#pydruid pydruid exposes a simple API to create, execute, and analyze Druid queries. pydruid can parse query results into Pandas DataFrame objects for subsequent data analysis -- this offers a tight integration between Druid, the SciPy stack (for scientific computing) and scikit-learn (for machine learning). Additionally, pydruid can export query results into TSV or JSON for further processing with your favorite tool, e.g., R, Julia, Matlab, Excel. It provides both synchronous and asynchronous clients.
To install:
pip install pydruid
# or, if you intend to use asynchronous client
pip install pydruid[async]
# or, if you intend to export query results into pandas
pip install pydruid[pandas]
# or, if you intend to do both
pip install pydruid[async, pandas]Documentation: https://pythonhosted.org/pydruid/.
#examples The following exampes show how to execute and analyze the results of three types of queries: timeseries, topN, and groupby. We will use these queries to ask simple questions about twitter's public data set.
timeseries
What was the average tweet length, per day, surrounding the 2014 Sochi olympics?
from pydruid.client import *
from pylab import plt
query = PyDruid(druid_url_goes_here, 'druid/v2')
ts = query.timeseries(
datasource='twitterstream',
granularity='day',
intervals='2014-02-02/p4w',
aggregations={'length': doublesum('tweet_length'), 'count': doublesum('count')},
post_aggregations={'avg_tweet_length': (Field('length') / Field('count'))},
filter=Dimension('first_hashtag') == 'sochi2014'
)
df = query.export_pandas()
df['timestamp'] = df['timestamp'].map(lambda x: x.split('T')[0])
df.plot(x='timestamp', y='avg_tweet_length', ylim=(80, 140), rot=20,
title='Sochi 2014')
plt.ylabel('avg tweet length (chars)')
plt.show()
topN
Who were the top ten mentions (@user_name) during the 2014 Oscars?
top = query.topn(
datasource='twitterstream',
granularity='all',
intervals='2014-03-03/p1d', # utc time of 2014 oscars
aggregations={'count': doublesum('count')},
dimension='user_mention_name',
filter=(Dimension('user_lang') == 'en') & (Dimension('first_hashtag') == 'oscars') &
(Dimension('user_time_zone') == 'Pacific Time (US & Canada)') &
~(Dimension('user_mention_name') == 'No Mention'),
metric='count',
threshold=10
)
df = query.export_pandas()
print df
count timestamp user_mention_name
0 1303 2014-03-03T00:00:00.000Z TheEllenShow
1 44 2014-03-03T00:00:00.000Z TheAcademy
2 21 2014-03-03T00:00:00.000Z MTV
3 21 2014-03-03T00:00:00.000Z peoplemag
4 17 2014-03-03T00:00:00.000Z THR
5 16 2014-03-03T00:00:00.000Z ItsQueenElsa
6 16 2014-03-03T00:00:00.000Z eonline
7 15 2014-03-03T00:00:00.000Z PerezHilton
8 14 2014-03-03T00:00:00.000Z realjohngreen
9 12 2014-03-03T00:00:00.000Z KevinSpaceygroupby
What does the social network of users replying to other users look like?
from igraph import *
from cairo import *
from pandas import concat
group = query.groupby(
datasource='twitterstream',
granularity='hour',
intervals='2013-10-04/pt12h',
dimensions=["user_name", "reply_to_name"],
filter=(~(Dimension("reply_to_name") == "Not A Reply")) &
(Dimension("user_location") == "California"),
aggregations={"count": doublesum("count")}
)
df = query.export_pandas()
# map names to categorical variables with a lookup table
names = concat([df['user_name'], df['reply_to_name']]).unique()
nameLookup = dict([pair[::-1] for pair in enumerate(names)])
df['user_name_lookup'] = df['user_name'].map(nameLookup.get)
df['reply_to_name_lookup'] = df['reply_to_name'].map(nameLookup.get)
# create the graph with igraph
g = Graph(len(names), directed=False)
vertices = zip(df['user_name_lookup'], df['reply_to_name_lookup'])
g.vs["name"] = names
g.add_edges(vertices)
layout = g.layout_fruchterman_reingold()
plot(g, "tweets.png", layout=layout, vertex_size=2, bbox=(400, 400), margin=25, edge_width=1, vertex_color="blue")
#asynchronous client
pydruid.async_client.AsyncPyDruid implements an asynchronous client. To achieve that, it utilizes an asynchronous
HTTP client from Tornado framework. The asynchronous client is suitable for use with async frameworks such as Tornado
and provides much better performance at scale. It lets you serve multiple requests at the same time, without blocking on
Druid executing your queries.
##example
from tornado import gen
from pydruid.async_client import AsyncPyDruid
from pydruid.utils.aggregators import longsum
from pydruid.utils.filters import Dimension
client = AsyncPyDruid(url_to_druid_broker, 'druid/v2')
@gen.coroutine
def your_asynchronous_method_serving_top10_mentions_for_day(day
top_mentions = yield client.topn(
datasource='twitterstream',
granularity='all',
intervals="%s/p1d" % (day, ),
aggregations={'count': doublesum('count')},
dimension='user_mention_name',
filter=(Dimension('user_lang') == 'en') & (Dimension('first_hashtag') == 'oscars') &
(Dimension('user_time_zone') == 'Pacific Time (US & Canada)') &
~(Dimension('user_mention_name') == 'No Mention'),
metric='count',
threshold=10)
# asynchronously return results
# can be simply ```return top_mentions``` in python 3.x
raise gen.Return(top_mentions)