### Execute Asynchronous TopN Query with AsyncPyDruid

Source: https://pythonhosted.org/pydruid/index.html

Demonstrates initializing the AsyncPyDruid client and executing a topN query. Includes examples of printing the query dictionary, raw results, and exporting to a pandas DataFrame.

```python
>>> from pydruid.async_client import *

>>> query = AsyncPyDruid('http://localhost:8083', 'druid/v2/')

>>> top = yield query.topn(
        datasource='twitterstream',
        granularity='all',
        intervals='2013-10-04/pt1h',
        aggregations={"count": doublesum("count")},
        dimension='user_name',
        filter = Dimension('user_lang') == 'en',
        metric='count',
        threshold=2
    )

>>> print json.dumps(top.query_dict, indent=2)
>>> {
      "metric": "count",
      "aggregations": [
        {
          "type": "doubleSum",
          "fieldName": "count",
          "name": "count"
        }
      ],
      "dimension": "user_name",
      "filter": {
        "type": "selector",
        "dimension": "user_lang",
        "value": "en"
      },
      "intervals": "2013-10-04/pt1h",
      "dataSource": "twitterstream",
      "granularity": "all",
      "threshold": 2,
      "queryType": "topN"
    }

>>> print top.result
>>> [{'timestamp': '2013-10-04T00:00:00.000Z',
    'result': [{'count': 7.0, 'user_name': 'user_1'}, {'count': 6.0, 'user_name': 'user_2'}]}]

>>> df = top.export_pandas()
>>> print df
>>>    count                 timestamp      user_name
    0      7  2013-10-04T00:00:00.000Z         user_1
    1      6  2013-10-04T00:00:00.000Z         user_2
```

--------------------------------

### topn query example with client.topn

Source: https://pythonhosted.org/pydruid/index.html

Example of using the topn method to query a datasource with a dimension filter, metric, and threshold. The query returns a list of results with the top items sorted by the specified metric.

```python
>>> top = client.topn(
            datasource='twitterstream',
            granularity='all',
            intervals='2013-06-14/pt1h',
            aggregations={"count": doublesum("count")},
            dimension='user_name',
            metric='count',
            filter=Dimension('user_lang') == 'en',
            threshold=1,
            context={"timeout": 1000}
        )
>>> print top
>>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': [{'count': 22.0, 'user': "cool_user"}}]}]
```

--------------------------------

### PyDruid client usage with topn query and export_pandas

Source: https://pythonhosted.org/pydruid/index.html

Complete example of creating a PyDruid client, running a topn query, printing the query dictionary and result, and exporting the result to a pandas DataFrame. Shows the full workflow from client instantiation to data export.

```python
>>> from pydruid.client import *

>>> query = PyDruid('http://localhost:8083', 'druid/v2/')

>>> top = query.topn(
        datasource='twitterstream',
        granularity='all',
        intervals='2013-10-04/pt1h',
        aggregations={"count": doublesum("count")},
        dimension='user_name',
        filter = Dimension('user_lang') == 'en',
        metric='count',
        threshold=2
    )

>>> print json.dumps(top.query_dict, indent=2)
>>> {
      "metric": "count",
      "aggregations": [
        {
          "type": "doubleSum",
          "fieldName": "count",
          "name": "count"
        }
      ],
      "dimension": "user_name",
      "filter": {
        "type": "selector",
        "dimension": "user_lang",
        "value": "en"
      },
      "intervals": "2013-10-04/pt1h",
      "dataSource": "twitterstream",
      "granularity": "all",
      "threshold": 2,
      "queryType": "topN"
    }

>>> print top.result
>>> [{'timestamp': '2013-10-04T00:00:00.000Z',
    'result': [{'count': 7.0, 'user_name': 'user_1'}, {'count': 6.0, 'user_name': 'user_2'}]}]

>>> df = top.export_pandas()
>>> print df
>>>    count                 timestamp      user_name
    0      7  2013-10-04T00:00:00.000Z         user_1
    1      6  2013-10-04T00:00:00.000Z         user_2
```

--------------------------------

### topn(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a topN query asynchronously. Accepts keyword arguments for the query parameters. Example usage is provided in the documentation.

```APIDOC
## topn(**kwargs)

### Description
Executes a topN query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the topN query.

### Example
```python
>>> top = yield query.topn(
        datasource='twitterstream',
        granularity='all',
        intervals='2013-10-04/pt1h',        aggregations={"count": doublesum("count")},
        dimension='user_name',        filter = Dimension('user_lang') == 'en',        metric='count',        threshold=2
    )```
```

--------------------------------

### QueryBuilder.select(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a select query that returns raw Druid rows and supports pagination. Returns a Query object.

