Coverage Report

Created: 2026-05-16 21:48

next uncovered line (L), next uncovered region (R), next uncovered branch (B)
be/src/exec/pipeline/pipeline_fragment_context.cpp
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Source
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// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements.  See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership.  The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
6
// "License"); you may not use this file except in compliance
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// with the License.  You may obtain a copy of the License at
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//
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//   http://www.apache.org/licenses/LICENSE-2.0
10
//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied.  See the License for the
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// specific language governing permissions and limitations
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// under the License.
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#include "exec/pipeline/pipeline_fragment_context.h"
19
20
#include <gen_cpp/DataSinks_types.h>
21
#include <gen_cpp/FrontendService.h>
22
#include <gen_cpp/FrontendService_types.h>
23
#include <gen_cpp/PaloInternalService_types.h>
24
#include <gen_cpp/PlanNodes_types.h>
25
#include <pthread.h>
26
27
#include <algorithm>
28
#include <cstdlib>
29
// IWYU pragma: no_include <bits/chrono.h>
30
#include <fmt/format.h>
31
#include <thrift/Thrift.h>
32
#include <thrift/protocol/TDebugProtocol.h>
33
#include <thrift/transport/TTransportException.h>
34
35
#include <chrono> // IWYU pragma: keep
36
#include <map>
37
#include <memory>
38
#include <ostream>
39
#include <utility>
40
41
#include "cloud/config.h"
42
#include "common/cast_set.h"
43
#include "common/config.h"
44
#include "common/exception.h"
45
#include "common/logging.h"
46
#include "common/status.h"
47
#include "exec/exchange/local_exchange_sink_operator.h"
48
#include "exec/exchange/local_exchange_source_operator.h"
49
#include "exec/exchange/local_exchanger.h"
50
#include "exec/exchange/vdata_stream_mgr.h"
51
#include "exec/operator/aggregation_sink_operator.h"
52
#include "exec/operator/aggregation_source_operator.h"
53
#include "exec/operator/analytic_sink_operator.h"
54
#include "exec/operator/analytic_source_operator.h"
55
#include "exec/operator/assert_num_rows_operator.h"
56
#include "exec/operator/blackhole_sink_operator.h"
57
#include "exec/operator/bucketed_aggregation_sink_operator.h"
58
#include "exec/operator/bucketed_aggregation_source_operator.h"
59
#include "exec/operator/cache_sink_operator.h"
60
#include "exec/operator/cache_source_operator.h"
61
#include "exec/operator/datagen_operator.h"
62
#include "exec/operator/dict_sink_operator.h"
63
#include "exec/operator/distinct_streaming_aggregation_operator.h"
64
#include "exec/operator/empty_set_operator.h"
65
#include "exec/operator/exchange_sink_operator.h"
66
#include "exec/operator/exchange_source_operator.h"
67
#include "exec/operator/file_scan_operator.h"
68
#include "exec/operator/group_commit_block_sink_operator.h"
69
#include "exec/operator/group_commit_scan_operator.h"
70
#include "exec/operator/hashjoin_build_sink.h"
71
#include "exec/operator/hashjoin_probe_operator.h"
72
#include "exec/operator/hive_table_sink_operator.h"
73
#include "exec/operator/iceberg_delete_sink_operator.h"
74
#include "exec/operator/iceberg_merge_sink_operator.h"
75
#include "exec/operator/iceberg_table_sink_operator.h"
76
#include "exec/operator/jdbc_scan_operator.h"
77
#include "exec/operator/jdbc_table_sink_operator.h"
78
#include "exec/operator/local_merge_sort_source_operator.h"
79
#include "exec/operator/materialization_opertor.h"
80
#include "exec/operator/maxcompute_table_sink_operator.h"
81
#include "exec/operator/memory_scratch_sink_operator.h"
82
#include "exec/operator/meta_scan_operator.h"
83
#include "exec/operator/multi_cast_data_stream_sink.h"
84
#include "exec/operator/multi_cast_data_stream_source.h"
85
#include "exec/operator/nested_loop_join_build_operator.h"
86
#include "exec/operator/nested_loop_join_probe_operator.h"
87
#include "exec/operator/olap_scan_operator.h"
88
#include "exec/operator/olap_table_sink_operator.h"
89
#include "exec/operator/olap_table_sink_v2_operator.h"
90
#include "exec/operator/partition_sort_sink_operator.h"
91
#include "exec/operator/partition_sort_source_operator.h"
92
#include "exec/operator/partitioned_aggregation_sink_operator.h"
93
#include "exec/operator/partitioned_aggregation_source_operator.h"
94
#include "exec/operator/partitioned_hash_join_probe_operator.h"
95
#include "exec/operator/partitioned_hash_join_sink_operator.h"
96
#include "exec/operator/rec_cte_anchor_sink_operator.h"
97
#include "exec/operator/rec_cte_scan_operator.h"
98
#include "exec/operator/rec_cte_sink_operator.h"
99
#include "exec/operator/rec_cte_source_operator.h"
100
#include "exec/operator/repeat_operator.h"
101
#include "exec/operator/result_file_sink_operator.h"
102
#include "exec/operator/result_sink_operator.h"
103
#include "exec/operator/schema_scan_operator.h"
104
#include "exec/operator/select_operator.h"
105
#include "exec/operator/set_probe_sink_operator.h"
106
#include "exec/operator/set_sink_operator.h"
107
#include "exec/operator/set_source_operator.h"
108
#include "exec/operator/sort_sink_operator.h"
109
#include "exec/operator/sort_source_operator.h"
110
#include "exec/operator/spill_iceberg_table_sink_operator.h"
111
#include "exec/operator/spill_sort_sink_operator.h"
112
#include "exec/operator/spill_sort_source_operator.h"
113
#include "exec/operator/streaming_aggregation_operator.h"
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#include "exec/operator/table_function_operator.h"
115
#include "exec/operator/tvf_table_sink_operator.h"
116
#include "exec/operator/union_sink_operator.h"
117
#include "exec/operator/union_source_operator.h"
118
#include "exec/pipeline/dependency.h"
119
#include "exec/pipeline/pipeline_task.h"
120
#include "exec/pipeline/task_scheduler.h"
121
#include "exec/runtime_filter/runtime_filter_mgr.h"
122
#include "exec/sort/topn_sorter.h"
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#include "exec/spill/spill_file.h"
124
#include "io/fs/stream_load_pipe.h"
125
#include "load/stream_load/new_load_stream_mgr.h"
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#include "runtime/exec_env.h"
127
#include "runtime/fragment_mgr.h"
128
#include "runtime/result_buffer_mgr.h"
129
#include "runtime/runtime_state.h"
130
#include "runtime/thread_context.h"
131
#include "service/backend_options.h"
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#include "util/client_cache.h"
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#include "util/countdown_latch.h"
134
#include "util/debug_util.h"
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#include "util/network_util.h"
136
#include "util/uid_util.h"
137
138
namespace doris {
139
PipelineFragmentContext::PipelineFragmentContext(
140
        TUniqueId query_id, const TPipelineFragmentParams& request,
141
        std::shared_ptr<QueryContext> query_ctx, ExecEnv* exec_env,
142
        const std::function<void(RuntimeState*, Status*)>& call_back)
143
326k
        : _query_id(std::move(query_id)),
144
326k
          _fragment_id(request.fragment_id),
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326k
          _exec_env(exec_env),
146
326k
          _query_ctx(std::move(query_ctx)),
147
326k
          _call_back(call_back),
148
326k
          _is_report_on_cancel(true),
149
326k
          _params(request),
150
326k
          _parallel_instances(_params.__isset.parallel_instances ? _params.parallel_instances : 0),
151
326k
          _need_notify_close(request.__isset.need_notify_close ? request.need_notify_close
152
326k
                                                               : false) {
153
326k
    _fragment_watcher.start();
154
326k
}
155
156
326k
PipelineFragmentContext::~PipelineFragmentContext() {
157
326k
    LOG_INFO("PipelineFragmentContext::~PipelineFragmentContext")
158
326k
            .tag("query_id", print_id(_query_id))
159
326k
            .tag("fragment_id", _fragment_id);
160
326k
    _release_resource();
161
326k
    {
162
        // The memory released by the query end is recorded in the query mem tracker.
163
326k
        SCOPED_SWITCH_THREAD_MEM_TRACKER_LIMITER(_query_ctx->query_mem_tracker());
164
326k
        _runtime_state.reset();
165
326k
        _query_ctx.reset();
166
326k
    }
167
326k
}
168
169
80
bool PipelineFragmentContext::is_timeout(timespec now) const {
170
80
    if (_timeout <= 0) {
171
0
        return false;
172
0
    }
173
80
    return _fragment_watcher.elapsed_time_seconds(now) > _timeout;
174
80
}
175
176
// notify_close() transitions the PFC from "waiting for external close notification" to
177
// "self-managed close". For recursive CTE fragments, the old PFC is kept alive until
178
// the rerun_fragment(wait_for_destroy) RPC calls this to trigger shutdown.
179
// Returns true if all tasks have already closed (i.e., the PFC can be safely destroyed).
180
8.35k
bool PipelineFragmentContext::notify_close() {
181
8.35k
    bool all_closed = false;
182
8.35k
    bool need_remove = false;
183
8.35k
    {
184
8.35k
        std::lock_guard<std::mutex> l(_task_mutex);
185
8.35k
        if (_closed_tasks >= _total_tasks) {
186
3.44k
            if (_need_notify_close) {
187
                // Fragment was cancelled and waiting for notify to close.
188
                // Record that we need to remove from fragment mgr, but do it
189
                // after releasing _task_mutex to avoid ABBA deadlock with
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                // dump_pipeline_tasks() (which acquires _pipeline_map lock
191
                // first, then _task_mutex via debug_string()).
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3.41k
                need_remove = true;
193
3.41k
            }
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3.44k
            all_closed = true;
195
3.44k
        }
196
        // make fragment release by self after cancel
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8.35k
        _need_notify_close = false;
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8.35k
    }
199
8.35k
    if (need_remove) {
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3.41k
        _exec_env->fragment_mgr()->remove_pipeline_context({_query_id, _fragment_id});
201
3.41k
    }
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8.35k
    return all_closed;
203
8.35k
}
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// Must not add lock in this method. Because it will call query ctx cancel. And
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// QueryCtx cancel will call fragment ctx cancel. And Also Fragment ctx's running
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// Method like exchange sink buffer will call query ctx cancel. If we add lock here
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// There maybe dead lock.
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4.87k
void PipelineFragmentContext::cancel(const Status reason) {
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4.87k
    LOG_INFO("PipelineFragmentContext::cancel")
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4.87k
            .tag("query_id", print_id(_query_id))
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4.87k
            .tag("fragment_id", _fragment_id)
213
4.87k
            .tag("reason", reason.to_string());
214
4.87k
    if (notify_close()) {
215
52
        return;
216
52
    }
217
    // Timeout is a special error code, we need print current stack to debug timeout issue.
218
4.82k
    if (reason.is<ErrorCode::TIMEOUT>()) {
219
1
        auto dbg_str = fmt::format("PipelineFragmentContext is cancelled due to timeout:\n{}",
220
1
                                   debug_string());
221
1
        LOG_LONG_STRING(WARNING, dbg_str);
222
1
    }
223
224
    // `ILLEGAL_STATE` means queries this fragment belongs to was not found in FE (maybe finished)
225
4.82k
    if (reason.is<ErrorCode::ILLEGAL_STATE>()) {
226
0
        LOG_WARNING("PipelineFragmentContext is cancelled due to illegal state : {}",
227
0
                    debug_string());
228
0
    }
229
230
4.82k
    if (reason.is<ErrorCode::MEM_LIMIT_EXCEEDED>() || reason.is<ErrorCode::MEM_ALLOC_FAILED>()) {
231
0
        print_profile("cancel pipeline, reason: " + reason.to_string());
232
0
    }
233
234
4.82k
    if (auto error_url = get_load_error_url(); !error_url.empty()) {
235
25
        _query_ctx->set_load_error_url(error_url);
236
25
    }
237
238
4.82k
    if (auto first_error_msg = get_first_error_msg(); !first_error_msg.empty()) {
239
25
        _query_ctx->set_first_error_msg(first_error_msg);
240
25
    }
241
242
4.82k
    _query_ctx->cancel(reason, _fragment_id);
243
4.82k
    if (reason.is<ErrorCode::LIMIT_REACH>()) {
244
187
        _is_report_on_cancel = false;
245
4.63k
    } else {
246
16.9k
        for (auto& id : _fragment_instance_ids) {
247
16.9k
            LOG(WARNING) << "PipelineFragmentContext cancel instance: " << print_id(id);
248
16.9k
        }
249
4.63k
    }
250
    // Get pipe from new load stream manager and send cancel to it or the fragment may hang to wait read from pipe
251
    // For stream load the fragment's query_id == load id, it is set in FE.
252
4.82k
    auto stream_load_ctx = _exec_env->new_load_stream_mgr()->get(_query_id);
253
4.82k
    if (stream_load_ctx != nullptr) {
254
35
        stream_load_ctx->pipe->cancel(reason.to_string());
255
        // Set error URL here because after pipe is cancelled, stream load execution may return early.
256
        // We need to set the error URL at this point to ensure error information is properly
257
        // propagated to the client.
258
35
        stream_load_ctx->error_url = get_load_error_url();
259
35
        stream_load_ctx->first_error_msg = get_first_error_msg();
260
35
    }
261
262
17.6k
    for (auto& tasks : _tasks) {
263
39.3k
        for (auto& task : tasks) {
264
39.3k
            task.first->unblock_all_dependencies();
265
39.3k
        }
266
17.6k
    }
267
4.82k
}
268
269
511k
PipelinePtr PipelineFragmentContext::add_pipeline(PipelinePtr parent, int idx) {
270
511k
    PipelineId id = _next_pipeline_id++;
271
511k
    auto pipeline = std::make_shared<Pipeline>(
272
511k
            id, parent ? std::min(parent->num_tasks(), _num_instances) : _num_instances,
273
511k
            parent ? parent->num_tasks() : _num_instances);
274
511k
    if (idx >= 0) {
275
94.4k
        _pipelines.insert(_pipelines.begin() + idx, pipeline);
276
416k
    } else {
277
416k
        _pipelines.emplace_back(pipeline);
278
416k
    }
279
511k
    if (parent) {
280
183k
        parent->set_children(pipeline);
281
183k
    }
282
511k
    return pipeline;
283
511k
}
284
285
326k
Status PipelineFragmentContext::_build_and_prepare_full_pipeline(ThreadPool* thread_pool) {
286
326k
    {
287
326k
        SCOPED_TIMER(_build_pipelines_timer);
288
        // 2. Build pipelines with operators in this fragment.
289
326k
        auto root_pipeline = add_pipeline();
290
326k
        RETURN_IF_ERROR(_build_pipelines(_runtime_state->obj_pool(), *_query_ctx->desc_tbl,
291
326k
                                         &_root_op, root_pipeline));
292
293
        // 3. Create sink operator
294
326k
        if (!_params.fragment.__isset.output_sink) {
295
0
            return Status::InternalError("No output sink in this fragment!");
296
0
        }
297
326k
        RETURN_IF_ERROR(_create_data_sink(_runtime_state->obj_pool(), _params.fragment.output_sink,
298
326k
                                          _params.fragment.output_exprs, _params,
299
326k
                                          root_pipeline->output_row_desc(), _runtime_state.get(),
300
326k
                                          *_desc_tbl, root_pipeline->id()));
301
326k
        RETURN_IF_ERROR(_sink->init(_params.fragment.output_sink));
302
326k
        RETURN_IF_ERROR(root_pipeline->set_sink(_sink));
303
304
416k
        for (PipelinePtr& pipeline : _pipelines) {
305
416k
            DCHECK(pipeline->sink() != nullptr) << pipeline->operators().size();
306
416k
            RETURN_IF_ERROR(pipeline->sink()->set_child(pipeline->operators().back()));
307
416k
        }
308
326k
    }
309
    // 4. Build local exchanger
310
326k
    if (_runtime_state->enable_local_shuffle()) {
311
323k
        SCOPED_TIMER(_plan_local_exchanger_timer);
312
323k
        RETURN_IF_ERROR(_plan_local_exchange(_params.num_buckets,
313
323k
                                             _params.bucket_seq_to_instance_idx,
314
323k
                                             _params.shuffle_idx_to_instance_idx));
315
323k
    }
316
317
    // 5. Initialize global states in pipelines.
318
511k
    for (PipelinePtr& pipeline : _pipelines) {
319
511k
        SCOPED_TIMER(_prepare_all_pipelines_timer);
320
511k
        pipeline->children().clear();
321
511k
        RETURN_IF_ERROR(pipeline->prepare(_runtime_state.get()));
322
511k
    }
323
324
324k
    {
325
324k
        SCOPED_TIMER(_build_tasks_timer);
326
        // 6. Build pipeline tasks and initialize local state.
327
324k
        RETURN_IF_ERROR(_build_pipeline_tasks(thread_pool));
328
324k
    }
329
330
324k
    return Status::OK();
331
324k
}
332
333
326k
Status PipelineFragmentContext::prepare(ThreadPool* thread_pool) {
334
326k
    if (_prepared) {
335
0
        return Status::InternalError("Already prepared");
336
0
    }
337
326k
    if (_params.__isset.query_options && _params.query_options.__isset.execution_timeout) {
338
326k
        _timeout = _params.query_options.execution_timeout;
339
326k
    }
340
341
326k
    _fragment_level_profile = std::make_unique<RuntimeProfile>("PipelineContext");
342
326k
    _prepare_timer = ADD_TIMER(_fragment_level_profile, "PrepareTime");
343
326k
    SCOPED_TIMER(_prepare_timer);
344
326k
    _build_pipelines_timer = ADD_TIMER(_fragment_level_profile, "BuildPipelinesTime");
345
326k
    _init_context_timer = ADD_TIMER(_fragment_level_profile, "InitContextTime");
346
326k
    _plan_local_exchanger_timer = ADD_TIMER(_fragment_level_profile, "PlanLocalLocalExchangerTime");
347
326k
    _build_tasks_timer = ADD_TIMER(_fragment_level_profile, "BuildTasksTime");
348
326k
    _prepare_all_pipelines_timer = ADD_TIMER(_fragment_level_profile, "PrepareAllPipelinesTime");
349
326k
    {
350
326k
        SCOPED_TIMER(_init_context_timer);
351
326k
        cast_set(_num_instances, _params.local_params.size());
352
326k
        _total_instances =
353
326k
                _params.__isset.total_instances ? _params.total_instances : _num_instances;
354
355
326k
        auto* fragment_context = this;
356
357
326k
        if (_params.query_options.__isset.is_report_success) {
358
325k
            fragment_context->set_is_report_success(_params.query_options.is_report_success);
359
325k
        }
360
361
        // 1. Set up the global runtime state.
