PySpark Structured Streaming ROS Kafka ApacheSpark Cassandra

PySpark-Structured-Streaming-ROS-Kafka-ApacheSpark-Cassandra

The purpose of this project is to demonstrate a structured streaming pipeline with Apache Spark. The process consists of given steps:

Installation Process
Prepare a robotic simulation environment to generate data to feed into the Kafka.
Prepare Kafka and Zookeeper environment to store discrete data.
Prepare Cassandra environment to store analyzed data.
Prepare Apache Spark structured streaming pipeline, integrate with Kafka and Cassandra.
Result

0. Installation Processes

You are able to install all required components to realize this project using the given steps.

Installation of ROS and Turtlebot3

We won't address the whole installation process of ROS and Turtlebot3 but you can access all required info from ROS & Turtlebot3 Installation.

After all installations are completed, you can demo our robotic environment using the given commands:

roslaunch turtlebot3_gazebo turtlebot3_world.launch

You should see a view like the one given below.

Installation of Kafka and Zookeeper

We won't address the whole installation process of Kafka and Zookeeper but you can access all required info from Kafka & Zookeeper Installation.

After all installations are completed, you can demo Kafka using the given commands:

# Change your path to Kafka folder and then run 
bin/zookeeper-server-start.sh config/zookeeper.properties

# Open second terminal and then run
bin/kafka-server-start.sh config/server.properties

# Create Kafka "demo" topic
bin/kafka-topics.sh --create --topic demo --partitions 1 --replication-factor 1 -bootstrap-server localhost:9092

Once you create "demo" topic, you can run kafka-demo/producer.py and kafka-demo/consumer.py respectively to check your setup.

❗ If you haven't installed kafka-python, use the given command and then run given files.

pip install kafka-python

producer.py

import time,json,random
from datetime import datetime
from data_generator import generate_message
from kafka import KafkaProducer

def serializer(message):
    return json.dumps(message).encode("utf-8")
    
producer = KafkaProducer(
    bootstrap_servers=["localhost:9092"],
    value_serializer=serializer
)

if __name__=="__main__":
    while True:
        dummy_messages=generate_message()
        print(f"Producing message {datetime.now()} | Message = {str(dummy_messages)}")
        producer.send("demo",dummy_messages)
        time.sleep(2)

consumer.py

import json
from kafka import KafkaConsumer

if __name__=="__main__":
    consumer=KafkaConsumer(
        "demo",
        bootstrap_servers="localhost:9092",
        auto_offset_reset="latest"    )

    for msg in consumer:
        print(json.loads(msg.value))

You should see a view like the one given below after run the commands:

python3 producer.py
python3 consumer.py

Installation of Cassandra

We won't address the whole installation process of Cassandra but you can access all required info from Cassandra Installation.

After all installations are completed, you can demo Cassandra using cqlsh. You can check this link.

Installation of Apache Spark

We won't address the whole installation process of Apache Spark but you can access all required info from Apache Spark Installation.

After all installations are completed, you can make a quick example like here.

1. Prepare a robotic simulation environment

ROS (Robot Operating System) allows us to design a robotic environment. We will use Turtlebot3, a robot in Gazebo simulation env, to generate data for our use case. Turtlebot3 publishes its data with ROS topics. Therefore, we will subscribe the topic and send data into Kafka.

Run the simulation environment and analysis the data we will use

Turtlebot3 publishes its odometry data with ROS "odom" topic. So, we can see the published data with the given command:

# run the simulation environment
roslaunch turtlebot3_gazebo turtlebot3_world.launch

# check the topic to see data
rostopic echo /odom

You should see a view like the one given below.

header: 
  seq: 10954
  stamp: 
    secs: 365
    nsecs: 483000000
  frame_id: "odom"
child_frame_id: "base_footprint"
pose: 
  pose: 
    position: 
      x: -2.000055643960576
      y: -0.4997879642933192
      z: -0.0010013932644100873
    orientation: 
      x: -1.3486164084605e-05
      y: 0.0038530870521455017
      z: 0.0016676819550213058
      w: 0.9999911861487526
  covariance: [1e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-05, 0.0, 0.0, 0.0, 0.0, 0.0,...
twist: 
  twist: 
    linear: 
      x: 5.8050405333644035e-08
      y: 7.749200305343809e-07
      z: 0.0
    angular: 
      x: 0.0
      y: 0.0
      z: 1.15143519181447e-05
  covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...

