flatdata-py 0.4.11

Python 3 implementation of Flatdata

flatdata-py

Python 3 implementation of flatdata.

Running the tests

python3 -m pytest

Basic usage

Once you have created a flatdata schema file, you can generate a Python module to read your existing flatdata archive:

flatdata-generator --gen py --schema locations.flatdata --output-file locations.py

Performance tips

flatdata-py supports two data access patterns with very different performance characteristics on large archives.

Iterating over a vector yields one Python object per element. Each field access unpacks bits from the underlying memory-mapped data. This is fine for accessing individual elements or small ranges, but has significant per-element overhead for bulk operations:

count = sum(1 for x in archive.links if x.speed_limit > 100)

For bulk operations, use the vectorized access methods that read fields directly into NumPy arrays:

# single column access, returns a pandas DataFrame
df = archive.links.speed_limit
count = len(df[df['speed_limit'] > 100])

# full NumPy structured array with all fields
arr = archive.links.to_numpy()
count = int(np.sum(arr['speed_limit'] > 100))

# slices work too
arr = archive.links[1000:2000].to_numpy()
df = archive.links[::10].to_data_frame()

• Use vector.field_name (column access) when you only need one or a few fields.
• Use vector.to_numpy() or vector.to_data_frame() when you need all fields at once.
• Use vector[i].field for random access to individual elements.
• The underlying data is memory-mapped; the OS pages it from disk on demand. Vectorized results are materialized as NumPy arrays in RAM.

Using the inspector

flatdata-py comes with a handy tool called the flatdata-inspector to inspect the contents of an archive:

• from the flatdata-py source directory:

./inspector.py
# or
python3 -m flatdata.lib.inspector

• if you want to install flatdata-py:

pip3 install flatdata-py[inspector]  # the inspector feature requires IPython
flatdata-inspector -p /path/to/my/flatdata.archive

Using the writer

flatdata-writer is an addition to flatdata-py that can create flatdata archives from a flatdata schema, with the following limitations:

• does not allow adding additional sub-archives to an existing archive

• supports only bulk-writing (no streaming)

• not optimized for performance

• from the flatdata-py source directory

./writer.py --schema archive.flatdata --output-dir testdir --json-file data.json --resource-name resourcename
#or
python3 -m flatdata.lib.writer --schema archive.flatdata --output-dir testdir --json-file data.json --resource-name resourcename

Note that the flatdata-writer CLI tool can only write one resource at a time. For archives that have multiple non-optional resources, the tool has to be executed separately for each resource. Only after all resources have been written can the archive be opened.

• if you want to install flatdata-py:

pip3 install flatdata-py[writer]
flatdata-writer --schema archive.flatdata --output-dir testdir --json-file data.json --resource-name resourcename