Fact   #chatgpt #deepseek #llm  

Recently ran into a task that needs to ingest multiple gzipped CSV files from multiple folders using spark.read.csv command, in another word, the task needs to specify multiple paths each with wildcards to spark.read.csv command. The problem solving process involves using Google/StackOverflow first, then #LLM such as Baidu, #Deepseek and #ChatGPT, while trying various LLMs, query prompt was refined, the final result is much better understanding of what spark.read.csv command is capable of than simply using Google/StackOverflow alone.

Below is my journey of solving the problem

Google / StackOverflow

Googled spark read csv files from multiple folders, found StackOverflow post, it gave me impression that I can use comma concatenated multiple paths containing wildcards such as spark.read.csv('hdfs://path/to/*.gz, filename1,hdfs://path/to/*.gz') may work, which I tried but got error. However single path containing wildcard such as `spark.read.csv('hdfs://path/to/*.gz') did work.

Baidu

Next tried Baidu using query for spark.read.csv, can I enter multiple paths with asterisks, got answer

在 Apache Spark 中，使用 spark.read.csv 方法时，如果你想一次性读取多个路径中的文件，可以使用 spark.read.csv 方法并结合 glob 模式来指定多个路径。但是，需要注意的是，spark.read.csv 本身并不直接支持使用通配符（如 *）来指定多个文件路径。相反，你可以使用 spark.read.text 或 spark.read.option("header", "true").csv 方法，并结合 glob 模式来实现这一点。

使用 spark.read.text 方法

如果你只是想读取文本文件，可以使用 spark.read.text 方法，然后使用 glob 模式来匹配多个路径。例如：

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()

使用 glob 模式读取多个路径

df = spark.read.text("path/to/folder/*") 使用 spark.read.csv 方法与 glob

如果你想处理 CSV 文件，可以使用 spark.read.option("header", "true").csv 方法，并结合 glob 模式：

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()

使用 glob 模式读取多个 CSV 文件路径

df = spark.read.option("header", "true").csv("path/to/folder/*") 使用 glob 模式读取多个路径

如果你需要更灵活地处理多个路径（例如，在不同的文件夹中），可以先使用 Python 的 glob 模块来获取所有文件的路径列表，然后一次性读取这些文件：

import glob from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()

获取所有 CSV 文件的路径

paths = glob.glob("path/to/folder//.csv")

一次性读取所有文件

df = spark.read.option("header", "true").csv(paths) 注意事项

确保你的路径模式正确无误，特别是在使用通配符时。例如，如果你想读取一个文件夹及其子文件夹中的所有 CSV 文件，你可以使用 "path/to/folder/*/.csv"。

使用 glob 模式可以灵活地处理复杂的文件路径结构。

在处理大量文件时，考虑到性能和资源使用情况，确保你的 Spark 集群配置得当。

通过上述方法，你可以有效地使用 Apache Spark 来读取多个路径中的 CSV 文件。

its answer confirmed my understanding that spark.read.csv cannot take multiple paths each containing wildcard. Although this answer turned out to be not accurate later on, this effort did help me to refine query prompt to for spark.read.csv, can I enter multiple paths with wildcard.

DeepSeek

Next tried Deepseek using prompt for spark.read.csv, can I enter multiple paths with wildcard, got answer

Yes, you can use wildcards and specify multiple paths when reading CSV files in Apache Spark using spark.read.csv. This allows you to read multiple files that match a specific pattern or are located in different directories.

Example: Using Wildcards

If you have multiple CSV files in a directory and want to read all of them, you can use a wildcard (*) in the path:

python df = spark.read.csv("path/to/files/*.csv")

This will read all CSV files in the path/to/files/ directory.

Example: Specifying Multiple Paths

You can also specify multiple paths explicitly by providing a comma-separated list:

python df = spark.read.csv("path/to/file1.csv,path/to/file2.csv,path/to/file3.csv")

Combining Wildcards and Multiple Paths

You can combine wildcards and multiple paths to read files from different directories or patterns:

python df = spark.read.csv("path/to/files1/*.csv,path/to/files2/*.csv")

This will read all CSV files in both path/to/files1/ and path/to/files2/.

Additional Options

You can also specify additional options, such as header, inferSchema, and sep, to customize how the CSV files are read:

python df = spark.read.csv("path/to/files/*.csv", header=True, inferSchema=True)

• header=True: Treats the first row as column names.
• inferSchema=True: Automatically infers the data types of the columns.

Notes

• Ensure that the paths and wildcards are correctly specified to match the files you want to read.
• If the files have different schemas, you may need to handle schema merging or specify a unified schema manually.

This flexibility makes spark.read.csv a powerful tool for reading large datasets distributed across multiple files.

ChatGPT

Shot prompt for spark.read.csv, can I enter multiple paths with wildcards? first, got answer

Yes, you can provide multiple paths with wildcards when using spark.read.csv() in PySpark.

Example:

python df = spark.read.csv("path/to/data/*.csv") This will read all CSV files in the specified directory.

You can also specify multiple paths explicitly: python df = spark.read.csv(["path/to/data/file1.csv", "path/to/data/file2.csv"])

Or use multiple wildcards: python df = spark.read.csv("path/to/data/*/2024-*.csv") This will match all CSV files in subdirectories that start with "2024-".

Let me know if you need further clarification! ?

Updated prompt to for spark.read.csv, can I enter multiple paths, each with one or more wildcards?, got answer

Yes, you can specify multiple paths, each containing wildcards, when using spark.read.csv() in PySpark.

Example:

python df = spark.read.csv(["path/to/data/*.csv", "another/path/**/*.csv"]) - The first path (path/to/data/*.csv) will match all CSV files in path/to/data/. - The second path (another/path/**/*.csv) will match all CSV files in any subdirectory of another/path/ (recursive matching with **).

Alternative Approach:

If you're dealing with many paths that follow a pattern, you can dynamically generate them: python paths = [f"path/to/data/{year}-*.csv" for year in range(2020, 2025)] df = spark.read.csv(paths) This reads files like path/to/data/2020-*.csv, path/to/data/2021-*.csv, etc.

Important Notes:

• Ensure that your paths are correctly formatted and exist.
• The wildcard expansion is handled by Spark internally, not by the shell.

Would you like help with performance optimization or filtering specific files? ?