Monday, March 2, 2015

Playing with ETCD cluster in Docker on Local

Martin Podval
I've started to write some management component last week. We would like to utilize CoreOs with the whole stack, as much as possible, at least within such early phase of our project.

The core component of our solution is ETCD - distributed key value store. Something like my favorite piece of software - Redis. Word 'distributed' means that the core of all things within your solution needs to be synchronized or 'consensused'. ETCD uses Raft. I'd love to know how my desired component works in real environment where everything can die.

In the age of docker - where every piece of software is docker-ized, it's pretty simple to start ETCD cluster on local in a second. Following piece of code starts three etcd instances linked together in one cluster.

docker run -d -p 4001:4001 -p 2380:2380 -p 2379:2379 --net=host --name etcd0 \
 -name etcd0 \
 -advertise-client-urls http://localhost:2379,http://localhost:4001 \
 -listen-client-urls http://localhost:2379,http://localhost:4001 \
 -initial-advertise-peer-urls http://localhost:2380 \
 -listen-peer-urls http://localhost:2380 \
 -initial-cluster-token etcd-cluster-1 \
 -initial-cluster etcd0=http://localhost:2380,etcd1=http://localhost:2480,etcd2=http://localhost:2580

docker run -d -p 4101:4101 -p 2480:2480 -p 2479:2479 --net=host --name etcd1 \
 -name etcd1 \
 -advertise-client-urls http://localhost:2479,http://localhost:4101 \
 -listen-client-urls http://localhost:2479,http://localhost:4101 \
 -initial-advertise-peer-urls http://localhost:2480 \
 -listen-peer-urls http://localhost:2480 \
 -initial-cluster-token etcd-cluster-1 \
 -initial-cluster etcd0=http://localhost:2380,etcd1=http://localhost:2480,etcd2=http://localhost:2580

docker run -d -p 4201:4201 -p 2580:2580 -p 2579:2579 --net=host --name etcd2 \
 -name etcd2 \
 -advertise-client-urls http://localhost:2579,http://localhost:4201 \
 -listen-client-urls http://localhost:2579,http://localhost:4201 \
 -initial-advertise-peer-urls http://localhost:2580 \
 -listen-peer-urls http://localhost:2580 \
 -initial-cluster-token etcd-cluster-1 \
 -initial-cluster etcd0=http://localhost:2380,etcd1=http://localhost:2480,etcd2=http://localhost:2580

The inspiration is obvious, but this stuff simply runs everything on your computer.  Parameter --net=host provides full transparency from port&network point of view.

You can now use following URL in a browser:


Good thing is also to check all members of your cluster. You will kill them later.


You can easily delete all keys in XYZ namespace using curl once you did you tests. Note that you can delete only one of your keys so you can't perform following command on your root namespace.


I also prefer to see http status code as ETCD uses http status codes.

curl -v

In advance to status codes, it always returns a json with their own errors codes. See a snippet at the end of the following listing. You can get something similar to:

* Hostname was NOT found in DNS cache
*   Trying
* Connected to localhost ( port 2379 (#0)
> GET /v2/keys/XYZ HTTP/1.1
> User-Agent: curl/7.35.0
> Host: localhost:2379
> Accept: */*

< HTTP/1.1 404 Not Found
< Content-Type: application/json
< X-Etcd-Cluster-Id: 65a1e86cb62588c5
< X-Etcd-Index: 6
< Date: Sun, 01 Mar 2015 22:55:14 GMT
< Content-Length: 69

{"errorCode":100,"message":"Key not found","cause":"/XYZ","index":6}
* Connection #0 to host localhost left intact

At the end of playing with ETCD cluster, you will probably want to remove all etcd's containers. I use simple script which removes every docker container, but you can improve it using grep to remove only those hosting ETCD.

sudo docker rm -f `docker ps --no-trunc -aq`

The last interesting thing is the performance. I've reminded Redis which can handle one million of transactions per second using one thread. I was surprised when ETCD responded usually in 20-30ms. Much worse fact is that I've also encountered client timeouts because of 400-500ms RT per request. Raft is obviously not for free. But the purpose of ETCD is massive reading scalability. Well, good to know.

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