The purpose of this article is to get you started quickly with a Home Assistant on a Raspberry Pi. It’s a simple walkthrough on how to install Home Assistant and configure it so it will boot with your PI.
I will use my old Raspberry PI V3 board.
Flashing the Raspberry PI OS
You will need a microSD card of reasonable size, I’m using a 16GB one and a USB Adapter to connect it with my PC.
Head over to Raspberry Pi OS website and download your preferred image, for my Home Assistant I’ve chosen Raspberry Pi OS with desktop and recommended software. After the download is completed, unzip the file and prepare to flash it.
To flash the OS image on the SD card I will use a program called balenaEtcher.
Download it, select your OS image, select the SD card, and hit flash.
After SD card flashing finishes, it is time to setup the Wi-Fi connection. If you’re using an ethernet cable you can skip this step, however, remember to enable SSH.
Setting up the Wi-Fi and enabling SSH
Unplug the SD card from the computer and plug it back. You should see two new drives D: and E:
Open your favorite text editor and create an empty file called ssh in drive E:. This will enable SSH access.
Create a new file called wpa_supplicant.conf using your text editor and paste the following contents in it:
Don’t forget to replace YOUR_WIFI_SSID and YOUR_WIFI_PASSWORD with the corresponding values regarding your Wi-Fi network.
Eject the SD card from your computer and plug it into the PI. At boot, the PI should automatically connect to your Wi-Fi network.
Installing Home Assistant Core
Find your Raspberry PI’s IP address and connect to it via ssh. You can run the command ssh pi@192.168.0.XXX. The password for the pi user should be raspberry.
After getting a shell, follow the instructions for installing Home Assistant from the official website.
Ensure that you run each command on its own line. Don’t directly copy the entire code block, copy each line individually.
Starting Home Assistant on boot
If you can access the Home Assistant web GUI using http://192.168.0.XXX:8123 then the next step would be to create a new systemd service so that some assistant starts at boot. Please replace XXX with your Raspberry PI’s IP address.
To create a new service:
Start a new shell on the Raspberry or ensure that you’re using the pi user. We will execute commands with sudo.
Use sudo nano /etc/systemd/system/hass.service to create a new file and paste the following contents into it:
If the service is running normally, everything is set up. You can safely reboot your PI and the Home Assistant service will run after boot.
Configuring Home Assistant
When visiting the Home Assistant’s web interface for the first time, you will be prompted to create a new user. You may also download the Home Assistant application for your mobile device if you wish to track things like battery, storage, steps, location and so on, in Home Assistant.
In future articles I will show you how to configure the BME680 enviromental sensor and how to activate the Apple Homekit integration. Until then, have fun exploring Home Assistant docs.
Things to do further:
Unattended Upgrades – Enable unattended upgrades for your Raspbian OS. Ensures that your OS’s is always patched and up to date.
UFW – Secure your Home Assistant server with the uncomplicated firewall.
Change default passwords or disable SSH login via password.
Click on mongodb-kafka-connect-mongodb-1.6.0.zip then unzip it and copy the directory into the plugin path /usr/share/java as defined in the CONNECT_PLUGIN_PATH: “/usr/share/java,/usr/share/confluent-hub-components” environment variable.
Connect needs to be restarted to pick-up the newly installed plugin. Verify that the connector plugin has been successfully installed:
➜ bin curl -s -X GET http://localhost:8083/connector-plugins | jq | head -n 20
[
{
"class": "com.mongodb.kafka.connect.MongoSinkConnector",
"type": "sink",
"version": "1.6.0"
},
{
"class": "com.mongodb.kafka.connect.MongoSourceConnector",
"type": "source",
"version": "1.6.0"
},
Note: If you don’t have jq installed you can omit it.
Creating the topics
Before starting the connector, let’s create the Kafka Topics events and events.deadletter, they will be used them in the connector.
To create the topics, we will need to download Confluent tools and run kafka-topics.
curl -s -O http://packages.confluent.io/archive/6.2/confluent-community-6.2.0.tar.gz
tar -xzf .\confluent-community-6.2.0.tar.gz
cd .\confluent-6.2.0\bin\
./kafka-topics --bootstrap-server localhost:9092 --list
__consumer_offsets
__transaction_state
_confluent-ksql-default__command_topic
_schemas
default_ksql_processing_log
docker-connect-configs
docker-connect-offsets
docker-connect-status
./kafka-topics --bootstrap-server localhost:9092 --create --topic events --partitions 3
Created topic events.
./kafka-topics --bootstrap-server localhost:9092 --create --topic events.deadletter --partitions 3
WARNING: Due to limitations in metric names, topics with a period ('.') or underscore ('_') could collide. To avoid issues, it is best to use either, but not both.
Created topic events.deadletter.
Note: You will need Java to run the Confluent tools if you’re on Ubuntu you can type sudo apt install openjdk-8-jdk.
Starting the connector 🚙
To start the connector, it is enough to do a single post request to the connector’s API with the connector’s configuration.
