Tuesday, November 30, 2010

Working with Chef attributes

It took me a while to really get how to use Chef attributes. It's fairly easy to understand what they are and where they are referenced in recipes, but it's not so clear from the documentation how and where to override them. Here's a quick note to clarify this.

A Chef attribute can be seen as a variable that:

1) gets initialized to a default value in cookbooks/mycookbook/attributes/default.rb

Examples:

default[:mycookbook][:swapfilesize] = '10485760'
default[:mycookbook][:tornado_version] = '1.1'
default[:mycookbook][:haproxy_version] = '1.4.8'
default[:mycookbook][:nginx_version] = '0.8.20'

2) gets used in cookbook recipes such as cookbooks.mycookbook/recipes/default.rb or any other myrecipefile.rb in the recipes directory; the syntax for using the attribute's value is of the form #{node[:mycookbook][:attribute_name]}

Example of using the haproxy_version attribute in a recipe called haproxy.rb:

# install haproxy from source
haproxy = "haproxy-#{node[:mycookbook][:haproxy_version]}"
haproxy_pkg = "#{haproxy}.tar.gz"

downloads = [
    "#{haproxy_pkg}",
]

downloads.each do |file|
    remote_file "/tmp/#{file}" do
        source "http://myserver.com/download/haproxy/#{file}"
    end
end

....etc.

Example of using the swapfilesize attribute in the default recipe default.rb:

# create swap file if it doesn't exist
SWAPFILE=/data/swapfile1

if [ -e "$SWAPFILE" ]
  then
   exit 0
fi
dd if=/dev/zero of=$SWAPFILE bs=1024 count=#{node[:mycookbook][:swapfilesize]}
mkswap $SWAPFILE
swapon $SWAPFILE
echo "$SWAPFILE swap swap defaults 0 0" >> /etc/fstab


3) can be overridden at either the role or the node level (and some other more obscure levels that I haven't used in practice)

I prefer to override attributes at the role level, because I want those overridden values to apply to all nodes pertaining to that role. 

For example, I have a role called appserver, which is defined in a file called appserver.rb in the roles directory:

# cat appserver.rb 
name "appserver"
description "Installs required packages and applications for an app server"
run_list "recipe[mycookbook::appserver]", "recipe[mycookbook::haproxy]", "recipe[memcached]"
override_attributes "mycookbook" => { "swapfilesize" => "4194304" }, "memcached" => { "memory" => "4096" }

Here I override the default swapfilesize value (an attribute from the mycookbook cookbook) of 10 GB and set it to 4 GB. I also override the default memcached memory value (an attribute from the Opscode memcached cookbook) of 128 MB and set it
to 4 GB.

If however you want to override some attributes at the node level, you could do this in the chef.json file on the node if you wanted for example to set the swap size to 2 GB:

"run_list": [ "role[base]", "role[appserver]" ]
"mycookbook": { "swapfilesize": "2097152"}

Anothe question is when should you use Chef attributes? My own observation is that wherever I use hardcoded values in my recipes, it's almost always better to use an attribute instead, and set the default value of the attribute to that hardcoded value, which can be then overridden as needed.

Tuesday, November 16, 2010

How to whip your infrastructure into shape

It's easy, just follow these steps:

Step 0. If you're fortunate enough to participate in the design of your infrastructure (as opposed to being thrown at the deep end and having to maintain some 'legacy' one), then try to aim for horizontal scalability. It's easier to scale out than to scale up, and failures in this mode will hopefully impact a smaller percentage of your users.

Step 1. Configure a good monitoring and alerting system

This is the single most important thing you need to do for your infrastructure. It's also a great way to learn a new infrastructure that you need to maintain.

I talked about different types of monitoring in another blog post. My preferred approach is to have 2 monitoring systems in place:
  • an internal monitoring system which I use to check the health of individual servers/devices
  • an external monitoring system used to check the behavior of the application/web site as a regular user would.

