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Replace ElasticSearch with jpetazzo/httpenv

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ElasticSearch slowly uses up to 2GB of RAM.
Eventually, on instances provisioned with
only 4GB of RAM and without swap, if more
than one ElasticSearch pod end up on the
same instance, it will cause the instance
to slow down and ultimately crash. Instead,
we now use a tiny Go web server that shows
its environment in JSON. It still highlights
that multiple backends are serving requests
but without the memory usage issue.
This commit is contained in:
Jerome Petazzoni
2018-09-12 15:49:27 -05:00
parent 510a37be44
commit db8e8377ac
1 changed files with 24 additions and 24 deletions
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48
slides/k8s/kubectlexpose.md
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@@ -59,9 +59,9 @@ The `LoadBalancer` type is currently only available on AWS, Azure, and GCE.
.exercise[
- Start a bunch of ElasticSearch containers:
- Start a bunch of HTTP servers:
```bash
kubectl run elastic --image=elasticsearch:2 --replicas=7
kubectl run httpenv --image=jpetazzo/httpenv --replicas=10
```
- Watch them being started:
@@ -70,15 +70,17 @@ The `LoadBalancer` type is currently only available on AWS, Azure, and GCE.
```
<!--
```wait elastic-```
```wait httpenv-```
```keys ^C```
-->
]
The `-w` option "watches" events happening on the specified resources.
The `jpetazzo/httpenv` image runs an HTTP server on port 8888.
<br/>
It serves its environment variables in JSON format.
Note: please DO NOT call the service `search`. It would collide with the TLD.
The `-w` option "watches" events happening on the specified resources.
---
@@ -88,9 +90,9 @@ Note: please DO NOT call the service `search`. It would collide with the TLD.
.exercise[
- Expose the ElasticSearch HTTP API port:
- Expose the HTTP port of our server:
```bash
kubectl expose deploy/elastic --port 9200
kubectl expose deploy/httpenv --port 8888
```
- Look up which IP address was allocated:
@@ -122,36 +124,34 @@ Note: please DO NOT call the service `search`. It would collide with the TLD.
## Testing our service
- We will now send a few HTTP requests to our ElasticSearch pods
- We will now send a few HTTP requests to our pods
.exercise[
- Let's obtain the IP address that was allocated for our service, *programmatically:*
```bash
IP=$(kubectl get svc elastic -o go-template --template '{{ .spec.clusterIP }}')
IP=$(kubectl get svc httpenv -o go-template --template '{{ .spec.clusterIP }}')
```
<!--
```hide kubectl wait deploy elastic --for condition=available```
```hide sleep 5``` (give some time for elasticsearch to start... hopefully this is enough!)
```hide kubectl wait deploy httpenv --for condition=available```
-->
- Send a few requests:
```bash
curl http://$IP:9200/
curl http://$IP:8888/
```
- Too much output? Filter it with `jq`:
```bash
curl -s http://$IP:8888/ | jq .HOSTNAME
```
]
--
We may see `curl: (7) Failed to connect to _IP_ port 9200: Connection refused`.
This is normal while the service starts up.
--
Once it's running, our requests are load balanced across multiple pods.
Our requests are load balanced across multiple pods.
---
@@ -205,9 +205,9 @@ class: extra-details
.exercise[
- Check the endpoints that Kubernetes has associated with our `elastic` service:
- Check the endpoints that Kubernetes has associated with our `httpenv` service:
```bash
kubectl describe service elastic
kubectl describe service httpenv
```
]
@@ -229,15 +229,15 @@ class: extra-details
- If we want to see the full list, we can use one of the following commands:
```bash
kubectl describe endpoints elastic
kubectl get endpoints elastic -o yaml
kubectl describe endpoints httpenv
kubectl get endpoints httpenv -o yaml
```
- These commands will show us a list of IP addresses
- These IP addresses should match the addresses of the corresponding pods:
```bash
kubectl get pods -l run=elastic -o wide
kubectl get pods -l run=httpenv -o wide
```
---