```APIDOC
## select(args)

### Description
A select query returns raw Druid rows and supports pagination.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - select query

### Example
N/A
```

--------------------------------

### Execute a select query with pydruid

Source: https://pythonhosted.org/pydruid/index.html

Demonstrates how to perform a select query on a datasource with specified granularity, intervals, paging specifications, and context timeout.

```python
    >>> raw_data = client.select(
            datasource=twitterstream,
            granularity='all',
            intervals='2013-06-14/pt1h',
            paging_spec={'pagingIdentifies': {}, 'threshold': 1},
            context={"timeout": 1000}
        )
    >>> print raw_data
    >>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'pagingIdentifiers': {'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1': 1, 'events': [{'segmentId': 'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1', 'offset': 0, 'event': {'timestamp': '2013-06-14T00:00:00.000Z', 'dim': 'value'}}]}}}]
```

--------------------------------

### select(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Performs a select query on a Druid data source, returning raw rows of data. It supports paging and context options.

```APIDOC
## select(**kwargs)

### Description
Performs a select query on a Druid data source, returning raw rows of data. It supports paging and context options.

### Method
select

### Parameters
#### Required Key/Value Pairs
- **datasource** (str) - Required - Data source to query
- **granularity** (str) - Required - Time bucket to aggregate data by hour, day, minute, etc.
- **intervals** (str or list) - Required - ISO-8601 intervals for which to run the query on

#### Optional Key/Value Pairs
- **filter** (pydruid.utils.filters.Filter) - Optional - Indicates which rows of data to include in the query
- **dimensions** (list) - Optional - The list of dimensions to select. If left empty, all dimensions are returned
- **metrics** (list) - Optional - The list of metrics to select. If left empty, all metrics are returned
- **context** (dict) - Optional - A dict of query context options

### Returns
The query result

### Return Type
Query

### Request Example
```python
raw_data = client.select(
    datasource=twitterstream,
    granularity='all',
    intervals='2013-06-14/pt1h',
    paging_spec={'pagingIdentifies': {}, 'threshold': 1},
    context={"timeout": 1000}
)
```

### Response Example
```python
[{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'pagingIdentifiers': {'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1': 1, 'events': [{'segmentId': 'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1', 'offset': 0, 'event': {'timestamp': '2013-06-14T00:00:00.000Z', 'dim': 'value'}}]}}]
```
```

--------------------------------

### select(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a select query asynchronously. Accepts keyword arguments for the query parameters.

```APIDOC
## select(**kwargs)

### Description
Executes a select query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the select query.
```

--------------------------------

### Export query results to a TSV file

Source: https://pythonhosted.org/pydruid/index.html

Demonstrates using the export_tsv method to write query results to a file path.

```python
    >>> top = client.topn(
            datasource='twitterstream',
            granularity='all',
            intervals='2013-10-04/pt1h',
            aggregations={"count": doublesum("count")},
            dimension='user_name',
            filter = Dimension('user_lang') == 'en',
            metric='count',
            threshold=2
        )

    >>> top.export_tsv('top.tsv')
    >>> !cat top.tsv
    >>> count       user_name       timestamp
        7.0 user_1  2013-10-04T00:00:00.000Z
        6.0 user_2  2013-10-04T00:00:00.000Z
```

--------------------------------

### Execute a time_boundary query

Source: https://pythonhosted.org/pydruid/index.html

Retrieves the minimum and maximum timestamps for a specified datasource.

```python
    >>> bound = client.time_boundary(datasource='twitterstream')
    >>> print bound
    >>> [{'timestamp': '2011-09-14T15:00:00.000Z', 'result': {'minTime': '2011-09-14T15:00:00.000Z', 'maxTime': '2014-03-04T23:44:00.000Z'}}]
```

--------------------------------

### AsyncPyDruid(url, endpoint)

Source: https://pythonhosted.org/pydruid/index.html

Constructor for the asynchronous PyDruid client. It initializes the client with the URL of the Druid Broker node and the endpoint that the Broker listens for queries on.

