If you are receiving data from Kafka, Wouldn't that be better in Json
format?
. try:
# construct a streaming dataframe streamingDataFrame that
subscribes to topic config['MDVariables']['topic']) -> md (market data)
streamingDataFrame = self.spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers",
config['MDVariables']['bootstrapServers'],) \
.option("schema.registry.url",
config['MDVariables']['schemaRegistryURL']) \
.option("group.id", config['common']['appName']) \
.option("zookeeper.connection.timeout.ms",
config['MDVariables']['zookeeperConnectionTimeoutMs']) \
.option("rebalance.backoff.ms",
config['MDVariables']['rebalanceBackoffMS']) \
.option("zookeeper.session.timeout.ms",
config['MDVariables']['zookeeperSessionTimeOutMs']) \
.option("auto.commit.interval.ms",
config['MDVariables']['autoCommitIntervalMS']) \
.option("subscribe", config['MDVariables']['topic']) \
.option("failOnDataLoss", "false") \
.option("includeHeaders", "true") \
.option("startingOffsets", "earliest") \
.load() \
.select(from_json(col("value").cast("string"),
schema).alias("parsed_value"))
return streamingDataFrame
except Exception as e:
print(f"""{e}, quitting""")
sys.exit(1)
and pass a class to the writer
result = streamingDataFrame. \
writeStream. \
foreach(*ForeachWriter()*). \
start()
You don't want to use a row by row (cursor) approach as it would leave a
lot of messages un processed (as you correctly stated it runs on a single
JVM).
I am doing the same trying to process and write messages to BigQuery.
HTH
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On Thu, 25 Feb 2021 at 06:27, Sachit Murarka <connectsac...@gmail.com>
wrote:
> Hello Users,
>
> I am using Spark 3.0.1 Structuring streaming with Pyspark.
>
> My use case::
> I get so many records in kafka(essentially some metadata with the location
> of actual data). I have to take that metadata from kafka and apply some
> processing.
> Processing includes : Reading the actual data location from metadata and
> fetching the actual data and applying some operation on actual data.
>
> What I have tried::
>
> def process_events(event):
> fetch_actual_data()
> #many more steps
>
> def fetch_actual_data():
> #applying operation on actual data
>
> df = spark.readStream.format("kafka") \
> .option("kafka.bootstrap.servers", KAFKA_URL) \
> .option("subscribe", KAFKA_TOPICS) \
> .option("startingOffsets",
> START_OFFSET).load() .selectExpr("CAST(value AS STRING)")
>
>
> query =
> df.writeStream.foreach(process_events).option("checkpointLocation",
> "/opt/checkpoint").trigger(processingTime="30 seconds").start()
>
>
> My Queries:
>
> 1. Will this foreach run across different executor processes? Generally in
> spark , foreach means it runs on a single executor.
>
> 2. I receive too many records in kafka and above code will run multiple
> times for each single message. If I change it for foreachbatch, will it
> optimize it?
>
>
> Kind Regards,
> Sachit Murarka
>
