Category Archives: Container

Kubernetes Control Plane Resiliency with HAProxy and Keepalived

I had a bit of fun setting up an on-premise Kubernetes cluster some time back, and thought I’d share an interesting part of the implementation.

Briefly, today’s post describes how to set up network load balancing to provide resiliency for the Kubernetes control plane using HAProxy. To ensure that this does not create a single point of failure, we’ll deploy redundant instances of HAProxy with fail-over protection by Keepalived

Brief Overview – Wait, the what and the what, now?

A production Kubernetes cluster has three or more concurrently active control plane nodes for resiliency. However, k8s does not have a built-in means of abstracting control plane node failures, nor balancing API access. An external network load balancer layer is needed to redirect connections to the nodes intelligently during a service failure. At the same time, such a layer is also useful to distribute API requests from users or from worker nodes to prevent overloading a single control plane node.

The diagram below from the Kubernetes website shows exactly where the load balancer should fit in (emphasis in red is mine).

By the way, I definitely did not figure all this out on my own, but started by referencing the rather excellent guide at How To Set Up Highly Available HAProxy Servers with Keepalived and Floating IPs on Ubuntu 14.04, then adapting the instructions for load balancing k8s API servers on-premise instead.

Initial DNS and Endpoint Set Up

To make this work, you need a domain name and the ability to add DNS entries. It’s not expensive, a non-premium .org or .net domain costs about USD13 per year (less during promotions). It is useful to have a domain for general lab use, and for experiments like what we are doing here. Of course, if you’ve got an in-house DNS server with a locally-relevant domain, that’ll work as well.

Whichever DNS service you go with, choose an fully-qualified domain name (FQDN) as a reference to your k8s control plane nodes. For example, I own the domain, and would like my k8s control plane API endpoint to be reachable via the FQDN

The steps to map the FQDN to an IP will differ by registrar. To start off, map the FQDN to the first k8s control plan node’s IP. We will update this later, but it is important to start with this to initialize the control plane. The mapping I use in my lab looks like the following:

  • DNS Record Type: A
  • DNS Name / Hostname: kube-control
  • IP Address:

With this DNS entry in place, all connectivity to goes to the first control plane node.

Initial DNS setup

At this point, set up the first and subsequent control plane nodes using the instructions at Creating Highly Available clusters with kubeadm. This post won’t go into the details of setting up the k8s cluster, as it’s all in the guide. A key point to note is that the FQDN to use as the k8s control plane endpoint must be explicitly specified during init. During my lab setup, that looks like:

kubeadm init

Updating DNS and Configuring HAProxy Load Balancing

To get resiliency on the Control Plane, we will insert a pair of HAProxy load balancers to direct traffic across the control plane nodes. At the same time, we want to make use of health-checks to identify failed nodes, and avoid sending traffic to them.

Also, to avoid the load balancer layer itself becoming a single point of failure, we’ll implement load balancer redundancy by making use of Keepalived to failover from the Master HAProxy to the Backup HAProxy if a failure occurs.

At the end of the setup, we should have the following in place:

End state with load balancing and redundancy

To start, first modify the DNS entry for to point at the Virtual IP that will be used by HAProxy pair.

  • DNS Record Type: A
  • DNS Name / Hostname: kube-control
  • IP Address:

Set up two Linux instances to be redundant load balancers; I used Ubuntu 18.04 LTS, though any distro should work as long as you can install HAProxy and Keepalived. We’ll call these two haproxy-lb1 and haproxy-lb2, and install HAProxy and Keepalived on both like so:

sudo apt install haproxy keepalived

Without changing the default configs, append the following frontend and backend config blocks into /etc/haproxy/haprox.cfg for both haproxy-lb1 and haproxy-lb2:

        bind *:6443
        default_backend k8s-managers

backend k8s-managers
        balance roundrobin
        mode tcp
        default-server check maxconn 20
        server k8s-master1
        server k8s-master2
        server k8s-master3

This configuration load balances incoming TCP connections on port 6443 to the k8s control plane nodes in the backend, as long as they are operational. Don’t forget to restart the haproxy service on both load balancers to apply the config.

