This typically includes all files smaller than X (see above). Find gaps: All sections of the file system that are not covered by step 1 or 2 are stored in gap chunks.However, the space savings due to this step are enormous and the user won’t care. If it is used, the re-assembled file system will likely not be identical as the original one, because unused space is rarely ever filled with zeros. Index empty space: Read the NTFS bitmap ( $Bitmap), find unused sections and mark them as sparse.For each chunk checksum, compare if it’s in the global chunk index and only store new chunks. 32M sized chunks and then hash each chunk using blake2b.
For each entry, read all data runs (extents, fragments) sequentially (i.e.
Index files within NTFS: Read all entries in the master file table ( $MFT) that are larger than X (e.g. Instead of fixed offset chunking with 4K chunks, I decided to parse the NTFS filesystem using a combination of content aware chunking (for the file system itself) and fixed offset chunking (for the files within the file system). Since this was a proof-of-concept, and I wanted to learn about NTFS and write some fun Go code, I chose to go a different route: That said, given that we’re talking about hundreds of thousands (maybe even millions? I should really check …) of NTFS file systems, it might not matter if the objects are 4K or 128K in size. On top of that, many object stores or file systems are not really good with small files like that, so a higher chunk size is desirable. In addition to that, the more chunks you produce the higher the chance of hash collisions (unless you increase the hash size, which in turn increases the metadata overhead …). While that will lead to the best dedup results, it also produces a lot of metadata and the metadata-to-data ratio (metadata overhead) is pretty bad. The obvious approach to dedup NTFS would be to simply dedup blocks (= clusters), so typically that would mean deduping with a fixed offset of 4096 bytes (4K cluster size). If you’re impatient, here’s a link to the fsdup tool I created as part of this proof-of-concept. Pandas dataframe with a new column deduplication_id. Score_threshold – Classification threshold to use for filtering before starting hierarchical clusteringĬluster_threshold – threshold to apply in hierarchical clusteringįill_missing – whether to apply missing value imputation on adjacency matrix X – Pandas dataframe with column as used when fitting deduplicator instance Predict on new data using the trained deduplicator. Trained deduplicator instance predict ( X :, score_threshold : float = 0.1, cluster_threshold : float = 0.5, fill_missing = True ) → ¶ N_samples – number of pairs to be created for active learning 
X – Pandas dataframe to be used for fitting Save_intermediate_steps – whether to save intermediate results in csv files for analysisįit ( X :, n_samples : int = 10000 ) → ¶ Recall – desired recall reached by blocking rules
Of blocking functions as values, if not provided, all default rules will be used for all columns Rules – list of blocking functions to use for all columns or a dict containing column names as keys and lists Interaction – whether to include interaction features Rows with the same deduplication_id areĬol_names – list of column names to be used for deduplication, if col_names is provided, field_info canįield_info – dict containing column names as keys and lists of metrics per column name as values, only used The result is a dataframe with a new column deduplication_id. myDedupliPy = Deduplicator () > myDedupliPy.
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