Skip to main content

Kafka integration for IBM Streams topology applications

Project description

Overview

Provides functions to read messages from Kafka brokers including the IBM Event Streams cloud service as a stream and submit tuples to Kafka brokers as messages.

The broker configuration must be done with properties in an application configuration or by using a dictionary variable. The minimum set of properties must contain the bootstrap.servers configuration, which is valid for both consumers and producers, i.e. for the KafkaConsumer and KafkaProducer classes.

It is also possible to use different application configurations for consumer and producer when special consumer or producer configs must be used.

Sample

A simple hello world example of a Streams application publishing to a topic and the same application consuming the same topic:

from streamsx.topology.topology import Topology
from streamsx.topology.schema import CommonSchema
from streamsx.topology.context import submit, ContextTypes
from streamsx.kafka import KafkaConsumer, KafkaProducer
import time

def delay(v):
    time.sleep(5.0)
    return True

topology = Topology('KafkaHelloWorld')

to_kafka = topology.source(['Hello', 'World!'])
to_kafka = to_kafka.as_string()
# delay tuple by tuple
to_kafka = to_kafka.filter(delay)

# Publish a stream to Kafka using TEST topic, the Kafka server is at localhost
producer = KafkaProducer(config={'bootstrap.servers': 'localhost:9092'},
                         topic='TEST')
to_kafka.for_each(producer)

# Subscribe to same topic as a stream
consumer = KafkaConsumer(config={'bootstrap.servers': 'localhost:9092'},
                         schema=CommonSchema.String,
                         topic='TEST')
from_kafka = topology.source(consumer)

# You'll find the Hello World! in stdout log file:
from_kafka.print()

submit(ContextTypes.DISTRIBUTED, topology)
# The Streams job is kept running.

Documentation

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

streamsx.kafka-1.9.0.tar.gz (26.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

streamsx.kafka-1.9.0-py2.py3-none-any.whl (29.1 kB view details)

Uploaded Python 2Python 3

File details

Details for the file streamsx.kafka-1.9.0.tar.gz.

File metadata

  • Download URL: streamsx.kafka-1.9.0.tar.gz
  • Upload date:
  • Size: 26.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.6

File hashes

Hashes for streamsx.kafka-1.9.0.tar.gz
Algorithm Hash digest
SHA256 92dd812d85cd627961e98be260bd0ce571fc0e562a4e164b160f92643a798b66
MD5 ae693015bbef4c5149daba85f4a4ab22
BLAKE2b-256 966ffa034959db355d3340338f10971beb397e4852feaf9b9a848bb5960104f1

See more details on using hashes here.

File details

Details for the file streamsx.kafka-1.9.0-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for streamsx.kafka-1.9.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 3e011d3507af666d64d44d882c095c1f3bbf32e8e7d848d177fc0035e020427f
MD5 21c74144814c3e52a1044a1edf2bf471
BLAKE2b-256 a72404dab5b10b90f93dbe555b41533b87b8f2a86b93c33286ffc1be74446ca0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page