nbthread_spark 0.0.5

Spark Structured Streaming multithread in IPython Notebooks

Spark multithread in IPython Notebooks.

It’s now simple to execute Spark Structured Streaming in Jupyter Notebooks

Install

pip install nbthread_spark --process-dependency-links

Usage

Given a Socket Stream:

TCP_IP = "localhost"
TCP_PORT = 9005

from pyspark.sql.functions import from_json
from pyspark.sql import SparkSession
from pyspark.sql.types import StructField, StructType, IntegerType

schema = StructType([
    StructField("bip", IntegerType(), True),
    StructField("is_on", IntegerType(), True)
])

spark = SparkSession \
    .builder \
    .appName("IOTStreamApp") \
    .getOrCreate()

iot_stream = spark \
    .readStream \
    .format("socket") \
    .option("host", TCP_IP) \
    .option("port", TCP_PORT) \
    .load()

iot_expanded = iot_stream.withColumn('value_json',
                                    from_json('value', schema)
                                    ).drop('value').select('value_json.*')

query = iot_expanded \
    .writeStream \
    .outputMode("update") \
    .format("memory") \
    .queryName("iot_table") \
    .start()

You can run queries using this:

from nbthread_spark.stream import StreamRunner

runner = StreamRunner(query)

runner.controls()
## you will see buttons ;)

runner.start() # start without controls

runner.status() # show stream status

runner.stop() # stop streaming and thread

For Stream Manager you can control lot of streams in a easy way:

from nbthread_spark.manager import StreamManager

sm = StreamManager()

sm.append(runner)
sm.append(runner1)
sm.append(runner2)

sm.all_controls()
## you will see all buttons from streams ;)

sm.start_all() # start all streams

sm.stop_all() # stop all streams

Special Thanks

Here the list of students that contribute with this module.