28356348514

Committed 29 Jun 2026 07:40AM UTC coverage: 52.174% (-0.1%) from 52.278%

Build # 28356348514

Build Type

Pull #83

github

Committed by

web-flow

Commit Message

Merge 278203630 into 2efed6649

Pull Request Pull Request #83: refactor and improve code QoL

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50.23

/src/dftracer/utils/python/arrow_stream_capsule.cpp

#include <dftracer/utils/core/common/config.h>
#include <dftracer/utils/python/py_type_helpers.h>
#ifdef DFTRACER_UTILS_ENABLE_ARROW

#define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <dftracer/utils/python/arrow_stream_capsule.h>
#include <dftracer/utils/python/batch_byte_size.h>
#include <dftracer/utils/python/schema_reconcile.h>
#include <nanoarrow/nanoarrow.h>

#include <cerrno>
#include <cstring>
#include <deque>
#include <exception>
#include <mutex>
#include <optional>
#include <string>

using ArrowExportResult =
    dftracer::utils::utilities::common::arrow::ArrowExportResult;

namespace {

// Drain until K consecutive batches add no new columns, bounded by MAX.
constexpr int STABLE_BATCHES = 5;
constexpr int MAX_DRAIN = 128;

struct StreamPrivate {
    std::shared_ptr<ArrowIteratorState> state;
    dftracer::utils::python::SchemaReconciler reconciler;
    // Drained during discovery, emitted first from get_next.
    std::deque<ArrowExportResult> pending;
    std::string last_error;
    bool initialized = false;
    // Sticky: once set, all entry points short-circuit to EIO.
    bool error_set = false;
};

static void mark_error(StreamPrivate *p, std::string msg) {
    if (p->last_error.empty()) p->last_error = std::move(msg);
    p->error_set = true;
    p->initialized = true;
}

static int initialize_stream(StreamPrivate *p) {
    if (p->error_set) return EIO;
    if (p->initialized) return 0;
    auto *astate = p->state.get();

    int stable_run = 0;
    int drained = 0;
    while (stable_run < STABLE_BATCHES && drained < MAX_DRAIN) {
        auto batch = astate->channel->blocking_receive();
        if (!batch.has_value()) {
            // End-of-stream or producer error before discovery converged.
            std::lock_guard<std::mutex> lock(astate->error_mtx);
            if (astate->error) {
                try {
                    std::rethrow_exception(astate->error);
                } catch (const std::exception &e) {
                    mark_error(p, e.what());
                } catch (...) {
                    mark_error(p, "unknown error in Arrow stream");
                }
                return EIO;
            }
            break;  // clean early EOS; finalize with whatever we have
        }
        auto dequeued = dftracer::utils::python::byte_size(*batch);
        astate->bytes_in_queue.fetch_sub(dequeued, std::memory_order_acq_rel);

        bool added = p->reconciler.merge(batch->get_schema());
        if (!p->reconciler.last_error().empty()) {
            mark_error(p, p->reconciler.last_error());
            return EIO;
        }
        p->pending.push_back(std::move(*batch));
        stable_run = added ? 0 : (stable_run + 1);
        ++drained;
    }

    if (p->reconciler.finalize() != 0) {
        mark_error(p, p->reconciler.last_error().empty()
                          ? "failed to finalize schema union"
                          : p->reconciler.last_error());
        return EIO;
    }
    p->initialized = true;
    return 0;
}

static int stream_get_schema(struct ArrowArrayStream *s,
                             struct ArrowSchema *out) {
    auto *p = static_cast<StreamPrivate *>(s->private_data);
    int rc = initialize_stream(p);
    if (rc != 0) return rc;
    if (p->error_set) return EIO;
    if (p->reconciler.copy_schema(out) != 0) {
        mark_error(p, p->reconciler.last_error().empty()
                          ? "failed to copy locked schema"
                          : p->reconciler.last_error());
        return EIO;
    }
    return 0;
}

static int stream_get_next(struct ArrowArrayStream *s, struct ArrowArray *out) {
    auto *p = static_cast<StreamPrivate *>(s->private_data);
    if (p->error_set) return EIO;
    if (!p->initialized) {
        int rc = initialize_stream(p);
        if (rc != 0) return rc;
    }