362
326k
        _runtime_state = RuntimeState::create_unique(
363
326k
                _params.query_id, _params.fragment_id, _params.query_options,
364
326k
                _query_ctx->query_globals, _exec_env, _query_ctx.get());
365
326k
        _runtime_state->set_task_execution_context(shared_from_this());
366
326k
        SCOPED_SWITCH_THREAD_MEM_TRACKER_LIMITER(_runtime_state->query_mem_tracker());
367
326k
        if (_params.__isset.backend_id) {
368
323k
            _runtime_state->set_backend_id(_params.backend_id);
369
323k
        }
370
326k
        if (_params.__isset.import_label) {
371
240
            _runtime_state->set_import_label(_params.import_label);
372
240
        }
373
326k
        if (_params.__isset.db_name) {
374
192
            _runtime_state->set_db_name(_params.db_name);
375
192
        }
376
326k
        if (_params.__isset.load_job_id) {
377
0
            _runtime_state->set_load_job_id(_params.load_job_id);
378
0
        }
379
380
326k
        if (_params.is_simplified_param) {
381
114k
            _desc_tbl = _query_ctx->desc_tbl;
382
211k
        } else {
383
211k
            DCHECK(_params.__isset.desc_tbl);
384
211k
            RETURN_IF_ERROR(DescriptorTbl::create(_runtime_state->obj_pool(), _params.desc_tbl,
385
211k
                                                  &_desc_tbl));
386
211k
        }
387
326k
        _runtime_state->set_desc_tbl(_desc_tbl);
388
326k
        _runtime_state->set_num_per_fragment_instances(_params.num_senders);
389
326k
        _runtime_state->set_load_stream_per_node(_params.load_stream_per_node);
390
326k
        _runtime_state->set_total_load_streams(_params.total_load_streams);
391
326k
        _runtime_state->set_num_local_sink(_params.num_local_sink);
392
393
        // init fragment_instance_ids
394
326k
        const auto target_size = _params.local_params.size();
395
326k
        _fragment_instance_ids.resize(target_size);
396
1.21M
        for (size_t i = 0; i < _params.local_params.size(); i++) {
397
887k
            auto fragment_instance_id = _params.local_params[i].fragment_instance_id;
398
887k
            _fragment_instance_ids[i] = fragment_instance_id;
399
887k
        }
400
326k
    }
401
402
326k
    RETURN_IF_ERROR(_build_and_prepare_full_pipeline(thread_pool));
403
404
324k
    _init_next_report_time();
405
406
324k
    _prepared = true;
407
324k
    return Status::OK();
408
326k
}
409
410
Status PipelineFragmentContext::_build_pipeline_tasks_for_instance(
411
        int instance_idx,
412
885k
        const std::vector<std::shared_ptr<RuntimeProfile>>& pipeline_id_to_profile) {
413
885k
    const auto& local_params = _params.local_params[instance_idx];
414
885k
    auto fragment_instance_id = local_params.fragment_instance_id;
415
885k
    auto runtime_filter_mgr = std::make_unique<RuntimeFilterMgr>(false);
416
885k
    std::map<PipelineId, PipelineTask*> pipeline_id_to_task;
417
885k
    auto get_shared_state = [&](PipelinePtr pipeline)
418
885k
            -> std::map<int, std::pair<std::shared_ptr<BasicSharedState>,
419
1.44M
                                       std::vector<std::shared_ptr<Dependency>>>> {
420
1.44M
        std::map<int, std::pair<std::shared_ptr<BasicSharedState>,
421
1.44M
                                std::vector<std::shared_ptr<Dependency>>>>
422
1.44M
                shared_state_map;
423
1.93M
        for (auto& op : pipeline->operators()) {
424
1.93M
            auto source_id = op->operator_id();
425
1.93M
            if (auto iter = _op_id_to_shared_state.find(source_id);
426
1.93M
                iter != _op_id_to_shared_state.end()) {
427
575k
                shared_state_map.insert({source_id, iter->second});
428
575k
            }
429
1.93M
        }
430
1.44M
        for (auto sink_to_source_id : pipeline->sink()->dests_id()) {
431
1.44M
            if (auto iter = _op_id_to_shared_state.find(sink_to_source_id);
432
1.44M
                iter != _op_id_to_shared_state.end()) {
433
239k
                shared_state_map.insert({sink_to_source_id, iter->second});
434
239k
            }
435
1.44M
        }
436
1.44M
        return shared_state_map;
437
1.44M
    };
438
439
2.66M
    for (size_t pip_idx = 0; pip_idx < _pipelines.size(); pip_idx++) {
440
1.77M
        auto& pipeline = _pipelines[pip_idx];
441
1.77M
        if (pipeline->num_tasks() > 1 || instance_idx == 0) {
442
1.44M
            auto task_runtime_state = RuntimeState::create_unique(
443
1.44M
                    local_params.fragment_instance_id, _params.query_id, _params.fragment_id,
444
1.44M
                    _params.query_options, _query_ctx->query_globals, _exec_env, _query_ctx.get());
445
1.44M
            {
446
                // Initialize runtime state for this task
447
1.44M
                task_runtime_state->set_query_mem_tracker(_query_ctx->query_mem_tracker());
448
449
1.44M
                task_runtime_state->set_task_execution_context(shared_from_this());
450
1.44M
                task_runtime_state->set_be_number(local_params.backend_num);
451
452
1.44M
                if (_params.__isset.backend_id) {
453
1.44M
                    task_runtime_state->set_backend_id(_params.backend_id);
454
1.44M
                }
455
1.44M
                if (_params.__isset.import_label) {
456
241
                    task_runtime_state->set_import_label(_params.import_label);
457
241
                }
458
1.44M
                if (_params.__isset.db_name) {
459
193
                    task_runtime_state->set_db_name(_params.db_name);
460
193
                }
461
1.44M
                if (_params.__isset.load_job_id) {
462
0
                    task_runtime_state->set_load_job_id(_params.load_job_id);
463
0
                }
464
1.44M
                if (_params.__isset.wal_id) {
465
114
                    task_runtime_state->set_wal_id(_params.wal_id);
466
114
                }
467
1.44M
                if (_params.__isset.content_length) {
468
32
                    task_runtime_state->set_content_length(_params.content_length);
469
32
                }
470
471
1.44M
                task_runtime_state->set_desc_tbl(_desc_tbl);
472
1.44M
                task_runtime_state->set_per_fragment_instance_idx(local_params.sender_id);
473
1.44M
                task_runtime_state->set_num_per_fragment_instances(_params.num_senders);
474
1.44M
                task_runtime_state->resize_op_id_to_local_state(max_operator_id());
475
1.44M
                task_runtime_state->set_max_operator_id(max_operator_id());
476
1.44M
                task_runtime_state->set_load_stream_per_node(_params.load_stream_per_node);
477
1.44M
                task_runtime_state->set_total_load_streams(_params.total_load_streams);
478
1.44M
                task_runtime_state->set_num_local_sink(_params.num_local_sink);
479
480
1.44M
                task_runtime_state->set_runtime_filter_mgr(runtime_filter_mgr.get());
481
1.44M
            }
482
1.44M
            auto cur_task_id = _total_tasks++;
483
1.44M
            task_runtime_state->set_task_id(cur_task_id);
484
1.44M
            task_runtime_state->set_task_num(pipeline->num_tasks());
485
1.44M
            auto task = std::make_shared<PipelineTask>(
486
1.44M
                    pipeline, cur_task_id, task_runtime_state.get(),
487
1.44M
                    std::dynamic_pointer_cast<PipelineFragmentContext>(shared_from_this()),
488
1.44M
                    pipeline_id_to_profile[pip_idx].get(), get_shared_state(pipeline),
489
1.44M
                    instance_idx);
490
1.44M
            pipeline->incr_created_tasks(instance_idx, task.get());
491
1.44M
            pipeline_id_to_task.insert({pipeline->id(), task.get()});
492
1.44M
            _tasks[instance_idx].emplace_back(
493
1.44M
                    std::pair<std::shared_ptr<PipelineTask>, std::unique_ptr<RuntimeState>> {
494
1.44M
                            std::move(task), std::move(task_runtime_state)});
495
1.44M
        }
496
1.77M
    }
497
498
    /**
499
         * Build DAG for pipeline tasks.
500
         * For example, we have
501
         *
502
         *   ExchangeSink (Pipeline1)     JoinBuildSink (Pipeline2)
503
         *            \                      /
504
         *          JoinProbeOperator1 (Pipeline1)    JoinBuildSink (Pipeline3)
505
         *                 \                          /
506
         *               JoinProbeOperator2 (Pipeline1)
507
         *
508
         * In this fragment, we have three pipelines and pipeline 1 depends on pipeline 2 and pipeline 3.
509
         * To build this DAG, `_dag` manage dependencies between pipelines by pipeline ID and
510
         * `pipeline_id_to_task` is used to find the task by a unique pipeline ID.
511
         *
512
         * Finally, we have two upstream dependencies in Pipeline1 corresponding to JoinProbeOperator1
513
         * and JoinProbeOperator2.
514
         */
515
1.77M
    for (auto& _pipeline : _pipelines) {
516
1.77M
        if (pipeline_id_to_task.contains(_pipeline->id())) {
517
1.44M
            auto* task = pipeline_id_to_task[_pipeline->id()];
518
1.44M
            DCHECK(task != nullptr);
519
520
            // If this task has upstream dependency, then inject it into this task.
521
1.44M
            if (_dag.contains(_pipeline->id())) {
522
902k
                auto& deps = _dag[_pipeline->id()];
523
1.45M
                for (auto& dep : deps) {
524
1.45M
                    if (pipeline_id_to_task.contains(dep)) {
525
788k
                        auto ss = pipeline_id_to_task[dep]->get_sink_shared_state();
526
788k
                        if (ss) {
527
308k
                            task->inject_shared_state(ss);
528
479k
                        } else {
529
479k
                            pipeline_id_to_task[dep]->inject_shared_state(
530
479k
                                    task->get_source_shared_state());
531
479k
                        }
532
788k
                    }
533
1.45M
                }
534
902k
            }
535
1.44M
        }
536
1.77M
    }
537
2.66M
    for (size_t pip_idx = 0; pip_idx < _pipelines.size(); pip_idx++) {
538
1.77M
        if (pipeline_id_to_task.contains(_pipelines[pip_idx]->id())) {
539
1.44M
            auto* task = pipeline_id_to_task[_pipelines[pip_idx]->id()];
540
1.44M
            DCHECK(pipeline_id_to_profile[pip_idx]);
541
1.44M
            std::vector<TScanRangeParams> scan_ranges;
542
1.44M
            auto node_id = _pipelines[pip_idx]->operators().front()->node_id();
543
1.44M
            if (local_params.per_node_scan_ranges.contains(node_id)) {
544
237k
                scan_ranges = local_params.per_node_scan_ranges.find(node_id)->second;
545
237k
            }
546
1.44M
            RETURN_IF_ERROR_OR_CATCH_EXCEPTION(task->prepare(scan_ranges, local_params.sender_id,
547
1.44M
                                                             _params.fragment.output_sink));
548
1.44M
        }
549
1.77M
    }
550
884k
    {
551
884k
        std::lock_guard<std::mutex> l(_state_map_lock);
552
884k
        _runtime_filter_mgr_map[instance_idx] = std::move(runtime_filter_mgr);
553
884k
    }
554
884k
    return Status::OK();
555
885k
}
556
557
325k
Status PipelineFragmentContext::_build_pipeline_tasks(ThreadPool* thread_pool) {
558
325k
    _total_tasks = 0;
559
325k
    _closed_tasks = 0;
560
325k
    const auto target_size = _params.local_params.size();
561
325k
    _tasks.resize(target_size);
562
325k
    _runtime_filter_mgr_map.resize(target_size);
563
835k
    for (size_t pip_idx = 0; pip_idx < _pipelines.size(); pip_idx++) {
564
510k
        _pip_id_to_pipeline[_pipelines[pip_idx]->id()] = _pipelines[pip_idx].get();
565
510k
    }
566
325k
    auto pipeline_id_to_profile = _runtime_state->build_pipeline_profile(_pipelines.size());
567
568
325k
    if (target_size > 1 &&
569
325k
        (_runtime_state->query_options().__isset.parallel_prepare_threshold &&
570
125k
         target_size > _runtime_state->query_options().parallel_prepare_threshold)) {
571
        // If instances parallelism is big enough ( > parallel_prepare_threshold), we will prepare all tasks by multi-threads
572
20.9k
        std::vector<Status> prepare_status(target_size);
573
20.9k
        int submitted_tasks = 0;
574
20.9k
        Status submit_status;
575
20.9k
        CountDownLatch latch((int)target_size);
576
229k
        for (int i = 0; i < target_size; i++) {
577
208k
            submit_status = thread_pool->submit_func([&, i]() {
578
208k
                SCOPED_ATTACH_TASK(_query_ctx.get());
579
208k
                prepare_status[i] = _build_pipeline_tasks_for_instance(i, pipeline_id_to_profile);
580
208k
                latch.count_down();
581
208k
            });
582
208k
            if (LIKELY(submit_status.ok())) {
583
208k
                submitted_tasks++;
584
18.4E
            } else {
585
18.4E
                break;
586
18.4E
            }
587
208k
        }
588
20.9k
        latch.arrive_and_wait(target_size - submitted_tasks);
589
20.9k
        if (UNLIKELY(!submit_status.ok())) {
590
0
            return submit_status;
591
0
        }
592
229k
        for (int i = 0; i < submitted_tasks; i++) {
593
208k
            if (!prepare_status[i].ok()) {
594
0
                return prepare_status[i];
595
0
            }
596
208k
        }
597
304k
    } else {
598
982k
        for (int i = 0; i < target_size; i++) {
599
678k
            RETURN_IF_ERROR(_build_pipeline_tasks_for_instance(i, pipeline_id_to_profile));
600
678k
        }
601
304k
    }
602
325k
    _pipeline_parent_map.clear();
603
325k
    _op_id_to_shared_state.clear();
604
    // Record task cardinality once when this fragment context finishes task initialization.
605
325k
    _query_ctx->add_total_task_num(_total_tasks.load(std::memory_order_relaxed));
606
607
325k
    return Status::OK();
608
325k
}
609
610
324k
void PipelineFragmentContext::_init_next_report_time() {
611
324k
    auto interval_s = config::pipeline_status_report_interval;
612
324k
    if (_is_report_success && interval_s > 0 && _timeout > interval_s) {
613
31.8k
        VLOG_FILE << "enable period report: fragment id=" << _fragment_id;
614
31.8k
        uint64_t report_fragment_offset = (uint64_t)(rand() % interval_s) * NANOS_PER_SEC;
615
        // We don't want to wait longer than it takes to run the entire fragment.
616
31.8k
        _previous_report_time =
617
31.8k
                MonotonicNanos() + report_fragment_offset - (uint64_t)(interval_s)*NANOS_PER_SEC;
618
31.8k
        _disable_period_report = false;
619
31.8k
    }
620
324k
}
621
622
3.67k
void PipelineFragmentContext::refresh_next_report_time() {
623
3.67k
    auto disable = _disable_period_report.load(std::memory_order_acquire);
624
3.67k
    DCHECK(disable == true);
625
3.67k
    _previous_report_time.store(MonotonicNanos(), std::memory_order_release);
626
3.67k
    _disable_period_report.compare_exchange_strong(disable, false);
627
3.67k
}
628
629
5.22M
void PipelineFragmentContext::trigger_report_if_necessary() {
630
5.22M
    if (!_is_report_success) {
631
4.88M
        return;
632
4.88M
    }
633
343k
    auto disable = _disable_period_report.load(std::memory_order_acquire);
634
343k
    if (disable) {
635
5.65k
        return;
636
5.65k
    }
637
337k
    int32_t interval_s = config::pipeline_status_report_interval;
638
337k
    if (interval_s <= 0) {
639
0
        LOG(WARNING) << "config::status_report_interval is equal to or less than zero, do not "
640
0
                        "trigger "
641
0
                        "report.";
642
0
    }
643
337k
    uint64_t next_report_time = _previous_report_time.load(std::memory_order_acquire) +
644
337k
                                (uint64_t)(interval_s)*NANOS_PER_SEC;
645
337k
    if (MonotonicNanos() > next_report_time) {
646
3.67k
        if (!_disable_period_report.compare_exchange_strong(disable, true,
647
3.67k
                                                            std::memory_order_acq_rel)) {
648
4
            return;
649
4
        }
650
3.67k
        if (VLOG_FILE_IS_ON) {
651
0
            VLOG_FILE << "Reporting "
652
0
                      << "profile for query_id " << print_id(_query_id)
653
0
                      << ", fragment id: " << _fragment_id;
654
655
0
            std::stringstream ss;
656
0
            _runtime_state->runtime_profile()->compute_time_in_profile();
657
0
            _runtime_state->runtime_profile()->pretty_print(&ss);
658
0
            if (_runtime_state->load_channel_profile()) {
659
0
                _runtime_state->load_channel_profile()->pretty_print(&ss);
660
0
            }
661
662
0
            VLOG_FILE << "Query " << print_id(get_query_id()) << " fragment " << get_fragment_id()
663
0
                      << " profile:\n"
664
0
                      << ss.str();
665
0
        }
666
3.67k
        auto st = send_report(false);
667
3.67k
        if (!st.ok()) {
668
0
            disable = true;
669
0
            _disable_period_report.compare_exchange_strong(disable, false,
670
0
                                                           std::memory_order_acq_rel);
671
0
        }
672
3.67k
    }
673
337k
}
674
675
Status PipelineFragmentContext::_build_pipelines(ObjectPool* pool, const DescriptorTbl& descs,
676
325k
                                                 OperatorPtr* root, PipelinePtr cur_pipe) {
677
325k
    if (_params.fragment.plan.nodes.empty()) {
678
0
        throw Exception(ErrorCode::INTERNAL_ERROR, "Invalid plan which has no plan node!");
679
0
    }
680
681
325k
    int node_idx = 0;
682
683
325k
    RETURN_IF_ERROR(_create_tree_helper(pool, _params.fragment.plan.nodes, descs, nullptr,
684
325k
                                        &node_idx, root, cur_pipe, 0, false, false));
685
686
325k
    if (node_idx + 1 != _params.fragment.plan.nodes.size()) {
687
0
        return Status::InternalError(
688
0
                "Plan tree only partially reconstructed. Not all thrift nodes were used.");
689
0
    }
690
325k
    return Status::OK();
691
325k
}
692
693
Status PipelineFragmentContext::_create_tree_helper(
694
        ObjectPool* pool, const std::vector<TPlanNode>& tnodes, const DescriptorTbl& descs,
695
        OperatorPtr parent, int* node_idx, OperatorPtr* root, PipelinePtr& cur_pipe, int child_idx,
696
521k
        const bool followed_by_shuffled_operator, const bool require_bucket_distribution) {
697
    // propagate error case
698
521k
    if (*node_idx >= tnodes.size()) {
699
0
        return Status::InternalError(
700
0
                "Failed to reconstruct plan tree from thrift. Node id: {}, number of nodes: {}",
701
0
                *node_idx, tnodes.size());
702
0
    }
703
521k
    const TPlanNode& tnode = tnodes[*node_idx];
704
705
521k
    int num_children = tnodes[*node_idx].num_children;
706
521k
    bool current_followed_by_shuffled_operator = followed_by_shuffled_operator;
707
521k
    bool current_require_bucket_distribution = require_bucket_distribution;
708
    // TODO: Create CacheOperator is confused now
709
521k
    OperatorPtr op = nullptr;
710
521k
    OperatorPtr cache_op = nullptr;
711
521k
    RETURN_IF_ERROR(_create_operator(pool, tnodes[*node_idx], descs, op, cur_pipe,
712
521k
                                     parent == nullptr ? -1 : parent->node_id(), child_idx,
713
521k
                                     followed_by_shuffled_operator,
714
521k
                                     current_require_bucket_distribution, cache_op));
715
    // Initialization must be done here. For example, group by expressions in agg will be used to
716
    // decide if a local shuffle should be planed, so it must be initialized here.
