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stream processing programs, with a focus on windows, aggregations, joins, state storage, checkpoints, and event-time processing. Its contents describe operator pipelines, state models and transitions, window and join semantics, and the relationship between event processing order and output results. It supports training and evaluating code understanding models for distributed event streaming, as well as debugging, maintenance, and education.\u003C\u002Fp>","2026-10-14 12:44:23","2026-10-19 17:19:07","0.00",24,0,5000,"stateful stream processing,stream processing code understanding,event stream processing,operator pipeline analysis,stream state tracking,event-time semantics,window and join semantics,checkpoint recovery,code SFT dataset","A code understanding dataset covering stateful stream processing, operator pipelines, state transitions, window and join semantics, event time, and recovery.","JSON","0.5GB","real-time stream processing, stateful data processing, event-driven system development","code understanding, state transition analysis, event-time semantics reasoning","V1.0","rivo\u002Fsample-packages\u002F3657d0ea-3be7-4c83-8e30-4dc7ed05ecff\u002F20261014.zip",[584,589,595,600,605,610,616,621,626,631],{"id":573,"name":585,"field":586,"intro":587,"type":588},"File Name","file_name","File name","string",{"id":590,"name":591,"field":592,"intro":593,"type":594},8744,"Code Understanding Explanation","explanation","A user-facing summary of the source code's operator pipeline, state transitions, time semantics, and event results.","object",{"id":596,"name":597,"field":598,"intro":599,"type":588},8745,"Source Code","source_code","Source code text to be analyzed for stateful stream processing behavior.",{"id":601,"name":602,"field":603,"intro":604,"type":594},8746,"State Model","state_model","Description of the keyed, aggregation, and join state used by the program and their lifecycles.",{"id":606,"name":607,"field":608,"intro":609,"type":594},8747,"Join Semantics","join_semantics","Description of join conditions and results for stream-to-stream, stream-to-table, or multiple stateful streams.",{"id":611,"name":612,"field":613,"intro":614,"type":615},8748,"Operator Pipeline","operator_chain","Stream processing operators parsed from the source code and their upstream and downstream relationships.","array",{"id":617,"name":618,"field":619,"intro":620,"type":594},8749,"Window Semantics","window_semantics","Structured details about window types, time boundaries, watermarks, late events, and trigger conditions.",{"id":622,"name":623,"field":624,"intro":625,"type":615},8750,"State Transition Sequence","state_transitions","State reads, updates, merges, expirations, and cleanup recorded in event processing order.",{"id":627,"name":628,"field":629,"intro":630,"type":615},8751,"Event-Time Results","event_time_results","Explanation of how event time, window assignment, late-event classification, and final outputs relate to one another.",{"id":632,"name":633,"field":634,"intro":635,"type":594},8752,"Checkpointing and Recovery","checkpoint_recovery","Description of how state snapshots, checkpoints, failure recovery, and consistency guarantees affect processing results.",[637,641,645,649],{"id":638,"question":639,"answer":640},6271,"What does the dataset cover?","It combines stateful stream processing source code with structured analyses of operator pipelines, state models and transitions, window and join semantics, checkpointing and recovery, and event-time results.",{"id":642,"question":643,"answer":644},6272,"Which model capabilities can it support?","It can support training models to explain stream processing code, trace upstream and downstream operators, follow state reads and updates, analyze window and join behavior, and interpret how event time and recovery affect outputs.",{"id":646,"question":647,"answer":648},6273,"What are the dataset size and quality specifications?","The available information does not specify sample counts or quantitative quality metrics. The schema defines source code and several structured analysis fields; check the product details or delivery documentation for sample volume, coverage, and format.",{"id":650,"question":651,"answer":652},6274,"How can I obtain the dataset, and what are the licensing terms?","Use the purchase or request process provided on the product page, and refer to the page or delivery agreement for applicable license terms. Confirm permissions before using the data for commercial training, redistribution, or public release.",[654,671,685],{"id":655,"relate_table":656,"relate_id":657,"file_size":573,"file_name":658,"path":8,"duration":573,"cate":483,"file_format":659,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":660,"cate_name":661,"explanation":662,"source_code":663,"state_model":664,"join_semantics":665,"operator_chain":666,"window_semantics":667,"state_transitions":668,"event_time_results":669,"checkpoint_recovery":670},15015,"union_cubo",178652907,"样例_3.json","json","{\"explanation\": \"{\\\"operator_chain_explanation\\\":\\\"After the click stream extracts event time, it is partitioned by user, and the rule stream is broadcast and joined with the click stream. The processor first performs event_id deduplication, then adds the event to all 5-minute sliding windows covering it, reads the active threshold from broadcast state, and outputs to the alert sink when the threshold is reached.\\\",\\\"state_change_explanation\\\":\\\"seen_event_ids prevents the same event from being counted multiple times, window_accumulators stores revenue and count by user, campaign, and window, and rule_state stores the latest rule for each campaign. Window state is cleared after the allowed lateness period ends, while rule state continuously retains the latest version.\\\",\\\"event_result_explanation\\\":\\\"e701 establishes a window accumulation of 55.0; although e702 is late, it can still revise the same window, making the accumulation reach 115.0 and triggering an alert; the e701 that arrives again is deduplicated and does not change the aggregation result.\\\",\\\"assumptions_and_uncertainties\\\":\\\"The example assumes that the latest version of the same campaign rule in broadcast state takes effect during click processing and that the sliding window maintains accumulation values separately for each covered interval. Because the checkpointing mode is AT_LEAST_ONCE, whether the external alert sink has an idempotency key will affect the risk of final duplicate alerts.\\\"}\", \"source_code\": \"class CampaignSlidingProcessor:\\n    def open(self):\\n        self.seen_event_ids = MapState('seen_event_ids')\\n        self.window_accumulators = MapState('window_accumulators')\\n        self.rule_state = BroadcastState('campaign_rules')\\n\\n    def on_rule(self, rule):\\n        self.rule_state.put(rule['campaign_id'], rule)\\n\\n    def on_click(self, event, ctx, out):\\n        if self.seen_event_ids.contains(event['event_id']):\\n            return\\n        self.seen_event_ids.put(event['event_id'], event['event_time_ms'])\\n        rule = self.rule_state.get(event['campaign_id'])\\n        for window_start in sliding_starts(event['event_time_ms'], size_ms=300000, slide_ms=60000):\\n            key = (event['user_id'], event['campaign_id'], window_start)\\n            current = self.window_accumulators.get(key, {'count': 0, 'revenue': 0.0})\\n            current['count'] += 1\\n            current['revenue'] += event['revenue']\\n            self.window_accumulators.put(key, current)\\n            if rule and rule['active'] and current['revenue'] >= rule['alert_threshold']:\\n                out.emit_alert(key, current)\\n        ctx.register_event_time_timer(event['event_time_ms'] + 330000)\\n\\nclicks = source('campaign-clicks').assign_timestamps_and_watermarks(max_out_of_orderness_ms=10000)\\nrules = source('campaign-rules').broadcast('campaign_rules')\\nalerts = clicks.key_by(lambda e: e['user_id']).connect(rules).process(\\n    CampaignSlidingProcessor(), window_size_ms=300000, slide_ms=60000, allowed_lateness_ms=30000\\n)\\nalerts.sink('campaign-alerts')\\ncheckpoint(enable=True, interval_ms=20000, mode='AT_LEAST_ONCE')\", \"state_model\": \"{\\\"has_state\\\":true,\\\"state_descriptors\\\":[{\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Partitioned by user_id, with event_id as the deduplication key.\\\",\\\"value_definition\\\":\\\"Stores the event_time_ms when the event_id was first seen.\\\",\\\"update_rule\\\":\\\"Written when an event appears for the first time; if event_id already exists, it is directly discarded and the window accumulation is not updated.\\\",\\\"retention_policy\\\":\\\"Cleared after the event time exceeds the window end plus the 30000-millisecond allowed lateness period.\\\"},{\\\"state_name\\\":\\\"window_accumulators\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Uses a composite key composed of user_id, campaign_id, and window_start.\\\",\\\"value_definition\\\":\\\"Stores count and revenue within the sliding window.\\\",\\\"update_rule\\\":\\\"After deduplication passes, the event is added to all 5-minute windows it covers; revenue is accumulated, count is incremented by one, and an alert is output when the broadcast rule threshold is met.\\\",\\\"retention_policy\\\":\\\"Retained for 30000 milliseconds after the window ends to accept late updates, then cleared by an event-time timer.\\\"},{\\\"state_name\\\":\\\"rule_state\\\",\\\"state_type\\\":\\\"Broadcast State\\\",\\\"key_definition\\\":\\\"Stored by campaign_id; all parallel instances have a copy of the rule.\\\",\\\"value_definition\\\":\\\"Stores fields such as active, alert_threshold, and rule version.\\\",\\\"update_rule\\\":\\\"When a rule event arrives, it overwrites the old rule by campaign_id; subsequent clicks read the current rule.\\\",\\\"retention_policy\\\":\\\"Retains the latest rule until a new rule overwrites it or the job stops.\\\"}],\\\"state_scope\\\":\\\"Click deduplication and window aggregation are scoped by user key, while rule state is scoped as broadcast per operator instance.\\\"}\", \"join_semantics\": \"{\\\"join_type\\\":\\\"Broadcast Join\\\",\\\"left_key\\\":\\\"click.campaign_id\\\",\\\"right_key\\\":\\\"rule.campaign_id\\\",\\\"join_window\\\":\\\"There is no separate rule time window; when processing a click, the current broadcast rule is read; the click itself is still constrained by the 5-minute sliding window.\\\",\\\"match_condition\\\":\\\"The rule campaign_id is the same and rule.active is true; alert_threshold is checked only when an active rule is matched.\\\",\\\"unmatched_record_policy\\\":\\\"When there is no corresponding rule, the click's window aggregation is still retained, but no alert is generated.\\\"}\", \"operator_chain\": \"[{\\\"node_id\\\":\\\"c1\\\",\\\"operator_type\\\":\\\"Data Source\\\",\\\"operator_name\\\":\\\"source('campaign-clicks')\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"c2\\\"],\\\"semantic_role\\\":\\\"Reads click events containing event_id, user_id, campaign_id, event_time_ms, and revenue.\\\"},{\\\"node_id\\\":\\\"c2\\\",\\\"operator_type\\\":\\\"Event Time and Watermarks\\\",\\\"operator_name\\\":\\\"assign_timestamps_and_watermarks\\\",\\\"input_nodes\\\":[\\\"c1\\\"],\\\"output_nodes\\\":[\\\"c3\\\"],\\\"semantic_role\\\":\\\"Extracts click event time and advances watermarks with a maximum out-of-orderness of 10 seconds.