Add database query utilities

datadog_checks_base/datadog_checks/base/utils/constants.py

@@ -1,5 +1,9 @@
 # (C) Datadog, Inc. 2019
 # All rights reserved
 # Licensed under a 3-clause BSD style license (see LICENSE)
+SECOND = 1
 MILLISECOND = 1000
 MICROSECOND = 1000000
+NANOSECOND = 1000000000
+
+TIME_UNITS = {'microsecond': MICROSECOND, 'millisecond': MILLISECOND, 'nanosecond': NANOSECOND, 'second': SECOND}

datadog_checks_base/datadog_checks/base/utils/db/__init__.py

@@ -0,0 +1,5 @@
+# (C) Datadog, Inc. 2019
+# All rights reserved
+# Licensed under a 3-clause BSD style license (see LICENSE)
+from .core import QueryManager
+from .query import Query

datadog_checks_base/datadog_checks/base/utils/db/core.py

@@ -0,0 +1,126 @@
+# (C) Datadog, Inc. 2019
+# All rights reserved
+# Licensed under a 3-clause BSD style license (see LICENSE)
+from itertools import chain
+
+from ...config import is_affirmative
+from ..containers import iter_unique
+from .query import Query
+from .transform import TRANSFORMERS
+from .utils import SUBMISSION_METHODS, create_submission_transformer
+
+
+class QueryManager(object):
+    def __init__(self, check, executor, queries=None, tags=None, error_handler=None):
+        self.check = check
+        self.executor = executor
+        self.queries = queries or []
+        self.tags = tags or []
+        self.error_handler = error_handler
+
+        custom_queries = list(self.check.instance.get('custom_queries', []))
+        use_global_custom_queries = self.check.instance.get('use_global_custom_queries', True)
+
+        # Handle overrides
+        if use_global_custom_queries == 'extend':
+            custom_queries.extend(self.check.init_config.get('global_custom_queries', []))
+        elif (
+            not custom_queries
+            and 'global_custom_queries' in self.check.init_config
+            and is_affirmative(use_global_custom_queries)
+        ):
+            custom_queries = self.check.init_config.get('global_custom_queries', [])
+
+        # Deduplicate
+        for i, custom_query in enumerate(iter_unique(custom_queries), 1):
+            query = Query(custom_query)
+            query.query_data.setdefault('name', 'custom query #{}'.format(i))
+            self.queries.append(query)
+
+    def compile_queries(self):
+        transformers = TRANSFORMERS.copy()
+
+        for submission_method in SUBMISSION_METHODS:
+            method = getattr(self.check, submission_method)
+            # Save each method in the initializer -> callable format
+            transformers[submission_method] = create_submission_transformer(method)
+
+        for query in self.queries:
+            query.compile(transformers)
+
+    def execute(self):
+        logger = self.check.log
+        global_tags = self.tags
+
+        for query in self.queries:
+            query_name = query.name
+            query_columns = query.columns
+            query_tags = query.tags
+            num_columns = len(query_columns)
+
+            try:
+                rows = self.execute_query(query.query)
+            except Exception as e:
+                if self.error_handler:
+                    logger.error('Error querying %s: %s', query_name, self.error_handler(str(e)))
+                else:
+                    logger.error('Error querying %s: %s', query_name, e)
+
+                continue
+
+            for row in rows:
+                if not row:
+                    logger.debug('Query %s returned an empty result', query_name)
+                    continue
+
+                if num_columns != len(row):
+                    logger.error(
+                        'Query %s expected %d column%s, got %d',
+                        query_name,
+                        num_columns,
+                        's' if num_columns > 1 else '',
+                        len(row),
+                    )
+                    continue
+
+                row_values = {}
+                submission_queue = []
+
+                tags = list(global_tags)
+                tags.extend(query_tags)
+
+                for (column_name, transformer), value in zip(query_columns, row):
+                    # Columns can be ignored via configuration
+                    if not column_name:
+                        continue
+
+                    row_values[column_name] = value
+
+                    column_type, transformer = transformer
+
+                    # The transformer can be None for `source` types. Those such columns do not submit
+                    # anything but are collected into the row values for other columns to reference.
+                    if transformer is None:
+                        continue
+                    elif column_type == 'tag':
+                        tags.append(transformer(value, None))
+                    else:
+                        submission_queue.append((transformer, value))
+
+                for transformer, value in submission_queue:
+                    transformer(value, row_values, tags=tags)
+
+    def execute_query(self, query):
+        rows = self.executor(query)
+        if rows is None:
+            return iter([])
+        else:
+            rows = iter(rows)
+
+        # Ensure we trigger query execution
+        try:
+            first_row = next(rows)
+        except StopIteration:
+            return iter([])
+
+        return chain((first_row,), rows)