717
521k
    RETURN_IF_ERROR(op->init(tnode, _runtime_state.get()));
718
    // assert(parent != nullptr || (node_idx == 0 && root_expr != nullptr));
719
521k
    if (parent != nullptr) {
720
        // add to parent's child(s)
721
196k
        RETURN_IF_ERROR(parent->set_child(cache_op ? cache_op : op));
722
325k
    } else {
723
325k
        *root = op;
724
325k
    }
725
    /**
726
     * `ExchangeType::HASH_SHUFFLE` should be used if an operator is followed by a shuffled operator (shuffled hash join, union operator followed by co-located operators).
727
     *
728
     * For plan:
729
     * LocalExchange(id=0) -> Aggregation(id=1) -> ShuffledHashJoin(id=2)
730
     *                           Exchange(id=3) -> ShuffledHashJoinBuild(id=2)
731
     * We must ensure data distribution of `LocalExchange(id=0)` is same as Exchange(id=3).
732
     *
733
     * If an operator's is followed by a local exchange without shuffle (e.g. passthrough), a
734
     * shuffled local exchanger will be used before join so it is not followed by shuffle join.
735
     */
736
521k
    auto required_data_distribution =
737
521k
            cur_pipe->operators().empty()
738
521k
                    ? cur_pipe->sink()->required_data_distribution(_runtime_state.get())
739
521k
                    : op->required_data_distribution(_runtime_state.get());
740
521k
    current_followed_by_shuffled_operator =
741
521k
            ((followed_by_shuffled_operator ||
742
521k
              (cur_pipe->operators().empty() ? cur_pipe->sink()->is_shuffled_operator()
743
462k
                                             : op->is_shuffled_operator())) &&
744
521k
             Pipeline::is_hash_exchange(required_data_distribution.distribution_type)) ||
745
521k
            (followed_by_shuffled_operator &&
746
412k
             required_data_distribution.distribution_type == ExchangeType::NOOP);
747
748
521k
    current_require_bucket_distribution =
749
521k
            ((require_bucket_distribution ||
750
521k
              (cur_pipe->operators().empty() ? cur_pipe->sink()->is_colocated_operator()
751
467k
                                             : op->is_colocated_operator())) &&
752
521k
             Pipeline::is_hash_exchange(required_data_distribution.distribution_type)) ||
753
521k
            (require_bucket_distribution &&
754
418k
             required_data_distribution.distribution_type == ExchangeType::NOOP);
755
756
521k
    if (num_children == 0) {
757
337k
        _use_serial_source = op->is_serial_operator();
758
337k
    }
759
    // rely on that tnodes is preorder of the plan
760
717k
    for (int i = 0; i < num_children; i++) {
761
196k
        ++*node_idx;
762
196k
        RETURN_IF_ERROR(_create_tree_helper(pool, tnodes, descs, op, node_idx, nullptr, cur_pipe, i,
763
196k
                                            current_followed_by_shuffled_operator,
764
196k
                                            current_require_bucket_distribution));
765
766
        // we are expecting a child, but have used all nodes
767
        // this means we have been given a bad tree and must fail
768
196k
        if (*node_idx >= tnodes.size()) {
769
0
            return Status::InternalError(
770
0
                    "Failed to reconstruct plan tree from thrift. Node id: {}, number of "
771
0
                    "nodes: {}",
772
0
                    *node_idx, tnodes.size());
773
0
        }
774
196k
    }
775
776
521k
    return Status::OK();
777
521k
}
778
779
void PipelineFragmentContext::_inherit_pipeline_properties(
780
        const DataDistribution& data_distribution, PipelinePtr pipe_with_source,
781
94.4k
        PipelinePtr pipe_with_sink) {
782
94.4k
    pipe_with_sink->set_num_tasks(pipe_with_source->num_tasks());
783
94.4k
    pipe_with_source->set_num_tasks(_num_instances);
784
94.4k
    pipe_with_source->set_data_distribution(data_distribution);
785
94.4k
}
786
787
Status PipelineFragmentContext::_add_local_exchange_impl(
788
        int idx, ObjectPool* pool, PipelinePtr cur_pipe, PipelinePtr new_pip,
789
        DataDistribution data_distribution, bool* do_local_exchange, int num_buckets,
790
        const std::map<int, int>& bucket_seq_to_instance_idx,
791
94.4k
        const std::map<int, int>& shuffle_idx_to_instance_idx) {
792
94.4k
    auto& operators = cur_pipe->operators();
793
94.4k
    const auto downstream_pipeline_id = cur_pipe->id();
794
94.4k
    auto local_exchange_id = next_operator_id();
795
    // 1. Create a new pipeline with local exchange sink.
796
94.4k
    DataSinkOperatorPtr sink;
797
94.4k
    auto sink_id = next_sink_operator_id();
798
799
    /**
800
     * `bucket_seq_to_instance_idx` is empty if no scan operator is contained in this fragment.
801
     * So co-located operators(e.g. Agg, Analytic) should use `HASH_SHUFFLE` instead of `BUCKET_HASH_SHUFFLE`.
802
     */
803
94.4k
    const bool followed_by_shuffled_operator =
804
94.4k
            operators.size() > idx ? operators[idx]->followed_by_shuffled_operator()
805
94.4k
                                   : cur_pipe->sink()->followed_by_shuffled_operator();
806
94.4k
    const bool use_global_hash_shuffle = bucket_seq_to_instance_idx.empty() &&
807
94.4k
                                         !shuffle_idx_to_instance_idx.contains(-1) &&
808
94.4k
                                         followed_by_shuffled_operator && !_use_serial_source;
809
94.4k
    sink = std::make_shared<LocalExchangeSinkOperatorX>(
810
94.4k
            sink_id, local_exchange_id, use_global_hash_shuffle ? _total_instances : _num_instances,
811
94.4k
            data_distribution.partition_exprs, bucket_seq_to_instance_idx);
812
94.4k
    if (bucket_seq_to_instance_idx.empty() &&
813
94.4k
        data_distribution.distribution_type == ExchangeType::BUCKET_HASH_SHUFFLE) {
814
8
        data_distribution.distribution_type = ExchangeType::HASH_SHUFFLE;
815
8
    }
816
94.4k
    RETURN_IF_ERROR(new_pip->set_sink(sink));
817
94.4k
    RETURN_IF_ERROR(new_pip->sink()->init(_runtime_state.get(), data_distribution.distribution_type,
818
94.4k
                                          num_buckets, use_global_hash_shuffle,
819
94.4k
                                          shuffle_idx_to_instance_idx));
820
821
    // 2. Create and initialize LocalExchangeSharedState.
822
94.4k
    std::shared_ptr<LocalExchangeSharedState> shared_state =
823
94.4k
            LocalExchangeSharedState::create_shared(_num_instances);
824
94.4k
    switch (data_distribution.distribution_type) {
825
9.93k
    case ExchangeType::HASH_SHUFFLE:
826
9.93k
        shared_state->exchanger = ShuffleExchanger::create_unique(
827
9.93k
                std::max(cur_pipe->num_tasks(), _num_instances), _num_instances,
828
9.93k
                use_global_hash_shuffle ? _total_instances : _num_instances,
829
9.93k
                _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
830
9.93k
                        ? cast_set<int>(
831
9.93k
                                  _runtime_state->query_options().local_exchange_free_blocks_limit)
832
9.93k
                        : 0);
833
9.93k
        break;
834
512
    case ExchangeType::BUCKET_HASH_SHUFFLE:
835
512
        shared_state->exchanger = BucketShuffleExchanger::create_unique(
836
512
                std::max(cur_pipe->num_tasks(), _num_instances), _num_instances, num_buckets,
837
512
                _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
838
512
                        ? cast_set<int>(
839
512
                                  _runtime_state->query_options().local_exchange_free_blocks_limit)
840
512
                        : 0);
841
512
        break;
842
80.7k
    case ExchangeType::PASSTHROUGH:
843
80.7k
        shared_state->exchanger = PassthroughExchanger::create_unique(
844
80.7k
                cur_pipe->num_tasks(), _num_instances,
845
80.7k
                _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
846
80.7k
                        ? cast_set<int>(
847
80.7k
                                  _runtime_state->query_options().local_exchange_free_blocks_limit)
848
80.7k
                        : 0);
849
80.7k
        break;
850
297
    case ExchangeType::BROADCAST:
851
297
        shared_state->exchanger = BroadcastExchanger::create_unique(
852
297
                cur_pipe->num_tasks(), _num_instances,
853
297
                _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
854
297
                        ? cast_set<int>(
855
297
                                  _runtime_state->query_options().local_exchange_free_blocks_limit)
856
297
                        : 0);
857
297
        break;
858
2.06k
    case ExchangeType::PASS_TO_ONE:
859
2.06k
        if (_runtime_state->enable_share_hash_table_for_broadcast_join()) {
860
            // If shared hash table is enabled for BJ, hash table will be built by only one task
861
845
            shared_state->exchanger = PassToOneExchanger::create_unique(
862
845
                    cur_pipe->num_tasks(), _num_instances,
863
845
                    _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
864
845
                            ? cast_set<int>(_runtime_state->query_options()
865
845
                                                    .local_exchange_free_blocks_limit)
866
845
                            : 0);
867
1.22k
        } else {
868
1.22k
            shared_state->exchanger = BroadcastExchanger::create_unique(
869
1.22k
                    cur_pipe->num_tasks(), _num_instances,
870
1.22k
                    _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
871
1.22k
                            ? cast_set<int>(_runtime_state->query_options()
872
1.22k
                                                    .local_exchange_free_blocks_limit)
873
1.22k
                            : 0);
874
1.22k
        }
875
2.06k
        break;
876
857
    case ExchangeType::ADAPTIVE_PASSTHROUGH:
877
857
        shared_state->exchanger = AdaptivePassthroughExchanger::create_unique(
878
857
                std::max(cur_pipe->num_tasks(), _num_instances), _num_instances,
879
857
                _runtime_state->query_options().__isset.local_exchange_free_blocks_limit
880
857
                        ? cast_set<int>(
881
857
                                  _runtime_state->query_options().local_exchange_free_blocks_limit)
882
857
                        : 0);
883
857
        break;
884
0
    default:
885
0
        return Status::InternalError("Unsupported local exchange type : " +
886
0
                                     std::to_string((int)data_distribution.distribution_type));
887
94.4k
    }
888
94.4k
    shared_state->create_source_dependencies(_num_instances, local_exchange_id, local_exchange_id,
889
94.4k
                                             "LOCAL_EXCHANGE_OPERATOR");
890
94.4k
    shared_state->create_sink_dependency(sink_id, local_exchange_id, "LOCAL_EXCHANGE_SINK");
891
94.4k
    _op_id_to_shared_state.insert({local_exchange_id, {shared_state, shared_state->sink_deps}});
892
893
    // 3. Set two pipelines' operator list. For example, split pipeline [Scan - AggSink] to
894
    // pipeline1 [Scan - LocalExchangeSink] and pipeline2 [LocalExchangeSource - AggSink].
895
896
    // 3.1 Initialize new pipeline's operator list.
897
94.4k
    std::copy(operators.begin(), operators.begin() + idx,
898
94.4k
              std::inserter(new_pip->operators(), new_pip->operators().end()));
899
900
    // 3.2 Erase unused operators in previous pipeline.
901
94.4k
    operators.erase(operators.begin(), operators.begin() + idx);
902
903
    // 4. Initialize LocalExchangeSource and insert it into this pipeline.
904
94.4k
    OperatorPtr source_op;
905
94.4k
    source_op = std::make_shared<LocalExchangeSourceOperatorX>(pool, local_exchange_id);
906
94.4k
    RETURN_IF_ERROR(source_op->set_child(new_pip->operators().back()));
907
94.4k
    RETURN_IF_ERROR(source_op->init(data_distribution.distribution_type));
908
94.4k
    if (!operators.empty()) {
909
44.6k
        RETURN_IF_ERROR(operators.front()->set_child(nullptr));
910
44.6k
        RETURN_IF_ERROR(operators.front()->set_child(source_op));
911
44.6k
    }
912
94.4k
    operators.insert(operators.begin(), source_op);
913
914
    // 5. Set children for two pipelines separately.
915
94.4k
    std::vector<std::shared_ptr<Pipeline>> new_children;
916
94.4k
    std::vector<PipelineId> edges_with_source;
917
110k
    for (auto child : cur_pipe->children()) {
918
110k
        bool found = false;
919
124k
        for (auto op : new_pip->operators()) {
920
124k
            if (child->sink()->node_id() == op->node_id()) {
921
11.8k
                new_pip->set_children(child);
922
11.8k
                found = true;
923
11.8k
            };
924
124k
        }
925
110k
        if (!found) {
926
99.1k
            new_children.push_back(child);
927
99.1k
            edges_with_source.push_back(child->id());
928
99.1k
        }
929
110k
    }
930
94.4k
    new_children.push_back(new_pip);
931
94.4k
    edges_with_source.push_back(new_pip->id());
932
933
    // 6. Set DAG for new pipelines.
934
94.4k
    if (!new_pip->children().empty()) {
935
6.61k
        std::vector<PipelineId> edges_with_sink;
936
11.7k
        for (auto child : new_pip->children()) {
937
11.7k
            edges_with_sink.push_back(child->id());
938
11.7k
        }
939
6.61k
        _dag.insert({new_pip->id(), edges_with_sink});
940
6.61k
    }
941
94.4k
    cur_pipe->set_children(new_children);
942
94.4k
    _dag[downstream_pipeline_id] = edges_with_source;
943
94.4k
    RETURN_IF_ERROR(new_pip->sink()->set_child(new_pip->operators().back()));
944
94.4k
    RETURN_IF_ERROR(cur_pipe->sink()->set_child(nullptr));
945
94.4k
    RETURN_IF_ERROR(cur_pipe->sink()->set_child(cur_pipe->operators().back()));
946
947
    // 7. Inherit properties from current pipeline.
948
94.4k
    _inherit_pipeline_properties(data_distribution, cur_pipe, new_pip);
949
94.4k
    return Status::OK();
950
94.4k
}
951
952
Status PipelineFragmentContext::_add_local_exchange(
953
        int pip_idx, int idx, int node_id, ObjectPool* pool, PipelinePtr cur_pipe,
954
        DataDistribution data_distribution, bool* do_local_exchange, int num_buckets,
955
        const std::map<int, int>& bucket_seq_to_instance_idx,
956
155k
        const std::map<int, int>& shuffle_idx_to_instance_idx) {
957
155k
    if (_num_instances <= 1 || cur_pipe->num_tasks_of_parent() <= 1) {
958
23.9k
        return Status::OK();
959
23.9k
    }
960
961
131k
    if (!cur_pipe->need_to_local_exchange(data_distribution, idx)) {
962
47.7k
        return Status::OK();
963
47.7k
    }
964
84.1k
    *do_local_exchange = true;
965
966
84.1k
    auto& operators = cur_pipe->operators();
967
84.1k
    auto total_op_num = operators.size();
968
84.1k
    auto new_pip = add_pipeline(cur_pipe, pip_idx + 1);
969
84.1k
    RETURN_IF_ERROR(_add_local_exchange_impl(
970
84.1k
            idx, pool, cur_pipe, new_pip, data_distribution, do_local_exchange, num_buckets,
971
84.1k
            bucket_seq_to_instance_idx, shuffle_idx_to_instance_idx));
972
973
84.1k
    CHECK(total_op_num + 1 == cur_pipe->operators().size() + new_pip->operators().size())
974
3
            << "total_op_num: " << total_op_num
975
3
            << " cur_pipe->operators().size(): " << cur_pipe->operators().size()
976
3
            << " new_pip->operators().size(): " << new_pip->operators().size();
977
978
    // There are some local shuffles with relatively heavy operations on the sink.
979
    // If the local sink concurrency is 1 and the local source concurrency is n, the sink becomes a bottleneck.
980
    // Therefore, local passthrough is used to increase the concurrency of the sink.