\\\"},{\\\"node_id\\\":\\\"c3\\\",\\\"operator_type\\\":\\\"Keyed Deduplication\\\",\\\"operator_name\\\":\\\"key_by(user_id)\\\",\\\"input_nodes\\\":[\\\"c2\\\"],\\\"output_nodes\\\":[\\\"c5\\\"],\\\"semantic_role\\\":\\\"Partitions by user and discards event_ids that have already been processed via seen_event_ids.\\\"},{\\\"node_id\\\":\\\"c4\\\",\\\"operator_type\\\":\\\"Rule Data Source\\\",\\\"operator_name\\\":\\\"source('campaign-rules').broadcast\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"c5\\\"],\\\"semantic_role\\\":\\\"Reads campaign threshold rules and broadcasts them to all processing instances.\\\"},{\\\"node_id\\\":\\\"c5\\\",\\\"operator_type\\\":\\\"Broadcast Join and Processing\\\",\\\"operator_name\\\":\\\"connect(rules).process(CampaignSlidingProcessor())\\\",\\\"input_nodes\\\":[\\\"c3\\\",\\\"c4\\\"],\\\"output_nodes\\\":[\\\"c6\\\"],\\\"semantic_role\\\":\\\"Combines click events with broadcast rules, accumulates revenue in sliding windows, and emits alerts when the rule threshold is reached.\\\"},{\\\"node_id\\\":\\\"c6\\\",\\\"operator_type\\\":\\\"Sink\\\",\\\"operator_name\\\":\\\"sink('campaign-alerts')\\\",\\\"input_nodes\\\":[\\\"c5\\\"],\\\"output_nodes\\\":[],\\\"semantic_role\\\":\\\"Outputs user window alerts that reach the campaign revenue threshold.\\\"}]\", \"window_semantics\": \"{\\\"window_type\\\":\\\"Sliding Window\\\",\\\"time_basis\\\":\\\"Event Time\\\",\\\"window_size_ms\\\":300000,\\\"slide_interval_ms\\\":60000,\\\"allowed_lateness_ms\\\":30000,\\\"watermark_strategy\\\":\\\"The watermark is approximately the currently observed maximum event time minus 10000 milliseconds.\\\",\\\"trigger_condition\\\":\\\"The current aggregation is output when the watermark reaches the window end time; during the allowed lateness period, events update the window and can trigger the threshold alert again.\\\",\\\"late_event_policy\\\":\\\"Late events within 30000 milliseconds after the window ends update windows that have not yet been cleared; events beyond the boundary no longer enter aggregation; duplicate event_ids are directly discarded regardless of whether they are late.\\\"}\", \"state_transitions\": \"[{\\\"sequence\\\":1,\\\"state_name\\\":\\\"rule_state\\\",\\\"trigger_event\\\":\\\"Rule r9: campaign_id=summer, active=true, alert_threshold=100.0.\\\",\\\"before_state\\\":\\\"summer: does not exist.\\\",\\\"after_state\\\":\\\"summer -> active=true, alert_threshold=100.0.\\\",\\\"transition_reason\\\":\\\"The broadcast rule arrives and initializes the active threshold, after which all processing instances can read the rule.\\\"},{\\\"sequence\\\":2,\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"trigger_event\\\":\\\"Click e701: user_id=u7, campaign_id=summer, event_time=09:02:30, revenue=55.0.\\\",\\\"before_state\\\":\\\"e701 is not in u7's seen_event_ids.\\\",\\\"after_state\\\":\\\"e701 -> 09:02:30 is added to u7's seen_event_ids.\\\",\\\"transition_reason\\\":\\\"The event appears for the first time, passes the deduplication check, and is allowed to enter window processing.\\\"},{\\\"sequence\\\":3,\\\"state_name\\\":\\\"window_accumulators\\\",\\\"trigger_event\\\":\\\"Click e701 enters sliding windows such as [09:00:00,09:05:00).\\\",\\\"before_state\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=0, revenue=0.0.\\\",\\\"after_state\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\\\",\\\"transition_reason\\\":\\\"The event is added by event time to the sliding windows covering it, and the 100.0 alert threshold has not yet been reached.\\\"},{\\\"sequence\\\":4,\\\"state_name\\\":\\\"window_accumulators\\\",\\\"trigger_event\\\":\\\"Late click e702: user_id=u7, campaign_id=summer, event_time=09:01:50, processing_time=09:03:20, revenue=60.0.\\\",\\\"before_state\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\\\",\\\"after_state\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=2, revenue=115.0.\\\",\\\"transition_reason\\\":\\\"e702 is earlier than the current watermark but still within the allowed lateness range, updating the window and making revenue exceed the rule threshold.\\\"},{\\\"sequence\\\":5,\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"trigger_event\\\":\\\"Duplicate click e701 arrives again.\\\",\\\"before_state\\\":\\\"u7's seen_event_ids already contains e701.\\\",\\\"after_state\\\":\\\"State unchanged; e701 is discarded.\\\",\\\"transition_reason\\\":\\\"event_id deduplication takes precedence over window aggregation, so duplicate records do not cause duplicate revenue accumulation.\\\"}]\", \"event_time_results\": \"[{\\\"event_id\\\":\\\"e701\\\",\\\"event_time\\\":\\\"2025-03-03T09:02:30Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:02:35Z\\\",\\\"window_id\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"count\\\\\\\":1,\\\\\\\"revenue\\\\\\\":55.0}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"e701 arrives for the first time and is added to multiple sliding windows; the current revenue of the example window is 55.0, which does not reach the campaign rule's 100.0 threshold, so no alert is generated.\\\"},{\\\"event_id\\\":\\\"e702\\\",\\\"event_time\\\":\\\"2025-03-03T09:01:50Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:03:20Z\\\",\\\"window_id\\\":\\\"u7\u002Fsummer\u002F[09:00:00,09:05:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"count\\\\\\\":2,\\\\\\\"revenue\\\\\\\":115.0}\\\",\\\"is_late_event\\\":true,\\\"emitted_result\\\":\\\"{\\\\\\\"user_id\\\\\\\":\\\\\\\"u7\\\\\\\",\\\\\\\"campaign_id\\\\\\\":\\\\\\\"summer\\\\\\\",\\\\\\\"window\\\\\\\":\\\\\\\"[09:00:00,09:05:00)\\\\\\\",\\\\\\\"revenue\\\\\\\":115.0,\\\\\\\"alert\\\\\\\":true}\\\",\\\"result_explanation\\\":\\\"e702's event time is earlier than the current watermark, so it is a late event, but it is still within the 30000-millisecond allowed lateness range; it updates the window revenue from 55.0 to 115.0, exceeds the broadcast rule threshold, and generates an alert.\\\"},{\\\"event_id\\\":\\\"e701-duplicate\\\",\\\"event_time\\\":\\\"2025-03-03T09:02:30Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:03:25Z\\\",\\\"window_id\\\":\\\"did not enter a window\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"dropped\\\\\\\":true,\\\\\\\"reason\\\\\\\":\\\\\\\"duplicate_event_id\\\\\\\"}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"This record carries an already existing event_id=e701 and is intercepted by seen_event_ids before updating the window, so it does not increase count or revenue again.\\\"}]\", \"checkpoint_recovery\": \"{\\\"checkpoint_enabled\\\":true,\\\"checkpoint_interval_ms\\\":20000,\\\"checkpointed_state\\\":[\\\"seen_event_ids\\\",\\\"window_accumulators\\\",\\\"rule_state\\\",\\\"click-source-offset\\\",\\\"rule-source-offset\\\"],\\\"recovery_strategy\\\":\\\"Restores the deduplication table, window accumulators, broadcast rules, and offsets of both input sources from the latest checkpoint, and then continues replay.\\\",\\\"exactly_once_semantics\\\":false,\\\"failure_effect\\\":\\\"Checkpoint recovery can restore critical state, but the code is configured for AT_LEAST_ONCE, so after a failure and restart the output sink may see duplicate alerts; deduplication state can reduce duplicate accumulation, but cannot by itself guarantee exactly-once external output.\\\"}\"}","文本","{\"operator_chain_explanation\":\"After the click stream extracts event time, it is partitioned by user, and the rule stream is broadcast and joined with the click stream. The processor first performs event_id deduplication, then adds the event to all 5-minute sliding windows covering it, reads the active threshold from broadcast state, and outputs to the alert sink when the threshold is reached.\",\"state_change_explanation\":\"seen_event_ids prevents the same event from being counted multiple times, window_accumulators stores revenue and count by user, campaign, and window, and rule_state stores the latest rule for each campaign. Window state is cleared after the allowed lateness period ends, while rule state continuously retains the latest version.\",\"event_result_explanation\":\"e701 establishes a window accumulation of 55.0; although e702 is late, it can still revise the same window, making the accumulation reach 115.0 and triggering an alert; the e701 that arrives again is deduplicated and does not change the aggregation result.\",\"assumptions_and_uncertainties\":\"The example assumes that the latest version of the same campaign rule in broadcast state takes effect during click processing and that the sliding window maintains accumulation values separately for each covered interval. Because the checkpointing mode is AT_LEAST_ONCE, whether the external alert sink has an idempotency key will affect the risk of final duplicate alerts.\"}","class CampaignSlidingProcessor:\n    def open(self):\n        self.seen_event_ids = MapState('seen_event_ids')\n        self.window_accumulators = MapState('window_accumulators')\n        self.rule_state = BroadcastState('campaign_rules')\n\n    def on_rule(self, rule):\n        self.rule_state.put(rule['campaign_id'], rule)\n\n    def on_click(self, event, ctx, out):\n        if self.seen_event_ids.contains(event['event_id']):\n            return\n        self.seen_event_ids.put(event['event_id'], event['event_time_ms'])\n        rule = self.rule_state.get(event['campaign_id'])\n        for window_start in sliding_starts(event['event_time_ms'], size_ms=300000, slide_ms=60000):\n            key = (event['user_id'], event['campaign_id'], window_start)\n            current = self.window_accumulators.get(key, {'count': 0, 'revenue': 0.0})\n            current['count'] += 1\n            current['revenue'] += event['revenue']\n            self.window_accumulators.put(key, current)\n            if rule and rule['active'] and current['revenue'] >= rule['alert_threshold']:\n                out.emit_alert(key, current)\n        ctx.register_event_time_timer(event['event_time_ms'] + 330000)\n\nclicks = source('campaign-clicks').assign_timestamps_and_watermarks(max_out_of_orderness_ms=10000)\nrules = source('campaign-rules').broadcast('campaign_rules')\nalerts = clicks.key_by(lambda e: e['user_id']).connect(rules).process(\n    CampaignSlidingProcessor(), window_size_ms=300000, slide_ms=60000, allowed_lateness_ms=30000\n)\nalerts.sink('campaign-alerts')\ncheckpoint(enable=True, interval_ms=20000, mode='AT_LEAST_ONCE')","{\"has_state\":true,\"state_descriptors\":[{\"state_name\":\"seen_event_ids\",\"state_type\":\"Map State\",\"key_definition\":\"Partitioned by user_id, with event_id as the deduplication key.\",\"value_definition\":\"Stores the event_time_ms when the event_id was first seen.\",\"update_rule\":\"Written when an event appears for the first time; if event_id already exists, it is directly discarded and the window accumulation is not updated.\",\"retention_policy\":\"Cleared after the event time exceeds the window end plus the 30000-millisecond allowed lateness period.\"},{\"state_name\":\"window_accumulators\",\"state_type\":\"Map State\",\"key_definition\":\"Uses a composite key composed of user_id, campaign_id, and window_start.\",\"value_definition\":\"Stores count and revenue within the sliding window.\",\"update_rule\":\"After deduplication passes, the event is added to all 5-minute windows it covers; revenue is accumulated, count is incremented by one, and an alert is output when the broadcast rule threshold is met.\",\"retention_policy\":\"Retained for 30000 milliseconds after the window ends to accept late updates, then cleared by an event-time timer.\"},{\"state_name\":\"rule_state\",\"state_type\":\"Broadcast State\",\"key_definition\":\"Stored by campaign_id; all parallel instances have a copy of the rule.