```APIDOC
## AsyncPyDruid(url, endpoint)

### Description
Asynchronous PyDruid client which mirrors functionality of the synchronous PyDruid, but it executes queries asynchronously (using an asynchronous http client from Tornado framework). Returns Query objects that can be used for exporting query results into TSV files or pandas.DataFrame objects for subsequent analysis.

### Parameters
- **url** (str) - Required - URL of Broker node in the Druid cluster
- **endpoint** (str) - Required - Endpoint that Broker listens for queries on

### Example
```python
>>> from pydruid.async_client import *
>>> query = AsyncPyDruid('http://localhost:8083', 'druid/v2/')
```
```

--------------------------------

### topn(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a TopN query against the Druid datasource. Returns a Query object containing the results.

```APIDOC
## topn(**kwargs)

### Description
A TopN query returns a set of the values in a given dimension, sorted by a specified metric. Conceptually, a topN can be thought of as an approximate GroupByQuery over a single dimension with an Ordering spec. TopNs are faster and more resource efficient than GroupBy for this use case.

### Parameters
#### Required Parameters
- **datasource** (str) - Required - Data source to query
- **granularity** (str) - Required - Aggregate data by hour, day, minute, etc.
- **intervals** (str or list) - Required - ISO-8601 intervals of data to query
- **aggregations** (dict) - Required - A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum
- **dimension** (str) - Required - Dimension to run the query against
- **metric** (str) - Required - Metric over which to sort the specified dimension by
- **threshold** (int) - Required - How many of the top items to return

#### Optional Parameters
- **filter** (pydruid.utils.filters.Filter) - Optional - Indicates which rows of data to include in the query
- **post_aggregations** - Optional - A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
- **context** (dict) - Optional - A dict of query context options

### Returns
- **Query** - The query result

### Example
```python
>>> top = client.topn(
            datasource='twitterstream',
            granularity='all',
            intervals='2013-06-14/pt1h',
            aggregations={"count": doublesum("count")},
            dimension='user_name',
            metric='count',
            filter=Dimension('user_lang') == 'en',
            threshold=1,
            context={"timeout": 1000}
        )
>>> print top
>>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': [{'count': 22.0, 'user': "cool_user"}}]}]
```
```

--------------------------------

### Execute a timeseries query with aggregations

Source: https://pythonhosted.org/pydruid/index.html

Performs a timeseries query with defined aggregations, post-aggregations, and context settings.

```python
    >>> counts = client.timeseries(
            datasource=twitterstream,
            granularity='hour',
            intervals='2013-06-14/pt1h',
            aggregations={"count": doublesum("count"), "rows": count("rows")},
            post_aggregations={'percent': (Field('count') / Field('rows')) * Const(100))},
            context={"timeout": 1000}
        )
    >>> print counts
    >>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'count': 9619.0, 'rows': 8007, 'percent': 120.13238416385663}}]
```

--------------------------------

### QueryBuilder.time_boundary(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a time boundary query that returns the min and max timestamps in a data source. Returns a Query object.

```APIDOC
## time_boundary(args)

### Description
A time boundary query returns the min and max timestamps present in a data source.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - time boundary query

### Example
N/A
```

--------------------------------

### PyDruid(url, endpoint)

Source: https://pythonhosted.org/pydruid/index.html

Constructor for the PyDruid class. Initializes a client to connect to a Druid Broker node and execute queries.

```APIDOC
## PyDruid(url, endpoint)

### Description
Initializes a PyDruid client for creating and executing Druid queries. Returns Query objects that can be used for exporting query results into TSV files or pandas.DataFrame objects.

### Parameters
- **url** (str) - Required - URL of Broker node in the Druid cluster
- **endpoint** (str) - Required - Endpoint that Broker listens for queries on

### Example
```python
>>> from pydruid.client import *
>>> query = PyDruid('http://localhost:8083', 'druid/v2/')
```
```

--------------------------------

### Execute a GroupBy Query

Source: https://pythonhosted.org/pydruid/index.html

Performs a groupby query on the 'twitterstream' datasource, grouping by user names with specific filters, aggregations, and a limit specification.

```python
    >>> group = client.groupby(
            datasource='twitterstream',
            granularity='hour',
            intervals='2013-10-04/pt1h',
            dimensions=["user_name", "reply_to_name"],
            filter=~(Dimension("reply_to_name") == "Not A Reply"),
            aggregations={"count": doublesum("count")},
            context={"timeout": 1000}
            limit_spec={
                "type": "default",
                "limit": 50,
                "columns" : ["count"]
            }
        )
    >>> for k in range(2):
        ...     print group[k]
    >>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_1', 'reply_to_name': 'user_2'}}
    >>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_2', 'reply_to_name': 'user_3'}}
```

--------------------------------

### QueryBuilder.topn(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a TopN query that returns a set of dimension values sorted by a specified metric. Returns a Query object.

