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MANIFEST.in
README.md
setup.py

README.md

Druidry

druidry is a lightweight Python library for building and executing Druid queries.

Its focus is on providing early feedback to the user when they've structured a query incorrectly, and on providing convenience methods for composing the components of a query (eg., filtering an aggregation or joining multiple filters).

All query-building classes, like Query or Aggregation, subclass dict, making it easy to gradually replace existing code with the query builder.

Why would I use druidry instead of pydruid?

The Druid maintainers graciously provide Python bindings. druidry has a different philosophy than pydruid: it aims to be more pythonic, to facilitate the gradual replacement of existing query-building code, to map as closely as possible to the JSON query format, and to solve common pain points around interval and granularity logic which were unaddressed in pydruid.

import druidry

client

druidry.client.Client enables users to configure a connection to a Druid broker, request the metadata from that broker and execute queries against.

Only the first two arguments, host and port, are required.

client = druidry.client.Client('localhost', '8080')

client.endpoint
# http://localhost:8080/druid/v2

If data_source is supplied, the fetch_schema method can be used to get the dimensions and metrics of the data_source.

client_with_data_source = druidry.client.Client(
    DRUID_BROKER_HOST, '8080', data_source=DATA_SOURCE)

client_with_data_source.fetch_schema()
# ([u'browser', u'os', u'device'], [u'count', u'length_of_visit'])

Likewise, one can pass fetch_schema=True to populate those properties on client off the bat.

client_with_data_source = druidry.client.Client(
    DRUID_BROKER_HOST, '8080', data_source=DATA_SOURCE, fetch_schema=True)

# Equivalent to above
client_with_data_source.dimensions, client_with_data_source.metrics
# ([u'browser', u'os', u'device'], [u'count', u'length_of_visit'])

Finally, one can pass an integer as the timeout keyword argument to wrap all queries in a timeout.

client_with_timeout = druidry.client.Client(
    DRUID_BROKER_HOST, '8080', timeout=10)

client_with_timeout.timeout
# 10

client.Client objects have a method called execute_query which processes all the contexts, validates the passed query, executes the query and validates the response.

client_with_data_source.execute_query({'queryType': 'timeBoundary'})
# [{u'result': {u'maxTime': u'2016-08-17T14:19:00.000Z',
#       u'minTime': u'2016-08-10T15:00:00.000Z'},
#       u'timestamp': u'2016-08-10T15:00:00.000Z'}]
try:
    client_with_data_source.execute_query({'queryType': 'NOT_A_REAL_QUERY_TYPE'})
except druidry.errors.DruidQueryError as e:
    print("Unsurprisingly, that is not a real query type.")

# Unsurprisingly, that is not a real query type.

queries

The druidry.queries.Query class helps in building queries by converting the case of its keyword arguments and performing validation.

query = druidry.queries.Query(query_type='timeBoundary')

Camel case or snake case both work.

query_1 = druidry.queries.Query(query_type='timeBoundary')
query_2 = druidry.queries.Query(queryType='timeBoundary')

print('Are the queries equal?', query_1 == query_2)
# Are the queries equal? True

Convenience classes exist for the following query types: druidry.queries.DataSourceMetadataQuery, druidry.queries.SegmentMetadataQuery, druidry.queries.TimeseriesQuery, druidry.queries.GroupByQuery, druidry.queries.ScanQuery, druidry.queries.TimeBoundaryQuery and druidry.queries.TopNQuery.

Query will validate the existence and type of required properties.

try:
    druidry.queries.TimeseriesQuery()
except druidry.errors.DruidQueryError as e:
    print(e)
#     Invalid Druid query:
#      Missing field: intervals required for type: timeseries
try:
    druidry.queries.TimeBoundaryQuery(bound=False)
except druidry.errors.DruidQueryError as e:
    print(e)
#    Invalid Druid query:
#      Field bound has mismatched type (expecting <type 'basestring'>, found <type 'bool'>)

Query provides static methods for validation that make it easy to upgrade code using dict objects.