\",\"value_definition\":\"Stores fields such as active, alert_threshold, and rule version.\",\"update_rule\":\"When a rule event arrives, it overwrites the old rule by campaign_id; subsequent clicks read the current rule.\",\"retention_policy\":\"Retains the latest rule until a new rule overwrites it or the job stops.\"}],\"state_scope\":\"Click deduplication and window aggregation are scoped by user key, while rule state is scoped as broadcast per operator instance.\"}","{\"join_type\":\"Broadcast Join\",\"left_key\":\"click.campaign_id\",\"right_key\":\"rule.campaign_id\",\"join_window\":\"There is no separate rule time window; when processing a click, the current broadcast rule is read; the click itself is still constrained by the 5-minute sliding window.\",\"match_condition\":\"The rule campaign_id is the same and rule.active is true; alert_threshold is checked only when an active rule is matched.\",\"unmatched_record_policy\":\"When there is no corresponding rule, the click's window aggregation is still retained, but no alert is generated.\"}","[{\"node_id\":\"c1\",\"operator_type\":\"Data Source\",\"operator_name\":\"source('campaign-clicks')\",\"input_nodes\":[],\"output_nodes\":[\"c2\"],\"semantic_role\":\"Reads click events containing event_id, user_id, campaign_id, event_time_ms, and revenue.\"},{\"node_id\":\"c2\",\"operator_type\":\"Event Time and Watermarks\",\"operator_name\":\"assign_timestamps_and_watermarks\",\"input_nodes\":[\"c1\"],\"output_nodes\":[\"c3\"],\"semantic_role\":\"Extracts click event time and advances watermarks with a maximum out-of-orderness of 10 seconds.\"},{\"node_id\":\"c3\",\"operator_type\":\"Keyed Deduplication\",\"operator_name\":\"key_by(user_id)\",\"input_nodes\":[\"c2\"],\"output_nodes\":[\"c5\"],\"semantic_role\":\"Partitions by user and discards event_ids that have already been processed via seen_event_ids.\"},{\"node_id\":\"c4\",\"operator_type\":\"Rule Data Source\",\"operator_name\":\"source('campaign-rules').broadcast\",\"input_nodes\":[],\"output_nodes\":[\"c5\"],\"semantic_role\":\"Reads campaign threshold rules and broadcasts them to all processing instances.\"},{\"node_id\":\"c5\",\"operator_type\":\"Broadcast Join and Processing\",\"operator_name\":\"connect(rules).process(CampaignSlidingProcessor())\",\"input_nodes\":[\"c3\",\"c4\"],\"output_nodes\":[\"c6\"],\"semantic_role\":\"Combines click events with broadcast rules, accumulates revenue in sliding windows, and emits alerts when the rule threshold is reached.\"},{\"node_id\":\"c6\",\"operator_type\":\"Sink\",\"operator_name\":\"sink('campaign-alerts')\",\"input_nodes\":[\"c5\"],\"output_nodes\":[],\"semantic_role\":\"Outputs user window alerts that reach the campaign revenue threshold.\"}]","{\"window_type\":\"Sliding Window\",\"time_basis\":\"Event Time\",\"window_size_ms\":300000,\"slide_interval_ms\":60000,\"allowed_lateness_ms\":30000,\"watermark_strategy\":\"The watermark is approximately the currently observed maximum event time minus 10000 milliseconds.\",\"trigger_condition\":\"The current aggregation is output when the watermark reaches the window end time; during the allowed lateness period, events update the window and can trigger the threshold alert again.\",\"late_event_policy\":\"Late events within 30000 milliseconds after the window ends update windows that have not yet been cleared; events beyond the boundary no longer enter aggregation; duplicate event_ids are directly discarded regardless of whether they are late.\"}","[{\"sequence\":1,\"state_name\":\"rule_state\",\"trigger_event\":\"Rule r9: campaign_id=summer, active=true, alert_threshold=100.0.\",\"before_state\":\"summer: does not exist.\",\"after_state\":\"summer -> active=true, alert_threshold=100.0.\",\"transition_reason\":\"The broadcast rule arrives and initializes the active threshold, after which all processing instances can read the rule.\"},{\"sequence\":2,\"state_name\":\"seen_event_ids\",\"trigger_event\":\"Click e701: user_id=u7, campaign_id=summer, event_time=09:02:30, revenue=55.0.\",\"before_state\":\"e701 is not in u7's seen_event_ids.\",\"after_state\":\"e701 -> 09:02:30 is added to u7's seen_event_ids.\",\"transition_reason\":\"The event appears for the first time, passes the deduplication check, and is allowed to enter window processing.\"},{\"sequence\":3,\"state_name\":\"window_accumulators\",\"trigger_event\":\"Click e701 enters sliding windows such as [09:00:00,09:05:00).\",\"before_state\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=0, revenue=0.0.\",\"after_state\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\",\"transition_reason\":\"The event is added by event time to the sliding windows covering it, and the 100.0 alert threshold has not yet been reached.\"},{\"sequence\":4,\"state_name\":\"window_accumulators\",\"trigger_event\":\"Late click e702: user_id=u7, campaign_id=summer, event_time=09:01:50, processing_time=09:03:20, revenue=60.0.\",\"before_state\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\",\"after_state\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00): count=2, revenue=115.0.\",\"transition_reason\":\"e702 is earlier than the current watermark but still within the allowed lateness range, updating the window and making revenue exceed the rule threshold.\"},{\"sequence\":5,\"state_name\":\"seen_event_ids\",\"trigger_event\":\"Duplicate click e701 arrives again.\",\"before_state\":\"u7's seen_event_ids already contains e701.\",\"after_state\":\"State unchanged; e701 is discarded.\",\"transition_reason\":\"event_id deduplication takes precedence over window aggregation, so duplicate records do not cause duplicate revenue accumulation.\"}]","[{\"event_id\":\"e701\",\"event_time\":\"2025-03-03T09:02:30Z\",\"processing_time\":\"2025-03-03T09:02:35Z\",\"window_id\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00)\",\"aggregation_result\":\"{\\\"count\\\":1,\\\"revenue\\\":55.0}\",\"is_late_event\":false,\"emitted_result\":\"null\",\"result_explanation\":\"e701 arrives for the first time and is added to multiple sliding windows; the current revenue of the example window is 55.0, which does not reach the campaign rule's 100.0 threshold, so no alert is generated.\"},{\"event_id\":\"e702\",\"event_time\":\"2025-03-03T09:01:50Z\",\"processing_time\":\"2025-03-03T09:03:20Z\",\"window_id\":\"u7\u002Fsummer\u002F[09:00:00,09:05:00)\",\"aggregation_result\":\"{\\\"count\\\":2,\\\"revenue\\\":115.0}\",\"is_late_event\":true,\"emitted_result\":\"{\\\"user_id\\\":\\\"u7\\\",\\\"campaign_id\\\":\\\"summer\\\",\\\"window\\\":\\\"[09:00:00,09:05:00)\\\",\\\"revenue\\\":115.0,\\\"alert\\\":true}\",\"result_explanation\":\"e702's event time is earlier than the current watermark, so it is a late event, but it is still within the 30000-millisecond allowed lateness range; it updates the window revenue from 55.0 to 115.0, exceeds the broadcast rule threshold, and generates an alert.\"},{\"event_id\":\"e701-duplicate\",\"event_time\":\"2025-03-03T09:02:30Z\",\"processing_time\":\"2025-03-03T09:03:25Z\",\"window_id\":\"did not enter a window\",\"aggregation_result\":\"{\\\"dropped\\\":true,\\\"reason\\\":\\\"duplicate_event_id\\\"}\",\"is_late_event\":false,\"emitted_result\":\"null\",\"result_explanation\":\"This record carries an already existing event_id=e701 and is intercepted by seen_event_ids before updating the window, so it does not increase count or revenue again.\"}]","{\"checkpoint_enabled\":true,\"checkpoint_interval_ms\":20000,\"checkpointed_state\":[\"seen_event_ids\",\"window_accumulators\",\"rule_state\",\"click-source-offset\",\"rule-source-offset\"],\"recovery_strategy\":\"Restores the deduplication table, window accumulators, broadcast rules, and offsets of both input sources from the latest checkpoint, and then continues replay.\",\"exactly_once_semantics\":false,\"failure_effect\":\"Checkpoint recovery can restore critical state, but the code is configured for AT_LEAST_ONCE, so after a failure and restart the output sink may see duplicate alerts; deduplication state can reduce duplicate accumulation, but cannot by itself guarantee exactly-once external output.\"}",{"id":672,"relate_table":656,"relate_id":673,"file_size":573,"file_name":674,"path":8,"duration":573,"cate":483,"file_format":659,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":675,"cate_name":661,"explanation":676,"source_code":677,"state_model":678,"join_semantics":679,"operator_chain":680,"window_semantics":681,"state_transitions":682,"event_time_results":683,"checkpoint_recovery":684},15014,1100640018,"样例_2.json","{\"explanation\": \"{\\\"operator_chain_explanation\\\":\\\"The order stream and payment stream separately extract event time and are partitioned by order_id, then enter a two-input interval join processor. The processor reads the keyed state of both sides, matches records whose time difference is no more than 5 seconds and whose payment is settled, and writes successful results to the transactional sink.\\\",\\\"state_change_explanation\\\":\\\"Orders and payments are stored in two map states respectively until the join window ends. When either side arrives, the other side's state is checked; after a successful join, the result is output, and event-time timers are responsible for clearing completed or unmatched keys. Checkpoints save both types of join state and both input offsets.\\\",\\\"event_result_explanation\\\":\\\"o501 is first written to the waiting state, and p501 arrives 4 seconds later and satisfies all matching conditions, so a settled order is produced. p502 has no corresponding order, can only wait until the interval ends, and is then cleared.\\\",\\\"assumptions_and_uncertainties\\\":\\\"The example abstracts the framework's two-stream join in a KeyedCoProcessFunction style and assumes that at most one valid record is retained for the same order_id within the join interval. Actual frameworks may differ in the details of duplicate payments, late side outputs, and timer deduplication.\\\"}\", \"source_code\": \"from datetime import timedelta\\n\\nclass OrderPaymentJoin:\\n    def open(self):\\n        self.orders = MapState('orders_by_id')\\n        self.payments = MapState('payments_by_id')\\n\\n    def process_order(self, order, ctx, out):\\n        self.orders.put(order['order_id'], order)\\n        payment = self.payments.get(order['order_id'])\\n        if payment and abs(order['event_time_ms'] - payment['event_time_ms']) \u003C= 5000 and payment['status'] == 'SETTLED':\\n            out.emit(join_record(order, payment))\\n        ctx.register_event_time_timer(order['event_time_ms'] + 5000)\\n\\n    def process_payment(self, payment, ctx, out):\\n        self.payments.put(payment['order_id'], payment)\\n        order = self.orders.get(payment['order_id'])\\n        if order and abs(order['event_time_ms'] - payment['event_time_ms']) \u003C= 5000 and payment['status'] == 'SETTLED':\\n            out.emit(join_record(order, payment))\\n        ctx.register_event_time_timer(payment['event_time_ms'] + 5000)\\n\\n    def on_timer(self, timestamp, ctx):\\n        for order_id in ctx.keys_with_expired_interval(timestamp):\\n            self.orders.remove(order_id)\\n            self.payments.remove(order_id)\\n\\norders = source('orders').assign_timestamps_and_watermarks(max_out_of_orderness_ms=3000)\\npayments = source('payments').assign_timestamps_and_watermarks(max_out_of_orderness_ms=3000)\\njoined = orders.key_by(lambda x: x['order_id']).connect(\\n    payments.key_by(lambda x: x['order_id'])\\n).process(OrderPaymentJoin(), interval_ms=5000, status='SETTLED')\\njoined.sink('settled-orders', transactional=True)\\ncheckpoint(enable=True, interval_ms=15000, mode='EXACTLY_ONCE')\", \"state_model\": \"{\\\"has_state\\\":true,\\\"state_descriptors\\\":[{\\\"state_name\\\":\\\"orders_by_id\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Partitioned by order_id; each join key stores at most the currently waiting order to be matched.