981
    // op -> local sink(1) -> local source (n)
982
    // op -> local passthrough(1) -> local passthrough(n) ->  local sink(n) -> local source (n)
983
84.1k
    if (cur_pipe->num_tasks() > 1 && new_pip->num_tasks() == 1 &&
984
84.1k
        Pipeline::heavy_operations_on_the_sink(data_distribution.distribution_type)) {
985
10.2k
        RETURN_IF_ERROR(_add_local_exchange_impl(
986
10.2k
                cast_set<int>(new_pip->operators().size()), pool, new_pip,
987
10.2k
                add_pipeline(new_pip, pip_idx + 2), DataDistribution(ExchangeType::PASSTHROUGH),
988
10.2k
                do_local_exchange, num_buckets, bucket_seq_to_instance_idx,
989
10.2k
                shuffle_idx_to_instance_idx));
990
10.2k
    }
991
84.1k
    return Status::OK();
992
84.1k
}
993
994
Status PipelineFragmentContext::_plan_local_exchange(
995
        int num_buckets, const std::map<int, int>& bucket_seq_to_instance_idx,
996
323k
        const std::map<int, int>& shuffle_idx_to_instance_idx) {
997
737k
    for (int pip_idx = cast_set<int>(_pipelines.size()) - 1; pip_idx >= 0; pip_idx--) {
998
414k
        _pipelines[pip_idx]->init_data_distribution(_runtime_state.get());
999
        // Set property if child pipeline is not join operator's child.
1000
414k
        if (!_pipelines[pip_idx]->children().empty()) {
1001
89.0k
            for (auto& child : _pipelines[pip_idx]->children()) {
1002
89.0k
                if (child->sink()->node_id() ==
1003
89.0k
                    _pipelines[pip_idx]->operators().front()->node_id()) {
1004
78.1k
                    _pipelines[pip_idx]->set_data_distribution(child->data_distribution());
1005
78.1k
                }
1006
89.0k
            }
1007
85.4k
        }
1008
1009
        // if 'num_buckets == 0' means the fragment is colocated by exchange node not the
1010
        // scan node. so here use `_num_instance` to replace the `num_buckets` to prevent dividing 0
1011
        // still keep colocate plan after local shuffle
1012
414k
        RETURN_IF_ERROR(_plan_local_exchange(num_buckets, pip_idx, _pipelines[pip_idx],
1013
414k
                                             bucket_seq_to_instance_idx,
1014
414k
                                             shuffle_idx_to_instance_idx));
1015
414k
    }
1016
323k
    return Status::OK();
1017
323k
}
1018
1019
Status PipelineFragmentContext::_plan_local_exchange(
1020
        int num_buckets, int pip_idx, PipelinePtr pip,
1021
        const std::map<int, int>& bucket_seq_to_instance_idx,
1022
413k
        const std::map<int, int>& shuffle_idx_to_instance_idx) {
1023
413k
    int idx = 1;
1024
413k
    bool do_local_exchange = false;
1025
458k
    do {
1026
458k
        auto& ops = pip->operators();
1027
458k
        do_local_exchange = false;
1028
        // Plan local exchange for each operator.
1029
521k
        for (; idx < ops.size();) {
1030
107k
            if (ops[idx]->required_data_distribution(_runtime_state.get()).need_local_exchange()) {
1031
96.4k
                RETURN_IF_ERROR(_add_local_exchange(
1032
96.4k
                        pip_idx, idx, ops[idx]->node_id(), _runtime_state->obj_pool(), pip,
1033
96.4k
                        ops[idx]->required_data_distribution(_runtime_state.get()),
1034
96.4k
                        &do_local_exchange, num_buckets, bucket_seq_to_instance_idx,
1035
96.4k
                        shuffle_idx_to_instance_idx));
1036
96.4k
            }
1037
107k
            if (do_local_exchange) {
1038
                // If local exchange is needed for current operator, we will split this pipeline to
1039
                // two pipelines by local exchange sink/source. And then we need to process remaining
1040
                // operators in this pipeline so we set idx to 2 (0 is local exchange source and 1
1041
                // is current operator was already processed) and continue to plan local exchange.
1042
44.6k
                idx = 2;
1043
44.6k
                break;
1044
44.6k
            }
1045
62.8k
            idx++;
1046
62.8k
        }
1047
458k
    } while (do_local_exchange);
1048
413k
    if (pip->sink()->required_data_distribution(_runtime_state.get()).need_local_exchange()) {
1049
59.2k
        RETURN_IF_ERROR(_add_local_exchange(
1050
59.2k
                pip_idx, idx, pip->sink()->node_id(), _runtime_state->obj_pool(), pip,
1051
59.2k
                pip->sink()->required_data_distribution(_runtime_state.get()), &do_local_exchange,
1052
59.2k
                num_buckets, bucket_seq_to_instance_idx, shuffle_idx_to_instance_idx));
1053
59.2k
    }
1054
413k
    return Status::OK();
1055
413k
}
1056
1057
Status PipelineFragmentContext::_create_data_sink(ObjectPool* pool, const TDataSink& thrift_sink,
1058
                                                  const std::vector<TExpr>& output_exprs,
1059
                                                  const TPipelineFragmentParams& params,
1060
                                                  const RowDescriptor& row_desc,
1061
                                                  RuntimeState* state, DescriptorTbl& desc_tbl,
1062
325k
                                                  PipelineId cur_pipeline_id) {
1063
325k
    switch (thrift_sink.type) {
1064
113k
    case TDataSinkType::DATA_STREAM_SINK: {
1065
113k
        if (!thrift_sink.__isset.stream_sink) {
1066
0
            return Status::InternalError("Missing data stream sink.");
1067
0
        }
1068
113k
        _sink = std::make_shared<ExchangeSinkOperatorX>(
1069
113k
                state, row_desc, next_sink_operator_id(), thrift_sink.stream_sink,
1070
113k
                params.destinations, _fragment_instance_ids);
1071
113k
        break;
1072
113k
    }
1073
181k
    case TDataSinkType::RESULT_SINK: {
1074
181k
        if (!thrift_sink.__isset.result_sink) {
1075
0
            return Status::InternalError("Missing data buffer sink.");
1076
0
        }
1077
1078
181k
        auto& pipeline = _pipelines[cur_pipeline_id];
1079
181k
        int child_node_id = pipeline->operators().back()->node_id();
1080
181k
        _sink = std::make_shared<ResultSinkOperatorX>(next_sink_operator_id(), child_node_id + 1,
1081
181k
                                                      row_desc, output_exprs,
1082
181k
                                                      thrift_sink.result_sink);
1083
181k
        break;
1084
181k
    }
1085
105
    case TDataSinkType::DICTIONARY_SINK: {
1086
105
        if (!thrift_sink.__isset.dictionary_sink) {
1087
0
            return Status::InternalError("Missing dict sink.");
1088
0
        }
1089
1090
105
        _sink = std::make_shared<DictSinkOperatorX>(next_sink_operator_id(), row_desc, output_exprs,
1091
105
                                                    thrift_sink.dictionary_sink);
1092
105
        break;
1093
105
    }
1094
0
    case TDataSinkType::GROUP_COMMIT_OLAP_TABLE_SINK:
1095
28.9k
    case TDataSinkType::OLAP_TABLE_SINK: {
1096
28.9k
        auto& pipeline = _pipelines[cur_pipeline_id];
1097
28.9k
        int child_node_id = pipeline->operators().back()->node_id();
1098
28.9k
        if (state->query_options().enable_memtable_on_sink_node &&
1099
28.9k
            !_has_inverted_index_v1_or_partial_update(thrift_sink.olap_table_sink) &&
1100
28.9k
            !config::is_cloud_mode()) {
1101
56
            _sink = std::make_shared<OlapTableSinkV2OperatorX>(
1102
56
                    pool, next_sink_operator_id(), child_node_id + 1, row_desc, output_exprs);
1103
28.8k
        } else {
1104
28.8k
            _sink = std::make_shared<OlapTableSinkOperatorX>(
1105
28.8k
                    pool, next_sink_operator_id(), child_node_id + 1, row_desc, output_exprs);
1106
28.8k
        }
1107
28.9k
        break;
1108
0
    }
1109
166
    case TDataSinkType::GROUP_COMMIT_BLOCK_SINK: {
1110
166
        DCHECK(thrift_sink.__isset.olap_table_sink);
1111
166
        DCHECK(state->get_query_ctx() != nullptr);
1112
166
        state->get_query_ctx()->query_mem_tracker()->is_group_commit_load = true;
1113
166
        _sink = std::make_shared<GroupCommitBlockSinkOperatorX>(next_sink_operator_id(), row_desc,
1114
166
                                                                output_exprs);
1115
166
        break;
1116
0
    }
1117
0
    case TDataSinkType::HIVE_TABLE_SINK: {
1118
0
        if (!thrift_sink.__isset.hive_table_sink) {
1119
0
            return Status::InternalError("Missing hive table sink.");
1120
0
        }
1121
0
        _sink = std::make_shared<HiveTableSinkOperatorX>(pool, next_sink_operator_id(), row_desc,
1122
0
                                                         output_exprs);
1123
0
        break;
1124
0
    }
1125
0
    case TDataSinkType::ICEBERG_TABLE_SINK: {
1126
0
        if (!thrift_sink.__isset.iceberg_table_sink) {
1127
0
            return Status::InternalError("Missing iceberg table sink.");
1128
0
        }
1129
0
        if (thrift_sink.iceberg_table_sink.__isset.sort_info) {
1130
0
            _sink = std::make_shared<SpillIcebergTableSinkOperatorX>(pool, next_sink_operator_id(),
1131
0
                                                                     row_desc, output_exprs);
1132
0
        } else {
1133
0
            _sink = std::make_shared<IcebergTableSinkOperatorX>(pool, next_sink_operator_id(),
1134
0
                                                                row_desc, output_exprs);
1135
0
        }
1136
0
        break;
1137
0
    }
1138
0
    case TDataSinkType::ICEBERG_DELETE_SINK: {
1139
0
        if (!thrift_sink.__isset.iceberg_delete_sink) {
1140
0
            return Status::InternalError("Missing iceberg delete sink.");
1141
0
        }
1142
0
        _sink = std::make_shared<IcebergDeleteSinkOperatorX>(pool, next_sink_operator_id(),
1143
0
                                                             row_desc, output_exprs);
1144
0
        break;
1145
0
    }
1146
0
    case TDataSinkType::ICEBERG_MERGE_SINK: {
1147
0
        if (!thrift_sink.__isset.iceberg_merge_sink) {
1148
0
            return Status::InternalError("Missing iceberg merge sink.");
1149
0
        }
1150
0
        _sink = std::make_shared<IcebergMergeSinkOperatorX>(pool, next_sink_operator_id(), row_desc,
1151
0
                                                            output_exprs);
1152
0
        break;
1153
0
    }
1154
0
    case TDataSinkType::MAXCOMPUTE_TABLE_SINK: {
1155
0
        if (!thrift_sink.__isset.max_compute_table_sink) {
1156
0
            return Status::InternalError("Missing max compute table sink.");
1157
0
        }
1158
0
        _sink = std::make_shared<MCTableSinkOperatorX>(pool, next_sink_operator_id(), row_desc,
1159
0
                                                       output_exprs);
1160
0
        break;
1161
0
    }
1162
0
    case TDataSinkType::JDBC_TABLE_SINK: {
1163
0
        if (!thrift_sink.__isset.jdbc_table_sink) {
1164
0
            return Status::InternalError("Missing data jdbc sink.");
1165
0
        }
1166
0
        if (config::enable_java_support) {
1167
0
            _sink = std::make_shared<JdbcTableSinkOperatorX>(row_desc, next_sink_operator_id(),
1168
0
                                                             output_exprs);
1169
0
        } else {
1170
0
            return Status::InternalError(
1171
0
                    "Jdbc table sink is not enabled, you can change be config "
1172
0
                    "enable_java_support to true and restart be.");
1173
0
        }
1174
0
        break;
1175
0
    }
1176
3
    case TDataSinkType::MEMORY_SCRATCH_SINK: {
1177
3
        if (!thrift_sink.__isset.memory_scratch_sink) {
1178
0
            return Status::InternalError("Missing data buffer sink.");
1179
0
        }
1180
1181
3
        _sink = std::make_shared<MemoryScratchSinkOperatorX>(row_desc, next_sink_operator_id(),
1182
3
                                                             output_exprs);
1183
3
        break;
1184
3
    }
1185
338
    case TDataSinkType::RESULT_FILE_SINK: {
1186
338
        if (!thrift_sink.__isset.result_file_sink) {
1187
0
            return Status::InternalError("Missing result file sink.");
1188
0
        }
1189
1190
        // Result file sink is not the top sink
1191
338
        if (params.__isset.destinations && !params.destinations.empty()) {
1192
0
            _sink = std::make_shared<ResultFileSinkOperatorX>(
1193
0
                    next_sink_operator_id(), row_desc, thrift_sink.result_file_sink,
1194
0
                    params.destinations, output_exprs, desc_tbl);
1195
338
        } else {
1196
338
            _sink = std::make_shared<ResultFileSinkOperatorX>(next_sink_operator_id(), row_desc,
1197
338
                                                              output_exprs);
1198
338
        }
1199
338
        break;
1200
338
    }
1201
609
    case TDataSinkType::MULTI_CAST_DATA_STREAM_SINK: {
1202
609
        DCHECK(thrift_sink.__isset.multi_cast_stream_sink);
1203
609
        DCHECK_GT(thrift_sink.multi_cast_stream_sink.sinks.size(), 0);
1204
609
        auto sink_id = next_sink_operator_id();
1205
609
        const int multi_cast_node_id = sink_id;
1206
609
        auto sender_size = thrift_sink.multi_cast_stream_sink.sinks.size();
1207
        // one sink has multiple sources.
1208
609
        std::vector<int> sources;
1209
2.22k
        for (int i = 0; i < sender_size; ++i) {
1210
1.61k
            auto source_id = next_operator_id();
1211
1.61k
            sources.push_back(source_id);
1212
1.61k
        }
1213
1214
609
        _sink = std::make_shared<MultiCastDataStreamSinkOperatorX>(
1215
609
                sink_id, multi_cast_node_id, sources, pool, thrift_sink.multi_cast_stream_sink);
1216
2.22k
        for (int i = 0; i < sender_size; ++i) {
1217
1.61k
            auto new_pipeline = add_pipeline();
1218
            // use to exchange sink
1219
1.61k
            RowDescriptor* exchange_row_desc = nullptr;
1220
1.61k
            {
1221
1.61k
                const auto& tmp_row_desc =
1222
1.61k
                        !thrift_sink.multi_cast_stream_sink.sinks[i].output_exprs.empty()
1223
1.61k
                                ? RowDescriptor(state->desc_tbl(),
1224
1.61k
                                                {thrift_sink.multi_cast_stream_sink.sinks[i]
1225
1.61k
                                                         .output_tuple_id})
1226
1.61k
                                : row_desc;
1227
1.61k
                exchange_row_desc = pool->add(new RowDescriptor(tmp_row_desc));
1228
1.61k
            }
1229
1.61k
            auto source_id = sources[i];
1230
1.61k
            OperatorPtr source_op;
1231
            // 1. create and set the source operator of multi_cast_data_stream_source for new pipeline
1232
1.61k
            source_op = std::make_shared<MultiCastDataStreamerSourceOperatorX>(
1233
1.61k
                    /*node_id*/ source_id, /*consumer_id*/ i, pool,
1234
1.61k
                    thrift_sink.multi_cast_stream_sink.sinks[i], row_desc,
1235
1.61k
                    /*operator_id=*/source_id);
1236
1.61k
            RETURN_IF_ERROR(new_pipeline->add_operator(
1237
1.61k
                    source_op, params.__isset.parallel_instances ? params.parallel_instances : 0));
1238
            // 2. create and set sink operator of data stream sender for new pipeline
1239
1240
1.61k
            DataSinkOperatorPtr sink_op;
1241
1.61k
            sink_op = std::make_shared<ExchangeSinkOperatorX>(
1242
1.61k
                    state, *exchange_row_desc, next_sink_operator_id(),
1243
1.61k
                    thrift_sink.multi_cast_stream_sink.sinks[i],
1244
1.61k
                    thrift_sink.multi_cast_stream_sink.destinations[i], _fragment_instance_ids);
1245
1246
1.61k
            RETURN_IF_ERROR(new_pipeline->set_sink(sink_op));
1247
1.61k
            {
1248
1.61k
                TDataSink* t = pool->add(new TDataSink());
1249
1.61k
                t->stream_sink = thrift_sink.multi_cast_stream_sink.sinks[i];
1250
1.61k
                RETURN_IF_ERROR(sink_op->init(*t));
1251
1.61k
            }
1252
1253
            // 3. set dependency dag
1254
1.61k
            _dag[new_pipeline->id()].push_back(cur_pipeline_id);
1255
1.61k
        }
1256
609
        if (sources.empty()) {
1257
0
            return Status::InternalError("size of sources must be greater than 0");
1258
0
        }
1259
609
        break;
1260
609
    }
1261
609
    case TDataSinkType::BLACKHOLE_SINK: {
1262
3
        if (!thrift_sink.__isset.blackhole_sink) {
1263
0
            return Status::InternalError("Missing blackhole sink.");
1264
0
        }
1265
1266
3
        _sink.reset(new BlackholeSinkOperatorX(next_sink_operator_id()));
1267
3
        break;
1268
3
    }
1269
0
    case TDataSinkType::TVF_TABLE_SINK: {
1270
0
        if (!thrift_sink.__isset.tvf_table_sink) {
1271
0
            return Status::InternalError("Missing TVF table sink.");
1272
0
        }
1273
0
        _sink = std::make_shared<TVFTableSinkOperatorX>(pool, next_sink_operator_id(), row_desc,
1274
0
                                                        output_exprs);
1275
0
        break;
1276
0
    }
1277
0
    default:
1278
0
        return Status::InternalError("Unsuported sink type in pipeline: {}", thrift_sink.type);
1279
325k
    }
1280
325k
    return Status::OK();
1281
325k
}
1282
1283
// NOLINTBEGIN(readability-function-size)
1284
// NOLINTBEGIN(readability-function-cognitive-complexity)
1285
Status PipelineFragmentContext::_create_operator(ObjectPool* pool, const TPlanNode& tnode,
1286
                                                 const DescriptorTbl& descs, OperatorPtr& op,
1287
                                                 PipelinePtr& cur_pipe, int parent_idx,
1288
                                                 int child_idx,
1289
                                                 const bool followed_by_shuffled_operator,
1290
                                                 const bool require_bucket_distribution,
1291
522k
                                                 OperatorPtr& cache_op) {
1292
522k
    std::vector<DataSinkOperatorPtr> sink_ops;
1293
522k
    Defer defer = Defer([&]() {
1294
522k
        if (op) {
1295
522k
            op->update_operator(tnode, followed_by_shuffled_operator, require_bucket_distribution);
1296
522k
        }
1297
521k
        for (auto& s : sink_ops) {
1298
88.7k
            s->update_operator(tnode, followed_by_shuffled_operator, require_bucket_distribution);
1299
88.7k
        }
1300
521k
    });
1301
    // We directly construct the operator from Thrift because the given array is in the order of preorder traversal.