The configuration that we will use is going to be:
In short, this POST will create a new connector named mongo-sink-connector using the com.mongodb.kafka.connect.MongoSinkConnector java class, run a single connector task that will get all the messages from the events topic and put them into the Mongo found at mongodb://mongodb:27017/my_events, database named my_events and collection named kafka_events. The records which will fail to be written into the database will be placed on a dead letter topic named events.deadletter, in my opinion this is better than discarding them, since we can inspect the topic to see what went wrong.
To verify that the connector is running, you can retrieve its first tasks status with:
➜ bin curl -s -X GET http://localhost:8083/connectors/mongo-sink-connector/tasks/0/status | jq
{
"id": 0,
"state": "RUNNING",
"worker_id": "connect:8083"
}
Querying the Database 🗃
Now that our Kafka Connect cluster is running and is configured, all that’s left to do is POST some dummy data into Kafka and check for it in the database.
That’s all! 🎉If we now connect to the database using mongosh or any other client, we can query the data.
mongosh
> use my_events
switched to db my_events
> db.kafka_events.findOne()
{
_id: ObjectId("6147242856623b0098fc756d"),
glossary: {
title: 'example glossary',
GlossDiv: {
title: 'S',
GlossList: {
GlossEntry: {
ID: 'SGML',
SortAs: 'SGML',
GlossTerm: 'Standard Generalized Markup Language',
Acronym: 'SGML',
Abbrev: 'ISO 8879:1986',
GlossDef: {
para: 'A meta-markup language, used to create markup languages such as DocBook.',
GlossSeeAlso: [ 'GML', 'XML' ]
},
GlossSee: 'markup'
}
}
}
}
}
Viewing Kafka Connect JMX Metrics
JConsole is a tool that can be used to view JMX metrics exposed by Kafka Connect, if you installed openjdk-8 it should come with it
Start JConsole and connect to localhost:9102. If you get a warning about an insecure connection, accept the connection, and ignore it.
After you’re connected click the MBeans tab and explore 🦹♀️
Summary
Getting into Kafka and Kafka Connect can be a bit overwhelming at first. I hope that this tutorial has provided you with the necessary basics so you can continue to play and explore on your own.
Spinning up a playground for Kafka and Connect using docker-compose isn’t that complicated, you can start from the confluent-cp-community repo, it will give you everything you need to get started. With some little modifications to the docker-compose file, we’ve spawned a MongoDB instance and exposed the JMX metrics in Kafka Connect.
Next, we’ve installed and configured the MongoDB connector and confirmed that it works as expected.
If you have any questions let me know in the comments.
In this article I will give you some tips on how to improve the throughput of a message producer.
I had to write a Golang based application which would consume messages from Apache Kafka and send them into a sink using HTTP JSON / HTTP Protocol Buffers.
To see if my idea works, I started using a naïve approach in which I polled Kafka for messages and then send each message into the sink, one at a time. This worked, but it was slow.
To better understand the system, a colleague has setup Grafana and a dashboard for monitoring, using Prometheus metrics provided by the Sink. This allowed us to test various versions of the producer and observe it’s behavior.
Let’s explore what we can do to improve the throughput.
Request batching 📪
A very important improvement is request batching.
Instead of sending one message at a time in a single HTTP request, try to send more, if the sink allows it.
As you can see in the image, this simple idea improved the throughput from 100msg/sec to ~4000msg/sec.
Batching is tricky, if your batches are large the receiver might be overwhelmed, or the producer might have a tough time building them. If your batches contain a few items you might not see an improvement. Try to choose a batch number which isn’t too high and not to low either.
Fast JSON libraries ⏩
If you’re using HTTP and JSON then it’s a good idea to replace the standard JSON library.
There are lots of open-source JSON libraries that provide much higher performance compared to standard JSON libraries that are built in the language.
There are several partitioning strategies that you can implement. It depends on your tech stack.
Kafka allows you to assign one consumer to one partition, if you have 3 partitions in a topic then you can run 3 consumer instances in parallel from that topic, in the same consumer group, this is called replication, I did not use this as the Sink does not allow it, only one instance of the Producer is running at a time.
If you have multiple topics that you want to consume from, you can partition on the topic name or topic name pattern by subscribing to multiple topics at once using regex. You can have 3 consumers consuming from highspeed.* and 3 consumer consuming from other.*. If each topic has 3 partitions.
Note: The standard build of librdkafka doesn’t support negative lookahead regex expressions, if that’s what you need you will need to build the library from source. See issues/2769. It’s easy to do and the confluent-kafka-go client supports custom builds of librdkafka.
Negative lookahead expressions allow you to ignore some patterns, see this example for a better understanding: regex101.com/r/jZ9AEz/1
Source Parallelization 🕊
The confluent-kafka-go client allows you to poll Kafka for messages. Since polling is thread safe, it can be done in multiple goroutines or threads for a performance improvement.
import (
"fmt"
"github.com/confluentinc/confluent-kafka-go/kafka"
)
func main() {
var wg sync.WaitGroup
c, err := kafka.NewConsumer(&kafka.ConfigMap{
"bootstrap.servers": "localhost",
"group.id": "myGroup",
"auto.offset.reset": "earliest",
})
if err != nil {
panic(err)
}
c.SubscribeTopics([]string{"myTopic", "^aRegex.*[Tt]opic"}, nil)
for i := 0; i < 5; i++ {
go func() {
wg.Add(1)
defer wg.Done()
for {
msg, err := c.ReadMessage(-1)
if err == nil {
fmt.Printf("Message on %s: %s\n", msg.TopicPartition, string(msg.Value))
// TODO: Send data through a channel to be processed by another goroutine.