datadog_checks_base/datadog_checks/base/utils/db/query.py

@@ -0,0 +1,96 @@
+# (C) Datadog, Inc. 2019
+# All rights reserved
+# Licensed under a 3-clause BSD style license (see LICENSE)
+from copy import deepcopy
+
+from six import raise_from
+
+
+class Query(object):
+    def __init__(self, query_data):
+        self.query_data = deepcopy(query_data or {})
+        self.name = None
+        self.query = None
+        self.columns = None
+        self.tags = None
+
+    def compile(self, transformers):
+        # Check for previous compilation
+        if self.name is not None:
+            return
+
+        query_name = self.query_data.get('name')
+        if not query_name:
+            raise ValueError('query field `name` is required')
+        elif not isinstance(query_name, str):
+            raise ValueError('query field `name` must be a string')
+
+        query = self.query_data.get('query')
+        if not query:
+            raise ValueError('field `query` for {} is required'.format(query_name))
+        elif not isinstance(query, str):
+            raise ValueError('field `query` for {} must be a string'.format(query_name))
+
+        columns = self.query_data.get('columns')
+        if not columns:
+            raise ValueError('field `columns` for {} is required'.format(query_name))
+        elif not isinstance(columns, list):
+            raise ValueError('field `columns` for {} must be a list'.format(query_name))
+
+        tags = self.query_data.get('tags')
+        if tags is not None and not isinstance(tags, list):
+            raise ValueError('field `tags` for {} must be a list'.format(query_name))
+
+        column_data = []
+        for i, column in enumerate(columns, 1):
+            # Columns can be ignored via configuration.
+            if not column:
+                column_data.append((None, None))
+                continue
+            elif not isinstance(column, dict):
+                raise ValueError('column #{} of {} is not a mapping'.format(i, query_name))
+
+            column_name = column.get('name')
+            if not column_name:
+                raise ValueError('field `name` for column #{} of {} is required'.format(i, query_name))
+            elif not isinstance(column_name, str):
+                raise ValueError('field `name` for column #{} of {} must be a string'.format(i, query_name))
+
+            column_type = column.get('type')
+            if not column_type:
+                raise ValueError('field `type` for column {} of {} is required'.format(column_name, query_name))
+            elif not isinstance(column_type, str):
+                raise ValueError('field `type` for column {} of {} must be a string'.format(column_name, query_name))
+            elif column_type == 'source':
+                column_data.append((column_name, (None, None)))
+                continue
+            elif column_type not in transformers:
+                raise ValueError('unknown type `{}` for column {} of {}'.format(column_type, column_name, query_name))
+
+            modifiers = {key: value for key, value in column.items() if key not in ('name', 'type')}
+
+            try:
+                transformer = transformers[column_type](column_name, transformers, **modifiers)
+            except Exception as e:
+                error = 'error compiling type `{}` for column {} of {}: {}'.format(
+                    column_type, column_name, query_name, e
+                )
+
+                # Prepend helpful error text.
+                #
+                # When an exception is raised in the context of another one, both will be printed. To avoid
+                # this we set the context to None. https://www.python.org/dev/peps/pep-0409/
+                raise_from(type(e)(error), None)
+            else:
+                if column_type == 'tag':
+                    column_data.append((column_name, (column_type, transformer)))
+                else:
+                    # All these would actually submit data. As that is the default case, we represent it as
+                    # a reference to None since if we use e.g. `value` it would never be checked anyway.
+                    column_data.append((column_name, (None, transformer)))
+
+        self.name = query_name
+        self.query = query
+        self.columns = tuple(column_data)
+        self.tags = tags
+        del self.query_data

