Build a dynamic query based on requested columns. Args: group_cols: Columns to group by where_clause: WHERE clause conditions low_time: Start time for data range high_time: End time for data range latency_columns:
(self,
group_cols: List[str],
where_clause: str = "1=1",
low_time: Optional[datetime] = None,
high_time: Optional[datetime] = None,
latency_columns: Optional[List[str]] = None,
limit: Optional[int] = None)
| 136 | return f"NULL AS {output_alias}" |
| 137 | |
| 138 | def build_dynamic_query(self, |
| 139 | group_cols: List[str], |
| 140 | where_clause: str = "1=1", |
| 141 | low_time: Optional[datetime] = None, |
| 142 | high_time: Optional[datetime] = None, |
| 143 | latency_columns: Optional[List[str]] = None, |
| 144 | limit: Optional[int] = None) -> str: |
| 145 | """ |
| 146 | Build a dynamic query based on requested columns. |
| 147 | |
| 148 | Args: |
| 149 | group_cols: Columns to group by |
| 150 | where_clause: WHERE clause conditions |
| 151 | low_time: Start time for data range |
| 152 | high_time: End time for data range |
| 153 | latency_columns: Additional latency/aggregate columns to include |
| 154 | limit: Row limit for results |
| 155 | |
| 156 | Returns: |
| 157 | Complete SQL query string |
| 158 | """ |
| 159 | # Standardize column names to lowercase |
| 160 | group_cols = [col.lower() for col in group_cols] |
| 161 | # Determine all requested columns |
| 162 | all_columns = set(group_cols) |
| 163 | all_columns.update(['samples', 'avg_threads']) # Always include these |
| 164 | if latency_columns: |
| 165 | all_columns.update([col.lower() for col in latency_columns]) |
| 166 | |
| 167 | # Determine required data sources |
| 168 | required_sources = self._determine_required_sources(all_columns) |
| 169 | |
| 170 | # Check for histogram requirements |
| 171 | # Check for histogram columns (case-insensitive) |
| 172 | all_columns_lower = {col.lower() for col in all_columns} |
| 173 | need_sc_histogram = 'sclat_histogram' in all_columns_lower |
| 174 | need_io_histogram = 'iolat_histogram' in all_columns_lower |
| 175 | |
| 176 | # Build the query parts |
| 177 | ctes = [] |
| 178 | |
| 179 | # 1. Build enriched_samples CTE with all computed columns |
| 180 | enriched_cte = self._build_enriched_samples_cte(low_time, high_time) |
| 181 | ctes.append(f"enriched_samples AS (\n{enriched_cte}\n)") |
| 182 | |
| 183 | # 2. Build base_samples CTE with JOINs and filters |
| 184 | base_cte = self._build_base_samples_cte( |
| 185 | required_sources, where_clause, low_time, high_time, |
| 186 | need_sc_histogram, need_io_histogram |
| 187 | ) |
| 188 | ctes.append(f"base_samples AS (\n{base_cte}\n)") |
| 189 | |
| 190 | # 3. Add histogram CTEs if needed |
| 191 | if need_sc_histogram: |
| 192 | hist_cte = self._build_histogram_cte( |
| 193 | 'sc', group_cols, 'sc_duration_ns', 'sc_lat_bkt_us' |
| 194 | ) |
| 195 | ctes.extend(hist_cte) |
nothing calls this directly
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