} else {
// The client will automatically try to recover from all errors.
fmt.Printf("Consumer error: %v (%v)\n", err, msg)
}
}
}()
}
wg.Wait()
c.Close()
}
Buffered Golang channels can also be used in this scenario in order to improve the performance.
Protocol Buffers 🔷
Finally, I saw a huge performance improvement when replacing the JSON body of the request with Protocol Buffers encoded and snappy compressed data.
If your Sink supports receiving protocol buffers, then it is a good idea to try sending it instead of JSON.
Honorable Mention: GZIP Compressed JSON 📚
The Sink supported receiving GZIP compressed JSON, but in my case I didn’t see any notable performance improvements.
I’ve compared the RAM and CPU usage of the Producer, the number of bytes sent over the network and the message throughput. While there were some improvements in some areas, I decided not to implement GZIP compression.
It’s all about trade-offs and needs.
Conclusion
As you could see, there are several things you can do to your producers in order to make them more efficient.
Request Batching
Fast JSON Libraries
Partitioning
Source Parallelization & Buffering
Protocol Buffers
Compression
I hope you’ve enjoyed this article and learned something! If you have some ideas, please let me know in the comments.
In this article I will explore the topic of sharding a Mongo Database that runs on Kubernetes. Before we get started, if you want to follow along, please install the tools listed in the prerequisites section, and if you want to learn more about sharding, check out this fantastic article Sharding Pattern.
After the installation completes, save the database’s root password and replica set key. While doing this the first time I messed up and didn’t save them properly.
Run the following commands to print the password and replica set key on the command line. If you’re on Windows I have provided you with a Powershell function for base64 and if you’re on Unix don’t forget to pass –decode to base64.
kubectl get secret --namespace default my-release-mongodb-sharded -o jsonpath="{.data.mongodb-root-password}" | base64
kubectl get secret --namespace default my-release-mongodb-sharded -o jsonpath="{.data.mongodb-replica-set-key}" | base64
Sharding the Database
Verify that all your pods are running and start a shell connection to the mongos server.
To enable sharding on the database and collection, I’m going to insert some dummy data in my_data database and my_users collections. The script used to insert the data is attached at the end of this blog post.
If you’ve made it this far, congrats, you’ve enabled sharding, now let’s define some rules.
Since we’re going to use a range sharding strategy based on the key t, and I have two shards available I would like my data to be distributed in the following way:
To test the rules, use the provided python script, modify the times variable and run it with various values.
You can run db.my_users.getShardDistribution() to view the data distribution on the shards.
[direct: mongos]> db.my_users.getShardDistribution()
Shard my-mongo-mongodb-sharded-shard-0 at my-mongo-mongodb-sharded-shard-0/my-mongo-mongodb-sharded-shard0-data-0.my-mongo-mongodb-sharded-headless.default.svc.cluster.local:27017,my-mongo-mongodb-sharded-shard0-data-1.my-mongo-mongodb-sharded-headless.default.svc.cluster.local:27017
{
data: '144KiB',
docs: 1667,
chunks: 1,
'estimated data per chunk': '144KiB',
'estimated docs per chunk': 1667
}
Shard my-mongo-mongodb-sharded-shard-1 at my-mongo-mongodb-sharded-shard-1/my-mongo-mongodb-sharded-shard1-data-0.my-mongo-mongodb-sharded-headless.default.svc.cluster.local:27017,my-mongo-mongodb-sharded-shard1-data-1.my-mongo-mongodb-sharded-headless.default.svc.cluster.local:27017
{
data: '195KiB',
docs: 2336,
chunks: 3,
'estimated data per chunk': '65KiB',
'estimated docs per chunk': 778
}
Adding More Shards
To add more shards to the cluster all we need to do is run helm upgrade, if you don’t mess up the replica set key like I did it should work on the first run.
After you save the correct password and replica set key, search for the volumes that belong to the shards which have the wrong replica set key and delete them. In my case I only delete the volumes which belong to the 3rd shard that I’ve added, since counting starts from 0, I’m looking for shard2 in the name.
Shading a MongoDB can seem intimidating at first, but with some practice in advance you can do it! If sharding doesn’t work out for you, you can Convert Sharded Cluster to Replica Set, but, be prepared with some backups.
Thanks for reading 📚 and happy hacking! 🔩🔨
Base64 Powershell Function
function global:Convert-From-Base64 {
[CmdletBinding()]
[Alias('base64')]
param (
[parameter(ValueFromPipeline,Mandatory=$True,Position=0)]
[string] $EncodedText
)
process {
[System.Text.Encoding]::ASCII.GetString([System.Convert]::FromBase64String($EncodedText))
}
}
Nucu Labs 