sparklyr批量导入多文件咨询：非原生方法是否符合Spark规范？

Hey there! Since you're new to sparklyr and Spark, let me break down the most efficient, Spark-native way to handle your batch import task—no need to mess with external file lists or loop through individual files, which would be far less efficient for your large dataset.

The Short Version: Let Spark Do the Heavy Lifting #

Spark (and sparklyr) is built to handle directory-wide file reads out of the box. Instead of reading single files one by one, you can point spark_read_csv directly at your folder x/, and it will automatically load all matching files into a single Spark DataFrame. Here's the code:

# Load all |-delimited .txt files in folder 'x' into a single Spark DataFrame
mydata <- spark_read_csv(
  sc = sc,
  name = "mydata",
  path = "x/",  # Trailing slash tells Spark to read the entire directory
  delimiter = "|",
  header = FALSE,
  infer_schema = TRUE  # Optional: auto-detect column types; replace with a predefined schema if needed
)

Why This Is Better Than Your Original Plan #

• No R memory bloat: If you loop through a file list outside sparklyr, you'd have to load each 300MB file into R's memory first before pushing to Spark—this is a huge waste of resources and could crash your R session. The directory read skips this entirely, letting Spark handle file loading across its cluster nodes.
• Built-in merging: Spark automatically concatenates all files in the directory into one DataFrame, so you don't have to manually merge them later.
• Distributed efficiency: Spark splits the files across its workers, making the import process much faster than sequential single-file reads.

Pro Tips for Your Use Case #

• Target only .txt files: If there are other file types in x/, use a wildcard to narrow it down: path = "x/*.txt"
• Enforce a schema: For large batches of files, it's safer to define a schema upfront instead of relying on infer_schema (which can cause mismatches if files have slightly different structures). Example:

  # Define a schema (adjust columns/types to match your data)
  my_schema <- struct_type(
    struct_field("col1", "integer"),
    struct_field("col2", "string"),
    struct_field("col3", "double")
  )
  
  # Use the predefined schema
  mydata <- spark_read_csv(
    sc = sc,
    name = "mydata",
    path = "x/*.txt",
    delimiter = "|",
    header = FALSE,
    schema = my_schema
  )
  

• Check partitions: Verify how Spark split your data with sdf_num_partitions(mydata)—you can adjust with sdf_repartition(mydata, n = 100) if needed for better performance.

内容的提问来源于stack exchange，提问作者shitoushan