Hi,
Are you using structured streaming, which is the spark version and Kafka
version, and where are you fetching the data from?
Semantically speaking if your data in Kafka represents an action to be
performed then it should be actually a queue like rabbitmq or SQS. If it is
simply data then it should be Kafka.
That once again begs the question, what is the data you are fetching, is it
just another field from a table to join or an explicit fetch operation
using Https or SQL call?
Regards
Gourav
On Tue, 2 Mar 2021, 12:57 Sachit Murarka, <connectsac...@gmail.com> wrote:
> Hi Mich,
>
> Thanks for reply. Will checkout this.
>
> Kind Regards,
> Sachit Murarka
>
>
> On Fri, Feb 26, 2021 at 2:14 AM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Hi Sachit,
>>
>> I managed to make mine work using the *foreachBatch function *in
>> writeStream.
>>
>> "foreach" performs custom write logic on each row and "foreachBatch"
>> performs custom write logic on each micro-batch through SendToBigQuery
>> function here
>> foreachBatch(SendToBigQuery) expects 2 parameters, first: micro-batch as
>> DataFrame or Dataset and second: unique id for each batch
>> Using foreachBatch, we write each micro batch to storage defined in our
>> custom logic. In this case, we store the output of our streaming
>> application to Google BigQuery table.
>> Note that we are appending data and column "rowkey" is defined as UUID
>> so it can be used as the primary key. batchId is just the counter
>> (monolithically increasing number).
>>
>> This is my code:
>>
>>
>> from __future__ import print_function
>> from config import config
>> import sys
>> from sparkutils import sparkstuff as s
>> from pyspark.sql import *
>> from pyspark.sql.functions import *
>> from pyspark.sql.types import StructType, StringType,IntegerType,
>> FloatType, TimestampType
>> from google.cloud import bigquery
>>
>>
>> def SendToBigQuery(df, batchId):
>>
>> """
>> Below uses standard Spark-BigQuery API to write to the table
>> Additional transformation logic will be performed here
>> """
>> s.writeTableToBQ(df, "append",
>> config['MDVariables']['targetDataset'],config['MDVariables']['targetTable'])
>>
>> class MDStreaming:
>> def __init__(self, spark_session,spark_context):
>> self.spark = spark_session
>> self.sc = spark_context
>> self.config = config
>>
>> def fetch_data(self):
>> self.sc.setLogLevel("ERROR")
>>
>> #{"rowkey":"c9289c6e-77f5-4a65-9dfb-d6b675d67cff","ticker":"MSFT",
>> "timeissued":"2021-02-23T08:42:23", "price":31.12}
>> schema = StructType().add("rowkey", StringType()).add("ticker",
>> StringType()).add("timeissued", TimestampType()).add("price", FloatType())
>> 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", "latest") \
>> .load() \
>> .select(from_json(col("value").cast("string"),
>> schema).alias("parsed_value"))
>>
>> #streamingDataFrame.printSchema()
>>
>> """
>> "foreach" performs custom write logic on each row and
>> "foreachBatch" performs custom write logic on each micro-batch through
>> SendToBigQuery function
>> foreachBatch(SendToBigQuery) expects 2 parameters, first:
>> micro-batch as DataFrame or Dataset and second: unique id for each batch
>> Using foreachBatch, we write each micro batch to storage
>> defined in our custom logic. In this case, we store the output of our
>> streaming application to Google BigQuery table.
>> Note that we are appending data and column "rowkey" is
>> defined as UUID so it can be used as the primary key
>> """
>> result = streamingDataFrame.select( \
>> col("parsed_value.rowkey").alias("rowkey") \
>> , col("parsed_value.ticker").alias("ticker") \
>> , col("parsed_value.timeissued").alias("timeissued") \
>> , col("parsed_value.price").alias("price")). \
>> withColumn("currency",
>> lit(config['MDVariables']['currency'])). \
>> withColumn("op_type",
>> lit(config['MDVariables']['op_type'])). \
>> withColumn("op_time", current_timestamp()). \
>> writeStream. \
>> * foreachBatch(SendToBigQuery). \*
>> outputMode("update"). \
>> start()
>> except Exception as e:
>> print(f"""{e}, quitting""")
>> sys.exit(1)
>>
>>
>> result.awaitTermination()
>>
>> if __name__ == "__main__":
>> appName = config['common']['appName']
>> spark_session = s.spark_session(appName)
>> spark_session = s.setSparkConfBQ(spark_session)
>> spark_context = s.sparkcontext()
>> mdstreaming = MDStreaming(spark_session, spark_context)
>> streamingDataFrame = mdstreaming.fetch_data()
>>
>>
>> My batch interval is 2 seconds and in this case I am sending 10 rows for
>> each ticker (security).
>>
>> HTH
>>
>>
>>
>> LinkedIn *
>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>
>>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
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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
>>>
>>