try:
    druidry.queries.Query.validate_query({'queryType': 'timeBoundary'})
except druidry.errors.DruidQueryError as e:
    print(e)
#    Invalid Druid query:
#      Invalid dataSource: None
try:
    druidry.queries.Query.validate_query({'queryType': 'WHY???'})
except druidry.errors.DruidQueryError as e:
    print(e)
#     Invalid Druid query:
#      Invalid dataSource: None
#      Invalid queryType "WHY???". Valid query types: groupBy, timeBoundary, timeseries, topN

context

druidry.context uses thread-local variables to register pre-processors which can alter all queries executed within a with block.

query = druidry.queries.TimeBoundaryQuery()

# Client.execute_query calls process_query behind the scenes.

print('Before', druidry.context.process_query(query))

with druidry.context.data_source_context('test-data-source'):
    print('During', druidry.context.process_query(query))

print('After', druidry.context.process_query(query))
#    Before {'queryType': 'timeBoundary'}
#    During {'queryType': 'timeBoundary', 'dataSource': 'test-data-source'}
#    After {'queryType': 'timeBoundary'}

Two other QueryContexts come out of the box: query_filter_context, which adds (or joins) a filter to all queries in the block, and timeout_context, which adds a timeout to all queries in the block.

One can also create their own QueryContexts by passing a unique name and a function which accepts a query and returns a query.

def add_granularity(query):
    return query.extend(granularity='P1D')

day_granularity_context = druidry.context.QueryContext('day-granularity', add_granularity)

aggregations

The druidry.aggregations.Aggregation assists in building and validating Druid aggregations.

try:
    druidry.aggregations.Aggregation('count')
except druidry.errors.DruidQueryError as e:
    print(e)

#    Invalid Druid query:
#      Missing field: name required for type: count
druidry.aggregations.Aggregation('longSum', field_name='time_on_site')
#    {'fieldName': 'time_on_site', 'type': 'longSum'}

Aggregations have a filter method.

druidry.aggregations.Aggregation('longSum', field_name='time_on_site').filter(
    druidry.filters.SelectorFilter(dimension='is_returning_user', value='t'))
#    {'aggregator': {'fieldName': 'time_on_site',
#      'name': 'time_on_site',
#      'type': 'longSum'},
#     'filter': {'dimension': 'is_returning_user',
#      'type': 'selector',
#      'value': 't'},
#     'type': 'filtered'}

druidry.aggregations.PostAggregation is the equivalent for post-aggregations.

druidry.aggregations.PostAggregation('fieldAccess', field_name='count')
#    {'fieldName': 'count', 'name': 'count', 'type': 'fieldAccess'}

Post-aggregations can be created from

druidry.aggregations.remove_duplicates removes aggregations with duplicate names.

filters

druidry.filters.Filter facilitates the creation and validation of filters, for queries, aggregations and post-aggregations.

druidry.filters.Filter('regex', dimension='browser', pattern='Chrom*')
#    {'dimension': 'browser', 'pattern': 'Chrom*', 'type': 'regex'}

Filters can be joined with join_filters.

druidry.filters.Filter.join_filters(
    druidry.filters.Filter('regex', dimension='browser', pattern='Chrom.*'),
    druidry.filters.Filter('regex', dimension='os', pattern='Windows.*'))
#     {'fields': [{'dimension': 'browser', 'pattern': 'Chrom.*', 'type': 'regex'},
#      {'dimension': 'os', 'pattern': 'Windows.*', 'type': 'regex'}],
#     'type': 'and'}

Filters can be negated.

druidry.filters.Filter('regex', dimension='browser', pattern='Chrom.*').negate()
#     {'field': {'dimension': 'browser', 'pattern': 'Chrom.*', 'type': 'regex'},
#     'type': 'not'}

Convenience classes exist for the following filter types: druidry.filters.SelectorFilter, druidry.filters.ColumnComparisonFilter, druidry.filters.RegexFilter, druidry.filters.AndFilter, druidry.filters.OrFilter, druidry.filters.NotFilter, druidry.filters.InFilter, druidry.filters.LikeFilter, druidry.filters.BoundFilter and druidry.filters.IntervalFilter.

granularities

druidry.granularities.SimpleGranularity, druidry.granularities.DurationGranularity, druidry.granularities.PeriodGranularity facilitate creating and validating granularities.

