| Conditions | 11 |
| Total Lines | 80 |
| Code Lines | 59 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like isomer.database._build_collections() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | #!/usr/bin/env python |
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| 131 | def _build_collections(store): |
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| 132 | """Generate database collections with indices from the schemastore""" |
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| 133 | |||
| 134 | result = {} |
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| 135 | |||
| 136 | client = pymongo.MongoClient(host=dbhost, port=dbport) |
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| 137 | db = client[dbname] |
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| 138 | |||
| 139 | for schemaname in store: |
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| 140 | |||
| 141 | schema = None |
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| 142 | indices = None |
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| 143 | |||
| 144 | try: |
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| 145 | schema = store[schemaname]["schema"] |
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| 146 | indices = store[schemaname].get("indices", None) |
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| 147 | except KeyError: |
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| 148 | db_log("No schema found for ", schemaname, lvl=critical) |
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| 149 | |||
| 150 | try: |
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| 151 | result[schemaname] = db[schemaname] |
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| 152 | except Exception: |
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| 153 | db_log( |
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| 154 | "Could not get collection for schema ", |
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| 155 | schemaname, |
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| 156 | schema, |
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| 157 | lvl=critical, |
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| 158 | exc=True, |
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| 159 | ) |
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| 160 | |||
| 161 | if indices is None: |
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| 162 | continue |
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| 163 | |||
| 164 | col = db[schemaname] |
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| 165 | db_log("Adding indices to", schemaname, lvl=debug) |
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| 166 | i = 0 |
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| 167 | keys = list(indices.keys()) |
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| 168 | |||
| 169 | while i < len(indices): |
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| 170 | index_name = keys[i] |
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| 171 | index = indices[index_name] |
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| 172 | |||
| 173 | index_type = index.get("type", None) |
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| 174 | index_unique = index.get("unique", False) |
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| 175 | index_sparse = index.get("sparse", True) |
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| 176 | index_reindex = index.get("reindex", False) |
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| 177 | |||
| 178 | if index_type in (None, "text"): |
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| 179 | index_type = pymongo.TEXT |
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| 180 | elif index_type == "2dsphere": |
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| 181 | index_type = pymongo.GEOSPHERE |
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| 182 | |||
| 183 | def do_index(): |
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| 184 | """Ensure index on a data class""" |
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| 185 | col.ensure_index( |
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| 186 | [(index_name, index_type)], unique=index_unique, sparse=index_sparse |
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| 187 | ) |
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| 188 | |||
| 189 | db_log("Enabling index of type", index_type, "on", index_name, lvl=debug) |
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| 190 | try: |
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| 191 | do_index() |
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| 192 | i += 1 |
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| 193 | except pymongo.errors.OperationFailure: |
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| 194 | db_log(col.list_indexes().__dict__, pretty=True, lvl=verbose) |
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| 195 | if not index_reindex: |
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| 196 | db_log("Index was not created!", lvl=warn) |
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| 197 | i += 1 |
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| 198 | else: |
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| 199 | try: |
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| 200 | col.drop_index(index_name) |
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| 201 | do_index() |
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| 202 | i += 1 |
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| 203 | except pymongo.errors.OperationFailure as e: |
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| 204 | db_log("Index recreation problem:", exc=True, lvl=error) |
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| 205 | col.drop_indexes() |
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| 206 | i = 0 |
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| 207 | |||
| 208 | # for index in col.list_indexes(): |
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| 209 | # db_log("Index: ", index) |
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| 210 | return result |
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| 211 | |||
| 281 |