datadog_checks_base/datadog_checks/base/utils/db/transform.py

@@ -0,0 +1,119 @@
+# (C) Datadog, Inc. 2019
+# All rights reserved
+# Licensed under a 3-clause BSD style license (see LICENSE)
+from ... import is_affirmative
+from .. import constants
+from ..common import total_time_to_temporal_percent
+
+
+def get_tag(column_name, transformers, **modifiers):
+    template = '{}:{{}}'.format(column_name)
+    boolean = is_affirmative(modifiers.pop('boolean', None))
+
+    def tag(value, *_, **kwargs):
+        if boolean:
+            value = str(is_affirmative(value)).lower()
+
+        return template.format(value)
+
+    return tag
+
+
+def get_monotonic_gauge(column_name, transformers, **modifiers):
+    gauge = transformers['gauge']('{}.total'.format(column_name), transformers, **modifiers)
+    monotonic_count = transformers['monotonic_count']('{}.count'.format(column_name), transformers, **modifiers)
+
+    def monotonic_gauge(value, *_, **kwargs):
+        gauge(value, **kwargs)
+        monotonic_count(value, **kwargs)
+
+    return monotonic_gauge
+
+
+def get_temporal_percent(column_name, transformers, **modifiers):
+    scale = modifiers.pop('scale', None)
+    if scale is None:
+        raise ValueError('the `scale` parameter is required')
+
+    if isinstance(scale, str):
+        scale = constants.TIME_UNITS.get(scale.lower())
+        if scale is None:
+            raise ValueError(
+                'the `scale` parameter must be one of: {}'.format(' | '.join(sorted(constants.TIME_UNITS)))
+            )
+    elif not isinstance(scale, int):
+        raise ValueError(
+            'the `scale` parameter must be an integer representing parts of a second e.g. 1000 for millisecond'
+        )
+
+    rate = transformers['rate'](column_name, transformers, **modifiers)
+
+    def temporal_percent(value, *_, **kwargs):
+        rate(total_time_to_temporal_percent(value, scale=scale), **kwargs)
+
+    return temporal_percent
+
+
+def get_match(column_name, transformers, **modifiers):
+    # Do work in a separate function to avoid having to `del` a bunch of variables
+    compiled_items = _compile_match_items(transformers, modifiers)
+
+    def match(value, row, *_, **kwargs):
+        if value in compiled_items:
+            source, transformer = compiled_items[value]
+            transformer(row[source], **kwargs)
+
+    return match
+
+
+TRANSFORMERS = {
+    'temporal_percent': get_temporal_percent,
+    'monotonic_gauge': get_monotonic_gauge,
+    'tag': get_tag,
+    'match': get_match,
+}
+
+
+def _compile_match_items(transformers, modifiers):
+    items = modifiers.pop('items', None)
+    if items is None:
+        raise ValueError('the `items` parameter is required')
+
+    if not isinstance(items, dict):
+        raise ValueError('the `items` parameter must be a mapping')
+
+    global_transform_source = modifiers.pop('source', None)
+
+    compiled_items = {}
+    for item, data in items.items():
+        if not isinstance(data, dict):
+            raise ValueError('item `{}` is not a mapping'.format(item))
+
+        transform_name = data.pop('name', None)
+        if not transform_name:
+            raise ValueError('the `name` parameter for item `{}` is required'.format(item))
+        elif not isinstance(transform_name, str):
+            raise ValueError('the `name` parameter for item `{}` must be a string'.format(item))
+
+        transform_type = data.pop('type', None)
+        if not transform_type:
+            raise ValueError('the `type` parameter for item `{}` is required'.format(item))
+        elif not isinstance(transform_type, str):
+            raise ValueError('the `type` parameter for item `{}` must be a string'.format(item))
+        elif transform_type not in transformers:
+            raise ValueError('unknown type `{}` for item `{}`'.format(transform_type, item))
+
+        transform_source = data.pop('source', global_transform_source)
+        if not transform_source:
+            raise ValueError('the `source` parameter for item `{}` is required'.format(item))
+        elif not isinstance(transform_source, str):
+            raise ValueError('the `source` parameter for item `{}` must be a string'.format(item))
+
+        transform_modifiers = modifiers.copy()
+        transform_modifiers.update(data)
+        compiled_items[item] = (
+            transform_source,
+            transformers[transform_type](transform_name, transformers, **transform_modifiers),
+        )
+
+    return compiled_items

datadog_checks_base/datadog_checks/base/utils/db/utils.py

@@ -0,0 +1,15 @@
+# (C) Datadog, Inc. 2019
+# All rights reserved
+# Licensed under a 3-clause BSD style license (see LICENSE)
+SUBMISSION_METHODS = {'gauge', 'count', 'monotonic_count', 'rate', 'histogram', 'historate'}
+
+
+def create_submission_transformer(submit_method):
+    def get_transformer(name, _, **modifiers):
+        def transformer(value, *_, **kwargs):
+            kwargs.update(modifiers)
+            submit_method(name, value, **kwargs)
+
+        return transformer
+
+    return get_transformer