```APIDOC
## topn(args)

### Description
A TopN query returns a set of the values in a given dimension, sorted by a specified metric. Conceptually, a topN can be thought of as an approximate GroupByQuery over a single dimension with an Ordering spec. TopNs are faster and more resource efficient than GroupBy for this use case.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of arguments

### Returns
- **Query** - topn query

### Example
N/A
```

--------------------------------

### Export query results to a Pandas DataFrame

Source: https://pythonhosted.org/pydruid/index.html

Demonstrates using the export_pandas method on a topN query result to convert it into a Pandas DataFrame.

```python
    >>> top = client.topn(
            datasource='twitterstream',
            granularity='all',
            intervals='2013-10-04/pt1h',
            aggregations={"count": doublesum("count")},
            dimension='user_name',
            filter = Dimension('user_lang') == 'en',
            metric='count',
            threshold=2
        )

    >>> df = top.export_pandas()
    >>> print df
    >>>    count                 timestamp      user_name
        0      7  2013-10-04T00:00:00.000Z         user_1
        1      6  2013-10-04T00:00:00.000Z         user_2
```

--------------------------------

### QueryBuilder.search(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a search query that returns dimension values matching the search specification. Returns a Query object.

```APIDOC
## search(args)

### Description
A search query returns dimension values that match the search specification.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - search query

### Example
N/A
```

--------------------------------

### BaseDruidClient.select

Source: https://pythonhosted.org/pydruid/index.html

Executes a select query that returns raw Druid rows and supports pagination. Requires datasource, granularity, paging_spec, and intervals.

```APIDOC
## select(**kwargs)

### Description
A select query returns raw Druid rows and supports pagination.

### Method
N/A (Python method)

### Parameters
#### Required Parameters
- **datasource** (str) - Required - Data source to query
- **granularity** (str) - Required - Time bucket to aggregate data by hour, day, minute, etc.
- **paging_spec** (dict) - Required - Indicates offsets into different scanned segments
- **intervals** (str or list) - Required - ISO-8601 intervals for which to run the query on

### Returns
- **Query** - The query result
```

--------------------------------

### QueryBuilder.timeseries(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a timeseries query that returns aggregated metric values for each timestamp. Returns a Query object.

```APIDOC
## timeseries(args)

### Description
A timeseries query returns the values of the requested metrics (in aggregate) for each timestamp.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - timeseries query

### Example
N/A
```

--------------------------------

### QueryBuilder.build_query(query_type, args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a query based on the given query type and arguments. Returns a Query object.

```APIDOC
## build_query(query_type, args)

### Description
Build query based on given query type and arguments.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **query_type** (string) - Required - a type of query
- **args** (dict) - Required - the dict of args to be sent

### Returns
- **Query** - the resulting query


### Example
N/A
```

--------------------------------

### time_boundary(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Returns the minimum and maximum timestamps present in a data source. This is useful for determining the time range of data.

```APIDOC
## time_boundary(**kwargs)

### Description
Returns the minimum and maximum timestamps present in a data source. This is useful for determining the time range of data.

### Method
time_boundary

### Parameters
#### Required Key/Value Pairs
- **datasource** (str) - Required - Data source to query

#### Optional Key/Value Pairs
- **context** (dict) - Optional - A dict of query context options

### Returns
The query result

### Return Type
Query

### Request Example
```python
bound = client.time_boundary(datasource='twitterstream')
```

### Response Example
```python
[{'timestamp': '2011-09-14T15:00:00.000Z', 'result': {'minTime': '2011-09-14T15:00:00.000Z', 'maxTime': '2014-03-04T23:44:00.000Z'}}]
```
```

--------------------------------

### time_boundary(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a timeBoundary query asynchronously. Accepts keyword arguments for the query parameters.

```APIDOC
## time_boundary(**kwargs)

### Description
Executes a timeBoundary query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the timeBoundary query.
```

--------------------------------

### Query.export_tsv(dest_path)

Source: https://pythonhosted.org/pydruid/index.html

Exports the current query result to a TSV file. Takes a destination path as a parameter.

