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> Identify and remove exact duplicates using MD5 hashing in a Ray-based workflow
# Exact Duplicate Removal
Remove character-for-character duplicate documents using NeMo Curator's exact duplicate removal workflow. This method computes MD5 hashes for each document's text and identifies documents with identical hashes as duplicates.
For an overview of all duplicate removal options, refer to [Deduplication ](/curate-text/process-data/deduplication).
## How It Works
Exact deduplication uses MD5 hashing to identify identical documents:
1. Computes MD5 hash for each document's text content
2. Groups documents by identical hash values
3. Identifies duplicates and saves IDs for removal
This method targets character-for-character duplicates and is recommended for removing exact copies of documents.
## Before You Start
**Prerequisites**:
* Ray cluster with GPU support (required for distributed processing)
* Stable document identifiers for removal (either existing IDs or IDs assigned by the workflow)
## Quick Start
Get started with exact deduplication using the following example of identifying duplicates, then remove them:
```python
from nemo_curator.core.client import RayClient
from nemo_curator.stages.deduplication.exact.workflow import ExactDeduplicationWorkflow
from nemo_curator.stages.text.deduplication.removal_workflow import TextDuplicatesRemovalWorkflow
ray_client = RayClient()
ray_client.start()
# Step 1: Identify duplicates
exact_workflow = ExactDeduplicationWorkflow(
input_path="input_data/",
output_path="./results",
text_field="text",
assign_id=True,
perform_removal=False,
input_filetype="parquet"
)
result = exact_workflow.run()
# result.metadata contains: total_time, num_duplicates, identification_time, id_generator_path
# Step 2: Remove duplicates
removal_workflow = TextDuplicatesRemovalWorkflow(
input_path="input_data/",
ids_to_remove_path="./results/ExactDuplicateIds",
output_path="./deduplicated",
input_filetype="parquet",
input_id_field="_curator_dedup_id",
ids_to_remove_duplicate_id_field="_curator_dedup_id",
id_generator_path="./results/exact_id_generator.json"
)
result = removal_workflow.run()
# result.metadata contains: total_time, num_duplicates_removed
```
## Configuration
Configure exact deduplication using these key parameters:
| Parameter | Type | Default | Description |
| -------------------------- | --------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `input_path` | str \| list\[str] | None | Path(s) to input files or directories |
| `output_path` | str | Required | Directory to write duplicate IDs and ID generator |
| `text_field` | str | "text" | Name of the text field in input data |
| `assign_id` | bool | True | Whether to automatically assign unique IDs |
| `id_field` | str \| None | None | Existing ID field name (if assign\_id=False) |
| `input_filetype` | str | "parquet" | Input file format ("parquet" or "jsonl") |
| `input_blocksize` | str \| int | "2GiB" | Size of input blocks for processing |
| `identification_batchsize` | int | 1 | Number of input blocks to concatenate and insert into the shuffler per call. Higher values increase GPU throughput at the cost of memory. For example, `input_blocksize="256MiB"` with `identification_batchsize=4` processes \~1 GB per call. |
| `perform_removal` | bool | False | Reserved; must remain `False`. Exact removal is performed with `TextDuplicatesRemovalWorkflow`. |
| `total_nparts` | int \| None | None | Total number of output partitions. If `None`, defaults to one-third of the number of input tasks. |
| `rmm_pool_size` | int \| "auto" \| None | "auto" | RMM GPU memory pool size in bytes. `"auto"` uses 90% of free GPU memory; `None` uses 50% with dynamic expansion. |
| `spill_memory_limit` | int \| "auto" \| None | "auto" | Device memory spill-to-host limit in bytes. `"auto"` sets the limit to 80% of the RMM pool size; `None` disables spilling. When `rmm_pool_size` is `None`, `"auto"` also resolves to no spilling. |
## Removing Duplicates
After identifying duplicates, use `TextDuplicatesRemovalWorkflow` to remove them:
```python
from nemo_curator.core.client import RayClient
from nemo_curator.stages.text.deduplication.removal_workflow import TextDuplicatesRemovalWorkflow
ray_client = RayClient()
ray_client.start()
removal_workflow = TextDuplicatesRemovalWorkflow(
input_path="/path/to/input/data",
ids_to_remove_path="/path/to/output/ExactDuplicateIds",
output_path="/path/to/deduplicated",
input_filetype="parquet",
input_id_field="_curator_dedup_id",
ids_to_remove_duplicate_id_field="_curator_dedup_id",
id_generator_path="/path/to/output/exact_id_generator.json" # Required if assign_id=True
)
result = removal_workflow.run()
```
**When `assign_id=True`**:
* Duplicate IDs file contains `_curator_dedup_id` column
* Set `ids_to_remove_duplicate_id_field="_curator_dedup_id"`
* `id_generator_path` is required
**When `assign_id=False`**:
* Duplicate IDs file contains the column specified by `id_field` (e.g., `"id"`)
* Set `ids_to_remove_duplicate_id_field` to match your `id_field` value
* `id_generator_path` not required
## Output Format
The exact deduplication process produces the following directory structure:
```s
output_path/
├── ExactDuplicateIds/ # Duplicate identification results
│ └── *.parquet # Parquet files with document IDs to remove
└── exact_id_generator.json # ID generator mapping (if assign_id=True)
```
### File Formats
The workflow produces these output files:
1. **Duplicate IDs** (`ExactDuplicateIds/*.parquet`):
* Contains document IDs to remove
* Format: Parquet files with a single ID column
* Column name depends on `assign_id`:
* When `assign_id=True`: Column is `"_curator_dedup_id"`
* When `assign_id=False`: Column matches the `id_field` parameter (e.g., `"id"`)
* **Important**: Contains only the IDs of documents to remove, not the full document content
2. **ID Generator** (`exact_id_generator.json`):
* JSON file containing ID generator state
* Required for removal workflow when `assign_id=True`
* Ensures consistent ID mapping across workflow stages
**Performance characteristics**:
* Uses MD5 hashing over the configured text field to derive duplicate groups
* Runs as a Ray-based workflow and writes duplicate IDs to the `ExactDuplicateIds/` directory
* Stores only document IDs to remove in the output files, not full document content
**Best practices**:
* Use smaller `input_blocksize` values (`256MiB` to `512MiB`) with a larger `identification_batchsize` to target 2-6 GB of overall batches as memory allows. For example, `input_blocksize="256MiB"` with `identification_batchsize=8` processes \~2 GB per insertion call. This improves both shuffle throughput and removal performance compared to the `2GiB` default.
* Clear output directory between runs
* Use `assign_id=True` for consistent ID tracking
* Setting `total_nparts` to smaller values (`256` or `512`) often leads to better shuffle performance compared to the defaults for really large runs
* For large cluster runs, tune `rmm_pool_size` and `spill_memory_limit` explicitly to match your hardware and dataset size
For comparison with other deduplication methods and guidance on when to use exact deduplication, refer to the [Deduplication overview ](/curate-text/process-data/deduplication).