mmap_ninja_dataframe

Memory-mapped dataframe abstraction based on mmap_ninja

Run tests:

uvx --with-editable . --with joblib --with zstandard --with dnn_cool_synthetic_dataset --with opencv-contrib-python --with transformers pytest

PropertyResult

PropertyResult is a sparse column of computed results, stored as two parallel mmaps under a directory:

• index — a numpy mmap of the indices the results were computed for
• results — an mmap-ninja mmap of the corresponding results ("numpy", "string", or "ragged")

results[j] is the result for index[j]. Because each result carries its own index, results can be recorded for any subset of indices, in any order.

Create and populate

Construct with an out_dir and the mmap_type for the results, then fill it with append / extend:

from mmap_ninja_dataframe import PropertyResult

pr = PropertyResult("my_store/sentiment", mmap_type="string")
pr.extend([0, 2], ["positive", "neutral"])   # results for indices 0 and 2
pr.append(1, "negative")                      # a single (index, result) pair

mmap_type selects the results mmap:

• "string" → StringsMmap (text labels, summaries, …)
• "numpy" → numpy mmap (scores, token counts, fixed-shape embeddings, …)
• "ragged" → RaggedMmap (variable-length sequences)

Results can be recorded partially and out of order:

pr = PropertyResult("my_store/embedding", mmap_type="numpy")
pr.extend([5], [np.random.rand(768).astype(np.float32)])   # index 5 first
pr.extend([0, 1], [emb0, emb1])                            # earlier indices later

Recording an index that already has a result doesn't overwrite it in place — it appends a new (index, result) pair. Lookups always return the most recently recorded result for that index:

pr = PropertyResult("my_store/sentiment", mmap_type="string")
pr.append(2, "neutral")
pr.append(2, "positive")
pr[2]   # "positive" -- the latest write wins

Look up by index

pr[2]              # result recorded for index 2 (KeyError if absent)
pr.get(2)          # same, but returns default (None) if absent
pr.get(2, "n/a")   # with an explicit default
2 in pr            # whether index 2 has a result

pr[idx] = value is equivalent to pr.append(idx, value); pr[indices] = values (a sequence of indices) is equivalent to pr.extend(indices, values).

Inspect

len(pr)         # number of recorded (index, result) pairs -- not the number of *distinct* indices
pr.indices()    # the indices, in insertion order (may contain repeats -- see "latest wins" above)
pr.mmap_type    # "string" / "numpy" / "ragged"
pr.name         # basename of out_dir, e.g. "sentiment"
pr.out_dir      # the directory it's persisted under
pr.index        # raw numpy index mmap (None until the first write)
pr.results      # raw results mmap (None until the first write)

Reopen

mmap_type is required on every construction call, including when reopening — it is then validated against what's already on disk, and a mismatch raises ValueError:

pr = PropertyResult("my_store/sentiment", mmap_type="string")
# ValueError if "my_store/sentiment" already holds results of a different mmap_type.

TextPropertiesMmap

TextPropertiesMmap is a deduplicated store of texts annotated with named, independently-computed properties (sentiment, embeddings, summaries, …). Each unique text gets a stable integer index; each property is a PropertyResult keyed by those indices, supplied explicitly by the caller.

How text lookup works

Texts are stored as a StringsMmap (store.text). Alongside it, each text's sha256 content hash is stored in a fixed-width numpy memmap (store.content_hash, dtype "<U64"), used to detect duplicates and to resolve a text back to its index.

Content hashes are never loaded into memory as a Python dict. Instead, a second numpy memmap (content_hash_sorter) holds the permutation that sorts content_hash; it is fully recomputed every time the store is (re)opened or texts are appended. Looking up a hash is then a binary search — np.searchsorted(content_hash, target, sorter=sorter) for a single text, or one vectorized np.searchsorted call for a batch — instead of a linear scan or an in-memory dict.

Create and add texts

from mmap_ninja_dataframe import TextPropertiesMmap, PropertyResult

store = TextPropertiesMmap.from_texts(
    "my_store",
    texts=["The quick brown fox.", "Hello, world!"],
    properties=[PropertyResult("my_store/sentiment", mmap_type="string")],
)

# Add more texts, deduplicating by content hash. Returns the index for each
# input text: texts already in the store resolve to their existing index,
# duplicates within the same call resolve to the same new index, and only
# genuinely new texts get appended.
indices = store.update(["The quick brown fox.", "A new sentence."])
# indices == [0, 2]  -- "The quick brown fox." already existed at index 0

from_texts builds a fresh store from a list of texts. To reopen an existing one, construct TextPropertiesMmap directly with the same out_dir — properties are not auto-discovered from disk, so pass the same properties list again:

store = TextPropertiesMmap(
    "my_store",
    properties=[PropertyResult("my_store/sentiment", mmap_type="string")],
)

Record and read property results

The store behaves like a named collection of properties. Resolve texts to indices, then write to the property directly:

store["sentiment"].extend(indices, ["neutral", "positive"])

store["sentiment"]                            # the PropertyResult
store.get_property("sentiment")               # same thing
store.get_property_names()                    # ["sentiment"]
store.add_property(PropertyResult("my_store/summary", mmap_type="string"))
store["summary"] = PropertyResult("my_store/summary", mmap_type="string")   # equivalent to add_property
store.delete_property("summary")              # unregisters it and deletes its directory

Property names "text", "content_hash", and "content_hash_sorter" are reserved (used internally) and raise ValueError if registered.

Look up texts

store.index_of_text("Hello, world!")            # 1, or None if not present
store.indices_for_texts(["Hello, world!"])       # [1] -- vectorized; raises KeyError naming
                                                  # the first text not found in the store

store.get_text_properties("Hello, world!")
# {"unprocessed": ["sentiment"]}   -- no result recorded yet for this text/property

store.get_properties_for_texts(["The quick brown fox."])
# {"text": [...], "content_hash": array([...], dtype='<U64'), "idx": [0], "sentiment": ["neutral"]}
# Raises ValueError if any requested text is missing a result for any registered property.

Check progress

store.get_unprocessed_indices_for_property("sentiment")   # numpy array of indices with no result yet
store.get_unprocessed_counts()                             # {"sentiment": 1} -- only not-yet-complete properties

Inspect

len(store)           # number of distinct texts
store.text           # StringsMmap of the texts
store.content_hash   # numpy memmap of sha256 hexdigests, dtype "<U64"