\\\",\\\"value_definition\\\":\\\"Stores the original order record and its fields such as event_time_ms and amount.\\\",\\\"update_rule\\\":\\\"When an order arrives, writes or overwrites the order for the corresponding order_id; if the payment state already exists and the time satisfies the interval condition, immediately outputs the join result.\\\",\\\"retention_policy\\\":\\\"Registers a timer at the order event time plus 5000 milliseconds and deletes the order state after the join waiting interval ends.\\\"},{\\\"state_name\\\":\\\"payments_by_id\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Partitioned by order_id; each join key stores the currently waiting payment to be matched.\\\",\\\"value_definition\\\":\\\"Stores the original payment record and its fields such as event_time_ms and status.\\\",\\\"update_rule\\\":\\\"When a payment arrives, writes or overwrites the payment for the corresponding order_id; output is generated only when status is SETTLED and the time difference from the order is no more than 5000 milliseconds.\\\",\\\"retention_policy\\\":\\\"Cleared by an event-time timer after the join interval ends; unmatched payments are not retained permanently.\\\"}],\\\"state_scope\\\":\\\"Isolated by the join key order_id and the join interval scope.\\\"}\", \"join_semantics\": \"{\\\"join_type\\\":\\\"Event-Time Interval Join\\\",\\\"left_key\\\":\\\"order.order_id\\\",\\\"right_key\\\":\\\"payment.order_id\\\",\\\"join_window\\\":\\\"payment.event_time_ms - 5000 \u003C= order.event_time_ms \u003C= payment.event_time_ms + 5000\\\",\\\"match_condition\\\":\\\"The two events have the same order_id, the absolute event-time difference is no more than 5000 milliseconds, and payment.status == 'SETTLED'.\\\",\\\"unmatched_record_policy\\\":\\\"Saved in the corresponding state before the join interval ends to wait for the other side; records still unmatched when the interval ends are cleared and not output.\\\"}\", \"operator_chain\": \"[{\\\"node_id\\\":\\\"o1\\\",\\\"operator_type\\\":\\\"Data Source\\\",\\\"operator_name\\\":\\\"source('orders')\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"o2\\\"],\\\"semantic_role\\\":\\\"Reads order events and provides order_id, event_time_ms, and amount.\\\"},{\\\"node_id\\\":\\\"o2\\\",\\\"operator_type\\\":\\\"Watermark\\\",\\\"operator_name\\\":\\\"orders.assign_timestamps_and_watermarks\\\",\\\"input_nodes\\\":[\\\"o1\\\"],\\\"output_nodes\\\":[\\\"o3\\\"],\\\"semantic_role\\\":\\\"Generates watermarks from order event time with a maximum out-of-orderness tolerance of 3 seconds.\\\"},{\\\"node_id\\\":\\\"o3\\\",\\\"operator_type\\\":\\\"Keyed Partitioning\\\",\\\"operator_name\\\":\\\"orders.key_by(order_id)\\\",\\\"input_nodes\\\":[\\\"o2\\\"],\\\"output_nodes\\\":[\\\"o5\\\"],\\\"semantic_role\\\":\\\"Routes order events to the corresponding join key by order ID.\\\"},{\\\"node_id\\\":\\\"o4\\\",\\\"operator_type\\\":\\\"Data Source\\\",\\\"operator_name\\\":\\\"source('payments')\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"o6\\\"],\\\"semantic_role\\\":\\\"Reads payment events and provides order_id, event_time_ms, and status.\\\"},{\\\"node_id\\\":\\\"o6\\\",\\\"operator_type\\\":\\\"Watermark and Keyed Partitioning\\\",\\\"operator_name\\\":\\\"payments.assign_timestamps_and_watermarks().key_by(order_id)\\\",\\\"input_nodes\\\":[\\\"o4\\\"],\\\"output_nodes\\\":[\\\"o5\\\"],\\\"semantic_role\\\":\\\"Generates watermarks for the payment stream and partitions by order_id.\\\"},{\\\"node_id\\\":\\\"o5\\\",\\\"operator_type\\\":\\\"Interval Join\\\",\\\"operator_name\\\":\\\"connect().process(OrderPaymentJoin())\\\",\\\"input_nodes\\\":[\\\"o3\\\",\\\"o6\\\"],\\\"output_nodes\\\":[\\\"o7\\\"],\\\"semantic_role\\\":\\\"Matches orders and payments under the same order_id whose event times differ by no more than 5000 milliseconds and whose payment status is SETTLED.\\\"},{\\\"node_id\\\":\\\"o7\\\",\\\"operator_type\\\":\\\"Sink\\\",\\\"operator_name\\\":\\\"sink('settled-orders')\\\",\\\"input_nodes\\\":[\\\"o5\\\"],\\\"output_nodes\\\":[],\\\"semantic_role\\\":\\\"Transactionally outputs successfully matched settled order records.\\\"}]\", \"window_semantics\": \"{\\\"window_type\\\":\\\"Interval Join Window\\\",\\\"time_basis\\\":\\\"Event Time\\\",\\\"window_size_ms\\\":10000,\\\"slide_interval_ms\\\":5000,\\\"allowed_lateness_ms\\\":3000,\\\"watermark_strategy\\\":\\\"Both input streams use a watermark of maximum event time minus 3000 milliseconds, and the join operator waits for both watermarks to advance.\\\",\\\"trigger_condition\\\":\\\"The join is triggered when an event arrives on either side and a qualifying record already exists on the other side; the waiting timer triggers cleanup at event time plus 5000 milliseconds.\\\",\\\"late_event_policy\\\":\\\"Records beyond the corresponding watermark and join waiting boundary no longer participate in the join and are handled by the actual framework's late side output or discard policy.\\\"}\", \"state_transitions\": \"[{\\\"sequence\\\":1,\\\"state_name\\\":\\\"orders_by_id\\\",\\\"trigger_event\\\":\\\"Order o501: order_id=A17, event_time=10:00:00, amount=42.50.\\\",\\\"before_state\\\":\\\"A17: does not exist.\\\",\\\"after_state\\\":\\\"A17 -> order(event_time=10:00:00, amount=42.50).\\\",\\\"transition_reason\\\":\\\"The order arrives first, and there is no corresponding payment yet, so it is written to the waiting state and a 10:00:05 timer is registered.\\\"},{\\\"sequence\\\":2,\\\"state_name\\\":\\\"payments_by_id\\\",\\\"trigger_event\\\":\\\"Payment p501: order_id=A17, event_time=10:00:04, status=SETTLED.\\\",\\\"before_state\\\":\\\"A17: does not exist.\\\",\\\"after_state\\\":\\\"A17 -> payment(event_time=10:00:04, status=SETTLED).\\\",\\\"transition_reason\\\":\\\"After the payment is written to state, order o501 is read; the time difference between them is 4 seconds, satisfying the interval join condition.\\\"},{\\\"sequence\\\":3,\\\"state_name\\\":\\\"orders_by_id\u002Fpayments_by_id\\\",\\\"trigger_event\\\":\\\"The join outputs order o501 and payment p501.\\\",\\\"before_state\\\":\\\"Order A17 and payment A17 are both stored in the join state.\\\",\\\"after_state\\\":\\\"Outputs joined(A17), and the state remains until the join interval timer cleans it up.\\\",\\\"transition_reason\\\":\\\"The join key is the same, the time difference is within 5 seconds, and the payment is settled.\\\"},{\\\"sequence\\\":4,\\\"state_name\\\":\\\"orders_by_id\u002Fpayments_by_id\\\",\\\"trigger_event\\\":\\\"The event-time timer for A17 triggers when the join waiting interval ends.\\\",\\\"before_state\\\":\\\"Order A17 and payment A17 are still in state.\\\",\\\"after_state\\\":\\\"Order A17 and payment A17 are both deleted.\\\",\\\"transition_reason\\\":\\\"The join interval has ended, and state is cleared to avoid unbounded growth.\\\"}]\", \"event_time_results\": \"[{\\\"event_id\\\":\\\"o501\\\",\\\"event_time\\\":\\\"2025-03-02T10:00:00Z\\\",\\\"processing_time\\\":\\\"2025-03-02T10:00:01Z\\\",\\\"window_id\\\":\\\"order_id=A17 \u002F interval[-5000ms,+5000ms]\\\",\\\"aggregation_result\\\":\\\"null\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"The order arrives first, is only written to orders_by_id, and waits for payment; unmatched orders are not output before the join time range ends.\\\"},{\\\"event_id\\\":\\\"p501\\\",\\\"event_time\\\":\\\"2025-03-02T10:00:04Z\\\",\\\"processing_time\\\":\\\"2025-03-02T10:00:05Z\\\",\\\"window_id\\\":\\\"order_id=A17 \u002F interval[-5000ms,+5000ms]\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"order_id\\\\\\\":\\\\\\\"A17\\\\\\\",\\\\\\\"amount\\\\\\\":42.5,\\\\\\\"payment_status\\\\\\\":\\\\\\\"SETTLED\\\\\\\"}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"{\\\\\\\"order_id\\\\\\\":\\\\\\\"A17\\\\\\\",\\\\\\\"amount\\\\\\\":42.5,\\\\\\\"payment_id\\\\\\\":\\\\\\\"p501\\\\\\\",\\\\\\\"payment_status\\\\\\\":\\\\\\\"SETTLED\\\\\\\"}\\\",\\\"result_explanation\\\":\\\"The payment event shares order_id with the order, and the event times differ by 4 seconds, satisfying the 5-second interval and SETTLED status conditions, so a join record is output.\\\"},{\\\"event_id\\\":\\\"p502\\\",\\\"event_time\\\":\\\"2025-03-02T10:01:20Z\\\",\\\"processing_time\\\":\\\"2025-03-02T10:01:21Z\\\",\\\"window_id\\\":\\\"order_id=A18 \u002F interval[-5000ms,+5000ms]\\\",\\\"aggregation_result\\\":\\\"null\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"This payment does not find an order within the join interval and is temporarily stored in payments_by_id; if the timer expires and there is still no order, it is cleared and produces no output.\\\"}]\", \"checkpoint_recovery\": \"{\\\"checkpoint_enabled\\\":true,\\\"checkpoint_interval_ms\\\":15000,\\\"checkpointed_state\\\":[\\\"orders_by_id\\\",\\\"payments_by_id\\\",\\\"orders-source-offset\\\",\\\"payments-source-offset\\\"],\\\"recovery_strategy\\\":\\\"After a failure, restores the join state on both sides, event-time timers, and the offsets of both input sources, and continues replaying from the latest checkpoint.\\\",\\\"exactly_once_semantics\\\":true,\\\"failure_effect\\\":\\\"Join state and source offsets remain consistent; during replay, joins may be computed again, but the transactional output sink deduplicates or atomically commits, thereby preventing already confirmed results from becoming visible multiple times.\\\"}\"}","{\"operator_chain_explanation\":\"The order stream and payment stream separately extract event time and are partitioned by order_id, then enter a two-input interval join processor. The processor reads the keyed state of both sides, matches records whose time difference is no more than 5 seconds and whose payment is settled, and writes successful results to the transactional sink.\",\"state_change_explanation\":\"Orders and payments are stored in two map states respectively until the join window ends. When either side arrives, the other side's state is checked; after a successful join, the result is output, and event-time timers are responsible for clearing completed or unmatched keys. Checkpoints save both types of join state and both input offsets.\",\"event_result_explanation\":\"o501 is first written to the waiting state, and p501 arrives 4 seconds later and satisfies all matching conditions, so a settled order is produced. p502 has no corresponding order, can only wait until the interval ends, and is then cleared.\",\"assumptions_and_uncertainties\":\"The example abstracts the framework's two-stream join in a KeyedCoProcessFunction style and assumes that at most one valid record is retained for the same order_id within the join interval. Actual frameworks may differ in the details of duplicate payments, late side outputs, and timer deduplication.