1302
    // Therefore, here we need to use a stack-like structure.
1303
522k
    _pipeline_parent_map.pop(cur_pipe, parent_idx, child_idx);
1304
522k
    std::stringstream error_msg;
1305
522k
    bool enable_query_cache = _params.fragment.__isset.query_cache_param;
1306
1307
522k
    bool fe_with_old_version = false;
1308
522k
    switch (tnode.node_type) {
1309
162k
    case TPlanNodeType::OLAP_SCAN_NODE: {
1310
162k
        op = std::make_shared<OlapScanOperatorX>(
1311
162k
                pool, tnode, next_operator_id(), descs, _num_instances,
1312
162k
                enable_query_cache ? _params.fragment.query_cache_param : TQueryCacheParam {});
1313
162k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1314
162k
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1315
162k
        break;
1316
162k
    }
1317
79
    case TPlanNodeType::GROUP_COMMIT_SCAN_NODE: {
1318
79
        DCHECK(_query_ctx != nullptr);
1319
79
        _query_ctx->query_mem_tracker()->is_group_commit_load = true;
1320
79
        op = std::make_shared<GroupCommitOperatorX>(pool, tnode, next_operator_id(), descs,
1321
79
                                                    _num_instances);
1322
79
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1323
79
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1324
79
        break;
1325
79
    }
1326
0
    case TPlanNodeType::JDBC_SCAN_NODE: {
1327
0
        if (config::enable_java_support) {
1328
0
            op = std::make_shared<JDBCScanOperatorX>(pool, tnode, next_operator_id(), descs,
1329
0
                                                     _num_instances);
1330
0
            RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1331
0
        } else {
1332
0
            return Status::InternalError(
1333
0
                    "Jdbc scan node is disabled, you can change be config enable_java_support "
1334
0
                    "to true and restart be.");
1335
0
        }
1336
0
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1337
0
        break;
1338
0
    }
1339
2.68k
    case TPlanNodeType::FILE_SCAN_NODE: {
1340
2.68k
        op = std::make_shared<FileScanOperatorX>(pool, tnode, next_operator_id(), descs,
1341
2.68k
                                                 _num_instances);
1342
2.68k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1343
2.68k
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1344
2.68k
        break;
1345
2.68k
    }
1346
113k
    case TPlanNodeType::EXCHANGE_NODE: {
1347
113k
        int num_senders = _params.per_exch_num_senders.contains(tnode.node_id)
1348
113k
                                  ? _params.per_exch_num_senders.find(tnode.node_id)->second
1349
18.4E
                                  : 0;
1350
113k
        DCHECK_GT(num_senders, 0);
1351
113k
        op = std::make_shared<ExchangeSourceOperatorX>(pool, tnode, next_operator_id(), descs,
1352
113k
                                                       num_senders);
1353
113k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1354
113k
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1355
113k
        break;
1356
113k
    }
1357
136k
    case TPlanNodeType::AGGREGATION_NODE: {
1358
136k
        if (tnode.agg_node.grouping_exprs.empty() &&
1359
136k
            descs.get_tuple_descriptor(tnode.agg_node.output_tuple_id)->slots().empty()) {
1360
0
            return Status::InternalError("Illegal aggregate node " + std::to_string(tnode.node_id) +
1361
0
                                         ": group by and output is empty");
1362
0
        }
1363
136k
        bool need_create_cache_op =
1364
136k
                enable_query_cache && tnode.node_id == _params.fragment.query_cache_param.node_id;
1365
136k
        auto create_query_cache_operator = [&](PipelinePtr& new_pipe) {
1366
10
            auto cache_node_id = _params.local_params[0].per_node_scan_ranges.begin()->first;
1367
10
            auto cache_source_id = next_operator_id();
1368
10
            op = std::make_shared<CacheSourceOperatorX>(pool, cache_node_id, cache_source_id,
1369
10
                                                        _params.fragment.query_cache_param);
1370
10
            RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1371
1372
10
            const auto downstream_pipeline_id = cur_pipe->id();
1373
10
            if (!_dag.contains(downstream_pipeline_id)) {
1374
10
                _dag.insert({downstream_pipeline_id, {}});
1375
10
            }
1376
10
            new_pipe = add_pipeline(cur_pipe);
1377
10
            _dag[downstream_pipeline_id].push_back(new_pipe->id());
1378
1379
10
            DataSinkOperatorPtr cache_sink(new CacheSinkOperatorX(
1380
10
                    next_sink_operator_id(), op->node_id(), op->operator_id()));
1381
10
            RETURN_IF_ERROR(new_pipe->set_sink(cache_sink));
1382
10
            return Status::OK();
1383
10
        };
1384
136k
        const bool group_by_limit_opt =
1385
136k
                tnode.agg_node.__isset.agg_sort_info_by_group_key && tnode.limit > 0;
1386
1387
        /// PartitionedAggSourceOperatorX does not support "group by limit opt(#29641)" yet.
1388
        /// If `group_by_limit_opt` is true, then it might not need to spill at all.
1389
136k
        const bool enable_spill = _runtime_state->enable_spill() &&
1390
136k
                                  !tnode.agg_node.grouping_exprs.empty() && !group_by_limit_opt;
1391
136k
        const bool is_streaming_agg = tnode.agg_node.__isset.use_streaming_preaggregation &&
1392
136k
                                      tnode.agg_node.use_streaming_preaggregation &&
1393
136k
                                      !tnode.agg_node.grouping_exprs.empty();
1394
        // TODO: distinct streaming agg does not support spill.
1395
136k
        const bool can_use_distinct_streaming_agg =
1396
136k
                (!enable_spill || is_streaming_agg) && tnode.agg_node.aggregate_functions.empty() &&
1397
136k
                !tnode.agg_node.__isset.agg_sort_info_by_group_key &&
1398
136k
                _params.query_options.__isset.enable_distinct_streaming_aggregation &&
1399
136k
                _params.query_options.enable_distinct_streaming_aggregation;
1400
1401
136k
        if (can_use_distinct_streaming_agg) {
1402
92.6k
            if (need_create_cache_op) {
1403
8
                PipelinePtr new_pipe;
1404
8
                RETURN_IF_ERROR(create_query_cache_operator(new_pipe));
1405
1406
8
                cache_op = op;
1407
8
                op = std::make_shared<DistinctStreamingAggOperatorX>(pool, next_operator_id(),
1408
8
                                                                     tnode, descs);
1409
8
                RETURN_IF_ERROR(new_pipe->add_operator(op, _parallel_instances));
1410
8
                RETURN_IF_ERROR(cur_pipe->operators().front()->set_child(op));
1411
8
                cur_pipe = new_pipe;
1412
92.6k
            } else {
1413
92.6k
                op = std::make_shared<DistinctStreamingAggOperatorX>(pool, next_operator_id(),
1414
92.6k
                                                                     tnode, descs);
1415
92.6k
                RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1416
92.6k
            }
1417
92.6k
        } else if (is_streaming_agg) {
1418
1.55k
            if (need_create_cache_op) {
1419
0
                PipelinePtr new_pipe;
1420
0
                RETURN_IF_ERROR(create_query_cache_operator(new_pipe));
1421
0
                cache_op = op;
1422
0
                op = std::make_shared<StreamingAggOperatorX>(pool, next_operator_id(), tnode,
1423
0
                                                             descs);
1424
0
                RETURN_IF_ERROR(cur_pipe->operators().front()->set_child(op));
1425
0
                RETURN_IF_ERROR(new_pipe->add_operator(op, _parallel_instances));
1426
0
                cur_pipe = new_pipe;
1427
1.55k
            } else {
1428
1.55k
                op = std::make_shared<StreamingAggOperatorX>(pool, next_operator_id(), tnode,
1429
1.55k
                                                             descs);
1430
1.55k
                RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1431
1.55k
            }
1432
42.2k
        } else {
1433
            // create new pipeline to add query cache operator
1434
42.2k
            PipelinePtr new_pipe;
1435
42.2k
            if (need_create_cache_op) {
1436
2
                RETURN_IF_ERROR(create_query_cache_operator(new_pipe));
1437
2
                cache_op = op;
1438
2
            }
1439
1440
42.2k
            if (enable_spill) {
1441
155
                op = std::make_shared<PartitionedAggSourceOperatorX>(pool, tnode,
1442
155
                                                                     next_operator_id(), descs);
1443
42.1k
            } else {
1444
42.1k
                op = std::make_shared<AggSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1445
42.1k
            }
1446
42.2k
            if (need_create_cache_op) {
1447
2
                RETURN_IF_ERROR(cur_pipe->operators().front()->set_child(op));
1448
2
                RETURN_IF_ERROR(new_pipe->add_operator(op, _parallel_instances));
1449
2
                cur_pipe = new_pipe;
1450
42.2k
            } else {
1451
42.2k
                RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1452
42.2k
            }
1453
1454
42.2k
            const auto downstream_pipeline_id = cur_pipe->id();
1455
42.2k
            if (!_dag.contains(downstream_pipeline_id)) {
1456
41.6k
                _dag.insert({downstream_pipeline_id, {}});
1457
41.6k
            }
1458
42.2k
            cur_pipe = add_pipeline(cur_pipe);
1459
42.2k
            _dag[downstream_pipeline_id].push_back(cur_pipe->id());
1460
1461
42.2k
            if (enable_spill) {
1462
155
                sink_ops.push_back(std::make_shared<PartitionedAggSinkOperatorX>(
1463
155
                        pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1464
42.1k
            } else {
1465
42.1k
                sink_ops.push_back(std::make_shared<AggSinkOperatorX>(
1466
42.1k
                        pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1467
42.1k
            }
1468
42.2k
            RETURN_IF_ERROR(cur_pipe->set_sink(sink_ops.back()));
1469
42.2k
            RETURN_IF_ERROR(cur_pipe->sink()->init(tnode, _runtime_state.get()));
1470
42.2k
        }
1471
136k
        break;
1472
136k
    }
1473
136k
    case TPlanNodeType::BUCKETED_AGGREGATION_NODE: {
1474
74
        if (tnode.bucketed_agg_node.grouping_exprs.empty()) {
1475
0
            return Status::InternalError(
1476
0
                    "Bucketed aggregation node {} should not be used without group by keys",
1477
0
                    tnode.node_id);
1478
0
        }
1479
1480
        // Create source operator (goes on the current / downstream pipeline).
1481
74
        op = std::make_shared<BucketedAggSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1482
74
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1483
1484
        // Create a new pipeline for the sink side.
1485
74
        const auto downstream_pipeline_id = cur_pipe->id();
1486
74
        if (!_dag.contains(downstream_pipeline_id)) {
1487
74
            _dag.insert({downstream_pipeline_id, {}});
1488
74
        }
1489
74
        cur_pipe = add_pipeline(cur_pipe);
1490
74
        _dag[downstream_pipeline_id].push_back(cur_pipe->id());
1491
1492
        // Create sink operator.
1493
74
        sink_ops.push_back(std::make_shared<BucketedAggSinkOperatorX>(
1494
74
                pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1495
74
        RETURN_IF_ERROR(cur_pipe->set_sink(sink_ops.back()));
1496
74
        RETURN_IF_ERROR(cur_pipe->sink()->init(tnode, _runtime_state.get()));
1497
1498
        // Pre-register a single shared state for ALL instances so that every
1499
        // sink instance writes its per-instance hash table into the same
1500
        // BucketedAggSharedState and every source instance can merge across
1501
        // all of them.
1502
74
        {
1503
74
            auto shared_state = BucketedAggSharedState::create_shared();
1504
74
            shared_state->id = op->operator_id();
1505
74
            shared_state->related_op_ids.insert(op->operator_id());
1506
1507
421
            for (int i = 0; i < _num_instances; i++) {
1508
347
                auto sink_dep = std::make_shared<Dependency>(op->operator_id(), op->node_id(),
1509
347
                                                             "BUCKETED_AGG_SINK_DEPENDENCY");
1510
347
                sink_dep->set_shared_state(shared_state.get());
1511
347
                shared_state->sink_deps.push_back(sink_dep);
1512
347
            }
1513
74
            shared_state->create_source_dependencies(_num_instances, op->operator_id(),
1514
74
                                                     op->node_id(), "BUCKETED_AGG_SOURCE");
1515
74
            _op_id_to_shared_state.insert(
1516
74
                    {op->operator_id(), {shared_state, shared_state->sink_deps}});
1517
74
        }
1518
74
        break;
1519
74
    }
1520
8.85k
    case TPlanNodeType::HASH_JOIN_NODE: {
1521
8.85k
        const auto is_broadcast_join = tnode.hash_join_node.__isset.is_broadcast_join &&
1522
8.85k
                                       tnode.hash_join_node.is_broadcast_join;
1523
8.85k
        const auto enable_spill = _runtime_state->enable_spill();
1524
8.85k
        if (enable_spill && !is_broadcast_join) {
1525
0
            auto tnode_ = tnode;
1526
0
            tnode_.runtime_filters.clear();
1527
0
            auto inner_probe_operator =
1528
0
                    std::make_shared<HashJoinProbeOperatorX>(pool, tnode_, 0, descs);
1529
1530
            // probe side inner sink operator is used to build hash table on probe side when data is spilled.
1531
            // So here use `tnode_` which has no runtime filters.
1532
0
            auto probe_side_inner_sink_operator =
1533
0
                    std::make_shared<HashJoinBuildSinkOperatorX>(pool, 0, 0, tnode_, descs);
1534
1535
0
            RETURN_IF_ERROR(inner_probe_operator->init(tnode_, _runtime_state.get()));
1536
0
            RETURN_IF_ERROR(probe_side_inner_sink_operator->init(tnode_, _runtime_state.get()));
1537
1538
0
            auto probe_operator = std::make_shared<PartitionedHashJoinProbeOperatorX>(
1539
0
                    pool, tnode_, next_operator_id(), descs);
1540
0
            probe_operator->set_inner_operators(probe_side_inner_sink_operator,
1541
0
                                                inner_probe_operator);
1542
0
            op = std::move(probe_operator);
1543
0
            RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1544
1545
0
            const auto downstream_pipeline_id = cur_pipe->id();
1546
0
            if (!_dag.contains(downstream_pipeline_id)) {
1547
0
                _dag.insert({downstream_pipeline_id, {}});
1548
0
            }
1549
0
            PipelinePtr build_side_pipe = add_pipeline(cur_pipe);
1550
0
            _dag[downstream_pipeline_id].push_back(build_side_pipe->id());
1551
1552
0
            auto inner_sink_operator =
1553
0
                    std::make_shared<HashJoinBuildSinkOperatorX>(pool, 0, 0, tnode, descs);
1554
0
            auto sink_operator = std::make_shared<PartitionedHashJoinSinkOperatorX>(
1555
0
                    pool, next_sink_operator_id(), op->operator_id(), tnode_, descs);
1556
0
            RETURN_IF_ERROR(inner_sink_operator->init(tnode, _runtime_state.get()));
1557
1558
0
            sink_operator->set_inner_operators(inner_sink_operator, inner_probe_operator);
1559
0
            sink_ops.push_back(std::move(sink_operator));
1560
0
            RETURN_IF_ERROR(build_side_pipe->set_sink(sink_ops.back()));
1561
0
            RETURN_IF_ERROR(build_side_pipe->sink()->init(tnode_, _runtime_state.get()));
1562
1563
0
            _pipeline_parent_map.push(op->node_id(), cur_pipe);
1564
0
            _pipeline_parent_map.push(op->node_id(), build_side_pipe);
1565
8.85k
        } else {
1566
8.85k
            op = std::make_shared<HashJoinProbeOperatorX>(pool, tnode, next_operator_id(), descs);
1567
8.85k
            RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1568
1569
8.85k
            const auto downstream_pipeline_id = cur_pipe->id();
1570
8.85k
            if (!_dag.contains(downstream_pipeline_id)) {
1571
7.14k
                _dag.insert({downstream_pipeline_id, {}});
1572
7.14k
            }
1573
8.85k
            PipelinePtr build_side_pipe = add_pipeline(cur_pipe);
1574
8.85k
            _dag[downstream_pipeline_id].push_back(build_side_pipe->id());
1575
1576
8.85k
            sink_ops.push_back(std::make_shared<HashJoinBuildSinkOperatorX>(
1577
8.85k
                    pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1578
8.85k
            RETURN_IF_ERROR(build_side_pipe->set_sink(sink_ops.back()));
1579
8.85k
            RETURN_IF_ERROR(build_side_pipe->sink()->init(tnode, _runtime_state.get()));
1580
1581
8.85k
            _pipeline_parent_map.push(op->node_id(), cur_pipe);
1582
8.85k
            _pipeline_parent_map.push(op->node_id(), build_side_pipe);
1583
8.85k
        }
1584
8.85k
        if (is_broadcast_join && _runtime_state->enable_share_hash_table_for_broadcast_join()) {
1585
2.08k
            std::shared_ptr<HashJoinSharedState> shared_state =
1586
2.08k
                    HashJoinSharedState::create_shared(_num_instances);
1587
12.0k
            for (int i = 0; i < _num_instances; i++) {
1588
9.97k
                auto sink_dep = std::make_shared<Dependency>(op->operator_id(), op->node_id(),
1589
9.97k
                                                             "HASH_JOIN_BUILD_DEPENDENCY");
1590
9.97k
                sink_dep->set_shared_state(shared_state.get());
1591
9.97k
                shared_state->sink_deps.push_back(sink_dep);
1592
9.97k
            }
1593
2.08k
            shared_state->create_source_dependencies(_num_instances, op->operator_id(),
1594
2.08k
                                                     op->node_id(), "HASH_JOIN_PROBE");
1595
2.08k
            _op_id_to_shared_state.insert(
1596
2.08k
                    {op->operator_id(), {shared_state, shared_state->sink_deps}});
1597
2.08k
        }
1598
8.85k
        break;
1599
8.85k
    }
1600
2.08k
    case TPlanNodeType::CROSS_JOIN_NODE: {
1601
2.08k
        op = std::make_shared<NestedLoopJoinProbeOperatorX>(pool, tnode, next_operator_id(), descs);
1602
2.08k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1603
1604
2.08k
        const auto downstream_pipeline_id = cur_pipe->id();
1605
2.08k
        if (!_dag.contains(downstream_pipeline_id)) {
1606
1.85k
            _dag.insert({downstream_pipeline_id, {}});
1607
1.85k
        }
1608
2.08k
        PipelinePtr build_side_pipe = add_pipeline(cur_pipe);
1609
2.08k
        _dag[downstream_pipeline_id].push_back(build_side_pipe->id());
1610
1611
2.08k
        sink_ops.push_back(std::make_shared<NestedLoopJoinBuildSinkOperatorX>(
1612
2.08k
                pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1613
2.08k
        RETURN_IF_ERROR(build_side_pipe->set_sink(sink_ops.back()));
1614
2.08k
        RETURN_IF_ERROR(build_side_pipe->sink()->init(tnode, _runtime_state.get()));
1615
2.08k
        _pipeline_parent_map.push(op->node_id(), cur_pipe);
1616
2.08k
        _pipeline_parent_map.push(op->node_id(), build_side_pipe);
1617
2.08k
        break;
1618
2.08k
    }
1619
49.6k
    case TPlanNodeType::UNION_NODE: {
1620
49.6k
        int child_count = tnode.num_children;
1621
49.6k
        op = std::make_shared<UnionSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1622
49.6k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1623
1624
49.6k
        const auto downstream_pipeline_id = cur_pipe->id();
1625
49.6k
        if (!_dag.contains(downstream_pipeline_id)) {
1626
49.4k
            _dag.insert({downstream_pipeline_id, {}});
1627
49.4k
        }
1628
50.6k
        for (int i = 0; i < child_count; i++) {
1629
1.01k
            PipelinePtr build_side_pipe = add_pipeline(cur_pipe);
1630
1.01k
            _dag[downstream_pipeline_id].push_back(build_side_pipe->id());
1631
1.01k
            sink_ops.push_back(std::make_shared<UnionSinkOperatorX>(
1632
1.01k
                    i, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1633
1.01k
            RETURN_IF_ERROR(build_side_pipe->set_sink(sink_ops.back()));
1634
1.01k
            RETURN_IF_ERROR(build_side_pipe->sink()->init(tnode, _runtime_state.get()));
1635
            // preset children pipelines. if any pipeline found this as its father, will use the prepared pipeline to build.
