Heuristic:TA Lib Ta lib python Thread Safety With Abstract API
| Knowledge Sources | |
|---|---|
| Domains | Optimization, Debugging |
| Last Updated | 2026-02-09 22:00 GMT |
Overview
The Abstract API uses thread-local storage for Function object state, enabling safe concurrent usage across threads but requiring `deepcopy` of DataFrames to avoid shared mutation.
Description
The Abstract API's `Function` class stores its internal state (input arrays, parameters, output buffers, function info) in a `threading.local()` object. This means each thread gets its own independent copy of the Function state, preventing race conditions when multiple threads call the same Function object concurrently. However, the input data itself (e.g., DataFrames or numpy arrays) is not automatically copied. If multiple threads share a mutable input object, they must create independent copies to avoid data races.
Usage
Apply this heuristic when using the Abstract API (`talib.abstract.Function`) in multi-threaded applications, such as computing indicators for multiple symbols or timeframes in parallel. While the Function objects are thread-safe, you must ensure your input data is not shared between threads without copying.
The Insight (Rule of Thumb)
- Action: Use `copy.deepcopy(df)` on input DataFrames before passing them to Abstract API Function objects in separate threads.
- Value: The Abstract API is thread-safe internally via `threading.local()`, but input data must be independently owned per thread.
- Trade-off: Deep copying DataFrames adds memory overhead and copy time, but prevents subtle data corruption bugs in concurrent code.
Reasoning
The thread-local storage pattern is implemented in the `Function.__init__` method and accessed via a `__local` property that lazily initializes per-thread state. This design allows a single Function object (e.g., `RSI`) to be used across threads without locking.
Thread-local storage initialization from `talib/_abstract.pxi:123-124`:
# thread-local storage
self.__localdata = threading.local()
Lazy per-thread initialization from `talib/_abstract.pxi:130-144`:
@property
def __local(self):
local = self.__localdata
if not hasattr(local, 'info'):
local.info = None
local.input_arrays = {}
local.input_names = OrderedDict()
local.opt_inputs = OrderedDict()
local.outputs = OrderedDict()
local.outputs_valid = False
local.info = _ta_getFuncInfo(self.__name)
# ... initialize per-thread state
Thread safety test from `tools/threads_talib.py:25-38`:
def loop():
global total
if threading.get_native_id() % 2 == 0:
df = copy.deepcopy(df_short)
else:
df = copy.deepcopy(df_long)
while total < LOOPS:
total += 1
try:
df['RSI'] = RSI(df)
except ValueError as msg:
raise ValueError(msg)
Note how the test uses `copy.deepcopy()` on input DataFrames to ensure each thread has its own data. Also note that RSI and threading tests are skipped during CI wheel builds (`pytest -k "not RSI and not threading"`), indicating these scenarios have environment-specific sensitivities.