Hivemind Microservice Workshop Exercise Material

Demo of a microservice architecture for sentiment analysis. It provides a simple ui for tweeting. Tweets are then sent for sentiment analysis (toxicity). The outcome can be seen in Kibana. This repo comes with batteries included, apart from docker-compose and vs code (for development) no further stuff required 😎(see Prerequisites for more information).

TOC

• Overview
• Prerequisites
• Running locally (non-development)
• Developing
• Exercises
• Troubleshooting

Overview

Architecture

Tech stack

Service	Tech	Published ports
Feeder ui	purescript, node, react	9000
Feeder http	scala, jvm, http4s, monix	9001
Sentiments	scala, jvm, monix	9002
Sentiment analysis	purescript, node, tensorflow	9003
Elastic search		9200, 9300
Kibana		5601
Zookeeper
Kafka {1-3}
Kafka-connect		8083

Prerequisites

Docker

Install Docker and docker-compose.

A couple of services will spin up, so make sure to give docker enough resources. E.g.

• CPUs: 8
• Memory: 10 GB
• Disk image size: 120 GB

VS Code (development only)

• Install vs code https://code.visualstudio.com/
• Afterwards install the remote development extensions https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack

For development, vscode will spin up a full, containerized development environment with all necessary tools and editor plugins.

Running locally (non-development)

docker-compose -f docker-compose.yml -f docker-compose.local.yml up

Now go, grab a ☕ while this is starting up...

Open the feeder ui http://localhost:9000/ and tweet something.

Setup Kafka Connect & Kibana

Kafka Connect

Run:

docker-compose exec kafka-connect sh setup.sh

This will create a connector between kafka topics (feeds, sentiments) and elasticsearch.

You should see the following output:

{"name":"tweets-to-es","config":{"connector.class":"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector","tasks.max":"1","topics":"tweets","key.ignore":"true","topic.schema.ignore":"true","schema.ignore":"true","connection.url":"http://elasticsearch:9200","type.name":"kafka-connect","name":"tweets-to-es"},"tasks":[],"type":"sink"}{"name":"sentiments-to-es","config":{"connector.class":"io.confluent.connect.elasticsearch.ElasticsearchSinkConnector","tasks.max":"1","topics":"sentiments","key.ignore":"true","topic.schema.ignore":"true","schema.ignore":"true","connection.url":"http://elasticsearch:9200","type.name":"kafka-connect","name":"sentiments-to-es"},"tasks":[],"type":"sink"}%

Kibana

• Open the Kibana management console and browse to Management -> Index Management -> Elasticsearch -> IndexManagement or open this link: http://localhost:5601/app/kibana#/management/elasticsearch/index_management/indices?_g=()
• You should see the indices tweets and sentiments.
• If not, check that you have set up kafka-connect correctly.
• Now we need to create the Kibana indices. On the same page, select Kibana -> IndexManagement or click http://localhost:5601/app/kibana#/management/kibana/index_patterns?_g=()
• Click on Create index pattern with the pattern sentiments* and click on Next and Create index pattern.
• You can do the same for tweets.
• On the main menu select Discover or click http://localhost:5601/app/kibana#/discover?_g=()
• You should now see your analysed tweets.

You can find some examples here.

Developing

Note: If you ran the local (non-development) setup before, make sure you have properly stopped & destroyed all containers.

First, start up the 3rd party services (zookeeper, kafka, elastic, kibana):

docker-compose -f docker-compose.yml up

Check whether all services are up & running.

docker-compose ps

                 Name                               Command                       State                               Ports
------------------------------------------------------------------------------------------------------------------------------------------------
microservices-workshop_elasticsearch_1   /usr/local/bin/docker-entr ...   Up                      0.0.0.0:9200->9200/tcp, 0.0.0.0:9300->9300/tcp
microservices-workshop_kafka-connect_1   /etc/confluent/docker/run        Up (health: starting)   0.0.0.0:8083->8083/tcp, 9092/tcp
microservices-workshop_kafka0_1          /etc/confluent/docker/run        Up (healthy)            9092/tcp
microservices-workshop_kafka1_1          /etc/confluent/docker/run        Up (healthy)            9092/tcp
microservices-workshop_kafka2_1          /etc/confluent/docker/run        Up (healthy)            9092/tcp
microservices-workshop_kibana_1          /usr/local/bin/dumb-init - ...   Up                      0.0.0.0:5601->5601/tcp
microservices-workshop_zookeeper_1       /etc/confluent/docker/run        Up (healthy)            2181/tcp, 2888/tcp, 3888/tcp

Feeder ui

Let's begin by spinning up the feeder ui dev container.