1636
1.01k
            _pipeline_parent_map.push(op->node_id(), build_side_pipe);
1637
1.01k
        }
1638
49.6k
        break;
1639
49.6k
    }
1640
49.6k
    case TPlanNodeType::SORT_NODE: {
1641
32.2k
        const auto should_spill = _runtime_state->enable_spill() &&
1642
32.2k
                                  tnode.sort_node.algorithm == TSortAlgorithm::FULL_SORT;
1643
32.2k
        const bool use_local_merge =
1644
32.2k
                tnode.sort_node.__isset.use_local_merge && tnode.sort_node.use_local_merge;
1645
32.2k
        if (should_spill) {
1646
9
            op = std::make_shared<SpillSortSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1647
32.2k
        } else if (use_local_merge) {
1648
30.3k
            op = std::make_shared<LocalMergeSortSourceOperatorX>(pool, tnode, next_operator_id(),
1649
30.3k
                                                                 descs);
1650
30.3k
        } else {
1651
1.84k
            op = std::make_shared<SortSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1652
1.84k
        }
1653
32.2k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1654
1655
32.2k
        const auto downstream_pipeline_id = cur_pipe->id();
1656
32.2k
        if (!_dag.contains(downstream_pipeline_id)) {
1657
32.2k
            _dag.insert({downstream_pipeline_id, {}});
1658
32.2k
        }
1659
32.2k
        cur_pipe = add_pipeline(cur_pipe);
1660
32.2k
        _dag[downstream_pipeline_id].push_back(cur_pipe->id());
1661
1662
32.2k
        if (should_spill) {
1663
9
            sink_ops.push_back(std::make_shared<SpillSortSinkOperatorX>(
1664
9
                    pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1665
32.2k
        } else {
1666
32.2k
            sink_ops.push_back(std::make_shared<SortSinkOperatorX>(
1667
32.2k
                    pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1668
32.2k
        }
1669
32.2k
        RETURN_IF_ERROR(cur_pipe->set_sink(sink_ops.back()));
1670
32.2k
        RETURN_IF_ERROR(cur_pipe->sink()->init(tnode, _runtime_state.get()));
1671
32.2k
        break;
1672
32.2k
    }
1673
32.2k
    case TPlanNodeType::PARTITION_SORT_NODE: {
1674
62
        op = std::make_shared<PartitionSortSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1675
62
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1676
1677
62
        const auto downstream_pipeline_id = cur_pipe->id();
1678
62
        if (!_dag.contains(downstream_pipeline_id)) {
1679
62
            _dag.insert({downstream_pipeline_id, {}});
1680
62
        }
1681
62
        cur_pipe = add_pipeline(cur_pipe);
1682
62
        _dag[downstream_pipeline_id].push_back(cur_pipe->id());
1683
1684
62
        sink_ops.push_back(std::make_shared<PartitionSortSinkOperatorX>(
1685
62
                pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1686
62
        RETURN_IF_ERROR(cur_pipe->set_sink(sink_ops.back()));
1687
62
        RETURN_IF_ERROR(cur_pipe->sink()->init(tnode, _runtime_state.get()));
1688
62
        break;
1689
62
    }
1690
1.60k
    case TPlanNodeType::ANALYTIC_EVAL_NODE: {
1691
1.60k
        op = std::make_shared<AnalyticSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1692
1.60k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1693
1694
1.60k
        const auto downstream_pipeline_id = cur_pipe->id();
1695
1.60k
        if (!_dag.contains(downstream_pipeline_id)) {
1696
1.59k
            _dag.insert({downstream_pipeline_id, {}});
1697
1.59k
        }
1698
1.60k
        cur_pipe = add_pipeline(cur_pipe);
1699
1.60k
        _dag[downstream_pipeline_id].push_back(cur_pipe->id());
1700
1701
1.60k
        sink_ops.push_back(std::make_shared<AnalyticSinkOperatorX>(
1702
1.60k
                pool, next_sink_operator_id(), op->operator_id(), tnode, descs));
1703
1.60k
        RETURN_IF_ERROR(cur_pipe->set_sink(sink_ops.back()));
1704
1.60k
        RETURN_IF_ERROR(cur_pipe->sink()->init(tnode, _runtime_state.get()));
1705
1.60k
        break;
1706
1.60k
    }
1707
1.60k
    case TPlanNodeType::MATERIALIZATION_NODE: {
1708
674
        op = std::make_shared<MaterializationOperator>(pool, tnode, next_operator_id(), descs);
1709
674
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1710
674
        break;
1711
674
    }
1712
674
    case TPlanNodeType::INTERSECT_NODE: {
1713
134
        RETURN_IF_ERROR(_build_operators_for_set_operation_node<true>(pool, tnode, descs, op,
1714
134
                                                                      cur_pipe, sink_ops));
1715
134
        break;
1716
134
    }
1717
134
    case TPlanNodeType::EXCEPT_NODE: {
1718
133
        RETURN_IF_ERROR(_build_operators_for_set_operation_node<false>(pool, tnode, descs, op,
1719
133
                                                                       cur_pipe, sink_ops));
1720
133
        break;
1721
133
    }
1722
296
    case TPlanNodeType::REPEAT_NODE: {
1723
296
        op = std::make_shared<RepeatOperatorX>(pool, tnode, next_operator_id(), descs);
1724
296
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1725
296
        break;
1726
296
    }
1727
910
    case TPlanNodeType::TABLE_FUNCTION_NODE: {
1728
910
        op = std::make_shared<TableFunctionOperatorX>(pool, tnode, next_operator_id(), descs);
1729
910
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1730
910
        break;
1731
910
    }
1732
910
    case TPlanNodeType::ASSERT_NUM_ROWS_NODE: {
1733
18
        op = std::make_shared<AssertNumRowsOperatorX>(pool, tnode, next_operator_id(), descs);
1734
18
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1735
18
        break;
1736
18
    }
1737
1.45k
    case TPlanNodeType::EMPTY_SET_NODE: {
1738
1.45k
        op = std::make_shared<EmptySetSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1739
1.45k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1740
1.45k
        break;
1741
1.45k
    }
1742
1.45k
    case TPlanNodeType::DATA_GEN_SCAN_NODE: {
1743
266
        op = std::make_shared<DataGenSourceOperatorX>(pool, tnode, next_operator_id(), descs);
1744
266
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1745
266
        fe_with_old_version = !tnode.__isset.is_serial_operator;
1746
266
        break;
1747
266
    }
1748
1.53k
    case TPlanNodeType::SCHEMA_SCAN_NODE: {
1749
1.53k
        op = std::make_shared<SchemaScanOperatorX>(pool, tnode, next_operator_id(), descs);
1750
1.53k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1751
1.53k
        break;
1752
1.53k
    }
1753
4.52k
    case TPlanNodeType::META_SCAN_NODE: {
1754
4.52k
        op = std::make_shared<MetaScanOperatorX>(pool, tnode, next_operator_id(), descs);
1755
4.52k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1756
4.52k
        break;
1757
4.52k
    }
1758
4.52k
    case TPlanNodeType::SELECT_NODE: {
1759
589
        op = std::make_shared<SelectOperatorX>(pool, tnode, next_operator_id(), descs);
1760
589
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1761
589
        break;
1762
589
    }
1763
589
    case TPlanNodeType::REC_CTE_NODE: {
1764
151
        op = std::make_shared<RecCTESourceOperatorX>(pool, tnode, next_operator_id(), descs);
1765
151
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1766
1767
151
        const auto downstream_pipeline_id = cur_pipe->id();
1768
151
        if (!_dag.contains(downstream_pipeline_id)) {
1769
148
            _dag.insert({downstream_pipeline_id, {}});
1770
148
        }
1771
1772
151
        PipelinePtr anchor_side_pipe = add_pipeline(cur_pipe);
1773
151
        _dag[downstream_pipeline_id].push_back(anchor_side_pipe->id());
1774
1775
151
        DataSinkOperatorPtr anchor_sink;
1776
151
        anchor_sink = std::make_shared<RecCTEAnchorSinkOperatorX>(next_sink_operator_id(),
1777
151
                                                                  op->operator_id(), tnode, descs);
1778
151
        RETURN_IF_ERROR(anchor_side_pipe->set_sink(anchor_sink));
1779
151
        RETURN_IF_ERROR(anchor_side_pipe->sink()->init(tnode, _runtime_state.get()));
1780
151
        _pipeline_parent_map.push(op->node_id(), anchor_side_pipe);
1781
1782
151
        PipelinePtr rec_side_pipe = add_pipeline(cur_pipe);
1783
151
        _dag[downstream_pipeline_id].push_back(rec_side_pipe->id());
1784
1785
151
        DataSinkOperatorPtr rec_sink;
1786
151
        rec_sink = std::make_shared<RecCTESinkOperatorX>(next_sink_operator_id(), op->operator_id(),
1787
151
                                                         tnode, descs);
1788
151
        RETURN_IF_ERROR(rec_side_pipe->set_sink(rec_sink));
1789
151
        RETURN_IF_ERROR(rec_side_pipe->sink()->init(tnode, _runtime_state.get()));
1790
151
        _pipeline_parent_map.push(op->node_id(), rec_side_pipe);
1791
1792
151
        break;
1793
151
    }
1794
1.95k
    case TPlanNodeType::REC_CTE_SCAN_NODE: {
1795
1.95k
        op = std::make_shared<RecCTEScanOperatorX>(pool, tnode, next_operator_id(), descs);
1796
1.95k
        RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1797
1.95k
        break;
1798
1.95k
    }
1799
1.95k
    default:
1800
0
        return Status::InternalError("Unsupported exec type in pipeline: {}",
1801
0
                                     print_plan_node_type(tnode.node_type));
1802
522k
    }
1803
521k
    if (_params.__isset.parallel_instances && fe_with_old_version) {
1804
0
        cur_pipe->set_num_tasks(_params.parallel_instances);
1805
0
        op->set_serial_operator();
1806
0
    }
1807
1808
521k
    return Status::OK();
1809
522k
}
1810
// NOLINTEND(readability-function-cognitive-complexity)
1811
// NOLINTEND(readability-function-size)
1812
1813
template <bool is_intersect>
1814
Status PipelineFragmentContext::_build_operators_for_set_operation_node(
1815
        ObjectPool* pool, const TPlanNode& tnode, const DescriptorTbl& descs, OperatorPtr& op,
1816
267
        PipelinePtr& cur_pipe, std::vector<DataSinkOperatorPtr>& sink_ops) {
1817
267
    op.reset(new SetSourceOperatorX<is_intersect>(pool, tnode, next_operator_id(), descs));
1818
267
    RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1819
1820
267
    const auto downstream_pipeline_id = cur_pipe->id();
1821
267
    if (!_dag.contains(downstream_pipeline_id)) {
1822
242
        _dag.insert({downstream_pipeline_id, {}});
1823
242
    }
1824
1825
895
    for (int child_id = 0; child_id < tnode.num_children; child_id++) {
1826
628
        PipelinePtr probe_side_pipe = add_pipeline(cur_pipe);
1827
628
        _dag[downstream_pipeline_id].push_back(probe_side_pipe->id());
1828
1829
628
        if (child_id == 0) {
1830
267
            sink_ops.push_back(std::make_shared<SetSinkOperatorX<is_intersect>>(
1831
267
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1832
361
        } else {
1833
361
            sink_ops.push_back(std::make_shared<SetProbeSinkOperatorX<is_intersect>>(
1834
361
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1835
361
        }
1836
628
        RETURN_IF_ERROR(probe_side_pipe->set_sink(sink_ops.back()));
1837
628
        RETURN_IF_ERROR(probe_side_pipe->sink()->init(tnode, _runtime_state.get()));
1838
        // prepare children pipelines. if any pipeline found this as its father, will use the prepared pipeline to build.
1839
628
        _pipeline_parent_map.push(op->node_id(), probe_side_pipe);
1840
628
    }
1841
1842
267
    return Status::OK();
1843
267
}
_ZN5doris23PipelineFragmentContext39_build_operators_for_set_operation_nodeILb1EEENS_6StatusEPNS_10ObjectPoolERKNS_9TPlanNodeERKNS_13DescriptorTblERSt10shared_ptrINS_13OperatorXBaseEERSB_INS_8PipelineEERSt6vectorISB_INS_21DataSinkOperatorXBaseEESaISK_EE
Line
Count
Source
1816
134
        PipelinePtr& cur_pipe, std::vector<DataSinkOperatorPtr>& sink_ops) {
1817
134
    op.reset(new SetSourceOperatorX<is_intersect>(pool, tnode, next_operator_id(), descs));
1818
134
    RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1819
1820
134
    const auto downstream_pipeline_id = cur_pipe->id();
1821
134
    if (!_dag.contains(downstream_pipeline_id)) {
1822
118
        _dag.insert({downstream_pipeline_id, {}});
1823
118
    }
1824
1825
481
    for (int child_id = 0; child_id < tnode.num_children; child_id++) {
1826
347
        PipelinePtr probe_side_pipe = add_pipeline(cur_pipe);
1827
347
        _dag[downstream_pipeline_id].push_back(probe_side_pipe->id());
1828
1829
347
        if (child_id == 0) {
1830
134
            sink_ops.push_back(std::make_shared<SetSinkOperatorX<is_intersect>>(
1831
134
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1832
213
        } else {
1833
213
            sink_ops.push_back(std::make_shared<SetProbeSinkOperatorX<is_intersect>>(
1834
213
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1835
213
        }
1836
347
        RETURN_IF_ERROR(probe_side_pipe->set_sink(sink_ops.back()));
1837
347
        RETURN_IF_ERROR(probe_side_pipe->sink()->init(tnode, _runtime_state.get()));
1838
        // prepare children pipelines. if any pipeline found this as its father, will use the prepared pipeline to build.
1839
347
        _pipeline_parent_map.push(op->node_id(), probe_side_pipe);
1840
347
    }
1841
1842
134
    return Status::OK();
1843
134
}
_ZN5doris23PipelineFragmentContext39_build_operators_for_set_operation_nodeILb0EEENS_6StatusEPNS_10ObjectPoolERKNS_9TPlanNodeERKNS_13DescriptorTblERSt10shared_ptrINS_13OperatorXBaseEERSB_INS_8PipelineEERSt6vectorISB_INS_21DataSinkOperatorXBaseEESaISK_EE
Line
Count
Source
1816
133
        PipelinePtr& cur_pipe, std::vector<DataSinkOperatorPtr>& sink_ops) {
1817
133
    op.reset(new SetSourceOperatorX<is_intersect>(pool, tnode, next_operator_id(), descs));
1818
133
    RETURN_IF_ERROR(cur_pipe->add_operator(op, _parallel_instances));
1819
1820
133
    const auto downstream_pipeline_id = cur_pipe->id();
1821
133
    if (!_dag.contains(downstream_pipeline_id)) {
1822
124
        _dag.insert({downstream_pipeline_id, {}});
1823
124
    }
1824
1825
414
    for (int child_id = 0; child_id < tnode.num_children; child_id++) {
1826
281
        PipelinePtr probe_side_pipe = add_pipeline(cur_pipe);
1827
281
        _dag[downstream_pipeline_id].push_back(probe_side_pipe->id());
1828
1829
281
        if (child_id == 0) {
1830
133
            sink_ops.push_back(std::make_shared<SetSinkOperatorX<is_intersect>>(
1831
133
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1832
148
        } else {
1833
148
            sink_ops.push_back(std::make_shared<SetProbeSinkOperatorX<is_intersect>>(
1834
148
                    child_id, next_sink_operator_id(), op->operator_id(), pool, tnode, descs));
1835
148
        }
1836
281
        RETURN_IF_ERROR(probe_side_pipe->set_sink(sink_ops.back()));
1837
281
        RETURN_IF_ERROR(probe_side_pipe->sink()->init(tnode, _runtime_state.get()));
1838
        // prepare children pipelines. if any pipeline found this as its father, will use the prepared pipeline to build.