    // Drain any discovery-phase batches first, then pull from the channel.
    std::optional<ArrowExportResult> batch;
    if (!p->pending.empty()) {
        batch = std::move(p->pending.front());
        p->pending.pop_front();
    } else {
        auto *astate = p->state.get();
        batch = astate->channel->blocking_receive();
        if (!batch.has_value()) {
            std::lock_guard<std::mutex> lock(astate->error_mtx);
            if (astate->error) {
                try {
                    std::rethrow_exception(astate->error);
                } catch (const std::exception &e) {
                    mark_error(p, e.what());
                } catch (...) {
                    mark_error(p, "unknown error in Arrow stream");
                }
                return EIO;
            }
            // End of stream per Arrow C spec: return success with
            // out->release == nullptr.
            out->release = nullptr;
            return 0;
        }
        auto dequeued = dftracer::utils::python::byte_size(*batch);
        astate->bytes_in_queue.fetch_sub(dequeued, std::memory_order_acq_rel);
    }

    if (p->reconciler.reconcile(batch->get_schema(), batch->get_array(), out) !=
        0) {
        mark_error(p, p->reconciler.last_error().empty()
                          ? "schema reconciliation failed"
                          : p->reconciler.last_error());
        return EIO;
    }
    return 0;
}

static const char *stream_get_last_error(struct ArrowArrayStream *s) {
    auto *p = static_cast<StreamPrivate *>(s->private_data);
    if (!p || p->last_error.empty()) return nullptr;
    return p->last_error.c_str();
}

static void stream_release(struct ArrowArrayStream *s) {
    auto *p = static_cast<StreamPrivate *>(s->private_data);
    if (p) {
        if (p->state) {
            p->state->cancelled.store(true, std::memory_order_release);
            if (p->state->channel) p->state->channel->close();
            if (p->state->task_future.valid()) {
                // Release the GIL if this callback was invoked from a
                // Python-holding context (e.g. capsule destructor during
                // GC). If the GIL is not held (pyarrow's C reader path),
                // _PyThreadState_UncheckedGet() returns null and we wait
                // without touching the Python thread state.
                if (Py_IsInitialized() && PyGILState_Check()) {
                    Py_BEGIN_ALLOW_THREADS p->state->task_future.wait();
                    Py_END_ALLOW_THREADS
                } else {
                    p->state->task_future.wait();
                }
            }
        }
        delete p;
    }
    s->private_data = nullptr;
    s->release = nullptr;
}

static void release_stream_capsule(PyObject *capsule) {
    auto *stream = static_cast<ArrowArrayStream *>(
        PyCapsule_GetPointer(capsule, "arrow_array_stream"));
    if (stream && stream->release) {
        stream->release(stream);
    }
    delete stream;
}

static PyObject *ArrowBatchStream_arrow_c_stream(ArrowBatchStreamObject *self,
                                                 PyObject *args) {
    PyObject *requested_schema = Py_None;
    if (!PyArg_ParseTuple(args, "|O", &requested_schema)) return NULL;

    // Per the PyCapsule protocol, a non-None `requested_schema` means the
    // caller wants the stream cast to that schema. We only emit our native
    // schema today; reject explicitly so misuse fails loudly instead of
    // silently returning arrays that don't match what the caller asked for.
    if (requested_schema != Py_None) {
        PyErr_SetString(PyExc_NotImplementedError,
                        "iter_arrow_stream does not support "
                        "requested_schema casting; pass None to use the "
                        "native schema.");
        return NULL;
    }

    if (self->consumed || !self->state) {
        PyErr_SetString(PyExc_RuntimeError,
                        "Arrow stream already exported via "
                        "__arrow_c_stream__; each stream can be "
                        "exported only once.");
        return NULL;
    }

    auto *priv = new StreamPrivate;
    priv->state = self->state;
    self->consumed = true;
    self->state.reset();

    auto *stream = new ArrowArrayStream;
    std::memset(stream, 0, sizeof(*stream));
    stream->get_schema = stream_get_schema;
    stream->get_next = stream_get_next;
    stream->get_last_error = stream_get_last_error;
    stream->release = stream_release;
    stream->private_data = priv;