```APIDOC
## export_tsv(dest_path)

### Description
Export the current query result to a tsv file.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **dest_path** (str) - Required - file to write query results to

### Returns
None

### Raises
- **NotImplementedError** - If the export is not implemented for the query type

### Example
```python
>>> top = client.topn(
        datasource='twitterstream',
        granularity='all',
        intervals='2013-10-04/pt1h',
        aggregations={"count": doublesum("count")},
        dimension='user_name',
        filter = Dimension('user_lang') == 'en',
        metric='count',
        threshold=2
    )

>>> top.export_tsv('top.tsv')
>>> !cat top.tsv
>>> count       user_name       timestamp
    7.0 user_1  2013-10-04T00:00:00.000Z
    6.0 user_2  2013-10-04T00:00:00.000Z
```
```

--------------------------------

### pydruid.utils.having.Having

Source: https://pythonhosted.org/pydruid/index.html

Base class for having clauses. Provides static method build_having(having_obj) and instance method show().

```APIDOC
## Having(**args)

### Description
Base class for having clauses.

### Methods
- **build_having(having_obj)** (static) - Builds a having clause from a having object.
- **show()** - Returns the having representation.
```

--------------------------------

### groupby(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a groupBy query asynchronously. Accepts keyword arguments for the query parameters.

```APIDOC
## groupby(**kwargs)

### Description
Executes a groupBy query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the groupBy query.
```

--------------------------------

### pydruid.utils.aggregators module

Source: https://pythonhosted.org/pydruid/index.html

Provides functions to build various Druid aggregators.

```APIDOC
## pydruid.utils.aggregators module

### Functions

#### build_aggregators(agg_input)
Builds aggregators from input.

#### cardinality(raw_column, by_row=False)
Creates a cardinality aggregator.

#### count(raw_metric)
Creates a count aggregator.

#### doublesum(raw_metric)
Creates a double sum aggregator.

#### filtered(filter, agg)
Creates a filtered aggregator.

#### hyperunique(raw_metric)
Creates a hyperUnique aggregator.

#### javascript(columns_list, fn_aggregate, fn_combine, fn_reset)
Creates a JavaScript aggregator.

#### longsum(raw_metric)
Creates a long sum aggregator.

#### max(raw_metric)
Creates a max aggregator.

#### min(raw_metric)
Creates a min aggregator.
```

--------------------------------

### QueryBuilder.groupby(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a group-by query that groups results by specified dimensions. Returns a Query object.

```APIDOC
## groupby(args)

### Description
A group-by query groups a results set (the requested aggregate metrics) by the specified dimension(s).

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - group by query

### Example
N/A
```

--------------------------------

### pydruid.utils.dimensions module

Source: https://pythonhosted.org/pydruid/index.html

Provides classes for building dimension specifications and extraction functions.

```APIDOC
## pydruid.utils.dimensions module

### Classes

#### BaseRegexExtraction(expr)
Builds a regex extraction function.

#### DimensionSpec(dimension, output_name, extraction_function=None)
Builds a dimension specification.

#### ExtractionFunction
Base class for extraction functions.

#### JavascriptExtraction(func, injective=False)
Builds a JavaScript extraction function.

#### LookupExtraction(retain_missing_values=False, replace_missing_values=None, injective=False)
Builds a lookup extraction function.

#### MapLookupExtraction(mapping, **kwargs)
Builds a map lookup extraction function.

#### NamespaceLookupExtraction(namespace, **kwargs)
Builds a namespace lookup extraction function.

#### PartialExtraction(expr)
Builds a partial extraction function.

#### RegexExtraction(expr)
Builds a regex extraction function.

#### TimeFormatExtraction(format, locale=None, time_zone=None)
Builds a time format extraction function.

### Functions
#### build_dimension(dim)
Builds a dimension from input.
```

--------------------------------

### timeseries(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a timeseries query asynchronously. Accepts keyword arguments for the query parameters.

```APIDOC
## timeseries(**kwargs)

### Description
Executes a timeseries query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the timeseries query.
```

--------------------------------

### pydruid.utils.filters.Filter

Source: https://pythonhosted.org/pydruid/index.html

Base class for filters. Provides static method build_filter(filter_obj) and instance method show().