1839
281
        _pipeline_parent_map.push(op->node_id(), probe_side_pipe);
1840
281
    }
1841
1842
133
    return Status::OK();
1843
133
}
1844
1845
324k
Status PipelineFragmentContext::submit() {
1846
324k
    if (_submitted) {
1847
0
        return Status::InternalError("submitted");
1848
0
    }
1849
324k
    _submitted = true;
1850
1851
324k
    int submit_tasks = 0;
1852
324k
    Status st;
1853
324k
    auto* scheduler = _query_ctx->get_pipe_exec_scheduler();
1854
886k
    for (auto& task : _tasks) {
1855
1.44M
        for (auto& t : task) {
1856
1.44M
            st = scheduler->submit(t.first);
1857
1.44M
            DBUG_EXECUTE_IF("PipelineFragmentContext.submit.failed",
1858
1.44M
                            { st = Status::Aborted("PipelineFragmentContext.submit.failed"); });
1859
1.44M
            if (!st) {
1860
0
                cancel(Status::InternalError("submit context to executor fail"));
1861
0
                std::lock_guard<std::mutex> l(_task_mutex);
1862
0
                _total_tasks = submit_tasks;
1863
0
                break;
1864
0
            }
1865
1.44M
            submit_tasks++;
1866
1.44M
        }
1867
886k
    }
1868
324k
    if (!st.ok()) {
1869
0
        bool need_remove = false;
1870
0
        {
1871
0
            std::lock_guard<std::mutex> l(_task_mutex);
1872
0
            if (_closed_tasks >= _total_tasks) {
1873
0
                need_remove = _close_fragment_instance();
1874
0
            }
1875
0
        }
1876
        // Call remove_pipeline_context() outside _task_mutex to avoid ABBA deadlock.
1877
0
        if (need_remove) {
1878
0
            _exec_env->fragment_mgr()->remove_pipeline_context({_query_id, _fragment_id});
1879
0
        }
1880
0
        return Status::InternalError("Submit pipeline failed. err = {}, BE: {}", st.to_string(),
1881
0
                                     BackendOptions::get_localhost());
1882
324k
    } else {
1883
324k
        return st;
1884
324k
    }
1885
324k
}
1886
1887
0
void PipelineFragmentContext::print_profile(const std::string& extra_info) {
1888
0
    if (_runtime_state->enable_profile()) {
1889
0
        std::stringstream ss;
1890
0
        for (auto runtime_profile_ptr : _runtime_state->pipeline_id_to_profile()) {
1891
0
            runtime_profile_ptr->pretty_print(&ss);
1892
0
        }
1893
1894
0
        if (_runtime_state->load_channel_profile()) {
1895
0
            _runtime_state->load_channel_profile()->pretty_print(&ss);
1896
0
        }
1897
1898
0
        auto profile_str =
1899
0
                fmt::format("Query {} fragment {} {}, profile, {}", print_id(this->_query_id),
1900
0
                            this->_fragment_id, extra_info, ss.str());
1901
0
        LOG_LONG_STRING(INFO, profile_str);
1902
0
    }
1903
0
}
1904
// If all pipeline tasks binded to the fragment instance are finished, then we could
1905
// close the fragment instance.
1906
// Returns true if the caller should call remove_pipeline_context() **after** releasing
1907
// _task_mutex. We must not call remove_pipeline_context() here because it acquires
1908
// _pipeline_map's shard lock, and this function is called while _task_mutex is held.
1909
// Acquiring _pipeline_map while holding _task_mutex creates an ABBA deadlock with
1910
// dump_pipeline_tasks(), which acquires _pipeline_map first and then _task_mutex
1911
// (via debug_string()).
1912
325k
bool PipelineFragmentContext::_close_fragment_instance() {
1913
325k
    if (_is_fragment_instance_closed) {
1914
0
        return false;
1915
0
    }
1916
325k
    Defer defer_op {[&]() { _is_fragment_instance_closed = true; }};
1917
325k
    _fragment_level_profile->total_time_counter()->update(_fragment_watcher.elapsed_time());
1918
325k
    if (!_need_notify_close) {
1919
321k
        auto st = send_report(true);
1920
321k
        if (!st) {
1921
0
            LOG(WARNING) << fmt::format("Failed to send report for query {}, fragment {}: {}",
1922
0
                                        print_id(_query_id), _fragment_id, st.to_string());
1923
0
        }
1924
321k
    }
1925
    // Print profile content in info log is a tempoeray solution for stream load and external_connector.
1926
    // Since stream load does not have someting like coordinator on FE, so
1927
    // backend can not report profile to FE, ant its profile can not be shown
1928
    // in the same way with other query. So we print the profile content to info log.
1929
1930
325k
    if (_runtime_state->enable_profile() &&
1931
325k
        (_query_ctx->get_query_source() == QuerySource::STREAM_LOAD ||
1932
2.40k
         _query_ctx->get_query_source() == QuerySource::EXTERNAL_CONNECTOR ||
1933
2.40k
         _query_ctx->get_query_source() == QuerySource::GROUP_COMMIT_LOAD)) {
1934
0
        std::stringstream ss;
1935
        // Compute the _local_time_percent before pretty_print the runtime_profile
1936
        // Before add this operation, the print out like that:
1937
        // UNION_NODE (id=0):(Active: 56.720us, non-child: 00.00%)
1938
        // After add the operation, the print out like that:
1939
        // UNION_NODE (id=0):(Active: 56.720us, non-child: 82.53%)
1940
        // We can easily know the exec node execute time without child time consumed.
1941
0
        for (auto runtime_profile_ptr : _runtime_state->pipeline_id_to_profile()) {
1942
0
            runtime_profile_ptr->pretty_print(&ss);
1943
0
        }
1944
1945
0
        if (_runtime_state->load_channel_profile()) {
1946
0
            _runtime_state->load_channel_profile()->pretty_print(&ss);
1947
0
        }
1948
1949
0
        LOG_INFO("Query {} fragment {} profile:\n {}", print_id(_query_id), _fragment_id, ss.str());
1950
0
    }
1951
1952
325k
    if (_query_ctx->enable_profile()) {
1953
2.40k
        _query_ctx->add_fragment_profile(_fragment_id, collect_realtime_profile(),
1954
2.40k
                                         collect_realtime_load_channel_profile());
1955
2.40k
    }
1956
1957
    // Return whether the caller needs to remove from the pipeline map.
1958
    // The caller must do this after releasing _task_mutex.
1959
325k
    return !_need_notify_close;
1960
325k
}
1961
1962
1.44M
void PipelineFragmentContext::decrement_running_task(PipelineId pipeline_id) {
1963
    // If all tasks of this pipeline has been closed, upstream tasks is never needed, and we just make those runnable here
1964
1.44M
    DCHECK(_pip_id_to_pipeline.contains(pipeline_id));
1965
1.44M
    if (_pip_id_to_pipeline[pipeline_id]->close_task()) {
1966
510k
        if (_dag.contains(pipeline_id)) {
1967
279k
            for (auto dep : _dag[pipeline_id]) {
1968
279k
                _pip_id_to_pipeline[dep]->make_all_runnable(pipeline_id);
1969
279k
            }
1970
226k
        }
1971
510k
    }
1972
1.44M
    bool need_remove = false;
1973
1.44M
    {
1974
1.44M
        std::lock_guard<std::mutex> l(_task_mutex);
1975
1.44M
        ++_closed_tasks;
1976
        // Update query-level finished task progress in real time.
1977
1.44M
        _query_ctx->inc_finished_task_num();
1978
1.44M
        if (_closed_tasks >= _total_tasks) {
1979
325k
            need_remove = _close_fragment_instance();
1980
325k
        }
1981
1.44M
    }
1982
    // Call remove_pipeline_context() outside _task_mutex to avoid ABBA deadlock.
1983
1.44M
    if (need_remove) {
1984
321k
        _exec_env->fragment_mgr()->remove_pipeline_context({_query_id, _fragment_id});
1985
321k
    }
1986
1.44M
}
1987
1988
41.7k
std::string PipelineFragmentContext::get_load_error_url() {
1989
41.7k
    if (const auto& str = _runtime_state->get_error_log_file_path(); !str.empty()) {
1990
0
        return to_load_error_http_path(str);
1991
0
    }
1992
88.8k
    for (auto& tasks : _tasks) {
1993
145k
        for (auto& task : tasks) {
1994
145k
            if (const auto& str = task.second->get_error_log_file_path(); !str.empty()) {
1995
163
                return to_load_error_http_path(str);
1996
163
            }
1997
145k
        }
1998
88.8k
    }
1999
41.5k
    return "";
2000
41.7k
}
2001
2002
41.7k
std::string PipelineFragmentContext::get_first_error_msg() {
2003
41.7k
    if (const auto& str = _runtime_state->get_first_error_msg(); !str.empty()) {
2004
0
        return str;
2005
0
    }
2006
88.8k
    for (auto& tasks : _tasks) {
2007
145k
        for (auto& task : tasks) {
2008
145k
            if (const auto& str = task.second->get_first_error_msg(); !str.empty()) {
2009
163
                return str;
2010
163
            }
2011
145k
        }
2012
88.8k
    }
2013
41.5k
    return "";
2014
41.7k
}
2015
2016
0
std::string PipelineFragmentContext::_to_http_path(const std::string& file_name) const {
2017
0
    std::stringstream url;
2018
0
    url << "http://" << BackendOptions::get_localhost() << ":" << config::webserver_port
2019
0
        << "/api/_download_load?"
2020
0
        << "token=" << _exec_env->token() << "&file=" << file_name;
2021
0
    return url.str();
2022
0
}
2023
2024
36.9k
void PipelineFragmentContext::_coordinator_callback(const ReportStatusRequest& req) {
2025
36.9k
    DBUG_EXECUTE_IF("FragmentMgr::coordinator_callback.report_delay", {
2026
36.9k
        int random_seconds = req.status.is<ErrorCode::DATA_QUALITY_ERROR>() ? 8 : 2;
2027
36.9k
        LOG_INFO("sleep : ").tag("time", random_seconds).tag("query_id", print_id(req.query_id));
2028
36.9k
        std::this_thread::sleep_for(std::chrono::seconds(random_seconds));
2029
36.9k
        LOG_INFO("sleep done").tag("query_id", print_id(req.query_id));
2030
36.9k
    });
2031
2032
36.9k
    DCHECK(req.status.ok() || req.done); // if !status.ok() => done
2033
36.9k
    if (req.coord_addr.hostname == "external") {
2034
        // External query (flink/spark read tablets) not need to report to FE.
2035
0
        return;
2036
0
    }
2037
36.9k
    int callback_retries = 10;
2038
36.9k
    const int sleep_ms = 1000;
2039
36.9k
    Status exec_status = req.status;
2040
36.9k
    Status coord_status;
2041
36.9k
    std::unique_ptr<FrontendServiceConnection> coord = nullptr;
2042
36.9k
    do {
2043
36.9k
        coord = std::make_unique<FrontendServiceConnection>(_exec_env->frontend_client_cache(),
2044
36.9k
                                                            req.coord_addr, &coord_status);
2045
36.9k
        if (!coord_status.ok()) {
2046
0
            std::this_thread::sleep_for(std::chrono::milliseconds(sleep_ms));
2047
0
        }
2048
36.9k
    } while (!coord_status.ok() && callback_retries-- > 0);
2049
2050
36.9k
    if (!coord_status.ok()) {
2051
0
        UniqueId uid(req.query_id.hi, req.query_id.lo);
2052
0
        static_cast<void>(req.cancel_fn(Status::InternalError(
2053
0
                "query_id: {}, couldn't get a client for {}, reason is {}", uid.to_string(),
2054
0
                PrintThriftNetworkAddress(req.coord_addr), coord_status.to_string())));
2055
0
        return;
2056
0
    }
2057
2058
36.9k
    TReportExecStatusParams params;
2059
36.9k
    params.protocol_version = FrontendServiceVersion::V1;
2060
36.9k
    params.__set_query_id(req.query_id);
2061
36.9k
    params.__set_backend_num(req.backend_num);
2062
36.9k
    params.__set_fragment_instance_id(req.fragment_instance_id);
2063
36.9k
    params.__set_fragment_id(req.fragment_id);
2064
36.9k
    params.__set_status(exec_status.to_thrift());
2065
36.9k
    params.__set_done(req.done);
2066
36.9k
    params.__set_query_type(req.runtime_state->query_type());
2067
36.9k
    params.__isset.profile = false;
2068
2069
36.9k
    DCHECK(req.runtime_state != nullptr);
2070
2071
36.9k
    if (req.runtime_state->query_type() == TQueryType::LOAD) {
2072
32.9k
        params.__set_loaded_rows(req.runtime_state->num_rows_load_total());
2073
32.9k
        params.__set_loaded_bytes(req.runtime_state->num_bytes_load_total());
2074
32.9k
    } else {
2075
4.01k
        DCHECK(!req.runtime_states.empty());
2076
4.01k
        if (!req.runtime_state->output_files().empty()) {
2077
0
            params.__isset.delta_urls = true;
2078
0
            for (auto& it : req.runtime_state->output_files()) {
2079
0
                params.delta_urls.push_back(_to_http_path(it));
2080
0
            }
2081
0
        }
2082
4.01k
        if (!params.delta_urls.empty()) {
2083
0
            params.__isset.delta_urls = true;
2084
0
        }
2085
4.01k
    }
2086
2087
36.9k
    static std::string s_dpp_normal_all = "dpp.norm.ALL";
2088
36.9k
    static std::string s_dpp_abnormal_all = "dpp.abnorm.ALL";
2089
36.9k
    static std::string s_unselected_rows = "unselected.rows";
2090
36.9k
    int64_t num_rows_load_success = 0;
2091
36.9k
    int64_t num_rows_load_filtered = 0;
2092
36.9k
    int64_t num_rows_load_unselected = 0;
2093
36.9k
    if (req.runtime_state->num_rows_load_total() > 0 ||
2094
36.9k
        req.runtime_state->num_rows_load_filtered() > 0 ||
2095
36.9k
        req.runtime_state->num_finished_range() > 0) {
2096
0
        params.__isset.load_counters = true;
2097
2098
0
        num_rows_load_success = req.runtime_state->num_rows_load_success();
2099
0
        num_rows_load_filtered = req.runtime_state->num_rows_load_filtered();
2100
0
        num_rows_load_unselected = req.runtime_state->num_rows_load_unselected();
2101
0
        params.__isset.fragment_instance_reports = true;
2102
0
        TFragmentInstanceReport t;
2103
0
        t.__set_fragment_instance_id(req.runtime_state->fragment_instance_id());
2104
0
        t.__set_num_finished_range(cast_set<int>(req.runtime_state->num_finished_range()));
2105
0
        t.__set_loaded_rows(req.runtime_state->num_rows_load_total());
2106
0
        t.__set_loaded_bytes(req.runtime_state->num_bytes_load_total());
2107
0
        params.fragment_instance_reports.push_back(t);
2108
36.9k
    } else if (!req.runtime_states.empty()) {
2109
106k
        for (auto* rs : req.runtime_states) {
2110
106k
            if (rs->num_rows_load_total() > 0 || rs->num_rows_load_filtered() > 0 ||
2111
106k
                rs->num_finished_range() > 0) {
2112
30.4k
                params.__isset.load_counters = true;
2113
30.4k
                num_rows_load_success += rs->num_rows_load_success();
2114
30.4k
                num_rows_load_filtered += rs->num_rows_load_filtered();
2115
30.4k
                num_rows_load_unselected += rs->num_rows_load_unselected();
2116
30.4k
                params.__isset.fragment_instance_reports = true;
2117
30.4k
                TFragmentInstanceReport t;
2118
30.4k
                t.__set_fragment_instance_id(rs->fragment_instance_id());
2119
30.4k
                t.__set_num_finished_range(cast_set<int>(rs->num_finished_range()));
2120
30.4k
                t.__set_loaded_rows(rs->num_rows_load_total());
2121
30.4k
                t.__set_loaded_bytes(rs->num_bytes_load_total());
2122
30.4k
                params.fragment_instance_reports.push_back(t);
2123
30.4k
            }
2124
106k
        }
2125
36.9k
    }
2126
36.9k
    params.load_counters.emplace(s_dpp_normal_all, std::to_string(num_rows_load_success));
2127
36.9k
    params.load_counters.emplace(s_dpp_abnormal_all, std::to_string(num_rows_load_filtered));
2128
36.9k
    params.load_counters.emplace(s_unselected_rows, std::to_string(num_rows_load_unselected));
2129
2130
36.9k
    if (!req.load_error_url.empty()) {
2131
146
        params.__set_tracking_url(req.load_error_url);
2132
146
    }
2133
36.9k
    if (!req.first_error_msg.empty()) {
2134
146
        params.__set_first_error_msg(req.first_error_msg);
2135
146
    }
2136
106k
    for (auto* rs : req.runtime_states) {
2137
106k
        if (rs->wal_id() > 0) {
2138
111
            params.__set_txn_id(rs->wal_id());
2139
111
            params.__set_label(rs->import_label());
2140
111
        }
2141
106k
    }
2142
36.9k
    if (!req.runtime_state->export_output_files().empty()) {
2143
0
        params.__isset.export_files = true;
2144
0
        params.export_files = req.runtime_state->export_output_files();
2145
36.9k
    } else if (!req.runtime_states.empty()) {
2146
106k
        for (auto* rs : req.runtime_states) {
2147
106k
            if (!rs->export_output_files().empty()) {
2148
0
                params.__isset.export_files = true;
2149
0
                params.export_files.insert(params.export_files.end(),
2150
0
                                           rs->export_output_files().begin(),
2151
0
                                           rs->export_output_files().end());
2152
0
            }
2153
106k
        }
2154
36.9k
    }
2155
36.9k
    if (auto tci = req.runtime_state->tablet_commit_infos(); !tci.empty()) {
2156
0
        params.__isset.commitInfos = true;
2157
0
        params.commitInfos.insert(params.commitInfos.end(), tci.begin(), tci.end());
2158
36.9k
    } else if (!req.runtime_states.empty()) {
2159
106k
        for (auto* rs : req.runtime_states) {
2160
106k