    PyObject *capsule =
        PyCapsule_New(stream, "arrow_array_stream", release_stream_capsule);
    if (!capsule) {
        stream->release(stream);
        delete stream;
        return NULL;
    }
    return capsule;
}

static void ArrowBatchStream_dealloc(ArrowBatchStreamObject *self) {
    if (self->state) {
        self->state->cancelled.store(true, std::memory_order_release);
        if (self->state->channel) self->state->channel->close();
        Py_BEGIN_ALLOW_THREADS if (self->state->task_future.valid()) {
            self->state->task_future.wait();
        }
        Py_END_ALLOW_THREADS
    }
    self->state.~shared_ptr<ArrowIteratorState>();
    Py_TYPE(self)->tp_free((PyObject *)self);
}

static PyMethodDef ArrowBatchStream_methods[] = {
    {"__arrow_c_stream__", (PyCFunction)ArrowBatchStream_arrow_c_stream,
     METH_VARARGS, "Export as Arrow C Data Interface stream PyCapsule"},
    {NULL}};

}  // namespace

PyTypeObject ArrowBatchStreamType = {
    PyVarObject_HEAD_INIT(NULL, 0) "dftracer_utils_ext._ArrowBatchStream",
    sizeof(ArrowBatchStreamObject), /* tp_basicsize */
    0,                              /* tp_itemsize */
    (destructor)ArrowBatchStream_dealloc,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    Py_TPFLAGS_DEFAULT,
    "Zero-iteration Arrow stream backed by a C++ coroutine channel",
    0,
    0,
    0,
    0,
    0,
    0,
    ArrowBatchStream_methods,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
    0,
};

int init_arrow_batch_stream(PyObject *m) {
    if (register_type(m, &ArrowBatchStreamType, "_ArrowBatchStream") < 0)
        return -1;
    return 0;
}

PyObject *make_arrow_batch_stream(std::shared_ptr<ArrowIteratorState> state) {
    auto *obj = (ArrowBatchStreamObject *)ArrowBatchStreamType.tp_alloc(
        &ArrowBatchStreamType, 0);
    if (!obj) return NULL;
    new (&obj->state) std::shared_ptr<ArrowIteratorState>(std::move(state));
    obj->consumed = false;
    return (PyObject *)obj;
}