$ sudo systemctl restart haproxy

Configuring Keepalived for Load Balancer Resiliency

Keepalived will use the Virtual Router Redundancy Protocol (VRRP) to ensure that one of the HAProxy instances will respond to any requests made to the Virtual IP (which, if you remember, we mapped to previously).

The following are configurations that need to be set at /etc/keepalived/keepalived.conf for the respective load balancer instances. There are slight differences between the configs of the nodes, so be sure to apply them to the correct nodes.

# For haproxy-lb1 VRRP Master

vrrp_script chk_haproxy {
    script "pgrep haproxy"
    interval 2
    rise 3
    fall 2

vrrp_instance vrrp33 {
    interface ens160
    state MASTER
    priority 120

    virtual_router_id 33
    unicast_peer {

    authentication {
        auth_type PASS
        auth_pass <PASSWORD>

    track_script {

    virtual_ipaddress { dev ens160 label ens160:1
# For haproxy-lb2 VRRP Backup

vrrp_script chk_haproxy {
    script "pgrep haproxy"
    interval 2
    rise 3
    fall 2

vrrp_instance vrrp33 {
    interface ens160
    state BACKUP
    priority 100

    virtual_router_id 33
    unicast_peer {

    authentication {
        auth_type PASS
        auth_pass <PASSWORD>

    track_script {

    virtual_ipaddress { dev ens160 label ens160:1

Very briefly, the vrrp_script chk_haproxy config block defines that Keepalived should first check and ensure that the haproxy process is running, before the load balancer node can be considered as a candidate to take up the virtual IP. There’s no point for a load balancer node to take up the Virtual IP, and not have a HAProxy operating to process connections. This check is also a fail-over criteria; if a load balancer node’s haproxy process fails, then it should give up the Virtual IP so that the backup load balancer can take over.

In the vrrp_instance vrrp33 block, we define the fail-over relationship between the two load balancer nodes, with haproxy-lb1 being the default active instance (with higher priority), and haproxy-lb2 taking over only if the first instance fails (lower priority). Note that the virtual IP is also defined here; This is the IP address identity that the active load balancer will assume to service incoming frontend connections.

Finally, restart the keepalived service on both nodes once the configs have been saved:

$ sudo systemctl restart keepalived

Peace of Mind

Well, that was somewhat involved, but we’re done. With this in place, barring a catastrophic outage, users using kubectl and k8s worker nodes configured to reach the control plane endpoint via FQDN / Virtual IP will not be affected by any single component failure at the Control Plane layer.

Part 2: Setting up Log Analytics on Datadog for QNAP NAS

In Part 2, we walk-through how to set up log parsing in Datadog for the QNAP NAS logs that the Datadog agent has been shipping out. This is an important step to allow filtering logs for troubleshooting, as well as creating facets for slicing, dicing and analyzing log data.

If you haven’t read Part 1: Setting up Log Analytics on Datadog for QNAP NAS yet, and arrived at this post without any clue as to how you got here, I highly recommend starting there.

To make your experience of this post better, here are some tips:

  • There are a number of screenshots in this post which may seem a bit small on their own. Click on them to pop-up a lightbox with an enlarged image. No, you don’t need a magnifying glass.
  • For easy understanding, we’ll split this out into the “Before”, “Setting Up”, and “After” sections.

Before Parsing Custom Logs

On the Datadog platform, navigate to Logs -> Search to get to the Log Explorer. Select only the qnap-nas Service facet. If you remember from Part 1, we had this Service configured on the agent configuration file. It might be a good idea to choose a longer time frame to view logs; In this case, we looking at logs from the “The Past Hour”. To generate some log activity, I logged into my NAS to start an antivirus scan as well as run a rapid test on one of the hard disks.

Yay logs.