\"}","from datetime import timedelta\n\nclass OrderPaymentJoin:\n    def open(self):\n        self.orders = MapState('orders_by_id')\n        self.payments = MapState('payments_by_id')\n\n    def process_order(self, order, ctx, out):\n        self.orders.put(order['order_id'], order)\n        payment = self.payments.get(order['order_id'])\n        if payment and abs(order['event_time_ms'] - payment['event_time_ms']) \u003C= 5000 and payment['status'] == 'SETTLED':\n            out.emit(join_record(order, payment))\n        ctx.register_event_time_timer(order['event_time_ms'] + 5000)\n\n    def process_payment(self, payment, ctx, out):\n        self.payments.put(payment['order_id'], payment)\n        order = self.orders.get(payment['order_id'])\n        if order and abs(order['event_time_ms'] - payment['event_time_ms']) \u003C= 5000 and payment['status'] == 'SETTLED':\n            out.emit(join_record(order, payment))\n        ctx.register_event_time_timer(payment['event_time_ms'] + 5000)\n\n    def on_timer(self, timestamp, ctx):\n        for order_id in ctx.keys_with_expired_interval(timestamp):\n            self.orders.remove(order_id)\n            self.payments.remove(order_id)\n\norders = source('orders').assign_timestamps_and_watermarks(max_out_of_orderness_ms=3000)\npayments = source('payments').assign_timestamps_and_watermarks(max_out_of_orderness_ms=3000)\njoined = orders.key_by(lambda x: x['order_id']).connect(\n    payments.key_by(lambda x: x['order_id'])\n).process(OrderPaymentJoin(), interval_ms=5000, status='SETTLED')\njoined.sink('settled-orders', transactional=True)\ncheckpoint(enable=True, interval_ms=15000, mode='EXACTLY_ONCE')","{\"has_state\":true,\"state_descriptors\":[{\"state_name\":\"orders_by_id\",\"state_type\":\"Map State\",\"key_definition\":\"Partitioned by order_id; each join key stores at most the currently waiting order to be matched.\",\"value_definition\":\"Stores the original order record and its fields such as event_time_ms and amount.\",\"update_rule\":\"When an order arrives, writes or overwrites the order for the corresponding order_id; if the payment state already exists and the time satisfies the interval condition, immediately outputs the join result.\",\"retention_policy\":\"Registers a timer at the order event time plus 5000 milliseconds and deletes the order state after the join waiting interval ends.\"},{\"state_name\":\"payments_by_id\",\"state_type\":\"Map State\",\"key_definition\":\"Partitioned by order_id; each join key stores the currently waiting payment to be matched.\",\"value_definition\":\"Stores the original payment record and its fields such as event_time_ms and status.\",\"update_rule\":\"When a payment arrives, writes or overwrites the payment for the corresponding order_id; output is generated only when status is SETTLED and the time difference from the order is no more than 5000 milliseconds.\",\"retention_policy\":\"Cleared by an event-time timer after the join interval ends; unmatched payments are not retained permanently.\"}],\"state_scope\":\"Isolated by the join key order_id and the join interval scope.\"}","{\"join_type\":\"Event-Time Interval Join\",\"left_key\":\"order.order_id\",\"right_key\":\"payment.order_id\",\"join_window\":\"payment.event_time_ms - 5000 \u003C= order.event_time_ms \u003C= payment.event_time_ms + 5000\",\"match_condition\":\"The two events have the same order_id, the absolute event-time difference is no more than 5000 milliseconds, and payment.status == 'SETTLED'.\",\"unmatched_record_policy\":\"Saved in the corresponding state before the join interval ends to wait for the other side; records still unmatched when the interval ends are cleared and not output.\"}","[{\"node_id\":\"o1\",\"operator_type\":\"Data Source\",\"operator_name\":\"source('orders')\",\"input_nodes\":[],\"output_nodes\":[\"o2\"],\"semantic_role\":\"Reads order events and provides order_id, event_time_ms, and amount.\"},{\"node_id\":\"o2\",\"operator_type\":\"Watermark\",\"operator_name\":\"orders.assign_timestamps_and_watermarks\",\"input_nodes\":[\"o1\"],\"output_nodes\":[\"o3\"],\"semantic_role\":\"Generates watermarks from order event time with a maximum out-of-orderness tolerance of 3 seconds.\"},{\"node_id\":\"o3\",\"operator_type\":\"Keyed Partitioning\",\"operator_name\":\"orders.key_by(order_id)\",\"input_nodes\":[\"o2\"],\"output_nodes\":[\"o5\"],\"semantic_role\":\"Routes order events to the corresponding join key by order ID.\"},{\"node_id\":\"o4\",\"operator_type\":\"Data Source\",\"operator_name\":\"source('payments')\",\"input_nodes\":[],\"output_nodes\":[\"o6\"],\"semantic_role\":\"Reads payment events and provides order_id, event_time_ms, and status.\"},{\"node_id\":\"o6\",\"operator_type\":\"Watermark and Keyed Partitioning\",\"operator_name\":\"payments.assign_timestamps_and_watermarks().key_by(order_id)\",\"input_nodes\":[\"o4\"],\"output_nodes\":[\"o5\"],\"semantic_role\":\"Generates watermarks for the payment stream and partitions by order_id.\"},{\"node_id\":\"o5\",\"operator_type\":\"Interval Join\",\"operator_name\":\"connect().process(OrderPaymentJoin())\",\"input_nodes\":[\"o3\",\"o6\"],\"output_nodes\":[\"o7\"],\"semantic_role\":\"Matches orders and payments under the same order_id whose event times differ by no more than 5000 milliseconds and whose payment status is SETTLED.\"},{\"node_id\":\"o7\",\"operator_type\":\"Sink\",\"operator_name\":\"sink('settled-orders')\",\"input_nodes\":[\"o5\"],\"output_nodes\":[],\"semantic_role\":\"Transactionally outputs successfully matched settled order records.\"}]","{\"window_type\":\"Interval Join Window\",\"time_basis\":\"Event Time\",\"window_size_ms\":10000,\"slide_interval_ms\":5000,\"allowed_lateness_ms\":3000,\"watermark_strategy\":\"Both input streams use a watermark of maximum event time minus 3000 milliseconds, and the join operator waits for both watermarks to advance.\",\"trigger_condition\":\"The join is triggered when an event arrives on either side and a qualifying record already exists on the other side; the waiting timer triggers cleanup at event time plus 5000 milliseconds.\",\"late_event_policy\":\"Records beyond the corresponding watermark and join waiting boundary no longer participate in the join and are handled by the actual framework's late side output or discard policy.\"}","[{\"sequence\":1,\"state_name\":\"orders_by_id\",\"trigger_event\":\"Order o501: order_id=A17, event_time=10:00:00, amount=42.50.\",\"before_state\":\"A17: does not exist.\",\"after_state\":\"A17 -> order(event_time=10:00:00, amount=42.50).\",\"transition_reason\":\"The order arrives first, and there is no corresponding payment yet, so it is written to the waiting state and a 10:00:05 timer is registered.\"},{\"sequence\":2,\"state_name\":\"payments_by_id\",\"trigger_event\":\"Payment p501: order_id=A17, event_time=10:00:04, status=SETTLED.\",\"before_state\":\"A17: does not exist.\",\"after_state\":\"A17 -> payment(event_time=10:00:04, status=SETTLED).\",\"transition_reason\":\"After the payment is written to state, order o501 is read; the time difference between them is 4 seconds, satisfying the interval join condition.\"},{\"sequence\":3,\"state_name\":\"orders_by_id\u002Fpayments_by_id\",\"trigger_event\":\"The join outputs order o501 and payment p501.\",\"before_state\":\"Order A17 and payment A17 are both stored in the join state.\",\"after_state\":\"Outputs joined(A17), and the state remains until the join interval timer cleans it up.\",\"transition_reason\":\"The join key is the same, the time difference is within 5 seconds, and the payment is settled.\"},{\"sequence\":4,\"state_name\":\"orders_by_id\u002Fpayments_by_id\",\"trigger_event\":\"The event-time timer for A17 triggers when the join waiting interval ends.\",\"before_state\":\"Order A17 and payment A17 are still in state.\",\"after_state\":\"Order A17 and payment A17 are both deleted.\",\"transition_reason\":\"The join interval has ended, and state is cleared to avoid unbounded growth.\"}]","[{\"event_id\":\"o501\",\"event_time\":\"2025-03-02T10:00:00Z\",\"processing_time\":\"2025-03-02T10:00:01Z\",\"window_id\":\"order_id=A17 \u002F interval[-5000ms,+5000ms]\",\"aggregation_result\":\"null\",\"is_late_event\":false,\"emitted_result\":\"null\",\"result_explanation\":\"The order arrives first, is only written to orders_by_id, and waits for payment; unmatched orders are not output before the join time range ends.\"},{\"event_id\":\"p501\",\"event_time\":\"2025-03-02T10:00:04Z\",\"processing_time\":\"2025-03-02T10:00:05Z\",\"window_id\":\"order_id=A17 \u002F interval[-5000ms,+5000ms]\",\"aggregation_result\":\"{\\\"order_id\\\":\\\"A17\\\",\\\"amount\\\":42.5,\\\"payment_status\\\":\\\"SETTLED\\\"}\",\"is_late_event\":false,\"emitted_result\":\"{\\\"order_id\\\":\\\"A17\\\",\\\"amount\\\":42.5,\\\"payment_id\\\":\\\"p501\\\",\\\"payment_status\\\":\\\"SETTLED\\\"}\",\"result_explanation\":\"The payment event shares order_id with the order, and the event times differ by 4 seconds, satisfying the 5-second interval and SETTLED status conditions, so a join record is output.\"},{\"event_id\":\"p502\",\"event_time\":\"2025-03-02T10:01:20Z\",\"processing_time\":\"2025-03-02T10:01:21Z\",\"window_id\":\"order_id=A18 \u002F interval[-5000ms,+5000ms]\",\"aggregation_result\":\"null\",\"is_late_event\":false,\"emitted_result\":\"null\",\"result_explanation\":\"This payment does not find an order within the join interval and is temporarily stored in payments_by_id; if the timer expires and there is still no order, it is cleared and produces no output.\"}]","{\"checkpoint_enabled\":true,\"checkpoint_interval_ms\":15000,\"checkpointed_state\":[\"orders_by_id\",\"payments_by_id\",\"orders-source-offset\",\"payments-source-offset\"],\"recovery_strategy\":\"After a failure, restores the join state on both sides, event-time timers, and the offsets of both input sources, and continues replaying from the latest checkpoint.\",\"exactly_once_semantics\":true,\"failure_effect\":\"Join state and source offsets remain consistent; during replay, joins may be computed again, but the transactional output sink deduplicates or atomically commits, thereby preventing already confirmed results from becoming visible multiple times.\"}",{"id":686,"relate_table":656,"relate_id":687,"file_size":573,"file_name":688,"path":8,"duration":573,"cate":483,"file_format":659,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":689,"cate_name":661,"explanation":690,"source_code":691,"state_model":692,"join_semantics":693,"operator_chain":694,"window_semantics":695,"state_transitions":696,"event_time_results":697,"checkpoint_recovery":698},15013,1546849546,"样例_1.json","{\"explanation\": \"{\\\"operator_chain_explanation\\\":\\\"Events first enter from meter-events, then after event timestamp extraction and watermark generation are partitioned by device_id, and then enter a 60-second tumbling window. The window aggregation maintains both sum and count; normal results are written to the transactional metrics sink, and records beyond the allowed lateness boundary are written from the side output to the late-events sink.\\\",\\\"state_change_explanation\\\":\\\"Each window of each device has independent sum\u002Fcount aggregation state. Events are accumulated when they arrive, the watermark triggers the first output, and allowed late events continue to update it; after the window ends and the 10-second retention period passes, the state is cleared. State, offsets, and watermark progress are all included in periodic checkpoints.\\\",\\\"event_result_explanation\\\":\\\"e101 and e102 enter the same window by event time, with results 4.0\u002F1 and 7.0\u002F2 respectively. Although e103 is processed later, its event time still belongs to the already triggered window and does not exceed the allowed lateness boundary, so it revises the result to 9.0\u002F3.