In this use case, we will just interest the given part of the data:

    position: 
      x: -2.000055643960576
      y: -0.4997879642933192
      z: -0.0010013932644100873
    orientation: 
      x: -1.3486164084605e-05
      y: 0.0038530870521455017
      z: 0.0016676819550213058
      w: 0.9999911861487526

2. Prepare Kafka and Zookeeper environment

The data produced by Turtlebot3 will stored into Kafka clusters.

Prepare Kafka for Use Case

First of all, we will create a new Kafka topic namely odometry for ROS odom data using the given commands:

# Change your path to Kafka folder and then run 
bin/zookeeper-server-start.sh config/zookeeper.properties

# Open second terminal and then run
bin/kafka-server-start.sh config/server.properties

# Create Kafka "odometry" topic for ROS odom data
bin/kafka-topics.sh --create --topic odometry --partitions 1 --replication-factor 1 -bootstrap-server localhost:9092

Then we will write a ROS subscriber to listen to the data from Turtlebot3. Also, since we need to send data to Kafka, it is necessary to add a producer script in it. We will use ros/publish2kafka.py to do it. This script subscribes to the odom topic and sends the content of the topic to Kafka.

import rospy
from nav_msgs.msg import Odometry
import json
from datetime import datetime
from kafka import KafkaProducer

count = 0
def callback(msg):
    global count
    messages={
        "id":count,
        "posex":float("{0:.5f}".format(msg.pose.pose.position.x)),
        "posey":float("{0:.5f}".format(msg.pose.pose.position.y)),
        "posez":float("{0:.5f}".format(msg.pose.pose.position.z)),
        "orientx":float("{0:.5f}".format(msg.pose.pose.orientation.x)),
        "orienty":float("{0:.5f}".format(msg.pose.pose.orientation.y)),
        "orientz":float("{0:.5f}".format(msg.pose.pose.orientation.z)),
        "orientw":float("{0:.5f}".format(msg.pose.pose.orientation.w))
        }

    print(f"Producing message {datetime.now()} Message :\n {str(messages)}")
    producer.send("odometry",messages)
    count+=1

producer = KafkaProducer(
    bootstrap_servers=["localhost:9092"],
    value_serializer=lambda message: json.dumps(message).encode('utf-8')
)

if __name__=="__main__":

    rospy.init_node('odomSubscriber', anonymous=True)
    rospy.Subscriber('odom',Odometry,callback)
    rospy.spin()

You can use ros/readFromKafka.py to check the data is really reach Kafka while ROS and publish2kafka.py is running.

import json
from kafka import KafkaConsumer

if __name__=="__main__":

    consumer=KafkaConsumer(
        "odometry",
        bootstrap_servers="localhost:9092",
        auto_offset_reset="earliest"
    )

    for msg in consumer:
        print(json.loads(msg.value))

3. Prepare Cassandra environment

Prepare Cassandra for Use Case

Initially, we will create a keyspace and then a topic in it using given command:

# Open the cqlsh and then run the command to create 'ros' keyspace
cqlsh> CREATE KEYSPACE ros WITH replication = {'class':'SimpleStrategy', 'replication_factor' : 1};

# Then, run the command to create 'odometry' topic in 'ros'
cqlsh> create table ros.odometry(
        id int primary key, 
        posex float,
        posey float,
        posez float,
        orientx float,
        orienty float,
        orientz float,
        orientw float);

# Check your setup is correct
cqlsh> DESCRIBE ros

#and
cqlsh> DESCRIBE ros.odometry

⚠️ The content of topic has to be the same as Spark schema: Be very careful here!