Clicking into any of the log lines, it’s soon clear that while the logs exist, the data is not actually parsed for easy slicing and dicing, which will be immensely useful if we want to perform log analysis and filtering. It would be nice to extract inline data into easy to use attributes.

No attributes, no fun.

Setting Up Custom Log Parsing

Navigate to Logs -> Configuration. Obseve that there are already a number of existing log parsing pipelines which come out of the box (Wait, do we have boxes for a SaaS?). These are automatically turned on when an associated monitoring integration is enabled. Did I mention that Datadog has 400+ vendors-supported integrations already available, and chances are that whatever you want to integrate for monitoring/tracing/log analytics is already here? Consider it said 🙂 And now, lets add on a custom log pipeline, just cuz we can. Click on “Add a new pipeline”.

To add a new log pipeline, click on “Add a new pipeline”. Whew, how hard was that?

First, we need to filter out log lines that we want to send through our QNAP log pipeline for parsing. We’ll simply use service:qnap-nas as our filter criteria. If you recall, we configured the agent in Part 1 to tag this attribute to all logs that come in from the QNAP NAS. Give this pipeline an easily distinguishable name; “QNAP NAS”, for example.

I call the pipeline “QNAP NAS”, just because.

Once the pipeline exists to snag the right logs, we need to apply some actions to the logs in order to parse it. In this case, the actions are called “Processors”. Click on “Add Processor”.

Add a “New Processor”, professor.

A pop-up appears to help configure the “New Processor”. For Step 1, let’s leave it as the default “Grok Parser”, because we are going to use Grok to extract attributes of interest. In Step 2, return to the “Log Explorer” screen to copy out a few log samples which will be used to test our parsing rules. From observation, there appear to two types of QNAP NAS logs; An event log type and a connection log type. Notice that all of the log samples have a red “No Match” indicator next to them, meaning we can’t extract any useful attributes yet.

Copy and paste in some sample logs from the QNAP NAS so we can test the Grok parsing rules

Going down to Step 3, place in the parsing rules. I’ve provided the rules in text format after the next screenshot, so you can easily copy/paste them for your own use. Essentially, we have main/general parsing rules for both types of logs. For readability, these will in turn call modular “Helper Rules” that need to be added in “Advanced Settings”. These “Helper Rules” will work their magic on specific sub-strings depending on where they are placed by the main parsing rules.

What a load of Grok!

Here are the main rules in text form; You can copy/paste this in into the Step 3 text box as in the screenshot above.

QNAP_Conn %{QNAP_initial} %{QNAP_conn_log}
QNAP_Event %{QNAP_initial} %{QNAP_event_log}

And here are the “Helper Rules”, which get called by the main rules. Pop open the “Advanced Settings” drop-down and copy/paste these in. If you’re curious, “QNAP_initial” will match and parse the beginning of every QNAP log, while “QNAP_conn_log” and “QNAP_event_log” will respectively match and parse connection or event logs, depending on what comes after the initial part of the log line.

QNAP_initial \<%{number:priority}\>%{date("MMM dd HH:mm:ss"):date}\s+%{ipOrHost:host}\s+%{word:process_name}\[%{number:process_id}\]\:

QNAP_conn_log conn\s+log\:\s+Users\:\s+%{word:user},\s+Source\s+IP\:\s+%{ip:source_ip},\s+Computer\s+name\:\s+%{data:computer_name},\s+Connection\s+type\:\s+%{word:connection_type},\s+Accessed\s+resources\:\s+%{data:accessed_resources},\s+Action\:\s+%{data:action}

QNAP_event_log event\s+log\:\s+Users\:\s+%{word:user},\s+Source\s+IP\:\s+%{ip:source_ip},\s+Computer\s+name\:\s+%{data:computer_name},\s+Content\:\s+%{data:msg}

Scrolling back up, notice that that all the sample log messages now show the “Match” in green.

We have a green slate

To see how well parsing works, select any of the sample log lines, and scroll down past Step 3 to see what attributes have been successfully parsed.

