Data Ingestion with CSV in Apache Spark: A Quick Guide

Data Ingestion with CSV in Apache Spark: A Quick Guide

Data ingestion is a crucial step in any data processing pipeline. Apache Spark simplifies loading and processing CSV files by providing a powerful DataFrame API to read, transform, and write tabular data. This guide walks through ingesting circuits and races CSV files from a Formula 1 dataset.

1. Data Ingestion Overview

Ingesting data means loading data from sources into a Spark DataFrame for analysis. CSV (Comma Separated Values) is a common tabular format. Spark’s DataFrameReader and DataFrame APIs make it straightforward to load, manipulate, and persist CSV data.

2. Circuits File - Requirements

• Contains circuit metadata such as circuit id, name, location, country, etc.
• Load the CSV into a DataFrame with an appropriate schema.
• Select only needed columns, rename for clarity, and add calculated columns if required.

3. Circuits File - DataFrame Reader

Use Spark’s DataFrameReader to load the CSV. A simple example:

• df_circuits = spark.read.csv("/path/to/circuits.csv", header=True, inferSchema=True)

• Optionally use format("csv") with option(...) to control header, delimiter, and encoding.

4. Circuits File - Specify Schema

For performance and type safety, explicitly define a schema rather than relying on schema inference:

• Define a StructType with StructField entries (e.g., IntegerType for IDs, StringType for names).

• Then read with spark.read.schema(schema).csv(path) to ensure correct types and avoid an extra pass for inference.

5. Circuits File - Select Columns

After loading, select only the columns you need for downstream processing to reduce memory and I/O:

• df_circuits_selected = df_circuits.select("circuitId", "name", "location")

6. Circuits File - WithColumnRenamed

Rename columns to improve readability or meet business naming conventions:

• df_circuits_renamed = df_circuits.withColumnRenamed("name", "circuitName")

7. Circuits File - WithColumn

Create or modify columns using expressions or functions:

• df_circuits_with_new_col = df_circuits.withColumn("newColumn", df_circuits["someCol"] * 2)

• Use Spark SQL functions (from pyspark.sql.functions) for date parsing, concatenation, and other transformations.

8. Circuits File - DataFrame Writer

Write the processed DataFrame back to storage in the desired format and layout:

• df_circuits_selected.write.csv("/path/to/output/circuits_processed.csv")

• Set write options like mode, compression, and partitioning as needed.

9. Races File - Requirements

The Races file typically contains race-specific data such as race id, date, circuit id, year, and results. Similar ingestion steps apply: load, cast types, filter, aggregate, and persist.

10. Races File - Spark Program (Assignment)

• Read the Races CSV with an explicit schema or inferSchema=True when appropriate.

• Perform transformations such as filtering by year, joining with circuits on circuitId, and computing aggregates (e.g., race counts per year).

• Example filter: df_races.filter(df_races.year == 2023)

11. Races File - Partitioning

For large outputs, partition by a high-cardinality, commonly-filtered column (like year) to speed future queries:

• df_races.write.partitionBy("year").csv("/path/to/output/races_partitioned")

• Partitioning organizes output into subfolders and reduces the data scanned for queries that filter on the partition column.

Conclusion

Using Spark’s DataFrame API to ingest CSVs is straightforward: prefer explicit schemas for large datasets, select and rename only needed columns, use withColumn for derived fields, and write outputs with sensible partitioning and options. These practices help keep circuits and races data efficient, readable, and ready for analysis.