\\\",\\\"assumptions_and_uncertainties\\\":\\\"The example assumes that the event-time field is in milliseconds, that the window operator supports updated output after allowed lateness, and that the sink with transactional=True is correctly integrated with the checkpointing protocol. How a specific framework version represents window update messages and side outputs needs to be confirmed against the actual runtime configuration.\\\"}\", \"source_code\": \"from pyflink.datastream import StreamExecutionEnvironment\\nfrom pyflink.common import Time\\n\\nenv = StreamExecutionEnvironment.get_execution_environment()\\nenv.enable_checkpointing(30000)\\nenv.get_checkpoint_config().set_checkpointing_mode('EXACTLY_ONCE')\\n\\nevents = env.from_source('meter-events')\\nwith_watermarks = events.assign_timestamps_and_watermarks(\\n    timestamp_field='event_time_ms',\\n    max_out_of_orderness_ms=5000\\n)\\nkeyed = with_watermarks.key_by(lambda event: event['device_id'])\\nwindowed = keyed.window(\\n    window_type='TUMBLING_EVENT_TIME',\\n    size_ms=60000,\\n    allowed_lateness_ms=10000\\n)\\naggregated = windowed.aggregate(sum_field='value', count_field='value')\\nlate_events = aggregated.get_side_output('late-events')\\naggregated.sink('minute-device-metrics', transactional=True)\\nlate_events.sink('late-meter-events')\", \"state_model\": \"{\\\"has_state\\\":true,\\\"state_descriptors\\\":[{\\\"state_name\\\":\\\"window_sum_count\\\",\\\"state_type\\\":\\\"Aggregation State\\\",\\\"key_definition\\\":\\\"Partitioned by device_id and the [window_start, window_end) window key.\\\",\\\"value_definition\\\":\\\"Stores the two fields value_sum and value_count.\\\",\\\"update_rule\\\":\\\"For each event in the window, reads the current accumulator, adds value to value_sum, and increments value_count by one; events within the allowed lateness range update an already triggered window.\\\",\\\"retention_policy\\\":\\\"Cleared after the window ends and the 10000-millisecond allowed lateness period has elapsed.\\\"}],\\\"state_scope\\\":\\\"Isolated by device key and event-time window scope.\\\"}\", \"join_semantics\": \"{\\\"join_type\\\":\\\"No Join\\\",\\\"left_key\\\":\\\"Not Applicable\\\",\\\"right_key\\\":\\\"Not Applicable\\\",\\\"join_window\\\":\\\"Not Applicable\\\",\\\"match_condition\\\":\\\"Not Applicable\\\",\\\"unmatched_record_policy\\\":\\\"Not applicable; the program has only a single input stream.\\\"}\", \"operator_chain\": \"[{\\\"node_id\\\":\\\"n1\\\",\\\"operator_type\\\":\\\"Data Source\\\",\\\"operator_name\\\":\\\"from_source('meter-events')\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"n2\\\"],\\\"semantic_role\\\":\\\"Reads meter events containing device_id, event_time_ms, and value.\\\"},{\\\"node_id\\\":\\\"n2\\\",\\\"operator_type\\\":\\\"Event Time and Watermarks\\\",\\\"operator_name\\\":\\\"assign_timestamps_and_watermarks\\\",\\\"input_nodes\\\":[\\\"n1\\\"],\\\"output_nodes\\\":[\\\"n3\\\"],\\\"semantic_role\\\":\\\"Extracts event time and generates watermarks using a maximum out-of-orderness of 5 seconds.\\\"},{\\\"node_id\\\":\\\"n3\\\",\\\"operator_type\\\":\\\"Keyed Partitioning\\\",\\\"operator_name\\\":\\\"key_by(device_id)\\\",\\\"input_nodes\\\":[\\\"n2\\\"],\\\"output_nodes\\\":[\\\"n4\\\"],\\\"semantic_role\\\":\\\"Partitions by device identifier so that each device's window aggregation state is isolated from others.\\\"},{\\\"node_id\\\":\\\"n4\\\",\\\"operator_type\\\":\\\"Window\\\",\\\"operator_name\\\":\\\"TUMBLING_EVENT_TIME\\\",\\\"input_nodes\\\":[\\\"n3\\\"],\\\"output_nodes\\\":[\\\"n5\\\"],\\\"semantic_role\\\":\\\"Creates 60-second event-time tumbling windows for each device and allows late events within 10 seconds after the window ends to still be received.\\\"},{\\\"node_id\\\":\\\"n5\\\",\\\"operator_type\\\":\\\"Aggregation\\\",\\\"operator_name\\\":\\\"aggregate(sum_field='value', count_field='value')\\\",\\\"input_nodes\\\":[\\\"n4\\\"],\\\"output_nodes\\\":[\\\"n6\\\",\\\"n7\\\"],\\\"semantic_role\\\":\\\"Maintains the sum and count of value within the window; normal results go to the main output, and events beyond the allowed lateness go to a side output.\\\"},{\\\"node_id\\\":\\\"n6\\\",\\\"operator_type\\\":\\\"Sink\\\",\\\"operator_name\\\":\\\"sink('minute-device-metrics')\\\",\\\"input_nodes\\\":[\\\"n5\\\"],\\\"output_nodes\\\":[],\\\"semantic_role\\\":\\\"Transactionally writes out the aggregated result for each device per minute.\\\"},{\\\"node_id\\\":\\\"n7\\\",\\\"operator_type\\\":\\\"Late Event Side Output\\\",\\\"operator_name\\\":\\\"sink('late-meter-events')\\\",\\\"input_nodes\\\":[\\\"n5\\\"],\\\"output_nodes\\\":[],\\\"semantic_role\\\":\\\"Receives events that exceed the allowed lateness boundary and can no longer update window results.\\\"}]\", \"window_semantics\": \"{\\\"window_type\\\":\\\"Tumbling Window\\\",\\\"time_basis\\\":\\\"Event Time\\\",\\\"window_size_ms\\\":60000,\\\"slide_interval_ms\\\":60000,\\\"allowed_lateness_ms\\\":10000,\\\"watermark_strategy\\\":\\\"The watermark is approximately the currently observed maximum event time minus 5000 milliseconds.\\\",\\\"trigger_condition\\\":\\\"First emitted when the watermark reaches the window end time; updated results are emitted again when events arrive within the allowed lateness period.\\\",\\\"late_event_policy\\\":\\\"Events within 10000 milliseconds after the window ends update the window and produce revised results; events beyond that boundary are sent to the late-events side output.\\\"}\", \"state_transitions\": \"[{\\\"sequence\\\":1,\\\"state_name\\\":\\\"window_sum_count\\\",\\\"trigger_event\\\":\\\"Event e101: device_id=d17, event_time=2025-03-01T12:00:12Z, value=4.0.\\\",\\\"before_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): does not exist.\\\",\\\"after_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=4.0, value_count=1.\\\",\\\"transition_reason\\\":\\\"The first event creates the window aggregation state for the device.\\\"},{\\\"sequence\\\":2,\\\"state_name\\\":\\\"window_sum_count\\\",\\\"trigger_event\\\":\\\"Event e102: device_id=d17, event_time=2025-03-01T12:00:45Z, value=3.0.\\\",\\\"before_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=4.0, value_count=1.\\\",\\\"after_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=7.0, value_count=2.\\\",\\\"transition_reason\\\":\\\"The event still belongs to the same event-time window, and the aggregation state is accumulated.\\\"},{\\\"sequence\\\":3,\\\"state_name\\\":\\\"window_sum_count\\\",\\\"trigger_event\\\":\\\"Late event e103: device_id=d17, event_time=2025-03-01T12:00:20Z, processing_time=2025-03-01T12:01:06Z, value=2.0.\\\",\\\"before_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=7.0, value_count=2, and the window has already been emitted once due to the watermark.\\\",\\\"after_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=9.0, value_count=3.\\\",\\\"transition_reason\\\":\\\"The event is later than the current watermark but still within the 10000-millisecond allowed lateness range, so it updates the already emitted window.\\\"},{\\\"sequence\\\":4,\\\"state_name\\\":\\\"window_sum_count\\\",\\\"trigger_event\\\":\\\"The allowed lateness timer for window [12:00:00,12:01:00) expires.\\\",\\\"before_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): value_sum=9.0, value_count=3.\\\",\\\"after_state\\\":\\\"d17 \u002F [12:00:00,12:01:00): cleared.\\\",\\\"transition_reason\\\":\\\"The allowed lateness period after the window end has ended, and subsequent events can only go to the side output.\\\"}]\", \"event_time_results\": \"[{\\\"event_id\\\":\\\"e101\\\",\\\"event_time\\\":\\\"2025-03-01T12:00:12Z\\\",\\\"processing_time\\\":\\\"2025-03-01T12:00:13Z\\\",\\\"window_id\\\":\\\"d17\u002F[12:00:00,12:01:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"sum\\\\\\\":4.0,\\\\\\\"count\\\\\\\":1}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"{\\\\\\\"device_id\\\\\\\":\\\\\\\"d17\\\\\\\",\\\\\\\"window\\\\\\\":\\\\\\\"[12:00:00,12:01:00)\\\\\\\",\\\\\\\"sum\\\\\\\":4.0,\\\\\\\"count\\\\\\\":1,\\\\\\\"update\\\\\\\":false}\\\",\\\"result_explanation\\\":\\\"The event time falls into the 12:00 to 12:01 window, creating the initial aggregation state; the watermark has not yet closed the window.\\\"},{\\\"event_id\\\":\\\"e102\\\",\\\"event_time\\\":\\\"2025-03-01T12:00:45Z\\\",\\\"processing_time\\\":\\\"2025-03-01T12:00:46Z\\\",\\\"window_id\\\":\\\"d17\u002F[12:00:00,12:01:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"sum\\\\\\\":7.0,\\\\\\\"count\\\\\\\":2}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"{\\\\\\\"device_id\\\\\\\":\\\\\\\"d17\\\\\\\",\\\\\\\"window\\\\\\\":\\\\\\\"[12:00:00,12:01:00)\\\\\\\",\\\\\\\"sum\\\\\\\":7.0,\\\\\\\"count\\\\\\\":2,\\\\\\\"update\\\\\\\":false}\\\",\\\"result_explanation\\\":\\\"The event belongs to the same device and same window as e101, and the sum and count are updated to 7.0 and 2, respectively.\\\"},{\\\"event_id\\\":\\\"e103\\\",\\\"event_time\\\":\\\"2025-03-01T12:00:20Z\\\",\\\"processing_time\\\":\\\"2025-03-01T12:01:06Z\\\",\\\"window_id\\\":\\\"d17\u002F[12:00:00,12:01:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"sum\\\\\\\":9.0,\\\\\\\"count\\\\\\\":3}\\\",\\\"is_late_event\\\":true,\\\"emitted_result\\\":\\\"{\\\\\\\"device_id\\\\\\\":\\\\\\\"d17\\\\\\\",\\\\\\\"window\\\\\\\":\\\\\\\"[12:00:00,12:01:00)\\\\\\\",\\\\\\\"sum\\\\\\\":9.0,\\\\\\\"count\\\\\\\":3,\\\\\\\"update\\\\\\\":true}\\\",\\\"result_explanation\\\":\\\"e103's event time is earlier than the current watermark, so it is a late event, but it is still within the allowed lateness range; it causes the already emitted window to produce a revised result rather than going to the side output.\\\"}]\", \"checkpoint_recovery\": \"{\\\"checkpoint_enabled\\\":true,\\\"checkpoint_interval_ms\\\":30000,\\\"checkpointed_state\\\":[\\\"window_sum_count\\\",\\\"source_offsets\\\",\\\"watermark_progress\\\"],\\\"recovery_strategy\\\":\\\"After a task failure, restores the window accumulator state and source offsets from the latest successful checkpoint, and replays events from the offset positions.\\\",\\\"exactly_once_semantics\\\":true,\\\"failure_effect\\\":\\\"Window state and source offsets are restored from the same checkpoint; the transactional sink ensures that committed results are not duplicately written due to replay, and uncommitted results are recomputed.\\\"}\"}","{\"operator_chain_explanation\":\"Events first enter from meter-events, then after event timestamp extraction and watermark generation are partitioned by device_id, and then enter a 60-second tumbling window. The window aggregation maintains both sum and count; normal results are written to the transactional metrics sink, and records beyond the allowed lateness boundary are written from the side output to the late-events sink.\",\"state_change_explanation\":\"Each window of each device has independent sum\u002Fcount aggregation state. Events are accumulated when they arrive, the watermark triggers the first output, and allowed late events continue to update it; after the window ends and the 10-second retention period passes, the state is cleared. State, offsets, and watermark progress are all included in periodic checkpoints.\",\"event_result_explanation\":\"e101 and e102 enter the same window by event time, with results 4.0\u002F1 and 7.0\u002F2 respectively. Although e103 is processed later, its event time still belongs to the already triggered window and does not exceed the allowed lateness boundary, so it revises the result to 9.0\u002F3.\",\"assumptions_and_uncertainties\":\"The example assumes that the event-time field is in milliseconds, that the window operator supports updated output after allowed lateness, and that the sink with transactional=True is correctly integrated with the checkpointing protocol. How a specific framework version represents window update messages and side outputs needs to be confirmed against the actual runtime configuration.