Open the folder feeder-ui in VS Code.

Open the command palette (on mac Cmd+Shift+p) and select Remote-Containers: Reopen in container.

This will open the current folder inside a dev container which has node, the purescript toolchain and the purescript ide extensions pre-installed.

This may take a couple of minutes. When finished, you should land in the dev container's terminal.

 # node_modules will be empty
ls node_modules/
# root@757003b08d76:/app# ls node_modules/   
npm install

Start the ui:

npm run dev  

This should output something like this:

> feeder-ui@1.0.0 dev /app
> spago build --watch & parcel -p 9000 dev.html

[info] Installation complete.
spago: <stdin>: hGetLine: end of file
Server running at http://localhost:9000 
⠴ Building index.js...

This will start the ui in dev mode with hot reload enabled. Open http://localhost:9000 to see the ui. Any change in the code should be automatically reflected in the ui.

Note: You'll need to start the other services before you can actually tweet and see the sentiment analysis.

Feeder http

Open the folder feeder-http in a new VS Code window and reopen it in container, just as you did in the previous section.

Again, this will open a new dev container with java, the scala toolchain and the scala ide extension installed.

You will probably be asked by scala metals to import the changes.

Click on import changes, this will give you scala support in vs code.

Start the http service:

# start sbt
sbt
# then run the service
run

On first time, this will take a quite few minutes as it will download all dependencies. Use this to grab another ☕ or 🍵 in the meantime.

The service should be up & running now:

sbt:feeder-http> run
[info] Compiling 6 Scala sources to /app/target/scala-2.13/classes ...
[info] running com.hivemindtechnologies.feeder.Main 
[ioapp-compute-0] INFO  o.h.b.c.n.NIO1SocketServerGroup - Service bound to address /0.0.0.0:9001 
[ioapp-compute-0] INFO  o.h.s.b.BlazeServerBuilder - 
  _   _   _        _ _
 | |_| |_| |_ _ __| | | ___
 | ' \  _|  _| '_ \_  _(_-<
 |_||_\__|\__| .__/ |_|/__/
             |_| 
[ioapp-compute-0] INFO  o.h.s.b.BlazeServerBuilder - http4s v0.21.0-RC2 on blaze v0.14.11 started at http://0.0.0.0:9001/ 

Open http://localhost:9001/api/health to verify.

Sentiment analyser

Open the folder sentiment-analyser in a new VS Code window and reopen it in container, just as you did in the previous section.

# install node dependencies
npm install
# run service
npx spago run

Should give something like

[info] Build succeeded.
2020-02-05 11:27:18.433853: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4069460 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-02-05 11:27:18.433902: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Listening on http://0.0.0.0:9003

Sentiments

Open the folder sentiments in a new VS Code window and reopen it in container, just as you did in the previous section.

# start sbt
sbt
# run service
run

You should get a lot of log messages.

[info] Compiling 8 Scala sources to /app/target/scala-2.13/classes ...
[info] running com.hivemindtechnologies.sentiments.Main 
[INFO] [02/05/2020 11:38:36.969] [system-akka.actor.default-dispatcher-3] [SingleSourceLogic(akka://system)] [d0ca5] Starting. StageActor Actor[akka:/
/system/system/StreamSupervisor-0/$$a#-2079864740]
[system-akka.kafka.default-dispatcher-5] INFO  o.a.k.c.c.ConsumerConfig - ConsumerConfig values: 
...

Testing

Post a few tweets.

Follow the instructions to setup Kafka Connect & Kibana.

Troubleshooting

Check that you have met the prerequisites.

Check whether you don't have any other containers or services running locally that conflict with the ports.

If the terminal in the dev container disappears, in the vs code menu click on Terminal -> New Terminal.

If you make any changes to the docker-compose or Dockerfiles, try rebuilding the containers when in dev mode.