apurvam

-   KafkaStreams streams = new KafkaStreams(
+   KafkaStreams streams = new ResponsiveKafkaStreams(
      builder.build(),
      props
    );

apurvam

#!/usr/bin/python

from urllib2 import Request, urlopen, URLError
import sys
from argparse import ArgumentParser
import json
import re
from dateutil.parser import parse

MAX_RESULTS = 50

apurvam

CREATE STREAM foo (col1 varchar, col2 varchar, timecol varchar) \
   WITH (kafka_topic=’foo_topic’, TIMESTAMP=’timecol’, \
         TIMESTAMP_FORMAT=’yyyy-MM-ddTHH:mm:SSZ’, value_format=’json’);

apurvam

CREATE TABLE users (userid bigint, zipcode int, created_at bigint) \
  WITH (kafka_topic=’users’, value_format=’json’, key=’userid’);
  
SELECT zipcode, count(*) from users GROUP BY zipcode;

apurvam

ksql> show queries;

 Query ID                      | Kafka Topic              | Query String                                                                                                              
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 CSAS_PAGEVIEWS_ENRICHED       | PAGEVIEWS_ENRICHED       | CREATE STREAM pageviews_enriched AS SELECT users_original.userid AS userid, pageid, regionid, gender FROM pageviews_original LEFT JOIN users_original ON pageviews_original.userid = users_original.userid;
 CSAS_PAGEVIEWS_FEMALE         | PAGEVIEWS_FEMALE         | CREATE STREAM pageviews_female AS SELECT * FROM pageviews_enriched WHERE gender = 'FEMALE';                               
 CSAS_PAGEVIEWS_FEMALE_LIKE_89 | pageviews_enriched_r8_r9 | CREATE STREAM pageviews_female_like_89 WITH (kaf

apurvam

$ ./bin/ksql-print-metrics
messages-consumed-avg: 96416.96196183885
messages-consumed-min: 88900.3329377909
error-rate: 0.0
num-persistent-queries: 2.0
messages-consumed-per-sec: 193024.78294586178
messages-produced-per-sec: 193025.4730374501
num-active-queries: 2.0
num-idle-queries: 0.0
messages-consumed-max: 103397.81191436431

apurvam

SELECT pageid, TOPKDISTINCT(viewtime, 5) FROM pageviews_users GROUP BY pageid;

apurvam

SELECT orderzip_code, TOPK(order_total, 5) FROM orders WINDOW TUMBLING (SIZE 1 HOUR) GROUP BY order_zipcode;

apurvam

ksql> print 'pageviews_kafka_topic_json';
Format:JSON
{"ROWTIME":1516753301093,"ROWKEY":"1","viewtime":1516753300961,"pageid":"Page_53","userid":"User_9"}
{"ROWTIME":1516753301458,"ROWKEY":"11","viewtime":1516753301458,"pageid":"Page_38","userid":"User_5"}
{"ROWTIME":1516753301660,"ROWKEY":"21","viewtime":1516753301660,"pageid":"Page_25","userid":"User_2"}
^C

apurvam

ksql> show topics;

 Kafka Topic                 | Registered | Partitions | Partition Replicas | Consumers | Consumer Groups
----------------------------------------------------------------------------------------------------------
 __confluent.support.metrics | false      | 1          | 1                  | 0         | 0
 _schemas                    | false      | 1          | 1                  | 0         | 0
 connect-configs             | false      | 1          | 1                  | 0         | 0
 connect-offsets             | false      | 25         | 1                  | 0         | 0
 connect-statuses            | false      | 5          | 1                  | 0         | 0
 groupbydevice               | true       | 4          | 1                  | 0         | 0