Hi
I’m using struct streaming(spark 2.2) to receive Kafka msg ,it works great.
The thing is I need to join the Kafka message with a relative static table
stored in mysql database (let’s call it metadata here).
So is it possible to reload the metadata table after some time interval(like
daily ) without restart running struct streaming?
Snippet code as following :
// df_meta contains important information to join with the dataframe read from
kafka
val df_meta = spark.read.format("jdbc").option("url",
mysql_url).option("dbtable", "v_entity_ap_rel").load()
df_meta.cache()
val df = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers",
“x.x.x.x:9092").option("fetch.message.max.bytes",
"50000000").option("kafka.max.partition.fetch.bytes", "50000000")
.option("subscribe", "rawdb.raw_data")
.option("failOnDataLoss", true)
.option("startingOffsets", "latest")
.load()
.select($"value".as[Array[Byte]])
.map(avroDeserialize(_))
.as[ApRawData].select("APMAC", "RSSI", "sourceMacAddress", "updatingTime")
.join(df_meta.as("b"), $"a.apmac" === $"b.apmac”)
df.selectExpr("ENTITYID", "CLIENTMAC", "STIME", "case when a.rrssi>=b.rssi then
'1' when a.rrssi < b.nearbyrssi then '3' else '2' end FLAG",
"substring(stime,1,13) STIME_HOUR")
.distinct().writeStream.format("parquet").partitionBy("STIME_HOUR")
.option("checkpointLocation",
"/user/root/t_cf_table_chpt").trigger(ProcessingTime("5 minutes"))
.start("T_CF_TABLE")
.awaitTermination()
Mason
