with


x = sdf.writeStream.foreachBatch(process_microbatch).start()


where process_microbatch is


def process_microbatch(self, batch_df: DataFrame, batch_id: int) -> None:
    """Processing of newly arrived messages. For each message replicate it if needed, and execute the parse_msg_proxy on each."""
    batch_df.rdd.flatMap(lambda msg: replicate_msg(msg)).map(lambda msg: parse_msg_proxy(msg))


and where parse_msg_proxy is


def parse_msg_proxy(self, msg: str) -> None:
            parsed_msg = parse_message(msg, element_mapping)
            # do some processing
            # create df_msg dataframe from parsed_msg
        except ParseException as e:
            spark.createDataFrame([{'msg': parsed_msg, 'error': str(e)}]).write.format("delta").mode("append").save(path_parse_errors)
            raise Exception("Parse error occured.")
        except UnknownMsgTypeException:
            spark.createDataFrame([{'msg': parsed_msg}]).write.format("delta").mode("append").save(path_unknown_msgs)


The streaming job starts without error message, but the delta tables are not created. Whats wrong? Maybe an action operations are missing, like collect()? Where should I put it?


Esteemed Contributor
Esteemed Contributor

There doesn't seem to any issue with code. But log needs to be analysed to get a clue of what is the issue. Could you please create a support ticket.

Community Manager
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