My preferred internal monitoring/alerting system is Nagios, but tools like Zabbix, OpenNMS, Zenoss, Monit etc. would definitely also do the job. I like Nagios because there is a wide variety of plugins already available (such as the extremely useful check_mysql_health plugin) and also because it's very easy to write custom plugin for your specific application needs. It's also relatively easy to generate Nagios configuration files automatically.

For external monitoring I use a combination of Pingdom and Akamai alerts. Pingdom runs checks against certain URLs within our application, whereas Akamai alerts us whenever the percentage of HTTP error codes returned by our application is greater than a certain threshold.

I'll talk more about correlating internal and external alerts below.

Step 2. Configure a good resource graphing system

This is the second most important thing you need to do for your infrastructure. It gives you visibility into how your system resources are used. If you don't have this visibility, it's very hard to do proper capacity planning. It's also hard to correlate monitoring alerts with resource limits you might have reached.

I use both Munin and Ganglia for resource graphing. I like the graphs that Munin produces and also some of the plugins that are available (such as the munin-mysql plugin), and I also like Ganglia's nice graph aggregation feature, which allows me to watch the same system resource across a cluster of nodes. Munin has this feature too, but Ganglia was designed from the get-go to work on clusters of machines.

Step 3. Dashboards, dashboards, dashboards

Everybody knows that whoever has the most dashboards wins. I am talking here about application-specific metrics that you want to track over time. I gave an example before of a dashboard I built for visualizing the outgoing email count through our system.

It's very easy to build such a dashboard with the Google Visualization API, so there's really no excuse for not having charts for critical metrics of your infrastructure. We use queuing a lot internally at Evite, so we have dashboards for tracking various queue sizes. We also track application errors from nginx logs and chart them in various ways: by server, by error code, by URL, aggregated, etc.

Dashboards offered by external monitoring tools such as Pingdom/Keynote/Gomez/Akamai are also very useful. They typically chart uptime and response time for various pages, and edge/origin HTTP traffic in the case of Akamai.

Step 4. Correlate errors with resource state and capacity

The combination of internal and external monitoring, resource charting and application dashboards is very powerful. As a rule, whenever you have an external alert firing off, you should have one or more internal ones firing off too. If you don't, then you don't have sufficient internal alerts, so you need to work on that aspect of your monitoring.

Once you do have external and internal alerts firing off in unison, you will be able to correlate external issues (such as increased percentages of HTTP error codes, or timeouts in certain application URLs) with server capacity issues/bottlenecks within your infrastructure. Of course, the fact that you are charting resources over time, and that you have a baseline to go from, will help you quickly identify outliers such as spikes in CPU usage or drops in Akamai traffic.

A typical work day for me starts with me opening a few tabs in my browser: the Nagios overview page, the Munin overview, the Ganglia overview, the Akamai HTTP content delivery dashboard, and various application-specific dashboards.

Let's say I get an alert from Akamai that the percentage of HTTP 500 error codes is over 1%. I start by checking the resource graphs for our database servers. I look at in-depth MySQL metrics in Munin, and at CPU metrics (especially CPU I/O wait time) in Ganglia. If nothing is out of the ordinary, I look at our various application services (our application consists of a multitude of RESTful Web services). The nginx log dashboard may show increased HTTP 500 errors from a particular server, or it may show an increase in such errors across the board. This may point to insufficient capacity at our services layer. Time to deploy more services on servers with enough CPU/RAM capacity. I know which servers those are, because I keep tabs on them with Munin and Ganglia.

As another example, I know that if the CPU I/O wait on my database servers approaches 30%, the servers will start huffing and puffing, and I'll see an increased number of slow queries. In this case, it's time to either identify queries to be optimized, or reduce the number of queries to the database -- or if everything else fails, time to add more database servers. (BTW, if you haven't yet read @allspaw's book "The Art of Capacity Planning", add reading it as a task for Step 0)

My point is that all these alerts and metric graphs are interconnected, and without looking at all of them, you're flying blind.