(
    druidry.granularities.SimpleGranularity('all'),
    druidry.granularities.SimpleGranularity('day'),
    druidry.granularities.SimpleGranularity('hour')
)
# ('all', 'day', 'hour')

try:
    druidry.granularities.SimpleGranularity('NOT REAL OBVIOUSLY')
except ValueError as e:
    print(e)
#    Invalid granularity: NOT REAL OBVIOUSLY

druidry.granularities.DurationGranularity(duration=43200000)
# {'duration': 43200000, 'type': 'duration'}

try:
    druidry.granularities.DurationGranularity(duration=43200000, origin='wow, *so* not an origin')
except ValueError as e:
    print(e)
#    Invalid origin: wow, *so* not an origin

druidry.granularities.DurationGranularity(duration=43200000, origin='2016-01-05T04:19:56.240773')
#    {'duration': 43200000,
#     'origin': '2016-01-05T04:19:56.240773',
#     'type': 'duration'}

druidry.granularities.PeriodGranularity(period='P2M3D')
#     {'period': 'P2M3D', 'type': 'period'}
try:
    druidry.granularities.PeriodGranularity(period='P2Z')
except ValueError as e:
    print(e)
#     Invalid period: P2Z

druidry.granularities.PeriodGranularity can create a period granularity either from a specified period or from any of a number of time delta args (eg days, weeks, months).

druidry.granularities.PeriodGranularity(months=2, days=3)
#    {'period': 'P2M3D', 'type': 'period'}

try:
    druidry.granularities.PeriodGranularity(period='P2Y', time_zone='Lilliput/Mildendo')
except ValueError as e:
    print(e)
#    Invalid timeZone: Lilliput/Mildendo

intervals

druidry.intervals.Interval facilitates interval creation in much the same way as granularities: it validates input and allows flexibility in using time arguments.

import datetime

druidry.intervals.Interval(
    start='1970-01-01', end='1975-12-31')
#  '1970-01-01/1975-12-31'

druidry.intervals.Interval(
    start=datetime.date(year=1970, month=1, day=1),
    end=datetime.date(year=1975, month=12, day=31))
#  '1970-01-01/1975-12-31'

druidry.intervals.Interval(
    start=datetime.date(year=1970, month=1, day=1), duration='P5Y')
#  '1970-01-01/P5Y'

druidry.intervals.Interval(
    start=datetime.date(year=1970, month=1, day=1),
    duration=datetime.timedelta(days=7))
#  '1970-01-01/P7D'

druidry.intervals.Interval(
    end=datetime.date(year=1970, month=1, day=1), duration='P5Y')
#  'P5Y/1970-01-01'

druidry.intervals.Interval(
    end=datetime.date(year=1970, month=1, day=1),
    duration=datetime.timedelta(days=7))
#  'P7D/1970-01-01'

druidry.intervals.Interval(
    end=datetime.date(year=1970, month=1, day=1), weeks=1)
#  'P7D/1970-01-01'

druidry.intervals.Interval(duration=datetime.timedelta(days=7))
#  'P7D'

druidry.intervals.Interval(years=5, months=3, days=25)
#  'P5Y3M25D'

druidry.intervals.Interval(interval='P5Y')
#  'P5Y'

Interval padding

It can be tricky to create timeseries with consistent and non-partial granule buckets. Say you want to display a timeseries chart with the last 24 hours of data, with hour granularity, up to the current moment. You might use an interval like this: P1D/2017-09-27T14:50:23 (where 14:50 is the current time). But now, every time you view the page, the buckets shift because they start at a different second or minute. Naturally you set an origin. But now you get a partial leading bucket.

To solve this problem, druidry provides methods which transform intervals of all kinds to start/end intervals with a floored start (relative to a provided timedelta, which can be generated from a granularity) and ceiled end.

interval = druidry.intervals.Interval(
    duration=datetime.timedelta(days=1),
    start=datetime.datetime(
        year=2017, month=9, day=27,
        hour=14, minute=50, second=23))
print(interval.pad_by_timedelta(datetime.timedelta(hours=1)))
# 2017-09-27T14:00:00/2017-09-28T15:00:00