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https://github.com/ethicalml/kafka-spark-streaming-zeppelin-docker
One click deploy docker-compose with Kafka, Spark Streaming, Zeppelin UI and Monitoring (Grafana + Kafka Manager)
https://github.com/ethicalml/kafka-spark-streaming-zeppelin-docker
docker docker-compose kafka kafka-spark kafka-spark-streaming kafka-zeppelin spark spark-kafka spark-streaming-kafka spark-zeppelin streaming zeppelin
Last synced: about 2 months ago
JSON representation
One click deploy docker-compose with Kafka, Spark Streaming, Zeppelin UI and Monitoring (Grafana + Kafka Manager)
- Host: GitHub
- URL: https://github.com/ethicalml/kafka-spark-streaming-zeppelin-docker
- Owner: EthicalML
- License: mit
- Created: 2019-04-29T13:58:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-07-20T21:44:22.000Z (over 3 years ago)
- Last Synced: 2023-03-05T07:52:29.092Z (almost 2 years ago)
- Topics: docker, docker-compose, kafka, kafka-spark, kafka-spark-streaming, kafka-zeppelin, spark, spark-kafka, spark-streaming-kafka, spark-zeppelin, streaming, zeppelin
- Homepage:
- Size: 1020 KB
- Stars: 99
- Watchers: 10
- Forks: 69
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# One Click Deploy: Kafka Spark Streaming with Zeppelin UI
This repository contains a docker-compose stack with Kafka and Spark Streaming, together with monitoring with Kafka Manager and a Grafana Dashboard. The networking is set up so Kafka brokers can be accessed from the host.
It also comes with a producer-consumer example using a small subset of the [US Census adult income prediction dataset](https://www.kaggle.com/johnolafenwa/us-census-data).
## High level features:
Monitoring with grafana
Zeppelin UI
Kafka access from host
Multiple spark interpreters
## Detail Summary
| Container | Image | Tag | Accessible |
|-|-|-|-|
| zookeeper | wurstmeister/zookeeper | latest | 172.25.0.11:2181 |
| kafka1 | wurstmeister/kafka | 2.12-2.2.0 | 172.25.0.12:9092 (port 8080 for JMX metrics) |
| kafka1 | wurstmeister/kafka | 2.12-2.2.0 | 172.25.0.13:9092 (port 8080 for JMX metrics) |
| kafka_manager | hlebalbau/kafka_manager | 1.3.3.18 | 172.25.0.14:9000 |
| prometheus | prom/prometheus | v2.8.1 | 172.25.0.15:9090 |
| grafana | grafana/grafana | 6.1.1 | 172.25.0.16:3000 |
| zeppelin | apache/zeppelin | 0.8.1 | 172.25.0.19:8080 |
# Quickstart
The easiest way to understand the setup is by diving into it and interacting with it.
## Running Docker Compose
To run docker compose simply run the following command in the current folder:
```
docker-compose up -d
```
This will run deattached. If you want to see the logs, you can run:
```
docker-compose logs -f -t --tail=10
```
To see the memory and CPU usage (which comes in handy to ensure docker has enough memory) use:
```
docker stats
```
## Accessing the notebook
You can access the default notebook by going to http://172.25.0.19:8080/#/notebook/2EAB941ZD. Now we can start running the cells.
### 1) Setup
#### Install python-kafka dependency
### 2) Producer
We have an interpreter called %producer.pyspark that we'll be able to run in parallel.
#### Load our example dummy dataset
We have made available a 1000-row version of the [US Census adult income prediction dataset](https://www.kaggle.com/johnolafenwa/us-census-data).
#### Start the stream of rows
We now take one row at random, and send it using our python-kafka producer. The topic will be created automatically if it doesn't exist (given that `auto.create.topics.enable` is set to true).
### 3) Consumer
We now use the %consumer.pyspark interpreter to run our pyspark job in parallel to the producer.
#### Connect to the stream and print
Now we can run the spark stream job to connect to the topic and listen to data. The job will listen for windows of 2 seconds and will print the ID and "label" for all the rows within that window.
### 4) Monitor Kafka
We can now use the kafka manager to dive into the current kafka setup.
#### Setup Kafka Manager
To set up kafka manager we need to configure it. In order to do this, access http://172.25.0.14:9000/addCluster and fill up the following two fields:
* Cluster name: Kafka
* Zookeeper hosts: 172.25.0.11:2181
Optionally:
* You can tick the following;
* Enable JMX Polling
* Poll consumer information
#### Access the topic information
If your cluster was named "Kafka", then you can go to http://172.25.0.14:9000/clusters/Kafka/topics/default_topic, where you will be able to see the partition offsets. Given that the topic was created automatically, it will have only 1 partition.
#### Visualise metrics in Grafana
Finally, you can access the default kafka dashboard in Grafana (username is "admin" and password is "password") by going to http://172.25.0.16:3000/d/xyAGlzgWz/kafka?orgId=1