4. Prepare Apache Spark structured streaming pipeline

You are able to write analysis results to either console or Cassandra.

(First Way) Prepare Apache Spark Structured Streaming Pipeline Kafka to Cassandra

We will write streaming script that read odometry topic from Kafka, analyze it and then write results to Cassandra. We will use spark-demo/streamingKafka2Cassandra.py to do it.

First of all, we create a schema same as we already defined in Cassandra.

⚠️ The content of schema has to be the same as Casssandra table: Be very careful here!

odometrySchema = StructType([
                StructField("id",IntegerType(),False),
                StructField("posex",FloatType(),False),
                StructField("posey",FloatType(),False),
                StructField("posez",FloatType(),False),
                StructField("orientx",FloatType(),False),
                StructField("orienty",FloatType(),False),
                StructField("orientz",FloatType(),False),
                StructField("orientw",FloatType(),False)
            ])

Then, we create a Spark Session using two packages:

for spark kafka connector : org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0
for spark cassandra connector : com.datastax.spark:spark-cassandra-connector_2.12:3.0.0

spark = SparkSession \
    .builder \
    .appName("SparkStructuredStreaming") \
    .config("spark.jars.packages","org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0,com.datastax.spark:spark-cassandra-connector_2.12:3.0.0") \
    .getOrCreate()

⚠️ If you use spark-submit you can specify the packages as:

spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.0,com.datastax.spark:spark-cassandra-connector_2.12:3.0.0 spark_cassandra.py

In order to read Kafka stream, we use readStream() and specify Kafka configurations as the given below:

df = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "odometry") \
  .option("delimeter",",") \
  .option("startingOffsets", "latest") \
  .load()

Since Kafka send data as binary, first we need to convert the binary value to String using selectExpr() as the given below:

df1 = df.selectExpr("CAST(value AS STRING)").select(from_json(col("value"),odometrySchema).alias("data")).select("data.*")
df1.printSchema()

Although Apache Spark isn't capable of directly write stream data to Cassandra yet (using writeStream()), we can do it with use foreachBatch() as the given below:

def writeToCassandra(writeDF, _):
  writeDF.write \
    .format("org.apache.spark.sql.cassandra")\
    .mode('append')\
    .options(table="odometry", keyspace="ros")\
    .save()

df1.writeStream \
    .option("spark.cassandra.connection.host","localhost:9042")\
    .foreachBatch(writeToCassandra) \
    .outputMode("update") \
    .start()\
    .awaitTermination()

Finally, we got the given script spark-demo/streamingKafka2Cassandra.py:

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

odometrySchema = StructType([
                StructField("id",IntegerType(),False),
                StructField("posex",FloatType(),False),
                StructField("posey",FloatType(),False),
                StructField("posez",FloatType(),False),
                StructField("orientx",FloatType(),False),
                StructField("orienty",FloatType(),False),
                StructField("orientz",FloatType(),False),
                StructField("orientw",FloatType(),False)
            ])

spark = SparkSession \
    .builder \
    .appName("SparkStructuredStreaming") \
    .config("spark.jars.packages","org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0,com.datastax.spark:spark-cassandra-connector_2.12:3.0.0") \
    .getOrCreate()

spark.sparkContext.setLogLevel("ERROR")


df = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "odometry") \
  .option("delimeter",",") \
  .option("startingOffsets", "latest") \
  .load() 

df.printSchema()

df1 = df.selectExpr("CAST(value AS STRING)").select(from_json(col("value"),odometrySchema).alias("data")).select("data.*")
df1.printSchema()

# It is possible to analysis data here using df1


def writeToCassandra(writeDF, _):
  writeDF.write \
    .format("org.apache.spark.sql.cassandra")\
    .mode('append')\
    .options(table="odometry", keyspace="ros")\
    .save()

df1.writeStream \
    .option("spark.cassandra.connection.host","localhost:9042")\
    .foreachBatch(writeToCassandra) \
    .outputMode("update") \
    .start()\
    .awaitTermination()

