python实现spark开发,使用Python生成Spark结构化流
I am trying to Spark structured streaming with Kafka and Python.
Requirement: I need to process streaming data from Kafka (in JSON format) in Spark (perform transformations) and then store it in a database.
I have data in JSON format like,
{"a": 120.56, "b": 143.6865998138807, "name": "niks", "time": "2012-12-01 00:00:09"}
I am planning to use spark.readStream for reading from Kafka like,
data = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "localhost:9092").option("subscribe","test").load()
I referred this link for reference but didn't get how to parse JSON data. I tried this,
data = data.selectExpr("CAST(a AS FLOAT)","CAST(b as FLOAT)", "CAST(name as STRING)", "CAST(time as STRING)").as[(Float, Float, String, String)]
but looks like it doesn't work.
Can anyone who has worked on Spark structured streaming with Python guide me to proceed with sample examples or links?
Using,
schema = StructType([
StructField("a", DoubleType()),
StructField("b", DoubleType()),
StructField("name", StringType()),
StructField("time", TimestampType())])
inData = spark.readStream.format("kafka").option("kafka.bootstrap.servers", "localhost:9092").option("subscribe","test").load()
data = inData.select(from_json(col("value").cast("string"), schema))
query = data.writeStream.outputMode("Append").format("console").start()
Program runs but I am getting values on console as,
+-----------------------------------+
|jsontostruct(CAST(value AS STRING))|
+-----------------------------------+
| [null,null,null,2...|
| [null,null,null,2...|
+-----------------------------------+
17/04/07 19:23:15 INFO StreamExecution: Streaming query made progress: {
"id" : "8e2355cb-0fd3-4233-89d8-34a855256b1e",
"runId" : "9fc462e0-385a-4b05-97ed-8093dc6ef37b",
"name" : null,
"timestamp" : "2017-04-07T19:23:15.013Z",
"numInputRows" : 2,
"inputRowsPerSecond" : 125.0,
"processedRowsPerSecond" : 12.269938650306749,
"durationMs" : {
"addBatch" : 112,
"getBatch" : 8,
"getOffset" : 2,
"queryPlanning" : 4,
"triggerExecution" : 163,
"walCommit" : 26
},
"eventTime" : {
"watermark" : "1970-01-01T00:00:00.000Z"
},
"stateOperators" : [ ],
"sources" : [ {
"description" : "KafkaSource[Subscribe[test]]",
"startOffset" : {
"test" : {
"0" : 366
}
},
"endOffset" : {
"test" : {
"0" : 368
}
},
"numInputRows" : 2,
"inputRowsPerSecond" : 125.0,
"processedRowsPerSecond" : 12.269938650306749
} ],
"sink" : {
"description" : "org.apache.spark.sql.execution.streaming.ConsoleSink@6aa91aa2"
}
}
Did I miss something here.
解决方案
You can either use from_json with schema:
from pyspark.sql.functions import col, from_json
from pyspark.sql.types import *
schema = StructType([
StructField("a", DoubleType()),
StructField("b", DoubleType()),
StructField("name", StringType()),
StructField("time", TimestampType())])
data.select(from_json(col("value").cast("string"), schema))
or get individual fields as strings with get_json_object:
from pyspark.sql.functions import get_json_object
data.select([
get_json_object(col("value").cast("string"), "$.{}".format(c)).alias(c)
for c in ["a", "b", "name", "time"]])
and cast them later according to your needs.