\"}","from pyflink.datastream import StreamExecutionEnvironment\nfrom pyflink.common import Time\n\nenv = StreamExecutionEnvironment.get_execution_environment()\nenv.enable_checkpointing(30000)\nenv.get_checkpoint_config().set_checkpointing_mode('EXACTLY_ONCE')\n\nevents = env.from_source('meter-events')\nwith_watermarks = events.assign_timestamps_and_watermarks(\n    timestamp_field='event_time_ms',\n    max_out_of_orderness_ms=5000\n)\nkeyed = with_watermarks.key_by(lambda event: event['device_id'])\nwindowed = keyed.window(\n    window_type='TUMBLING_EVENT_TIME',\n    size_ms=60000,\n    allowed_lateness_ms=10000\n)\naggregated = windowed.aggregate(sum_field='value', count_field='value')\nlate_events = aggregated.get_side_output('late-events')\naggregated.sink('minute-device-metrics', transactional=True)\nlate_events.sink('late-meter-events')","{\"has_state\":true,\"state_descriptors\":[{\"state_name\":\"window_sum_count\",\"state_type\":\"Aggregation State\",\"key_definition\":\"Partitioned by device_id and the [window_start, window_end) window key.\",\"value_definition\":\"Stores the two fields value_sum and value_count.\",\"update_rule\":\"For each event in the window, reads the current accumulator, adds value to value_sum, and increments value_count by one; events within the allowed lateness range update an already triggered window.\",\"retention_policy\":\"Cleared after the window ends and the 10000-millisecond allowed lateness period has elapsed.\"}],\"state_scope\":\"Isolated by device key and event-time window scope.\"}","{\"join_type\":\"No Join\",\"left_key\":\"Not Applicable\",\"right_key\":\"Not Applicable\",\"join_window\":\"Not Applicable\",\"match_condition\":\"Not Applicable\",\"unmatched_record_policy\":\"Not applicable; the program has only a single input stream.\"}","[{\"node_id\":\"n1\",\"operator_type\":\"Data Source\",\"operator_name\":\"from_source('meter-events')\",\"input_nodes\":[],\"output_nodes\":[\"n2\"],\"semantic_role\":\"Reads meter events containing device_id, event_time_ms, and value.\"},{\"node_id\":\"n2\",\"operator_type\":\"Event Time and Watermarks\",\"operator_name\":\"assign_timestamps_and_watermarks\",\"input_nodes\":[\"n1\"],\"output_nodes\":[\"n3\"],\"semantic_role\":\"Extracts event time and generates watermarks using a maximum out-of-orderness of 5 seconds.\"},{\"node_id\":\"n3\",\"operator_type\":\"Keyed Partitioning\",\"operator_name\":\"key_by(device_id)\",\"input_nodes\":[\"n2\"],\"output_nodes\":[\"n4\"],\"semantic_role\":\"Partitions by device identifier so that each device's window aggregation state is isolated from others.\"},{\"node_id\":\"n4\",\"operator_type\":\"Window\",\"operator_name\":\"TUMBLING_EVENT_TIME\",\"input_nodes\":[\"n3\"],\"output_nodes\":[\"n5\"],\"semantic_role\":\"Creates 60-second event-time tumbling windows for each device and allows late events within 10 seconds after the window ends to still be received.\"},{\"node_id\":\"n5\",\"operator_type\":\"Aggregation\",\"operator_name\":\"aggregate(sum_field='value', count_field='value')\",\"input_nodes\":[\"n4\"],\"output_nodes\":[\"n6\",\"n7\"],\"semantic_role\":\"Maintains the sum and count of value within the window; normal results go to the main output, and events beyond the allowed lateness go to a side output.\"},{\"node_id\":\"n6\",\"operator_type\":\"Sink\",\"operator_name\":\"sink('minute-device-metrics')\",\"input_nodes\":[\"n5\"],\"output_nodes\":[],\"semantic_role\":\"Transactionally writes out the aggregated result for each device per minute.\"},{\"node_id\":\"n7\",\"operator_type\":\"Late Event Side Output\",\"operator_name\":\"sink('late-meter-events')\",\"input_nodes\":[\"n5\"],\"output_nodes\":[],\"semantic_role\":\"Receives events that exceed the allowed lateness boundary and can no longer update window results.\"}]","{\"window_type\":\"Tumbling Window\",\"time_basis\":\"Event Time\",\"window_size_ms\":60000,\"slide_interval_ms\":60000,\"allowed_lateness_ms\":10000,\"watermark_strategy\":\"The watermark is approximately the currently observed maximum event time minus 5000 milliseconds.\",\"trigger_condition\":\"First emitted when the watermark reaches the window end time; updated results are emitted again when events arrive within the allowed lateness period.\",\"late_event_policy\":\"Events within 10000 milliseconds after the window ends update the window and produce revised results; events beyond that boundary are sent to the late-events side output.\"}","[{\"sequence\":1,\"state_name\":\"window_sum_count\",\"trigger_event\":\"Event e101: device_id=d17, event_time=2025-03-01T12:00:12Z, value=4.0.\",\"before_state\":\"d17 \u002F [12:00:00,12:01:00): does not exist.\",\"after_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=4.0, value_count=1.\",\"transition_reason\":\"The first event creates the window aggregation state for the device.\"},{\"sequence\":2,\"state_name\":\"window_sum_count\",\"trigger_event\":\"Event e102: device_id=d17, event_time=2025-03-01T12:00:45Z, value=3.0.\",\"before_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=4.0, value_count=1.\",\"after_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=7.0, value_count=2.\",\"transition_reason\":\"The event still belongs to the same event-time window, and the aggregation state is accumulated.\"},{\"sequence\":3,\"state_name\":\"window_sum_count\",\"trigger_event\":\"Late event e103: device_id=d17, event_time=2025-03-01T12:00:20Z, processing_time=2025-03-01T12:01:06Z, value=2.0.\",\"before_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=7.0, value_count=2, and the window has already been emitted once due to the watermark.\",\"after_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=9.0, value_count=3.\",\"transition_reason\":\"The event is later than the current watermark but still within the 10000-millisecond allowed lateness range, so it updates the already emitted window.\"},{\"sequence\":4,\"state_name\":\"window_sum_count\",\"trigger_event\":\"The allowed lateness timer for window [12:00:00,12:01:00) expires.\",\"before_state\":\"d17 \u002F [12:00:00,12:01:00): value_sum=9.0, value_count=3.\",\"after_state\":\"d17 \u002F [12:00:00,12:01:00): cleared.\",\"transition_reason\":\"The allowed lateness period after the window end has ended, and subsequent events can only go to the side output.\"}]","[{\"event_id\":\"e101\",\"event_time\":\"2025-03-01T12:00:12Z\",\"processing_time\":\"2025-03-01T12:00:13Z\",\"window_id\":\"d17\u002F[12:00:00,12:01:00)\",\"aggregation_result\":\"{\\\"sum\\\":4.0,\\\"count\\\":1}\",\"is_late_event\":false,\"emitted_result\":\"{\\\"device_id\\\":\\\"d17\\\",\\\"window\\\":\\\"[12:00:00,12:01:00)\\\",\\\"sum\\\":4.0,\\\"count\\\":1,\\\"update\\\":false}\",\"result_explanation\":\"The event time falls into the 12:00 to 12:01 window, creating the initial aggregation state; the watermark has not yet closed the window.\"},{\"event_id\":\"e102\",\"event_time\":\"2025-03-01T12:00:45Z\",\"processing_time\":\"2025-03-01T12:00:46Z\",\"window_id\":\"d17\u002F[12:00:00,12:01:00)\",\"aggregation_result\":\"{\\\"sum\\\":7.0,\\\"count\\\":2}\",\"is_late_event\":false,\"emitted_result\":\"{\\\"device_id\\\":\\\"d17\\\",\\\"window\\\":\\\"[12:00:00,12:01:00)\\\",\\\"sum\\\":7.0,\\\"count\\\":2,\\\"update\\\":false}\",\"result_explanation\":\"The event belongs to the same device and same window as e101, and the sum and count are updated to 7.0 and 2, respectively.\"},{\"event_id\":\"e103\",\"event_time\":\"2025-03-01T12:00:20Z\",\"processing_time\":\"2025-03-01T12:01:06Z\",\"window_id\":\"d17\u002F[12:00:00,12:01:00)\",\"aggregation_result\":\"{\\\"sum\\\":9.0,\\\"count\\\":3}\",\"is_late_event\":true,\"emitted_result\":\"{\\\"device_id\\\":\\\"d17\\\",\\\"window\\\":\\\"[12:00:00,12:01:00)\\\",\\\"sum\\\":9.0,\\\"count\\\":3,\\\"update\\\":true}\",\"result_explanation\":\"e103's event time is earlier than the current watermark, so it is a late event, but it is still within the allowed lateness range; it causes the already emitted window to produce a revised result rather than going to the side output.\"}]","{\"checkpoint_enabled\":true,\"checkpoint_interval_ms\":30000,\"checkpointed_state\":[\"window_sum_count\",\"source_offsets\",\"watermark_progress\"],\"recovery_strategy\":\"After a task failure, restores the window accumulator state and source offsets from the latest successful checkpoint, and replays events from the offset positions.\",\"exactly_once_semantics\":true,\"failure_effect\":\"Window state and source offsets are restored from the same checkpoint; the transactional sink ensures that committed results are not duplicately written due to replay, and uncommitted results are recomputed.\"}","{\"explanation\":\"{\\\"operator_chain_explanation\\\":\\\"After the click stream extracts event time, it is partitioned by user, and the rule stream is broadcast and joined with the click stream. The processor first performs event_id deduplication, then adds the event to all 5-minute sliding windows covering it, reads the active threshold from broadcast state, and outputs to the alert sink when the threshold is reached.\\\",\\\"state_change_explanation\\\":\\\"seen_event_ids prevents the same event from being counted multiple times, window_accumulators stores revenue and count by user, campaign, and window, and rule_state stores the latest rule for each campaign. Window state is cleared after the allowed lateness period ends, while rule state continuously retains the latest version.\\\",\\\"event_result_explanation\\\":\\\"e701 establishes a window accumulation of 55.0; although e702 is late, it can still revise the same window, making the accumulation reach 115.0 and triggering an alert; the e701 that arrives again is deduplicated and does not change the aggregation result.\\\",\\\"assumptions_and_uncertainties\\\":\\\"The example assumes that the latest version of the same campaign rule in broadcast state takes effect during click processing and that the sliding window maintains accumulation values separately for each covered interval. Because the checkpointing mode is AT_LEAST_ONCE, whether the external alert sink has an idempotency key will affect the risk of final duplicate alerts.\\\"}\",\"source_code\":\"class CampaignSlidingProcessor:\\n    def open(self):\\n        self.seen_event_ids = MapState('seen_event_ids')\\n        self.window_accumulators = MapState('window_accumulators')\\n        self.rule_state = BroadcastState('campaign_rules')\\n\\n    def on_rule(self, rule):\\n        self.rule_state.put(rule['campaign_id'], rule)\\n\\n    def on_click(self, event, ctx, out):\\n        if self.seen_event_ids.contains(event['event_id']):\\n            return\\n        self.seen_event_ids.put(event['event_id'], event['event_time_ms'])\\n        rule = self.rule_state.get(event['campaign_id'])\\n        for window_start in sliding_starts(event['event_time_ms'], size_ms=300000, slide_ms=60000):\\n            key = (event['user_id'], event['campaign_id'], window_start)\\n            current = self.window_accumulators.get(key, {'count': 0, 'revenue': 0.0})\\n            current['count'] += 1\\n            current['revenue'] += event['revenue']\\n            self.window_accumulators.put(key, current)\\n            if rule and rule['active'] and current['revenue'] >= rule['alert_threshold']:\\n                out.emit_alert(key, current)\\n        ctx.register_event_time_timer(event['event_time_ms'] + 330000)\\n\\nclicks = source('campaign-clicks').assign_timestamps_and_watermarks(max_out_of_orderness_ms=10000)\\nrules = source('campaign-rules').broadcast('campaign_rules')\\nalerts = clicks.key_by(lambda e: e['user_id']).connect(rules).process(\\n    CampaignSlidingProcessor(), window_size_ms=300000, slide_ms=60000, allowed_lateness_ms=30000\\n)\\nalerts.sink('campaign-alerts')\\ncheckpoint(enable=True, interval_ms=20000, mode='AT_LEAST_ONCE')\",\"state_model\":\"{\\\"has_state\\\":true,\\\"state_descriptors\\\":[{\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Partitioned by user_id, with event_id as the deduplication key.