(Second Way) Prepare Apache Spark Structured Streaming Pipeline Kafka to Console

There are a few differences between writing to the console and writing to Cassandra. First of all, we don't need to use cassandra connector, so we remove it from packages.

spark = SparkSession \
    .builder \
    .appName("SSKafka") \
    .config("spark.jars.packages","org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0") \
    .getOrCreate()

With writeStream() we can write stream data directly to the console.

df1.writeStream \
  .outputMode("update") \
  .format("console") \
  .option("truncate", False) \
  .start() \
  .awaitTermination()

The rest of the process takes place in the same way as the previous one. Finally, we got the given script spark-demo/streamingKafka2Console.py:

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType,StructField,LongType,IntegerType,FloatType,StringType
from pyspark.sql.functions import split,from_json,col

odometrySchema = StructType([
                StructField("id",IntegerType(),False),
                StructField("posex",FloatType(),False),
                StructField("posey",FloatType(),False),
                StructField("posez",FloatType(),False),
                StructField("orientx",FloatType(),False),
                StructField("orienty",FloatType(),False),
                StructField("orientz",FloatType(),False),
                StructField("orientw",FloatType(),False)
            ])

spark = SparkSession \
    .builder \
    .appName("SSKafka") \
    .config("spark.jars.packages","org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0") \
    .getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

df = spark \
  .readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "odometry") \
  .option("delimeter",",") \
  .option("startingOffsets", "latest") \
  .load() 

df1 = df.selectExpr("CAST(value AS STRING)").select(from_json(col("value"),odometrySchema).alias("data")).select("data.*")
df1.printSchema()

df1.writeStream \
  .outputMode("update") \
  .format("console") \
  .option("truncate", False) \
  .start() \
  .awaitTermination()

5. Result

After all the process is done, we got the data in our Cassandra table as the given below:

You can query the given command to see your table:

# Open the cqlsh 
cqlsh
# Then write select query to see content of the table
cqlsh> select * from ros.odometry

PySpark Structured Streaming ROS Kafka ApacheSpark Cassandra

Related tags

Overview

PySpark-Structured-Streaming-ROS-Kafka-ApacheSpark-Cassandra

0. Installation Processes

Installation of ROS and Turtlebot3

Installation of Kafka and Zookeeper

Installation of Cassandra

Installation of Apache Spark

1. Prepare a robotic simulation environment

Run the simulation environment and analysis the data we will use

2. Prepare Kafka and Zookeeper environment

Prepare Kafka for Use Case

3. Prepare Cassandra environment

Prepare Cassandra for Use Case

4. Prepare Apache Spark structured streaming pipeline

(First Way) Prepare Apache Spark Structured Streaming Pipeline Kafka to Cassandra

(Second Way) Prepare Apache Spark Structured Streaming Pipeline Kafka to Console

5. Result

Owner

Zekeriyya Demirci

Python library for creating data pipelines with chain functional programming

DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis.

This tool parses log data and allows to define analysis pipelines for anomaly detection.

OpenDrift is a software for modeling the trajectories and fate of objects or substances drifting in the ocean, or even in the atmosphere.

CSV database for chihuahua (HUAHUA) blockchain transactions

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

Fit models to your data in Python with Sherpa.

NumPy aware dynamic Python compiler using LLVM

This creates a ohlc timeseries from downloaded CSV files from NSE India website and makes a SQLite database for your research.

Creating a statistical model to predict 10 year treasury yields

General Assembly's 2015 Data Science course in Washington, DC

Orchest is a browser based IDE for Data Science.

A neural-based binary analysis tool

MidTerm Project for the Data Analysis FT Bootcamp, Adam Tycner and Florent ZAHOUI

COVID-19 deaths statistics around the world

Project under the certification "Data Analysis with Python" on FreeCodeCamp

BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Desafio proposto pela IGTI em seu bootcamp de Cloud Data Engineer

Python package to transfer data in a fast, reliable, and packetized form.

Performance analysis of predictive (alpha) stock factors