TsFile Python Document

Introduction

This directory contains the Python implementation of TsFile. The Python version is built on the CPP version and uses the Cython package to integrate TsFile's read and write capabilities into the Python environment. Users can read and write TsFile as easily as they use read_csv and write_csv in Pandas.

The source code can be found in the ./tsfile directory. Files ending with .pyx and .pyd are wrapper code written in Cython. The tsfile/tsfile.py defines some user interfaces. You can find some examples of reading and writing in the .examples/examples.py.

How to make contributions

Using pylint to check Python code is recommended. However, there is no suitable style checking tool for Cython code, and this part of the code should be consistent with the Python style required by pylint.

Feature List

• In pywrapper, invoke the batch reading interface implemented in CPP version of TsFile.
• Supports writing multiple DataFrames into one single TsFile.

Build

Before constructing Python version of TsFile, it is necessary to build CPP version of TsFile first, because Python version of TsFile relies on the shared library files provided by CPP version of TsFile.

Build by mvn in root directory:

mvn -P with-cpp,with-python clean verify

Build by python command:

python setup.py build_ext --inplace

Dataset read resource limits

TsFileDataFrame accepts keyword-only resource options when use_index=True:

from tsfile import TsFileDataFrame

with TsFileDataFrame(
    ["part1.tsfile", "part2.tsfile"],
    use_index=True,
    max_prepared_series=32,
    descriptor_cache_size=32,
    max_open_files=16,
    query_workers=4,
) as frame:
    values = frame[0][:]

An explicit parameter takes precedence over its environment variable. None uses the environment variable, or the built-in default if it is unset. Options are resolved once when constructing the frame; later environment changes do not affect it. Subsets share their parent's runtime and limits. Invalid values raise an error before dataset files or indexes are opened. Explicit resource options require use_index=True; the default non-indexed mode ignores these environment variables.

The prepared-series cache retains parsed native series metadata. It evicts the least recently used idle entries and releases their native handles. Active queries and dependent series pin shared time metadata, so the entry count can temporarily exceed the limit until those queries finish. This is an entry-count limit per runtime, not a process-wide memory limit. A value of 0 disables retention after use. descriptor_cache_size bounds each of the named-descriptor and series-route caches; 0 disables both. max_open_files bounds open readers, and query_workers=1 runs query groups serially. Multiple runtimes or worker processes have separate limits.

File-level properties

TsFileWriter and TsFileTableWriter accept binary properties while they are open. The setter accepts bytes only. Readers return dict[str, bytes | None], preserving null and empty values separately.

with TsFileWriter("example.tsfile") as writer:
    writer.add_tsfile_property("binary-property", b"\x01\x00\xff")

with TsFileReader("example.tsfile") as reader:
    properties = reader.get_tsfile_properties()

Values do not carry a data type; use an explicit portable encoding when storing numbers or structures.

Dataset indexes

Enable the persistent dataset index with use_index=True. The first open builds the index; subsequent opens reuse it. By default, trust_index=True assumes the indexed TsFiles will not change and skips source file size, modification time, and generation checks during index loading and queries. Index format and locator bounds are always checked.

from tsfile import TsFileDataFrame

with TsFileDataFrame("dataset/", use_index=True) as dataset:
    series = dataset[0]

# Check source generations when loading the index and acquiring readers.
with TsFileDataFrame("dataset/", use_index=True, trust_index=False) as dataset:
    series = dataset[0]

With trust_index=False, a stale index is rebuilt when the dataset opens; changes detected while acquiring a query reader raise an error. trust_index is keyword-only and has no effect when use_index=False, which remains the default.

Local File Read Backend

Python readers inherit the process-wide backend setting when they open a file. PREAD is the default to preserve the traditional positioned-read behavior, MMAP requires memory mapping, and AUTO prefers mapping with a fallback to PREAD.

from tsfile import FileReadBackend, TsFileReader, set_file_read_backend

set_file_read_backend(FileReadBackend.MMAP)
with TsFileReader("example.tsfile") as reader:
    ...

# The configuration-dictionary API is equivalent.
from tsfile import set_tsfile_config
set_tsfile_config({"file_read_backend_": FileReadBackend.AUTO})

The setting only affects readers opened afterward. Do not modify or truncate a file while it is open through the memory-mapped backend.

Seekable binary file objects

TsFileReader also accepts a seekable binary file object. This allows remote files opened by libraries such as fsspec to be read without first copying the whole file to local storage.

import fsspec
from tsfile import TsFileReader

with fsspec.open("s3://bucket/example.tsfile", "rb") as source:
    with TsFileReader(source) as reader:
        result = reader.query_table("table_name", ["column_name"])

The object must provide seek(), tell(), and explicitly sized binary read(size) operations. The caller owns the object: TsFileReader keeps it alive while in use, preserves its cursor position, and does not close it. The local FileReadBackend setting does not apply to file objects.