            if (auto rs_tci = rs->tablet_commit_infos(); !rs_tci.empty()) {
2161
24.9k
                params.__isset.commitInfos = true;
2162
24.9k
                params.commitInfos.insert(params.commitInfos.end(), rs_tci.begin(), rs_tci.end());
2163
24.9k
            }
2164
106k
        }
2165
36.9k
    }
2166
36.9k
    if (auto eti = req.runtime_state->error_tablet_infos(); !eti.empty()) {
2167
0
        params.__isset.errorTabletInfos = true;
2168
0
        params.errorTabletInfos.insert(params.errorTabletInfos.end(), eti.begin(), eti.end());
2169
36.9k
    } else if (!req.runtime_states.empty()) {
2170
106k
        for (auto* rs : req.runtime_states) {
2171
106k
            if (auto rs_eti = rs->error_tablet_infos(); !rs_eti.empty()) {
2172
0
                params.__isset.errorTabletInfos = true;
2173
0
                params.errorTabletInfos.insert(params.errorTabletInfos.end(), rs_eti.begin(),
2174
0
                                               rs_eti.end());
2175
0
            }
2176
106k
        }
2177
36.9k
    }
2178
36.9k
    if (auto hpu = req.runtime_state->hive_partition_updates(); !hpu.empty()) {
2179
0
        params.__isset.hive_partition_updates = true;
2180
0
        params.hive_partition_updates.insert(params.hive_partition_updates.end(), hpu.begin(),
2181
0
                                             hpu.end());
2182
36.9k
    } else if (!req.runtime_states.empty()) {
2183
106k
        for (auto* rs : req.runtime_states) {
2184
106k
            if (auto rs_hpu = rs->hive_partition_updates(); !rs_hpu.empty()) {
2185
0
                params.__isset.hive_partition_updates = true;
2186
0
                params.hive_partition_updates.insert(params.hive_partition_updates.end(),
2187
0
                                                     rs_hpu.begin(), rs_hpu.end());
2188
0
            }
2189
106k
        }
2190
36.9k
    }
2191
36.9k
    if (auto icd = req.runtime_state->iceberg_commit_datas(); !icd.empty()) {
2192
0
        params.__isset.iceberg_commit_datas = true;
2193
0
        params.iceberg_commit_datas.insert(params.iceberg_commit_datas.end(), icd.begin(),
2194
0
                                           icd.end());
2195
36.9k
    } else if (!req.runtime_states.empty()) {
2196
106k
        for (auto* rs : req.runtime_states) {
2197
106k
            if (auto rs_icd = rs->iceberg_commit_datas(); !rs_icd.empty()) {
2198
0
                params.__isset.iceberg_commit_datas = true;
2199
0
                params.iceberg_commit_datas.insert(params.iceberg_commit_datas.end(),
2200
0
                                                   rs_icd.begin(), rs_icd.end());
2201
0
            }
2202
106k
        }
2203
36.9k
    }
2204
2205
36.9k
    if (auto mcd = req.runtime_state->mc_commit_datas(); !mcd.empty()) {
2206
0
        params.__isset.mc_commit_datas = true;
2207
0
        params.mc_commit_datas.insert(params.mc_commit_datas.end(), mcd.begin(), mcd.end());
2208
36.9k
    } else if (!req.runtime_states.empty()) {
2209
106k
        for (auto* rs : req.runtime_states) {
2210
106k
            if (auto rs_mcd = rs->mc_commit_datas(); !rs_mcd.empty()) {
2211
0
                params.__isset.mc_commit_datas = true;
2212
0
                params.mc_commit_datas.insert(params.mc_commit_datas.end(), rs_mcd.begin(),
2213
0
                                              rs_mcd.end());
2214
0
            }
2215
106k
        }
2216
36.9k
    }
2217
2218
36.9k
    req.runtime_state->get_unreported_errors(&(params.error_log));
2219
36.9k
    params.__isset.error_log = (!params.error_log.empty());
2220
2221
36.9k
    if (_exec_env->cluster_info()->backend_id != 0) {
2222
36.9k
        params.__set_backend_id(_exec_env->cluster_info()->backend_id);
2223
36.9k
    }
2224
2225
36.9k
    TReportExecStatusResult res;
2226
36.9k
    Status rpc_status;
2227
2228
36.9k
    VLOG_DEBUG << "reportExecStatus params is "
2229
9
               << apache::thrift::ThriftDebugString(params).c_str();
2230
36.9k
    if (!exec_status.ok()) {
2231
1.53k
        LOG(WARNING) << "report error status: " << exec_status.msg()
2232
1.53k
                     << " to coordinator: " << req.coord_addr
2233
1.53k
                     << ", query id: " << print_id(req.query_id);
2234
1.53k
    }
2235
36.9k
    try {
2236
36.9k
        try {
2237
36.9k
            (*coord)->reportExecStatus(res, params);
2238
36.9k
        } catch ([[maybe_unused]] apache::thrift::transport::TTransportException& e) {
2239
#ifndef ADDRESS_SANITIZER
2240
            LOG(WARNING) << "Retrying ReportExecStatus. query id: " << print_id(req.query_id)
2241
                         << ", instance id: " << print_id(req.fragment_instance_id) << " to "
2242
                         << req.coord_addr << ", err: " << e.what();
2243
#endif
2244
0
            rpc_status = coord->reopen();
2245
2246
0
            if (!rpc_status.ok()) {
2247
0
                req.cancel_fn(rpc_status);
2248
0
                return;
2249
0
            }
2250
0
            (*coord)->reportExecStatus(res, params);
2251
0
        }
2252
2253
36.9k
        rpc_status = Status::create<false>(res.status);
2254
36.9k
    } catch (apache::thrift::TException& e) {
2255
0
        rpc_status = Status::InternalError("ReportExecStatus() to {} failed: {}",
2256
0
                                           PrintThriftNetworkAddress(req.coord_addr), e.what());
2257
0
    }
2258
2259
36.9k
    if (!rpc_status.ok()) {
2260
0
        LOG_INFO("Going to cancel query {} since report exec status got rpc failed: {}",
2261
0
                 print_id(req.query_id), rpc_status.to_string());
2262
0
        req.cancel_fn(rpc_status);
2263
0
    }
2264
36.9k
}
2265
2266
325k
Status PipelineFragmentContext::send_report(bool done) {
2267
325k
    Status exec_status = _query_ctx->exec_status();
2268
    // If plan is done successfully, but _is_report_success is false,
2269
    // no need to send report.
2270
    // Load will set _is_report_success to true because load wants to know
2271
    // the process.
2272
325k
    if (!_is_report_success && done && exec_status.ok()) {
2273
288k
        return Status::OK();
2274
288k
    }
2275
2276
    // If both _is_report_success and _is_report_on_cancel are false,
2277
    // which means no matter query is success or failed, no report is needed.
2278
    // This may happen when the query limit reached and
2279
    // a internal cancellation being processed
2280
    // When limit is reached the fragment is also cancelled, but _is_report_on_cancel will
2281
    // be set to false, to avoid sending fault report to FE.
2282
37.0k
    if (!_is_report_success && !_is_report_on_cancel) {
2283
117
        if (done) {
2284
            // if done is true, which means the query is finished successfully, we can safely close the fragment instance without sending report to FE, and just return OK status here.
2285
117
            return Status::OK();
2286
117
        }
2287
0
        return Status::NeedSendAgain("");
2288
117
    }
2289
2290
36.9k
    std::vector<RuntimeState*> runtime_states;
2291
2292
71.4k
    for (auto& tasks : _tasks) {
2293
106k
        for (auto& task : tasks) {
2294
106k
            runtime_states.push_back(task.second.get());
2295
106k
        }
2296
71.4k
    }
2297
2298
36.9k
    std::string load_eror_url = _query_ctx->get_load_error_url().empty()
2299
36.9k
                                        ? get_load_error_url()
2300
36.9k
                                        : _query_ctx->get_load_error_url();
2301
36.9k
    std::string first_error_msg = _query_ctx->get_first_error_msg().empty()
2302
36.9k
                                          ? get_first_error_msg()
2303
36.9k
                                          : _query_ctx->get_first_error_msg();
2304
2305
36.9k
    ReportStatusRequest req {.status = exec_status,
2306
36.9k
                             .runtime_states = runtime_states,
2307
36.9k
                             .done = done || !exec_status.ok(),
2308
36.9k
                             .coord_addr = _query_ctx->coord_addr,
2309
36.9k
                             .query_id = _query_id,
2310
36.9k
                             .fragment_id = _fragment_id,
2311
36.9k
                             .fragment_instance_id = TUniqueId(),
2312
36.9k
                             .backend_num = -1,
2313
36.9k
                             .runtime_state = _runtime_state.get(),
2314
36.9k
                             .load_error_url = load_eror_url,
2315
36.9k
                             .first_error_msg = first_error_msg,
2316
36.9k
                             .cancel_fn = [this](const Status& reason) { cancel(reason); }};
2317
36.9k
    auto ctx = std::dynamic_pointer_cast<PipelineFragmentContext>(shared_from_this());
2318
36.9k
    return _exec_env->fragment_mgr()->get_thread_pool()->submit_func([this, req, ctx]() {
2319
36.9k
        SCOPED_ATTACH_TASK(ctx->get_query_ctx()->query_mem_tracker());
2320
36.9k
        _coordinator_callback(req);
2321
36.9k
        if (!req.done) {
2322
3.67k
            ctx->refresh_next_report_time();
2323
3.67k
        }
2324
36.9k
    });
2325
37.0k
}
2326
2327
0
size_t PipelineFragmentContext::get_revocable_size(bool* has_running_task) const {
2328
0
    size_t res = 0;
2329
    // _tasks will be cleared during ~PipelineFragmentContext, so that it's safe
2330
    // here to traverse the vector.
2331
0
    for (const auto& task_instances : _tasks) {
2332
0
        for (const auto& task : task_instances) {
2333
0
            if (task.first->is_running()) {
2334
0
                LOG_EVERY_N(INFO, 50) << "Query: " << print_id(_query_id)
2335
0
                                      << " is running, task: " << (void*)task.first.get()
2336
0
                                      << ", is_running: " << task.first->is_running();
2337
0
                *has_running_task = true;
2338
0
                return 0;
2339
0
            }
2340
2341
0
            size_t revocable_size = task.first->get_revocable_size();
2342
0
            if (revocable_size >= SpillFile::MIN_SPILL_WRITE_BATCH_MEM) {
2343
0
                res += revocable_size;
2344
0
            }
2345
0
        }
2346
0
    }
2347
0
    return res;
2348
0
}
2349
2350
0
std::vector<PipelineTask*> PipelineFragmentContext::get_revocable_tasks() const {
2351
0
    std::vector<PipelineTask*> revocable_tasks;
2352
0
    for (const auto& task_instances : _tasks) {
2353
0
        for (const auto& task : task_instances) {
2354
0
            size_t revocable_size_ = task.first->get_revocable_size();
2355
2356
0
            if (revocable_size_ >= SpillFile::MIN_SPILL_WRITE_BATCH_MEM) {
2357
0
                revocable_tasks.emplace_back(task.first.get());
2358
0
            }
2359
0
        }
2360
0
    }
2361
0
    return revocable_tasks;
2362
0
}
2363
2364
81
std::string PipelineFragmentContext::debug_string() {
2365
81
    std::lock_guard<std::mutex> l(_task_mutex);
2366
81
    fmt::memory_buffer debug_string_buffer;
2367
81
    fmt::format_to(debug_string_buffer,
2368
81
                   "PipelineFragmentContext Info: _closed_tasks={}, _total_tasks={}, "
2369
81
                   "need_notify_close={}, fragment_id={}, _rec_cte_stage={}\n",
2370
81
                   _closed_tasks, _total_tasks, _need_notify_close, _fragment_id, _rec_cte_stage);
2371
278
    for (size_t j = 0; j < _tasks.size(); j++) {
2372
197
        fmt::format_to(debug_string_buffer, "Tasks in instance {}:\n", j);
2373
905
        for (size_t i = 0; i < _tasks[j].size(); i++) {
2374
708
            fmt::format_to(debug_string_buffer, "Task {}: {}\n", i,
2375
708
                           _tasks[j][i].first->debug_string());
2376
708
        }
2377
197
    }
2378
2379
81
    return fmt::to_string(debug_string_buffer);
2380
81
}
2381
2382
std::vector<std::shared_ptr<TRuntimeProfileTree>>
2383
2.40k
PipelineFragmentContext::collect_realtime_profile() const {
2384
2.40k
    std::vector<std::shared_ptr<TRuntimeProfileTree>> res;
2385
2386
    // we do not have mutex to protect pipeline_id_to_profile
2387
    // so we need to make sure this funciton is invoked after fragment context
2388
    // has already been prepared.
2389
2.40k
    if (!_prepared) {
2390
0
        std::string msg =
2391
0
                "Query " + print_id(_query_id) + " collecting profile, but its not prepared";
2392
0
        DCHECK(false) << msg;
2393
0
        LOG_ERROR(msg);
2394
0
        return res;
2395
0
    }
2396
2397
    // Make sure first profile is fragment level profile
2398
2.40k
    auto fragment_profile = std::make_shared<TRuntimeProfileTree>();
2399
2.40k
    _fragment_level_profile->to_thrift(fragment_profile.get(), _runtime_state->profile_level());
2400
2.40k
    res.push_back(fragment_profile);
2401
2402
    // pipeline_id_to_profile is initialized in prepare stage
2403
4.69k
    for (auto pipeline_profile : _runtime_state->pipeline_id_to_profile()) {
2404
4.69k
        auto profile_ptr = std::make_shared<TRuntimeProfileTree>();
2405
4.69k
        pipeline_profile->to_thrift(profile_ptr.get(), _runtime_state->profile_level());
2406
4.69k
        res.push_back(profile_ptr);
2407
4.69k
    }
2408
2409
2.40k
    return res;
2410
2.40k
}
2411
2412
std::shared_ptr<TRuntimeProfileTree>
2413
2.40k
PipelineFragmentContext::collect_realtime_load_channel_profile() const {
2414
    // we do not have mutex to protect pipeline_id_to_profile
2415
    // so we need to make sure this funciton is invoked after fragment context
2416
    // has already been prepared.
2417
2.40k
    if (!_prepared) {
2418
0
        std::string msg =
2419
0
                "Query " + print_id(_query_id) + " collecting profile, but its not prepared";
2420
0
        DCHECK(false) << msg;
2421
0
        LOG_ERROR(msg);
2422
0
        return nullptr;
2423
0
    }
2424
2425
7.14k
    for (const auto& tasks : _tasks) {
2426
14.6k
        for (const auto& task : tasks) {
2427
14.6k
            if (task.second->load_channel_profile() == nullptr) {
2428
0
                continue;
2429
0
            }
2430
2431
14.6k
            auto tmp_load_channel_profile = std::make_shared<TRuntimeProfileTree>();
2432
2433
14.6k
            task.second->load_channel_profile()->to_thrift(tmp_load_channel_profile.get(),
2434
14.6k
                                                           _runtime_state->profile_level());
2435
14.6k
            _runtime_state->load_channel_profile()->update(*tmp_load_channel_profile);
2436
14.6k
        }
2437
7.14k
    }
2438
2439
2.40k
    auto load_channel_profile = std::make_shared<TRuntimeProfileTree>();
2440
2.40k
    _runtime_state->load_channel_profile()->to_thrift(load_channel_profile.get(),
2441
2.40k
                                                      _runtime_state->profile_level());
2442
2.40k
    return load_channel_profile;
2443
2.40k
}
2444
2445
// Collect runtime filter IDs registered by all tasks in this PFC.
2446
// Used during recursive CTE stage transitions to know which filters to deregister
2447
// before creating the new PFC for the next recursion round.
2448
// Called from rerun_fragment(wait_for_destroy) while tasks are still closing.
2449
// Thread safety: safe because _tasks is structurally immutable after prepare() —
2450
// the vector sizes do not change, and individual RuntimeState filter sets are
2451
// written only during open() which has completed by the time we reach rerun.
2452
3.28k
std::set<int> PipelineFragmentContext::get_deregister_runtime_filter() const {
2453
3.28k
    std::set<int> result;
2454
6.98k
    for (const auto& _task : _tasks) {
2455
12.5k
        for (const auto& task : _task) {
2456
12.5k
            auto set = task.first->runtime_state()->get_deregister_runtime_filter();
2457
12.5k
            result.merge(set);
2458
12.5k
        }
2459
6.98k
    }
2460
3.28k
    if (_runtime_state) {
2461
3.28k
        auto set = _runtime_state->get_deregister_runtime_filter();
2462
3.28k
        result.merge(set);
2463
3.28k
    }
2464
3.28k
    return result;
2465
3.28k
}
2466
2467
326k
void PipelineFragmentContext::_release_resource() {
2468
326k
    std::lock_guard<std::mutex> l(_task_mutex);
2469
    // The memory released by the query end is recorded in the query mem tracker.
2470
326k
    SCOPED_SWITCH_THREAD_MEM_TRACKER_LIMITER(_query_ctx->query_mem_tracker());
2471
326k
    auto st = _query_ctx->exec_status();
2472
887k
    for (auto& _task : _tasks) {
2473
887k
        if (!_task.empty()) {
2474
887k
            _call_back(_task.front().first->runtime_state(), &st);
2475
887k
        }
2476
887k
    }
2477
326k
    _tasks.clear();
2478
326k
    _dag.clear();
2479
326k
    _pip_id_to_pipeline.clear();
2480
326k
    _pipelines.clear();
2481
326k
    _sink.reset();
2482
326k
    _root_op.reset();
2483
326k
    _runtime_filter_mgr_map.clear();
2484
326k
    _op_id_to_shared_state.clear();
2485
326k
}
2486
2487
} // namespace doris