These values are all extracted from the sample log line, and assigned to attributes. It’s kinda like a key-value pair.

It works! Let’s clean up by giving the processor a name and saving it into the log pipeline.

It’s a log parser, what else could you call it?

If all goes well, we should now see the “QNAP NAS Log Parser” log processor attached to the QNAP NAS log pipeline.

Pipeline ready!

After: Slice and Dice Log Data like a Pro

So the net is now cast, let’s see what we can catch! Return to the Log Explorer and filter for service:qnap-nas. Click on any of the recent logs, and observe that we now have attributes which have been extracted from the raw log line by the QNAP NAS Log Parser. The next screenshot shows the data extracted from a user’s action of writing a file on the NAS using Windows File Share.

More attributes than you can shake a stick at! (please don’t shake sticks at stuff)

We want to set these attributes as facets in order to index, slice and dice the logs. Let’s start with the “action” attribute, since this is a useful log facet that tells us what action a user performed. Mouse over the left area of each attribute, then look out for a small settings icon (symbol of a gear). Click on it to pop open a menu.

Mouse over, and click… Side note here, Pomplamoose covers are awesome and you should check them out on Youtube.

Select “Create facet for @action” from the menu…

Create a new facet

… Which will pop-up a confirmation dialog. No changes needed here, just click on the “Add” button. Repeat the steps to add facets with the “computer_name”, “connection_type”, “host”, “source_ip”, and “user” attributes.

Hurry up and click “Add” already

Observe that once these attributes have been added as facets, they now appear on the facet selector/menu on the left. You can see a list of facet values for manipulating the log view now.

Options, options, and more options. Options are good.

For example, let’s use the “user” facet to select ONLY the “admin” user. This will show a list of all logs that are related to the “admin” user, and filter everything else out.

This “admin” guy looks suspicious, let’s see what he’s been up to.

Observe that the “user:admin” term is now added automatically to the search bar, and that the visible logs are only those which are caused by the “admin” user. In this case, it’s a list of files that is being accessed by the user.

Apparently “admin” enjoys a cover of a Jim Croce song. Great taste!

Having facets is also fantastic for running log analytics. Say for example, I wanted to understand what actions are frequently performed on the NAS by users. It’s easy to click on the “graph” icon on the “action” facet.

… and “ACTION!”

With that simple click, a visualization of all the actions performed on the NAS by the users within the selected timeframe is displayed. Here we can see that by far the most frequently performed operation by the NAS is the “Read” operation.

So the users seem to like reading files off the NAS. Color me surprised…

There’s a lot more that can be done now that we’re able to parse the QNAP NAS logs, like adding this to a dashboard of related applications or systems, or setting up monitoring and alerting against specific thresholds. It’s all up to your imagination!

Part 1: Setting up Log Analytics on Datadog for QNAP NAS

I recently had the opportunity to join Datadog, a modern monitoring-as-a-service solution provider with a focus on Cloud Native applications. On its own, Datadog has substantial integration for monitoring/tracing/log analytics for enterprise cloud and applications out of the box. Not to toot any horns here, but you can pop by Datadog HQ to sign up for a trial if you need an easy-to-use cloud-based monitoring platform that’s good to go live in 5 minutes.

To get up to speed on log analytics, I wanted to learn how to set up log analytics for custom log sources, which could be a home-grown application or any system which Datadog has not integrated log parsing for yet. Note that this is NOT how most folks would use it in production, since there are already tonnes of out-of-the-box and supported integrations for log parsing/analytics. This is more “corner-case” testing, and to let myself learn how to make custom log parsing work. Also, having had several faults with my QNAP NAS recently which went undiscovered for too long, I thought that would be the perfect target to try this on.

