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DE-6867: Adding example for running PyFlink on Decodable
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secret | ||
snow*.log* | ||
.DS_Store | ||
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.venv |
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.PHONY: clean deploy | ||
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TARGET = target | ||
LIBS = $(TARGET)/libs | ||
PYTHON_LIBS = $(TARGET)/python-libs | ||
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PY = python3 | ||
VENV = $(TARGET)/venv | ||
BIN=$(VENV)/bin | ||
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build: $(TARGET) $(LIBS)/flink-sql-connector-kafka-3.0.2-1.18.jar $(LIBS)/flink-python-1.18.1.jar $(TARGET)/pyflink-job.zip $(VENV) | ||
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$(TARGET): | ||
mkdir $(TARGET) | ||
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$(VENV): requirements.txt | ||
$(PY) -m venv $(VENV) | ||
$(BIN)/pip install -r requirements.txt --target=${PYTHON_LIBS} | ||
touch $(VENV) | ||
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$(LIBS)/flink-sql-connector-kafka-3.0.2-1.18.jar: | $(TARGET) | ||
mkdir -p $(LIBS) | ||
wget -N -P $(LIBS) https://repo.maven.apache.org/maven2/org/apache/flink/flink-sql-connector-kafka/3.0.2-1.18/flink-sql-connector-kafka-3.0.2-1.18.jar | ||
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$(LIBS)/flink-python-1.18.1.jar: | $(TARGET) | ||
mkdir -p $(LIBS) | ||
wget -N -P $(LIBS) https://repo1.maven.org/maven2/org/apache/flink/flink-python/1.18.1/flink-python-1.18.1.jar | ||
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$(TARGET)/pyflink-job.zip: main.py $(LIBS)/flink-sql-connector-kafka-3.0.2-1.18.jar $(LIBS)/flink-python-1.18.1.jar $(VENV) | ||
cp main.py $(TARGET) | ||
cd $(TARGET) && zip -r pyflink-job.zip main.py libs python-libs | ||
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clean: | ||
@rm -rf $(TARGET) | ||
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deploy: build | ||
decodable apply decodable-resources.yaml |
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# Getting Started With PyFlink On Decodable | ||
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This example project shows how to run PyFlink jobs on Decodable. | ||
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By deploying your PyFlink jobs as [https://docs.decodable.co/pipelines/create-pipelines-using-your-own-apache-flink-jobs.html](Custom Pipelines) onto Decodable, you can solely focus on implementing your job, | ||
while leaving all the aspects of running the job, like provisioning Flink clusters and the underlying hardware, | ||
keeping them secure and up-to-date, scaling them, monitoring and observing them, to the fully-managed Decodable platform. | ||
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## Prerequisites | ||
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You'll need the following things in place to run this example: | ||
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* A free Decodable account ([https://app.decodable.co/-/accounts/create](sign up)) | ||
* The [Decodable CLI](https://docs.decodable.co/cli.html) | ||
* [GNU Make](https://www.gnu.org/software/make/) | ||
* A Kafka cluster which can be accessed via Decodable; For instance, the free tier of [Upstash](https://upstash.com/) can be used | ||
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Make sure you are logged into your Decodable account on the CLI by running `decodable login`. | ||
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## Description | ||
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This example shows how to run a simple [PyFlink](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/python/overview/) job on Decodable. | ||
With the help of the built-in DataGen connector, a random `Todo` event is emitted per second. | ||
A user-defined function (UDF) is used for enriching each event with user information retrieved from a remote REST API, | ||
leveraging the `requests` and `jmespath` 3rd-party libraries. | ||
The enriched events are sent to a Kafka topic. | ||
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## Running the Example | ||
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After checking out the project, provide the broker address and credentials for your Kafka cluster in the files _.secret\_kafka\_bootstrap\_servers_, ._secret\_kafka\_user\_name_, and _secret\_kafka\_password_, respectively. | ||
If your cluster is using another securiy protocol than SASL_SSL with SCRAM-SHA-256, | ||
adjust the connector configuration of the `enriched_todos` table in _main.py_ accordingly. | ||
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Next, build the PyFlink job and deploy it to your Decodable account: | ||
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``` | ||
$ make | ||
$ make deploy | ||
``` | ||
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Take note of the id generated for the `pyflink_on_decodable` pipeline. | ||
Then activate this pipeline using the Decodable CLI: | ||
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``` | ||
$ decodable pipeline activate <id> | ||
``` | ||