```APIDOC
## Filter(**args)

### Description
Base class for filter objects.

### Methods
- **build_filter(filter_obj)** (static) - Builds a filter from a filter object.
- **show()** - Returns the filter representation.
```

--------------------------------

### pydruid.utils.postaggregator.Quantiles

Source: https://pythonhosted.org/pydruid/index.html

Postaggregator that computes multiple quantiles.

```APIDOC
## Quantiles(name, probabilities)

### Description
Postaggregator that computes multiple quantiles.

### Constructor Parameters
- **name** - The field name.
- **probabilities** - A list of quantile probabilities.
```

--------------------------------

### QueryBuilder.segment_metadata(args)

Source: https://pythonhosted.org/pydruid/index.html

Builds a segment metadata query that returns column type, estimated size, interval, and segment ID. Returns a Query object.

```APIDOC
## segment_metadata(args)

### Description
A segment metadata query returns information about segments including column type, estimated size, interval, and segment ID.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
- **args** (dict) - Required - dict of args

### Returns
- **Query** - segment metadata query

### Example
N/A
```

--------------------------------

### pydruid.utils.postaggregator.Const

Source: https://pythonhosted.org/pydruid/index.html

Postaggregator that returns a constant value.

```APIDOC
## Const(value, output_name=None)

### Description
Postaggregator that returns a constant value.

### Constructor Parameters
- **value** - The constant value.
- **output_name** (optional) - The output name for the postaggregator.
```

--------------------------------

### pydruid.utils.postaggregator.Quantile

Source: https://pythonhosted.org/pydruid/index.html

Postaggregator that computes a quantile.

```APIDOC
## Quantile(name, probability)

### Description
Postaggregator that computes a quantile.

### Constructor Parameters
- **name** - The field name.
- **probability** - The quantile probability.
```

--------------------------------

### validate_query(query_type, valid_parts, args)

Source: https://pythonhosted.org/pydruid/index.html

Validates the query parts so only allowed objects are sent. Each query type can have an optional 'context' object attached which is used to set certain query context settings, etc. timeout or priority.

```APIDOC
## validate_query(query_type, valid_parts, args)

### Description
Validates the query parts so only allowed objects are sent. Each query type can have an optional 'context' object attached which is used to set certain query context settings, etc. timeout or priority.

### Parameters
- **query_type** (string) - Required - a type of query
- **valid_parts** (list) - Required - a list of valid object names
- **args** (dict) - Required - the dict of args to be sent

### Raises
- **ValueError** - if an invalid object is given
```

--------------------------------

### segment_metadata(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Executes a segmentMetadata query asynchronously. Accepts keyword arguments for the query parameters.

```APIDOC
## segment_metadata(**kwargs)

### Description
Executes a segmentMetadata query asynchronously. Accepts keyword arguments for the query parameters.

### Parameters
- **kwargs** - Required - Query parameters for the segmentMetadata query.
```

--------------------------------

### timeseries(**kwargs)

Source: https://pythonhosted.org/pydruid/index.html

Returns aggregated metric values for each timestamp in the specified intervals. Supports aggregations, post-aggregations, and filtering.

```APIDOC
## timeseries(**kwargs)

### Description
Returns aggregated metric values for each timestamp in the specified intervals. Supports aggregations, post-aggregations, and filtering.

### Method
timeseries

### Parameters
#### Required Key/Value Pairs
- **datasource** (str) - Required - Data source to query
- **granularity** (str) - Required - Time bucket to aggregate data by hour, day, minute, etc.
- **intervals** (str or list) - Required - ISO-8601 intervals for which to run the query on
- **aggregations** (dict) - Required - A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum

#### Optional Key/Value Pairs
- **filter** (pydruid.utils.filters.Filter) - Optional - Indicates which rows of data to include in the query
- **post_aggregations** - Optional - A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
- **context** (dict) - Optional - A dict of query context options

### Returns
The query result

### Return Type
Query

### Request Example
```python
counts = client.timeseries(
    datasource=twitterstream,
    granularity='hour', n    intervals='2013-06-14/pt1h', n    aggregations={"count": doublesum("count"), "rows": count("rows")},
    post_aggregations={'percent': (Field('count') / Field('rows')) * Const(100))},
    context={"timeout": 1000}
)
``` n
### Response Example
```python
[{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'count': 9619.0, 'rows': 8007, 'percent': 120.13238416385663}}]
```
```

--------------------------------

### pydruid.utils.postaggregator.Field

Source: https://pythonhosted.org/pydruid/index.html

Postaggregator that references a field.