#endif

1	#include <dftracer/utils/core/common/config.h>
2	#include <dftracer/utils/python/py_type_helpers.h>
3	#ifdef DFTRACER_UTILS_ENABLE_ARROW
4
5	#define PY_SSIZE_T_CLEAN
6	#include <Python.h>
7	#include <dftracer/utils/python/arrow_stream_capsule.h>
8	#include <dftracer/utils/python/batch_byte_size.h>
9	#include <dftracer/utils/python/schema_reconcile.h>
10	#include <nanoarrow/nanoarrow.h>
11
12	#include <cerrno>
13	#include <cstring>
14	#include <deque>
15	#include <exception>
16	#include <mutex>
17	#include <optional>
18	#include <string>
19
20	using ArrowExportResult =
21	dftracer::utils::utilities::common::arrow::ArrowExportResult;
22
23	namespace {
24
25	// Drain until K consecutive batches add no new columns, bounded by MAX.
26	constexpr int STABLE_BATCHES = 5;
27	constexpr int MAX_DRAIN = 128;
28
29	struct StreamPrivate {	50✔
30	std::shared_ptr<ArrowIteratorState> state;
31	dftracer::utils::python::SchemaReconciler reconciler;
32	// Drained during discovery, emitted first from get_next.
33	std::deque<ArrowExportResult> pending;
34	std::string last_error;
35	bool initialized = false;	25✔
36	// Sticky: once set, all entry points short-circuit to EIO.
37	bool error_set = false;	25✔
38	};
39
40	static void mark_error(StreamPrivate *p, std::string msg) {	×
41	if (p->last_error.empty()) p->last_error = std::move(msg);	×
42	p->error_set = true;	×
43	p->initialized = true;	×
44	}	×
45
46	static int initialize_stream(StreamPrivate *p) {	48✔
47	if (p->error_set) return EIO;	48!
48	if (p->initialized) return 0;	48!
49	auto *astate = p->state.get();	48✔
50
51	int stable_run = 0;	48✔
52	int drained = 0;	48✔
53	while (stable_run < STABLE_BATCHES && drained < MAX_DRAIN) {	178✔
54	auto batch = astate->channel->blocking_receive();	176!
55	if (!batch.has_value()) {	176✔
56	// End-of-stream or producer error before discovery converged.
57	std::lock_guard<std::mutex> lock(astate->error_mtx);	46!
58	if (astate->error) {	46✔
59	try {
60	std::rethrow_exception(astate->error);	×
61	} catch (const std::exception &e) {	×
62	mark_error(p, e.what());	×
63	} catch (...) {	×
64	mark_error(p, "unknown error in Arrow stream");	×
65	}	×
66	return EIO;	×
67	}
68	break; // clean early EOS; finalize with whatever we have	46✔
69	}	46!
70	auto dequeued = dftracer::utils::python::byte_size(*batch);	130!
71	astate->bytes_in_queue.fetch_sub(dequeued, std::memory_order_acq_rel);	130✔
72
73	bool added = p->reconciler.merge(batch->get_schema());	130!
74	if (!p->reconciler.last_error().empty()) {	130!
75	mark_error(p, p->reconciler.last_error());	×
76	return EIO;	×
77	}
78	p->pending.push_back(std::move(*batch));	130!
79	stable_run = added ? 0 : (stable_run + 1);	130✔
80	++drained;	130✔
81	}	176!
82
83	if (p->reconciler.finalize() != 0) {	48!
84	mark_error(p, p->reconciler.last_error().empty()	×
85	? "failed to finalize schema union"	×
86	: p->reconciler.last_error());	×
87	return EIO;	×
88	}
89	p->initialized = true;	48✔
90	return 0;	48✔
91	}	24✔
92
93	static int stream_get_schema(struct ArrowArrayStream *s,	48✔
94	struct ArrowSchema *out) {
95	auto p = static_cast<StreamPrivate >(s->private_data);	48✔
96	int rc = initialize_stream(p);	48✔
97	if (rc != 0) return rc;	48!
98	if (p->error_set) return EIO;	48!
99	if (p->reconciler.copy_schema(out) != 0) {	48!
100	mark_error(p, p->reconciler.last_error().empty()	×
101	? "failed to copy locked schema"	×
102	: p->reconciler.last_error());	×
103	return EIO;	×