\\\",\\\"value_definition\\\":\\\"Stores the event_time_ms when the event_id was first seen.\\\",\\\"update_rule\\\":\\\"Written when an event appears for the first time; if event_id already exists, it is directly discarded and the window accumulation is not updated.\\\",\\\"retention_policy\\\":\\\"Cleared after the event time exceeds the window end plus the 30000-millisecond allowed lateness period.\\\"},{\\\"state_name\\\":\\\"window_accumulators\\\",\\\"state_type\\\":\\\"Map State\\\",\\\"key_definition\\\":\\\"Uses a composite key composed of user_id, campaign_id, and window_start.\\\",\\\"value_definition\\\":\\\"Stores count and revenue within the sliding window.\\\",\\\"update_rule\\\":\\\"After deduplication passes, the event is added to all 5-minute windows it covers; revenue is accumulated, count is incremented by one, and an alert is output when the broadcast rule threshold is met.\\\",\\\"retention_policy\\\":\\\"Retained for 30000 milliseconds after the window ends to accept late updates, then cleared by an event-time timer.\\\"},{\\\"state_name\\\":\\\"rule_state\\\",\\\"state_type\\\":\\\"Broadcast State\\\",\\\"key_definition\\\":\\\"Stored by campaign_id; all parallel instances have a copy of the rule.\\\",\\\"value_definition\\\":\\\"Stores fields such as active, alert_threshold, and rule version.\\\",\\\"update_rule\\\":\\\"When a rule event arrives, it overwrites the old rule by campaign_id; subsequent clicks read the current rule.\\\",\\\"retention_policy\\\":\\\"Retains the latest rule until a new rule overwrites it or the job stops.\\\"}],\\\"state_scope\\\":\\\"Click deduplication and window aggregation are scoped by user key, while rule state is scoped as broadcast per operator instance.\\\"}\",\"join_semantics\":\"{\\\"join_type\\\":\\\"Broadcast Join\\\",\\\"left_key\\\":\\\"click.campaign_id\\\",\\\"right_key\\\":\\\"rule.campaign_id\\\",\\\"join_window\\\":\\\"There is no separate rule time window; when processing a click, the current broadcast rule is read; the click itself is still constrained by the 5-minute sliding window.\\\",\\\"match_condition\\\":\\\"The rule campaign_id is the same and rule.active is true; alert_threshold is checked only when an active rule is matched.\\\",\\\"unmatched_record_policy\\\":\\\"When there is no corresponding rule, the click's window aggregation is still retained, but no alert is generated.\\\"}\",\"operator_chain\":\"[{\\\"node_id\\\":\\\"c1\\\",\\\"operator_type\\\":\\\"Data Source\\\",\\\"operator_name\\\":\\\"source('campaign-clicks')\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"c2\\\"],\\\"semantic_role\\\":\\\"Reads click events containing event_id, user_id, campaign_id, event_time_ms, and revenue.\\\"},{\\\"node_id\\\":\\\"c2\\\",\\\"operator_type\\\":\\\"Event Time and Watermarks\\\",\\\"operator_name\\\":\\\"assign_timestamps_and_watermarks\\\",\\\"input_nodes\\\":[\\\"c1\\\"],\\\"output_nodes\\\":[\\\"c3\\\"],\\\"semantic_role\\\":\\\"Extracts click event time and advances watermarks with a maximum out-of-orderness of 10 seconds.\\\"},{\\\"node_id\\\":\\\"c3\\\",\\\"operator_type\\\":\\\"Keyed Deduplication\\\",\\\"operator_name\\\":\\\"key_by(user_id)\\\",\\\"input_nodes\\\":[\\\"c2\\\"],\\\"output_nodes\\\":[\\\"c5\\\"],\\\"semantic_role\\\":\\\"Partitions by user and discards event_ids that have already been processed via seen_event_ids.\\\"},{\\\"node_id\\\":\\\"c4\\\",\\\"operator_type\\\":\\\"Rule Data Source\\\",\\\"operator_name\\\":\\\"source('campaign-rules').broadcast\\\",\\\"input_nodes\\\":[],\\\"output_nodes\\\":[\\\"c5\\\"],\\\"semantic_role\\\":\\\"Reads campaign threshold rules and broadcasts them to all processing instances.\\\"},{\\\"node_id\\\":\\\"c5\\\",\\\"operator_type\\\":\\\"Broadcast Join and Processing\\\",\\\"operator_name\\\":\\\"connect(rules).process(CampaignSlidingProcessor())\\\",\\\"input_nodes\\\":[\\\"c3\\\",\\\"c4\\\"],\\\"output_nodes\\\":[\\\"c6\\\"],\\\"semantic_role\\\":\\\"Combines click events with broadcast rules, accumulates revenue in sliding windows, and emits alerts when the rule threshold is reached.\\\"},{\\\"node_id\\\":\\\"c6\\\",\\\"operator_type\\\":\\\"Sink\\\",\\\"operator_name\\\":\\\"sink('campaign-alerts')\\\",\\\"input_nodes\\\":[\\\"c5\\\"],\\\"output_nodes\\\":[],\\\"semantic_role\\\":\\\"Outputs user window alerts that reach the campaign revenue threshold.\\\"}]\",\"window_semantics\":\"{\\\"window_type\\\":\\\"Sliding Window\\\",\\\"time_basis\\\":\\\"Event Time\\\",\\\"window_size_ms\\\":300000,\\\"slide_interval_ms\\\":60000,\\\"allowed_lateness_ms\\\":30000,\\\"watermark_strategy\\\":\\\"The watermark is approximately the currently observed maximum event time minus 10000 milliseconds.\\\",\\\"trigger_condition\\\":\\\"The current aggregation is output when the watermark reaches the window end time; during the allowed lateness period, events update the window and can trigger the threshold alert again.\\\",\\\"late_event_policy\\\":\\\"Late events within 30000 milliseconds after the window ends update windows that have not yet been cleared; events beyond the boundary no longer enter aggregation; duplicate event_ids are directly discarded regardless of whether they are late.\\\"}\",\"state_transitions\":\"[{\\\"sequence\\\":1,\\\"state_name\\\":\\\"rule_state\\\",\\\"trigger_event\\\":\\\"Rule r9: campaign_id=summer, active=true, alert_threshold=100.0.\\\",\\\"before_state\\\":\\\"summer: does not exist.\\\",\\\"after_state\\\":\\\"summer -> active=true, alert_threshold=100.0.\\\",\\\"transition_reason\\\":\\\"The broadcast rule arrives and initializes the active threshold, after which all processing instances can read the rule.\\\"},{\\\"sequence\\\":2,\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"trigger_event\\\":\\\"Click e701: user_id=u7, campaign_id=summer, event_time=09:02:30, revenue=55.0.\\\",\\\"before_state\\\":\\\"e701 is not in u7's seen_event_ids.\\\",\\\"after_state\\\":\\\"e701 -> 09:02:30 is added to u7's seen_event_ids.\\\",\\\"transition_reason\\\":\\\"The event appears for the first time, passes the deduplication check, and is allowed to enter window processing.\\\"},{\\\"sequence\\\":3,\\\"state_name\\\":\\\"window_accumulators\\\",\\\"trigger_event\\\":\\\"Click e701 enters sliding windows such as [09:00:00,09:05:00).\\\",\\\"before_state\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00): count=0, revenue=0.0.\\\",\\\"after_state\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\\\",\\\"transition_reason\\\":\\\"The event is added by event time to the sliding windows covering it, and the 100.0 alert threshold has not yet been reached.\\\"},{\\\"sequence\\\":4,\\\"state_name\\\":\\\"window_accumulators\\\",\\\"trigger_event\\\":\\\"Late click e702: user_id=u7, campaign_id=summer, event_time=09:01:50, processing_time=09:03:20, revenue=60.0.\\\",\\\"before_state\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00): count=1, revenue=55.0.\\\",\\\"after_state\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00): count=2, revenue=115.0.\\\",\\\"transition_reason\\\":\\\"e702 is earlier than the current watermark but still within the allowed lateness range, updating the window and making revenue exceed the rule threshold.\\\"},{\\\"sequence\\\":5,\\\"state_name\\\":\\\"seen_event_ids\\\",\\\"trigger_event\\\":\\\"Duplicate click e701 arrives again.\\\",\\\"before_state\\\":\\\"u7's seen_event_ids already contains e701.\\\",\\\"after_state\\\":\\\"State unchanged; e701 is discarded.\\\",\\\"transition_reason\\\":\\\"event_id deduplication takes precedence over window aggregation, so duplicate records do not cause duplicate revenue accumulation.\\\"}]\",\"event_time_results\":\"[{\\\"event_id\\\":\\\"e701\\\",\\\"event_time\\\":\\\"2025-03-03T09:02:30Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:02:35Z\\\",\\\"window_id\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"count\\\\\\\":1,\\\\\\\"revenue\\\\\\\":55.0}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"e701 arrives for the first time and is added to multiple sliding windows; the current revenue of the example window is 55.0, which does not reach the campaign rule's 100.0 threshold, so no alert is generated.\\\"},{\\\"event_id\\\":\\\"e702\\\",\\\"event_time\\\":\\\"2025-03-03T09:01:50Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:03:20Z\\\",\\\"window_id\\\":\\\"u7\\\u002Fsummer\\\u002F[09:00:00,09:05:00)\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"count\\\\\\\":2,\\\\\\\"revenue\\\\\\\":115.0}\\\",\\\"is_late_event\\\":true,\\\"emitted_result\\\":\\\"{\\\\\\\"user_id\\\\\\\":\\\\\\\"u7\\\\\\\",\\\\\\\"campaign_id\\\\\\\":\\\\\\\"summer\\\\\\\",\\\\\\\"window\\\\\\\":\\\\\\\"[09:00:00,09:05:00)\\\\\\\",\\\\\\\"revenue\\\\\\\":115.0,\\\\\\\"alert\\\\\\\":true}\\\",\\\"result_explanation\\\":\\\"e702's event time is earlier than the current watermark, so it is a late event, but it is still within the 30000-millisecond allowed lateness range; it updates the window revenue from 55.0 to 115.0, exceeds the broadcast rule threshold, and generates an alert.\\\"},{\\\"event_id\\\":\\\"e701-duplicate\\\",\\\"event_time\\\":\\\"2025-03-03T09:02:30Z\\\",\\\"processing_time\\\":\\\"2025-03-03T09:03:25Z\\\",\\\"window_id\\\":\\\"did not enter a window\\\",\\\"aggregation_result\\\":\\\"{\\\\\\\"dropped\\\\\\\":true,\\\\\\\"reason\\\\\\\":\\\\\\\"duplicate_event_id\\\\\\\"}\\\",\\\"is_late_event\\\":false,\\\"emitted_result\\\":\\\"null\\\",\\\"result_explanation\\\":\\\"This record carries an already existing event_id=e701 and is intercepted by seen_event_ids before updating the window, so it does not increase count or revenue again.\\\"}]\",\"checkpoint_recovery\":\"{\\\"checkpoint_enabled\\\":true,\\\"checkpoint_interval_ms\\\":20000,\\\"checkpointed_state\\\":[\\\"seen_event_ids\\\",\\\"window_accumulators\\\",\\\"rule_state\\\",\\\"click-source-offset\\\",\\\"rule-source-offset\\\"],\\\"recovery_strategy\\\":\\\"Restores the deduplication table, window accumulators, broadcast rules, and offsets of both input sources from the latest checkpoint, and then continues replay.\\\",\\\"exactly_once_semantics\\\":false,\\\"failure_effect\\\":\\\"Checkpoint recovery can restore critical state, but the code is configured for AT_LEAST_ONCE, so after a failure and restart the output sink may see duplicate alerts; deduplication state can reduce duplicate accumulation, but cannot by itself guarantee exactly-once external output.\\\"}\"}","MOBIUSI INC","https:\u002F\u002Fstatic.mobiusi.com\u002Fmobiwusi\u002F20250829\u002F7bf4ac62b4533e2b809998a6f1c5491e.png?Expires=1792463130&OSSAccessKeyId=LTAI5tH8MQqhgeiAtrzCHrxM&Signature=v6Tr8jBrdm4s5wU1SnnyGsYg%2Bhw%3D&x-oss-process=image%2Fresize%2Cm_fill%2Cw_35%2Ch_35",[703,709],{"type":704,"title":705,"count_data":706},"bar","Length distribution",[707],{"name":708,"value":483},"1k lower",{"type":710,"title":711,"count_data":712},"pie","Language distribution",[713],{"name":714,"value":483},"English"]