```APIDOC
## Field(name)

### Description
Postaggregator that references a field.

### Constructor Parameters
- **name** - The field name.
```

--------------------------------

### BaseDruidClient.export_tsv

Source: https://pythonhosted.org/pydruid/index.html

Exports the current query result to a TSV file. Deprecated; use Query.export_tsv() instead.

```APIDOC
## export_tsv(dest_path)

### Description
Export the current query result to a tsv file.

### Parameters
- **dest_path** - Required - Destination path for the TSV file

### Deprecation
Deprecated since version: Use Query.export_tsv() method instead.
```

--------------------------------

### pydruid.utils.postaggregator.Postaggregator

Source: https://pythonhosted.org/pydruid/index.html

Base class for postaggregators. Provides static method build_post_aggregators(postaggs) and instance method fields(other).

```APIDOC
## Postaggregator(fn, fields, name)

### Description
Base class for postaggregators.

### Constructor Parameters
- **fn** - The function to apply.
- **fields** - The fields to use.
- **name** - The output name.

### Methods
- **build_post_aggregators(postaggs)** (static) - Builds post-aggregators from a list of post-aggregator objects.
- **fields(other)** - Combines fields with another postaggregator.
```

--------------------------------

### BaseDruidClient.groupby

Source: https://pythonhosted.org/pydruid/index.html

Executes a group-by query that groups a result set by specified dimensions. Required parameters include datasource, granularity, intervals, aggregations, and dimensions; optional parameters include filter, having, post_aggregations, context, and limit_spec.

```APIDOC
## groupby(**kwargs)

### Description
A group-by query groups a results set (the requested aggregate metrics) by the specified dimension(s).

### Method
N/A (Python method)

### Parameters
#### Required Parameters
- **datasource** (str) - Required - Data source to query
- **granularity** (str) - Required - Time bucket to aggregate data by hour, day, minute, etc.
- **intervals** (str or list) - Required - ISO-8601 intervals for which to run the query on
- **aggregations** (dict) - Required - A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum
- **dimensions** (list) - Required - The dimensions to group by

#### Optional Parameters
- **filter** (pydruid.utils.filters.Filter) - Optional - Indicates which rows of data to include in the query
- **having** (pydruid.utils.having.Having) - Optional - Indicates which groups in results set of query to keep
- **post_aggregations** - Optional - A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
- **context** (dict) - Optional - A dict of query context options
- **limit_spec** (dict) - Optional - A dict of parameters defining how to limit the rows returned, as specified in the Druid api documentation

### Returns
- **Query** - The query result

### Example
```python
>>> group = client.groupby(
        datasource='twitterstream',
        granularity='hour',
        intervals='2013-10-04/pt1h',
        dimensions=["user_name", "reply_to_name"],
        filter=~(Dimension("reply_to_name") == "Not A Reply"),
        aggregations={"count": doublesum("count")},
        context={"timeout": 1000}
        limit_spec={
            "type": "default",
            "limit": 50,
            "columns" : ["count"]
        }
    )
>>> for k in range(2):
    ...     print group[k]
>>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_1', 'reply_to_name': 'user_2'}}
>>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_2', 'reply_to_name': 'user_3'}}
```
```

--------------------------------

### pydruid.utils.having.Aggregation

Source: https://pythonhosted.org/pydruid/index.html

Class representing an aggregation for having clauses.

```APIDOC
## Aggregation(agg)

### Description
Class representing an aggregation for having clauses.

### Constructor Parameters
- **agg** - The aggregation object.
```

--------------------------------

### pydruid.utils.filters.JavaScript

Source: https://pythonhosted.org/pydruid/index.html

Class representing a JavaScript filter.

```APIDOC
## JavaScript(dim)

### Description
Class representing a JavaScript filter.

### Constructor Parameters
- **dim** - The dimension name.
```

--------------------------------

### Retrieve Segment Metadata

Source: https://pythonhosted.org/pydruid/index.html

Queries segment metadata for the 'twitterstream' datasource and displays column information.

```python
    >>> meta = client.segment_metadata(datasource='twitterstream', intervals = '2013-10-04/pt1h')
    >>> print meta[0].keys()
    >>> ['intervals', 'id', 'columns', 'size']
    >>> print meta[0]['columns']['tweet_length']
    >>> {'errorMessage': None, 'cardinality': None, 'type': 'FLOAT', 'size': 30908008}
```

--------------------------------

### BaseDruidClient.export_pandas

Source: https://pythonhosted.org/pydruid/index.html

Exports the current query result to a Pandas DataFrame object. Deprecated; use Query.export_pandas() instead.

