File size: 12,496 Bytes
4df2ab3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3219310
 
4df2ab3
3219310
4df2ab3
 
3219310
 
 
4df2ab3
 
 
3219310
 
4df2ab3
3219310
 
4df2ab3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3219310
4df2ab3
3219310
 
 
 
 
4df2ab3
3219310
4df2ab3
 
3219310
 
 
 
 
4df2ab3
 
 
 
 
 
3219310
4df2ab3
 
 
 
 
3219310
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4df2ab3
3219310
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4df2ab3
3219310
 
4df2ab3
 
3219310
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
import datetime
import json
import os
from opik import Opik
import parameters
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed


class DateTimeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime.datetime):
            return obj.isoformat()
        return super().default(obj)

def get_trace_content(opik, trace_id):
    try:
        trace_content = opik.get_trace_content(trace_id)
        return trace_content.dict()
    except Exception as e:
        print(f"Error getting trace content {trace_id}: {e}")
        return None

def get_span_content(opik, trace_id, span):
    try:
        content = opik.get_span_content(span.id)
        return {"trace_id": trace_id, "span_id": span.id, "content": content.dict()}
    except Exception as e:
        print(f"Error getting span content {span.id}: {e}")
        return None

def get_traces_on_date(start_date_str, end_date_str, project_name, api_key,max_workers=10):
    try:
        print("Step 1: Converting date strings")
        date = datetime.date.fromisoformat(start_date_str)
        start_date_str = date.isoformat() + "T00:00:00Z"
        
        if not end_date_str:
            end_date = date + datetime.timedelta(days=1)
            end_date_str = end_date.isoformat() + "T00:00:00Z"
        else:
            end_date = datetime.date.fromisoformat(end_date_str)
            end_date_str = end_date.isoformat() + "T00:00:00Z"
        
        print(f"Start: {start_date_str} and end: {end_date_str}")
        filter_string = f'start_time >= "{start_date_str}" and end_time <= "{end_date_str}"'
        print("Filter string: ", filter_string)

        print("Step 2: Initializing Opik client")
        try:
            opik = Opik(api_key=api_key, project_name=project_name, workspace='verba-tech-ninja')
            print("Opik client initialized successfully")
        except Exception as e:
            print(f"Error initializing Opik client: {e}")
            return [], []

        print("Step 3: Searching traces")
        try:
            traces = opik.search_traces(filter_string=filter_string, project_name=project_name)
            print("Total searches: ", len(traces))
        except Exception as e:
            print(f"Error searching traces: {e}")
            return [], []

        print("Step 4: Processing traces in parallel")
        all_traces_content = []
        try:
            with ThreadPoolExecutor(max_workers=max_workers) as executor:
                future_to_trace = {executor.submit(get_trace_content, opik, trace.id): trace for trace in traces}
                for future in as_completed(future_to_trace):
                    result = future.result()
                    if result:
                        all_traces_content.append(result)
            print(f"Completed processing {len(all_traces_content)} traces")
        except Exception as e:
            print(f"Error processing traces in parallel: {e}")

        print("Step 5: Processing spans in parallel")
        all_spans_content = []
        try:
            with ThreadPoolExecutor(max_workers=max_workers) as executor:
                future_to_span = {}
                for i, trace in enumerate(traces):
                    try:
                        print(f"Searching spans for trace_id: {trace.id}:{i+1}/{len(traces)}")
                        spans = opik.search_spans(project_name=parameters.project, trace_id=trace.id)
                        print(f"Found {len(spans)} spans for trace_id: {trace.id}")
                        for span in spans:
                            future_to_span[executor.submit(get_span_content, opik, trace.id, span)] = span
                    except Exception as e:
                        print(f"Error searching spans for trace {trace.id}: {e}")
                
                for future in as_completed(future_to_span):
                    result = future.result()
                    if result:
                        all_spans_content.append(result)
            print(f"Completed processing {len(all_spans_content)} spans")
        except Exception as e:
            print(f"Error processing spans in parallel: {e}")

        print("Step 6: Saving to JSON files")
        traces_file = 'all_traces_content.json'
        spans_file = 'all_spans_content.json'
        try:
            if os.path.exists(traces_file):
                os.remove(traces_file)
                print(f"Removed existing {traces_file}")
            if os.path.exists(spans_file):
                os.remove(spans_file)
                print(f"Removed existing {spans_file}")

            print(f"Writing {len(all_traces_content)} traces to {traces_file}")
            with open(traces_file, 'w') as f:
                json.dump(all_traces_content, f, indent=2, cls=DateTimeEncoder)
            print(f"Saved traces to {traces_file}")

            print(f"Writing {len(all_spans_content)} spans to {spans_file}")
            with open(spans_file, 'w') as f:
                json.dump(all_spans_content, f, indent=2, cls=DateTimeEncoder)
            print(f"Saved spans to {spans_file}")
        except Exception as e:
            print(f"Error saving to JSON files: {e}")
            with open('partial_traces_content.json', 'w') as f:
                json.dump(all_traces_content, f, indent=2, cls=DateTimeEncoder)
            with open('partial_spans_content.json', 'w') as f:
                json.dump(all_spans_content, f, indent=2, cls=DateTimeEncoder)
            print("Saved partial data to partial_traces_content.json and partial_spans_content.json")

        print("Step 7: Returning results")
        return all_traces_content, all_spans_content

    except Exception as e:
        print(f"Main function error: {e}")
        return [], []

