Thanks Jungtaek.
I am stuck on how to add rows to BigQuery. Spark API in PySpark does it
fine. However, we are talking about structured streaming with PySpark.
This is my code that reads and display data on the console fine
class MDStreaming:
def __init__(self, spark_session,spark_context):
self.spark = spark_session
self.sc = spark_context
self.config = config
def startStreaming(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", "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)
def processData(self, streamingDataFrame):
result = streamingDataFrame. \
writeStream. \
* foreach(ForeachWriter()).* \
start()
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.startStreaming()
mdstreaming.processData(streamingDataFrame)
That class ForeachWriter() is supposed to add data (batchsize 10 rows) to
the GCP BigQuery table. My code is as follows: However, it does not seem to
invoke methods in this class. Every 2 seconds a batch of 10 rows passed to
this class. Specifically in the method process(self,row) what
is rows_to_insert = [... signify?
class ForeachWriter:
'''
Class to send a set of rows to BigQuery.
When used with `foreach`, copies of this class is going to be used to
write
multiple rows in the executor. See the python docs for
`DataStreamWriter.foreach`
for more details.
'''
def open(self):
# This is called first when preparing to send multiple rows.
# Put all the initialization code inside open() so that a fresh
# copy of this class is initialized in the executor where open()
# will be called.
self.config = config
self.table_id =
self.config['MDVariables']['fullyQualifiedoutputTableId']
self.client =
bigquery.Client(self.config['MDVariables']['projectId'])
self.table_ref =
client.dataset(self.config['MDVariables']['targetDataset']).table(self.table_id)
return True
def process(self,row):
# This is called for each row after open() has been called.
# This implementation sends one row at a time.
# A more efficient implementation can be to send batches of rows at
a time.
rows_to_insert = [
{u"full_name": u"Phred Phlyntstone", u"age": 32},
{u"full_name": u"Wylma Phlyntstone", u"age": 29},
]
errors = client.insert_rows_json(self.table_id,
self.config['MDVariables']['rows_to_insert'], row_ids=[None] *
len(self.config['MDVariables']['rows_to_insert'])
) # Make an API request.
if errors == []:
print("New rows have been added.")
else:
print("Encountered errors while inserting rows:
{}".format(errors))
def close(self, err):
# This is called after all the rows have been processed.
if err:
raise err
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Tue, 23 Feb 2021 at 22:48, Jungtaek Lim <kabhwan.opensou...@gmail.com>
wrote:
> If your code doesn't require "end to end exactly-once" then you could
> leverage foreachBatch which enables you to use batch sink.
>
> If your code requires "end to end exactly-once", then well, that's the
> different story. I'm not familiar with BigQuery and even have no idea how
> sink is implemented, but from quick googling tells me a transaction with
> multiple DML isn't supported, so end to end exactly-once cannot be
> implemented in any way.
>
> If you ensure the write in the query is idempotent then no matter at all.
>
> On Tue, Feb 23, 2021 at 10:35 PM Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> With the ols spark streaming (example in Scala), this would have been
>> easier through RDD. You could read data
>>
>> val dstream = KafkaUtils.createDirectStream[String, String,
>> StringDecoder, StringDecoder](streamingContext, kafkaParams, topicsValue)
>>
>> dstream.foreachRDD
>>
>> { pricesRDD =>
>>
>> if (!pricesRDD.isEmpty) // data exists in RDD
>>
>> {
>>
>> write to DB
>> }
>>
>>
>> Now with structured streaming in Python, you read data into a dataframe
>> with reaSstream and load
>>
>>
>> schema = StructType().add("rowkey", StringType()).add("ticker",
>> StringType()).add("timeissued", TimestampType()).add("price", FloatType())
>>
>> ds = self.spark \
>>
>> .readStream \
>>
>> .format("kafka") \
>>
>> .......
>>
>> .load() \
>>
>> .select(from_json(col("value").cast("string"),
>> schema).alias("parsed_value"))
>>
>> ds2 = ds \
>>
>> .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())
>>
>> # write to console
>>
>> query = ds2. \
>> writeStream. \
>> outputMode("append"). \
>> format("console"). \
>> start()
>> ds2.printSchema()
>>
>>
>> But writing to BigQuery through BigQuery API does not work
>>
>>
>> s.writeTableToBQ(ds2, "overwrite",
>> config['MDVariables']['targetDataset'],config['MDVariables']['targetTable'])
>>
>>
>> query.awaitTermination()
>>
>>
>> So this is the run result and the error
>>
>>
>> root
>>
>> |-- rowkey: string (nullable = true)
>>
>> |-- ticker: string (nullable = true)
>>
>> |-- timeissued: timestamp (nullable = true)
>>
>> |-- price: float (nullable = true)
>>
>> |-- currency: string (nullable = false)
>>
>> |-- op_type: string (nullable = false)
>>
>> |-- op_time: timestamp (nullable = false)
>>
>>
>> *'write' can not be called on streaming Dataset/DataFrame;, quitting*
>>
>> I gather need to create RDD from the dataframe or maybe there is another
>> way to write streaming data to DB directly from the dataframe?
>>
>> Thanks
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>