```APIDOC
## export_pandas()

### Description
Export the current query result to a Pandas DataFrame object.

### Deprecation
Deprecated since version: Use Query.export_pandas() method instead.
```

--------------------------------

### Query.export_pandas()

Source: https://pythonhosted.org/pydruid/index.html

Exports the current query result to a Pandas DataFrame object. Returns a DataFrame representing the query result.

```APIDOC
## export_pandas()

### Description
Export the current query result to a Pandas DataFrame object.

### Method
N/A (Python method)

### Endpoint
N/A

### Parameters
None

### Returns
- **DataFrame** - The DataFrame representing the query result

### Raises
- **NotImplementedError** - If the export is not implemented for the query type

### Example
```python
>>> top = client.topn(
        datasource='twitterstream',
        granularity='all',
        intervals='2013-10-04/pt1h',
        aggregations={"count": doublesum("count")},
        dimension='user_name',
        filter = Dimension('user_lang') == 'en',
        metric='count',
        threshold=2
    )

>>> df = top.export_pandas()
>>> print df
>>>    count                 timestamp      user_name
    0      7  2013-10-04T00:00:00.000Z         user_1
    1      6  2013-10-04T00:00:00.000Z         user_2
```
```

--------------------------------

### BaseDruidClient.segment_metadata

Source: https://pythonhosted.org/pydruid/index.html

Executes a segment metadata query that returns per segment information about column cardinality, column type, estimated sizes, interval, and segment ID. Requires datasource and intervals; optional context parameter.

```APIDOC
## segment_metadata(**kwargs)

### Description
A segment meta-data query returns per segment information about:
- Cardinality of all the columns present
- Column type
- Estimated size in bytes
- Estimated size in bytes of each column
- Interval the segment covers
- Segment ID

### Method
N/A (Python method)

### Parameters
#### Required Parameters
- **datasource** (str) - Required - Data source to query
- **intervals** (str or list) - Required - ISO-8601 intervals for which to run the query on

#### Optional Parameters
- **context** (dict) - Optional - A dict of query context options

### Returns
- **Query** - The query result

### Example
```python
>>> meta = client.segment_metadata(datasource='twitterstream', intervals = '2013-10-04/pt1h')
>>> print meta[0].keys()
>>> ['intervals', 'id', 'columns', 'size']
>>> print meta[0]['columns']['tweet_length']
>>> {'errorMessage': None, 'cardinality': None, 'type': 'FLOAT', 'size': 30908008}
```
```

--------------------------------

### pydruid.utils.filters.Bound

Source: https://pythonhosted.org/pydruid/index.html

Bound filter to filter by comparing dimension values to an upper and/or lower value. Constructor parameters: dimension (str), lower (str), upper (str), lowerStrict (bool, default False), upperStrict (bool, default False), alphaNumeric (bool, default False).

```APIDOC
## Bound(dimension, lower, upper, lowerStrict=False, upperStrict=False, alphaNumeric=False)

### Description
Bound filter can be used to filter by comparing dimension values to an upper value or/and a lower value.

### Constructor Parameters
- **dimension** (str) - Required - Dimension to filter on.
- **lower** (str) - Required - Lower bound.
- **upper** (str) - Required - Upper bound.
- **lowerStrict** (bool) - Optional - Strict lower inclusion. Initial value: False
- **upperStrict** (bool) - Optional - Strict upper inclusion. Initial value: False
- **alphaNumeric** (bool) - Optional - Numeric comparison. Initial value: False
```

--------------------------------

### pydruid.utils.postaggregator.HyperUniqueCardinality

Source: https://pythonhosted.org/pydruid/index.html

Postaggregator that computes the cardinality of a hyperUnique field.

```APIDOC
## HyperUniqueCardinality(name)

### Description
Postaggregator that computes the cardinality of a hyperUnique field.

### Constructor Parameters
- **name** - The field name.
```

--------------------------------

### pydruid.utils.filters.Dimension

Source: https://pythonhosted.org/pydruid/index.html

Class representing a dimension for filtering.

```APIDOC
## Dimension(dim)

### Description
Class representing a dimension for filtering.

### Constructor Parameters
- **dim** - The dimension name.
```

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