def find_errors_and_metrics(traces, spans):
    try:
        print("Step 8: Analyzing outputs for errors")
        error_spans = []
        error_metrics = defaultdict(list)

        for span in spans:
            content = span['content']
            output = content.get("output")
            error_info = content.get("error_info", {})  

            if isinstance(output, dict) and 'output' in output:
                output_value = output.get("output")
            else:
                output_value = output

            if ((output_value is None or (isinstance(output, list) and len(output) == 0)) and len(error_info) > 0):
                error_type = error_info.get("exception_type", "unknown_error")
                error_spans.append({
                    "trace_id": span["trace_id"],
                    "span_id": span["span_id"],
                    "error_content": output, 
                    "exception_type": error_type
                })
                
                error_metrics[error_type].append({"trace_id": span["trace_id"], "span_id": span["span_id"]})

        print(f"Found {len(error_spans)} outputs with errors (empty/null)")

        print("Step 9: Saving error spans")
        error_file = 'error_spans.json'
        try:
            if os.path.exists(error_file):
                os.remove(error_file)
                print(f"Removed existing {error_file}")
            print(f"Writing {len(error_spans)} error outputs to {error_file}")
            with open(error_file, 'w') as f:
                json.dump(error_spans, f, indent=2, cls=DateTimeEncoder)
            print(f"Saved error outputs to {error_file}")
        except Exception as e:
            print(f"Error saving error spans: {e}")

        print("Step 10: Calculating metrics")
        metrics = {
            "total_errors": len(error_spans),
            "error_types": {
                error_type: {
                    "count": len(entries),
                    "instances": [
                        {"trace_id": entry["trace_id"], "span_id": entry["span_id"]}
                        for entry in entries
                    ]
                }
                for error_type, entries in error_metrics.items()
            }
        }
        print(f"Metrics calculated: {len(metrics['error_types'])} error types")
        for error_type, data in metrics["error_types"].items():
            print(f"Error Type: {error_type}, Count: {data['count']}")
            for instance in data["instances"]:
                print(f"  Trace ID: {instance['trace_id']}, Span ID: {instance['span_id']}")
        return metrics

    except Exception as e:
        print(f"Error in find_errors_and_metrics: {e}")
        return {}

def process_dates(start_date, end_date, project):
    try:
        print("Pipeline Start: Processing dates")
        traces, spans = get_traces_on_date(start_date, end_date, project, parameters.api_key)
        metrics = find_errors_and_metrics(traces, spans)
        
        html_output = """

        <div style='font-family: Arial, sans-serif; padding: 20px; background-color: #f5f5f5; border-radius: 10px;'>

            <h2 style='color: #2c3e50; border-bottom: 2px solid #3498db; padding-bottom: 5px;'>Metrics Report</h2>

            

            <div style='margin: 15px 0;'>

                <span style='color: #e74c3c; font-weight: bold;'>Total Empty/Null Outputs Found: </span>

                <span style='color: #2980b9'>{empty_count}</span>

            </div>

            

            <div style='margin: 15px 0;'>

                <span style='color: #27ae60; font-weight: bold;'>Total Traces Found: </span>

                <span style='color: #2980b9'>{traces_count}</span>

                <br>

                <span style='color: #27ae60; font-weight: bold;'>Total Spans Processed: </span>

                <span style='color: #2980b9'>{spans_count}</span>

            </div>

            

            <h3 style='color: #8e44ad; margin-top: 20px;'>Error Metrics</h3>

            {error_section}

        </div>

        """

        if metrics.get('error_types', {}):
            error_html = "<div style='background-color: #fff; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1);'>"
            for error_type, data in metrics.get('error_types', {}).items():
                error_html += f"""

                <div style='margin: 10px 0;'>

                    <span style='color: #d35400; font-weight: bold;'>{error_type.replace('_', ' ').title()}: </span>

                    <span style='color: #2980b9'>{data['count']} occurrences</span>

                    <div style='margin-left: 20px;'>

                        <span style='color: #7f8c8d;'>Instances:</span>

                        <ul style='list-style-type: none; padding-left: 10px;'>

                """
                for instance in data['instances'][:5]:
                    error_html += f"""

                    <li style='color: #34495e;'>

                        Trace ID: <span style='color: #16a085'>{instance['trace_id']}</span>, 

                        Span ID: <span style='color: #16a085'>{instance['span_id']}</span>

                    </li>

                    """
                if len(data['instances']) > 5:
                    error_html += "<li style='color: #7f8c8d;'>... (and more)</li>"
                error_html += "</ul></div></div>"
            error_html += "</div>"
        else:
            error_html = """

            <div style='color: #27ae60; background-color: #ecf0f1; padding: 10px; border-radius: 5px;'>

                No empty or null outputs with exceptions detected.

            </div>

            """

        formatted_output = html_output.format(
            empty_count=metrics.get('total_errors', 0),
            traces_count=len(traces),
            spans_count=len(spans),
            error_section=error_html
        )
        
        print("Pipeline End: Results formatted")
        return formatted_output
        
    except Exception as e:
        print(f"Error processing dates: {e}")
        error_html = f"""

        <div style='font-family: Arial, sans-serif; padding: 20px; background-color: #f5f5f5; border-radius: 10px;'>

            <h2 style='color: #e74c3c;'>Error Occurred</h2>

            <p style='color: #c0392b;'>Error processing dates: {str(e)}</p>

        </div>

        """
        return error_html