Bit of a disclaimer before going any further: All views here are mine and do not reflect in any way the official position of my employer. Yadda Yadda. Mistakes were very likely made, and are mine. Got it? Good, let’s move on. 🙂

Now, Datadog relies on using a single agent to collect all manner of information, be it metrics, application traces, or logs. This agent can also be configured as a remote syslog collector, used to forward syslogs sent at it to the Datadog cloud for analytics. The set up that I did looked something like the following diagram.

Who’s talking to who?

And, just so we already have the source of custom logs already set up, I configured my QNAP NAS to send all its logs, hopes, fears, anger, failures and frustrations to the Datadog Monitor VM, where my Datadog Agent is installed.

QNAP is set to tell the Datadog Agent about all its problems. Everyone needs a sympathetic ear, and a good doggo to cuddle away their problems. Yes, even a QNAP NAS.

Now that’s done, we’ll deploy the Datadog agent as a Docker container. Using Ubuntu 18.04 as the base OS, install Docker by following the instructions at the Docker Installation Page. For test setups, you can also also run the Docker setup script (not recommended for production) here. Also, remember to install Docker Compose by following the setup instructions here, I’ve got a docker-compose.yaml further down that you can get the agent going in seconds.

To start off, create the following directory structure in your home directory. Use touch and mkdir as you see fit.

-> docker-compose.yaml
-> datadog-agent
   -> conf.d
      -> qnap.d
         -> conf.yaml

Here are the contents of ~/datadog-monitor/datadog-agent/conf.d/qnap.d/conf.yaml. It configures the agent that it to listen on UDP port 15141, and for any ingested logs to be marked with qnap-nas service and qnap source. I’ve added a number of other tags for easy correlation in my environment later on, but they are optional for the purposes of what we’re trying to do here.

  type: udp
  port: 15141
  service: qnap-nas
  source: qnap
    - cloud_provider:vsphere
    - availability_zone:sgp1
    - env:prod
    - vendor:qnap

Here are the contents of ~/datadog-monitor/docker-compose.yaml. It’s a nice easy way to have Docker Compose bring up the agent container for us and start listening for logs immediately. We’re really setting in the correct environment variables to allow the agent to call home, and also enable logging. You can see we have also allowed the agent to mount and access anything in the ~/datadog-monitor/datadog-agent/conf.d we made earlier.

version: '3.8'
    image: 'datadog/agent:7'
      - DD_API_KEY=<REDACTED - Refer to your own API Key>
      - DD_LOGS_ENABLED=true
      - DD_AC_EXCLUDE="name:dd-agent"
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /proc/:/host/proc/:ro
      - /sys/fs/cgroup/:/host/sys/fs/cgroup:ro
      - ./datadog-agent/conf.d:/conf.d:ro
      - "15141:15141/udp"
    restart: 'always'

Let make sure we are in the ~/datadog-monitor directory, and run docker-compose.

$ sudo docker-compose up -d
Creating network "datadog-monitor_default" with the default driver
Creating datadog-monitor_dd-agent_1 … done

It’s probably a good idea to verify that the agent container started correctly, and that it is ready to forward logs from the QNAP NAS.

$ sudo docker ps
c9d0e3d05441 datadog/agent:7 "/init" 4 seconds ago Up 2 seconds (health: starting) 8125/udp, 8126/tcp,>15141/udp datadog-monitor_dd-agent_1

$ sudo docker exec c9d0e3d05441 agent status
Agent (v7.18.1)
Status date: 2020-06-10 13:58:06.211055 UTC
Agent start: 2020-06-10 13:57:44.291976 UTC
Pid: 348
Go Version: go1.12.9
Python Version: 3.8.1
Build arch: amd64
Check Runners: 4
Log Level: info
Logs Agent
Sending uncompressed logs in HTTPS to on port 0 
BytesSent: 26753
EncodedBytesSent: 26753
LogsProcessed: 73
LogsSent: 56
Type: udp
Port: 15141
Status: OK

Perfect, we’re looking good for now. We’ve got both the log source (QNAP NAS) and the log collector (Datadog agent) set up. In the next post, we will set up custom log parsing for the QNAP NAS.