Beginning Apache Spark 3 Pdf Online

df = spark.read.parquet("sales.parquet") df.filter("amount > 1000").groupBy("region").count().show() You can register DataFrames as temporary views and run SQL:

Run with:

query.awaitTermination() Structured Streaming uses checkpointing and write‑ahead logs to guarantee end‑to‑end exactly‑once processing. 6.4 Event Time and Watermarks Handle late data efficiently: beginning apache spark 3 pdf

df.createOrReplaceTempView("sales") result = spark.sql("SELECT region, COUNT(*) FROM sales WHERE amount > 1000 GROUP BY region") This makes Spark accessible to analysts familiar with SQL. 4.1 Reading and Writing Data Supported formats: Parquet, ORC, Avro, JSON, CSV, text, JDBC, and more. df = spark

from pyspark.sql import SparkSession spark = SparkSession.builder .appName("MyApp") .config("spark.sql.adaptive.enabled", "true") .getOrCreate() 3.1 RDD – The Original Foundation RDDs (Resilient Distributed Datasets) are low‑level, immutable, partitioned collections. They provide fault tolerance via lineage. However, they are not recommended for new projects because they lack optimization. from pyspark

squared_udf = udf(squared, IntegerType()) df.withColumn("squared_val", squared_udf(df.value))

from pyspark.sql.functions import udf def squared(x): return x * x