Read Existing Data#

Use Curator’s JsonlReader and ParquetReader to read existing datasets into a pipeline, then optionally add processing stages.

JSONL Reader

Example: Read JSONL and Filter

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.modules import ScoreFilter
from nemo_curator.stages.text.filters import WordCountFilter

# Create pipeline for processing existing JSONL files
pipeline = Pipeline(name="jsonl_data_processing")

# Read JSONL files
reader = JsonlReader(
    file_paths="/path/to/data/*.jsonl",
    files_per_partition=4,
    fields=["text", "url"]  # Only read specific columns
)
pipeline.add_stage(reader)

# Add filtering stage
word_filter = ScoreFilter(
    filter_obj=WordCountFilter(min_words=50, max_words=1000),
    text_field="text"
)
pipeline.add_stage(word_filter)

# Execute pipeline
results = pipeline.run()

Parquet Reader

Example: Read Parquet and Filter

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import ParquetReader
from nemo_curator.stages.text.modules import ScoreFilter
from nemo_curator.stages.text.filters import WordCountFilter

# Create pipeline for processing existing Parquet files
pipeline = Pipeline(name="parquet_data_processing")

# Read Parquet files with PyArrow engine
reader = ParquetReader(
    file_paths="/path/to/data",
    files_per_partition=4,
    fields=["text", "metadata"]  # Only read specific columns
)
pipeline.add_stage(reader)

# Add filtering stage
word_filter = ScoreFilter(
    filter_obj=WordCountFilter(min_words=50, max_words=1000),
    text_field="text"
)
pipeline.add_stage(word_filter)

# Execute pipeline
results = pipeline.run()

Reader Configuration#

Common Parameters#

Both JsonlReader and ParquetReader support these configuration options:

Parameter	Type	Description	Default
`file_paths`	str \| list[str]	File paths or glob patterns to read	Required
`files_per_partition`	int \| None	Number of files per partition	None
`blocksize`	int \| str \| None	Target partition size (e.g., “128MB”)	None
`fields`	list[str] \| None	Column names to read (column selection)	None (all columns)
`read_kwargs`	dict[str, Any] \| None	Extra arguments for the underlying reader	None

Parquet-Specific Features#

ParquetReader provides these optimizations:

PyArrow Engine: Uses pyarrow engine by default for better performance
Storage Options: Supports cloud storage via storage_options in read_kwargs
Schema Handling: Automatic schema inference and validation
Columnar Efficiency: Optimized for reading specific columns

Performance Tips#

Use fields parameter to read required columns for better performance
Set files_per_partition based on your cluster size and memory constraints
For cloud storage, configure storage_options in read_kwargs
Use blocksize for fine-grained control over partition sizes

Output Integration#

Both readers produce DocumentBatch tasks that integrate seamlessly with:

Processing Stages: Apply filters, transformations, and quality checks
Writer Stages: Export to JSONL, Parquet, or other formats
Analysis Tools: Convert to Pandas/PyArrow for inspection and debugging