104	}
105	return 0;	48✔
106	}	24✔
107
108	static int stream_get_next(struct ArrowArrayStream s, struct ArrowArray out) {	170✔
109	auto p = static_cast<StreamPrivate >(s->private_data);	170✔
110	if (p->error_set) return EIO;	170!
111	if (!p->initialized) {	170✔
112	int rc = initialize_stream(p);	×
113	if (rc != 0) return rc;	×
114	}
115
116	// Drain any discovery-phase batches first, then pull from the channel.
117	std::optional<ArrowExportResult> batch;	170✔
118	if (!p->pending.empty()) {	170✔
119	batch = std::move(p->pending.front());	126!
120	p->pending.pop_front();	126!
121	} else {	63✔
122	auto *astate = p->state.get();	44✔
123	batch = astate->channel->blocking_receive();	44!
124	if (!batch.has_value()) {	44!
125	std::lock_guard<std::mutex> lock(astate->error_mtx);	44!
126	if (astate->error) {	44✔
127	try {
128	std::rethrow_exception(astate->error);	×
129	} catch (const std::exception &e) {	×
130	mark_error(p, e.what());	×
131	} catch (...) {	×
132	mark_error(p, "unknown error in Arrow stream");	×
133	}	×
134	return EIO;	×
135	}
136	// End of stream per Arrow C spec: return success with
137	// out->release == nullptr.
138	out->release = nullptr;	44✔
139	return 0;	44✔
140	}	44✔
141	auto dequeued = dftracer::utils::python::byte_size(*batch);	×
142	astate->bytes_in_queue.fetch_sub(dequeued, std::memory_order_acq_rel);	×
143	}
144
145	if (p->reconciler.reconcile(batch->get_schema(), batch->get_array(), out) !=	126!
146	0) {
147	mark_error(p, p->reconciler.last_error().empty()	×
148	? "schema reconciliation failed"	×
149	: p->reconciler.last_error());	×
150	return EIO;	×
151	}
152	return 0;	126✔
153	}	170✔
154
155	static const char stream_get_last_error(struct ArrowArrayStream s) {	×
156	auto p = static_cast<StreamPrivate >(s->private_data);	×
157	if (!p \|\| p->last_error.empty()) return nullptr;	×
158	return p->last_error.c_str();	×
159	}
160
161	static void stream_release(struct ArrowArrayStream *s) {	50✔
162	auto p = static_cast<StreamPrivate >(s->private_data);	50✔
163	if (p) {	50✔
164	if (p->state) {	50✔
165	p->state->cancelled.store(true, std::memory_order_release);	50✔
166	if (p->state->channel) p->state->channel->close();	50✔
167	if (p->state->task_future.valid()) {	50✔
168	// Release the GIL if this callback was invoked from a
169	// Python-holding context (e.g. capsule destructor during
170	// GC). If the GIL is not held (pyarrow's C reader path),
171	// _PyThreadState_UncheckedGet() returns null and we wait
172	// without touching the Python thread state.
173	if (Py_IsInitialized() && PyGILState_Check()) {	50!
174	Py_BEGIN_ALLOW_THREADS p->state->task_future.wait();	2✔
175	Py_END_ALLOW_THREADS	2✔
176	} else {	1✔
177	p->state->task_future.wait();	48✔
178	}
179	}	25✔
180	}	25✔
181	delete p;	50✔
182	}	25✔
183	s->private_data = nullptr;	50✔
184	s->release = nullptr;	50✔
185	}	50✔
186
187	static void release_stream_capsule(PyObject *capsule) {	50✔
188	auto stream = static_cast<ArrowArrayStream >(	25✔
189	PyCapsule_GetPointer(capsule, "arrow_array_stream"));	50✔
190	if (stream && stream->release) {	50!
191	stream->release(stream);	2✔
192	}	1✔
193	delete stream;	50✔
194	}	50✔
195
196	static PyObject ArrowBatchStream_arrow_c_stream(ArrowBatchStreamObject self,	52✔
197	PyObject *args) {
198	PyObject *requested_schema = Py_None;	52✔
199	if (!PyArg_ParseTuple(args, "\|O", &requested_schema)) return NULL;	52!
200
201	// Per the PyCapsule protocol, a non-None `requested_schema` means the
