val spark = SparkSession.builder() .appName("DeptSalary") .getOrCreate()
## 6. Quick Reference Cheatsheet (Spark 2.4)
## 5. Tips for Maximising Marks
words = lines.flatMap(lambda line: line.split()) # optional cleaning cleaned = words.map(lambda w: w.lower().strip('.,!?"\'')) distinct_words = cleaned.distinct() count = distinct_words.count()
**Solution (PySpark):**
1. **Ingestion** – `spark.read.json` or `textFile`. 2. **Parsing** – `withColumn` + `from_unixtime`, `regexp_extract`. 3. **Cleaning** – filter out malformed rows, `na.drop`. 4. **Enrichment** – join with a static lookup table (broadcast). 5. **Aggregation** – `groupBy(date, status).agg(count("*").as("cnt"))`. 6. **Output** – write to Parquet partitioned by `date` **or** stream to console for debugging.
| Operation | PySpark | Scala | |-----------|---------|-------| | **Read CSV** | `spark.read.option("header","true").csv(path)` | `spark.read.option("header","true").csv(path)` | | **Write Parquet** | `df.write.parquet("out.parquet")` | `df.write.parquet("out.parquet")` | | **Cache** | `df.cache()` | `df.cache()` | | **Repartition** | `df.repartition(10)` | `df.repartition(10)` | | **Window** | `from pyspark.sql.window import Window` | `import org.apache.spark.sql.expressions.Window` | | **UDF** | `spark.udf.register("toUpper", lambda s: s.upper(), StringType())` | `udf((s: String) => s.toUpperCase, StringType)` | | **Streaming read** | `spark.readStream.format("socket")...` | `spark.readStream.format("socket")...` | | **Stop Spark** | `spark.stop()` | `spark.stop()` | spark 2 workbook answers
val result = df .groupBy($"department") .agg(count("*").as("emp_cnt"), avg($"salary").as("avg_salary")) .filter($"emp_cnt" > 5)