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Once the pipeline is running (use `decodable pipeline get <id>` to query its state), | ||
you can observe the enriched `Todo` events in the Kafka topic, for instance via the web console when using Upstash. | ||
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## Clean-Up | ||
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To shut down the pipeline and clean up all the resources in your Decodable account, | ||
run the following commands: | ||
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``` | ||
$ decodable pipeline deactivate <id> | ||
$ decodable pipeline delete <id> | ||
# Obtain secret ids via decodable secret list | ||
$ decodable secret delete <id1> | ||
$ decodable secret delete <id2> | ||
$ decodable secret delete <id3> | ||
``` |
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--- | ||
kind: secret | ||
metadata: | ||
name: todo_kafka_user_name | ||
spec_version: v1 | ||
spec: | ||
value_file: .secret_kafka_user_name | ||
--- | ||
kind: secret | ||
metadata: | ||
name: todo_kafka_password | ||
spec_version: v1 | ||
spec: | ||
value_file: .secret_kafka_password | ||
--- | ||
kind: secret | ||
metadata: | ||
name: todo_kafka_bootstrap_servers | ||
spec_version: v1 | ||
spec: | ||
value_file: .secret_kafka_bootstrap_servers | ||
--- | ||
kind: pipeline | ||
metadata: | ||
name: pyflink_on_decodable | ||
spec_version: v1 | ||
spec: | ||
type: PYTHON | ||
job_file_path: target/pyflink-job.zip | ||
properties: | ||
secrets: | ||
- todo_kafka_user_name | ||
- todo_kafka_password | ||
- todo_kafka_bootstrap_servers | ||
flink_version: 1.18-python310 | ||
additional_metrics: [] |
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################################################################################ | ||
# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
################################################################################ | ||
import logging | ||
import sys | ||
import os | ||
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import requests | ||
import jmespath | ||
import json | ||
import sys | ||
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from pyflink.datastream import StreamExecutionEnvironment | ||
from pyflink.table import StreamTableEnvironment, DataTypes | ||
from pyflink.table.udf import udf | ||
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@udf(input_types=[DataTypes.BIGINT()], result_type=DataTypes.STRING()) | ||
def get_user_name(id): | ||
r = requests.get('https://jsonplaceholder.typicode.com/users/' + str(id)) | ||
return jmespath.search("name", json.loads(r.text)) | ||
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def process_todos(): | ||
with open('/opt/pipeline-secrets/gm_todo_kafka_user_name', 'r') as file: | ||
user_name = file.read() | ||
with open('/opt/pipeline-secrets/gm_todo_kafka_password', 'r') as file: | ||
password = file.read() | ||
with open('/opt/pipeline-secrets/gm_todo_kafka_bootstrap_servers', 'r') as file: | ||
bootstrap_servers = file.read() | ||
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env = StreamExecutionEnvironment.get_execution_environment() | ||
env.set_parallelism(1) | ||
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t_env = StreamTableEnvironment.create(stream_execution_environment=env) | ||
t_env.create_temporary_system_function("user_name", get_user_name) | ||
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kafka_jar = os.path.join(os.path.abspath(os.path.dirname(__file__)) + "/libs", | ||
'flink-sql-connector-kafka-3.0.2-1.18.jar') | ||
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flink_python_jar = os.path.join(os.path.abspath(os.path.dirname(__file__)) + "/libs", | ||
'flink-python-1.18.1.jar') | ||
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t_env.get_config()\ | ||
.get_configuration()\ | ||
.set_string("pipeline.jars", "file://{}".format(kafka_jar) + ";file://{}".format(flink_python_jar)) | ||
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t_env.execute_sql(""" | ||
CREATE TABLE todos ( | ||
id BIGINT, | ||
text STRING, | ||
user_id BIGINT, | ||
due TIMESTAMP(3) | ||
) WITH ( | ||
'connector' = 'datagen', | ||
'rows-per-second' = '1' | ||
)""") | ||
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t_env.execute_sql(f""" | ||
CREATE TABLE enriched_todos ( | ||
id BIGINT, | ||
text STRING, | ||
user_id BIGINT, | ||
due TIMESTAMP(3), | ||
user_name STRING | ||
) WITH ( | ||
'connector' = 'kafka', | ||
'topic' = 'todos', | ||
'properties.bootstrap.servers' = '{bootstrap_servers}', | ||
'properties.sasl.mechanism' = 'SCRAM-SHA-256', | ||
'properties.security.protocol' = 'SASL_SSL', | ||
'properties.sasl.jaas.config' = 'org.apache.flink.kafka.shaded.org.apache.kafka.common.security.scram.ScramLoginModule required username=\"{user_name}\" password=\"{password}\";', | ||
'properties.group.id' = 'todos-sink', | ||
'format' = 'json' | ||
)""") | ||
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t_env.execute_sql(""" | ||
INSERT INTO enriched_todos SELECT *, user_name(ABS(MOD(todos.user_id, 10))) FROM todos""") | ||
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if __name__ == '__main__': | ||
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(message)s") | ||
process_todos() |
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jmespath==1.0.1 |