202	// caller wants the stream cast to that schema. We only emit our native
203	// schema today; reject explicitly so misuse fails loudly instead of
204	// silently returning arrays that don't match what the caller asked for.
205	if (requested_schema != Py_None) {	52!
206	PyErr_SetString(PyExc_NotImplementedError,	×
207	"iter_arrow_stream does not support "
208	"requested_schema casting; pass None to use the "
209	"native schema.");
210	return NULL;	×
211	}
212
213	if (self->consumed \|\| !self->state) {	52!
214	PyErr_SetString(PyExc_RuntimeError,	2!
215	"Arrow stream already exported via "
216	"__arrow_c_stream__; each stream can be "
217	"exported only once.");
218	return NULL;	2✔
219	}
220
221	auto *priv = new StreamPrivate;	50!
222	priv->state = self->state;	50✔
223	self->consumed = true;	50✔
224	self->state.reset();	50✔
225
226	auto *stream = new ArrowArrayStream;	50!
227	std::memset(stream, 0, sizeof(*stream));	50✔
228	stream->get_schema = stream_get_schema;	50✔
229	stream->get_next = stream_get_next;	50✔
230	stream->get_last_error = stream_get_last_error;	50✔
231	stream->release = stream_release;	50✔
232	stream->private_data = priv;	50✔
233
234	PyObject *capsule =	25✔
235	PyCapsule_New(stream, "arrow_array_stream", release_stream_capsule);	50!
236	if (!capsule) {	50✔
237	stream->release(stream);	×
238	delete stream;	×
239	return NULL;	×
240	}
241	return capsule;	50✔
242	}	26✔
243
244	static void ArrowBatchStream_dealloc(ArrowBatchStreamObject *self) {	54✔
245	if (self->state) {	54✔
246	self->state->cancelled.store(true, std::memory_order_release);	4✔
247	if (self->state->channel) self->state->channel->close();	4✔
248	Py_BEGIN_ALLOW_THREADS if (self->state->task_future.valid()) {	4✔
249	self->state->task_future.wait();	4✔
250	}	2✔
251	Py_END_ALLOW_THREADS	4✔
252	}	2✔
253	self->state.~shared_ptr<ArrowIteratorState>();	54✔
254	Py_TYPE(self)->tp_free((PyObject *)self);	54✔
255	}	54✔
256
257	static PyMethodDef ArrowBatchStream_methods[] = {
258	{"__arrow_c_stream__", (PyCFunction)ArrowBatchStream_arrow_c_stream,
259	METH_VARARGS, "Export as Arrow C Data Interface stream PyCapsule"},
260	{NULL}};
261
262	} // namespace
263
264	PyTypeObject ArrowBatchStreamType = {
265	PyVarObject_HEAD_INIT(NULL, 0) "dftracer_utils_ext._ArrowBatchStream",
266	sizeof(ArrowBatchStreamObject), /* tp_basicsize */
267	0, /* tp_itemsize */
268	(destructor)ArrowBatchStream_dealloc,
269	0,
270	0,
271	0,
272	0,
273	0,
274	0,
275	0,
276	0,
277	0,
278	0,
279	0,
280	0,
281	0,
282	0,
283	Py_TPFLAGS_DEFAULT,
284	"Zero-iteration Arrow stream backed by a C++ coroutine channel",
285	0,
286	0,
287	0,
288	0,
289	0,
290	0,
291	ArrowBatchStream_methods,
292	0,
293	0,
294	0,
295	0,
296	0,
297	0,
298	0,
299	0,
300	0,
301	0,
302	};
303
304	int init_arrow_batch_stream(PyObject *m) {	2✔
305	if (register_type(m, &ArrowBatchStreamType, "_ArrowBatchStream") < 0)	2✔
UNCOV 306	return -1;	×
307	return 0;	2✔
308	}	1✔
309
310	PyObject *make_arrow_batch_stream(std::shared_ptr<ArrowIteratorState> state) {	54✔
311	auto obj = (ArrowBatchStreamObject )ArrowBatchStreamType.tp_alloc(	54✔
312	&ArrowBatchStreamType, 0);
313	if (!obj) return NULL;	54✔
314	new (&obj->state) std::shared_ptr<ArrowIteratorState>(std::move(state));	54✔
315	obj->consumed = false;	54✔
316	return (PyObject *)obj;	54✔
317	}	27✔
318
319	#endif

llnl / dftracer-utils / 28356348514

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