Initial Commit

11-StreamTableJoinDemo/StreamTableJoinDemo.py

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+from pyspark.sql import SparkSession
+from pyspark.sql.functions import from_json, col, to_timestamp
+from pyspark.sql.types import StructType, StructField, StringType
+
+from lib.logger import Log4j
+
+
+def write_to_cassandra(target_df, batch_id):
+    target_df.write \
+        .format("org.apache.spark.sql.cassandra") \
+        .option("keyspace", "spark_db") \
+        .option("table", "users") \
+        .mode("append") \
+        .save()
+    target_df.show()
+
+
+if __name__ == "__main__":
+    spark = SparkSession \
+        .builder \
+        .master("local[3]") \
+        .appName("Stream Table Join Demo") \
+        .config("spark.streaming.stopGracefullyOnShutdown", "true") \
+        .config("spark.sql.shuffle.partitions", 2) \
+        .config("spark.cassandra.connection.host", "localhost") \
+        .config("spark.cassandra.connection.port", "9042") \
+        .config("spark.sql.extensions", "com.datastax.spark.connector.CassandraSparkExtensions") \
+        .config("spark.sql.catalog.lh", "com.datastax.spark.connector.datasource.CassandraCatalog") \
+        .getOrCreate()
+
+    logger = Log4j(spark)
+
+    login_schema = StructType([
+        StructField("created_time", StringType()),
+        StructField("login_id", StringType())
+    ])
+
+    kafka_source_df = spark \
+        .readStream \
+        .format("kafka") \
+        .option("kafka.bootstrap.servers", "localhost:9092") \
+        .option("subscribe", "logins") \
+        .option("startingOffsets", "earliest") \
+        .load()
+
+    value_df = kafka_source_df.select(from_json(col("value").cast("string"), login_schema).alias("value"))
+
+    login_df = value_df.select("value.*") \
+        .withColumn("created_time", to_timestamp(col("created_time"), "yyyy-MM-dd HH:mm:ss"))
+
+    user_df = spark.read \
+        .format("org.apache.spark.sql.cassandra") \
+        .option("keyspace", "spark_db") \
+        .option("table", "users") \
+        .load()
+
+    join_expr = login_df.login_id == user_df.login_id
+    join_type = "inner"
+
+    joined_df = login_df.join(user_df, join_expr, join_type) \
+        .drop(login_df.login_id)
+
+    output_df = joined_df.select(col("login_id"), col("user_name"),
+                                 col("created_time").alias("last_login"))
+
+    output_query = output_df.writeStream \
+        .foreachBatch(write_to_cassandra) \
+        .outputMode("update") \
+        .option("checkpointLocation", "chk-point-dir") \
+        .trigger(processingTime="1 minute") \
+        .start()
+
+    logger.info("Waiting for Query")
+    output_query.awaitTermination()

11-StreamTableJoinDemo/kafka-scripts/01-start-zookeeper.cmd

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+%KAFKA_HOME%\bin\windows\zookeeper-server-start.bat %KAFKA_HOME%\config\zookeeper.properties

11-StreamTableJoinDemo/kafka-scripts/02-start-kafka.cmd

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+%KAFKA_HOME%\bin\windows\kafka-server-start.bat %KAFKA_HOME%\config\server-0.properties

11-StreamTableJoinDemo/kafka-scripts/03-create-input-topic.cmd

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+%KAFKA_HOME%\bin\windows\kafka-topics.bat --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic logins

11-StreamTableJoinDemo/kafka-scripts/05-start-producer.cmd

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+%KAFKA_HOME%\bin\windows\kafka-console-producer.bat --broker-list localhost:9092 --topic logins

11-StreamTableJoinDemo/lib/logger.py

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+class Log4j:
+    def __init__(self, spark):
+        log4j = spark._jvm.org.apache.log4j
+
+        root_class = "guru.learningjournal.spark.examples"
+        conf = spark.sparkContext.getConf()
+        app_name = conf.get("spark.app.name")
+
+        self.logger = log4j.LogManager.getLogger(root_class + "." + app_name)
+
+    def warn(self, message):
+        self.logger.warn(message)
+
+    def info(self, message):
+        self.logger.info(message)
+
+    def error(self, message):
+        self.logger.error(message)
+
+    def debug(self, message):
+        self.logger.debug(message)

11-StreamTableJoinDemo/log4j.properties

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+# Set everything to be logged to the console
+log4j.rootCategory=WARN, console
+
+# define console appender
+log4j.appender.console=org.apache.log4j.ConsoleAppender
+log4j.appender.console.target=System.out
+log4j.appender.console.layout=org.apache.log4j.PatternLayout
+log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+
+#application log
+log4j.logger.guru.learningjournal.spark.examples=INFO, console
+log4j.additivity.guru.learningjournal.spark.examples=false
+
+#define following in Java System
+# -Dlog4j.configuration=file:log4j.properties
+
+# Recommendations from Spark template
+log4j.logger.org.apache.spark.repl.Main=WARN
+log4j.logger.org.spark_project.jetty=WARN
+log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
+log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
+log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+log4j.logger.org.apache.parquet=ERROR
+log4j.logger.parquet=ERROR
+log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
+log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR
+

11-StreamTableJoinDemo/static-data/user.cql

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+CREATE KEYSPACE spark_db WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1};
+USE spark_db;
+CREATE TABLE users(Login_id text PRIMARY KEY, user_name text, last_login timestamp);
+
+INSERT INTO users (Login_id, user_name, last_login) VALUES( '100001', 'Prashant', '2019-02-05 10:05:00');
+INSERT INTO users (Login_id, user_name, last_login) VALUES( '100009', 'Alisha', '2019-03-07 11:03:00');
+INSERT INTO users (Login_id, user_name, last_login) VALUES( '100087', 'Abdul', '2019-06-12 09:43:00');
+
+INSERT INTO users (Login_id, user_name, last_login) VALUES( '100091', 'New User', '2019-06-12 09:43:00');

11-StreamTableJoinDemo/streaming-data/logins.txt

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+{"login_id": "100001", "created_time": "2020-09-09 10:18:00"}
+{"login_id": "100009", "created_time": "2020-09-18 07:15:00"}
+{"login_id": "100087", "created_time": "2020-09-18 07:15:00"}
+{"login_id": "100091", "created_time": "2020-09-18 07:15:00"}