Step 5. Expect failures and recover quickly and gracefully

It's not a question whether failures will happen, it's WHEN they will happen. When they do happen, you need to be prepared. Hopefully you designed your infrastructure in a way that allows you to bounce back quickly from failures. If a database server goes down, hopefully you have a slave that you can quickly promote to a master, or even better you have another passive master ready to become the active server. Even better, maybe you have a fancy self-healing distributed database -- kudos to you then ;-)

One thing that you can do here is to have various knobs that turn on and off certain features or pieces of functionality within your application (again, John Allspaw has some blog posts and presentations on that from his days at Flickr and his current role at Etsy). These knobs allow you to survive an application server outage, or even (God forbid) a database outage, while still being able to present *something* to your end-users.

To quickly bounce back from a server failure, I recommend you use automated deployment and configuration management tools such as Chef, Puppet, Fabric, etc. (see some posts of mine on this topic). I personally use a combination of Chef (to bootstrap a new machine and do things as file system layout, pre-requisite installation etc) and Fabric (to actually deploy the application code).


Update #1:

Comment on Twitter:

@ericholscher:

"@griggheo Good stuff. Any thoughts on figuring out symptom's from causes? eg. load balancer is having issues which causes db load to drop?"

Good question, and something similar actually has happened to us. To me, it's a matter of knowing your baseline graphs. In our case, whenever I see an Akamai traffic drop, it's usually correlated to an increase in the percentage of HTTP 500 errors returned by our Web services. If I also see DB traffic dropping, then I know the bottleneck is at the application services layer. If the DB traffic is increasing, then the bottleneck is most likely the DB. Depending on the bottleneck, we need to add capacity at that layer, or to optimize code or DB queries at the respective layer.

Main accomplishment of this post

I'm most proud of the fact that I haven't used the following words in the post above: 'devops', 'cloud', 'noSQL' and 'agile'.

Thursday, October 28, 2010

MySQL load balancing with HAProxy

In an earlier blog post I was advising people to use HAProxy 1.4 and above if they need MySQL load balancing with health checks. It turns out that I didn't have much luck with that solution either. HAProxy shines when it load balances HTTP traffic, and its health checks are really meant to be run over HTTP and not plain TCP. So the solution I found was to have a small HTTP Web service (which I wrote using tornado) listening on a configurable port on each  MySQL node.

For the health check, the Web service connects via MySQLdb to the MySQL instance running on a given port and issues a 'show databases' command. For more in-depth checking you can obviously run fancier SQL statements.

The code for my small tornado server is here. The default port it listens on is 31337.

Now on the HAProxy side I have a "listen" section for each collection of MySQL nodes that I want to load balance. Example:
listen mysql-m0 0.0.0.0:33306
  mode tcp
  option httpchk GET /mysqlchk/?port=3306
  balance roundrobin
  server db101 10.10.10.1:3306 check port 31337 inter 5000 rise 3 fall 3
  server db201 10.10.10.2:3306 check port 31337 inter 5000 rise 3 fall 3 backup
In this case, HAProxy listens on port 33306 and load balances MySQL traffic between db101 and db201, with db101 being the primary node and db201 being the backup node (which means that traffic only goes to db101 unless it's considered down by the health check, in which case traffic is directed to db201). This scenario is especially useful when db101 and db201 are in a master-master replication setup, and you want traffic to hit only 1 of them at any given time. Note also that I could have had HAProxy listen on port 3306, but I preferred to have it listen and be contacted by the application on port 33306, in case I also wanted to run a MySQL server in port 3306 on the same server as HAProxy.

I specify how to call the HTTP check handler via "option httpchk GET /mysqlchk/?port=3306". I specify the port the handler listens on via the "port" option in the "server" line. In my case the port is 31337. So HAProxy will do a GET against http://10.10.10.1:31337/mysqlchk/?port=3306. If the result is an HTTP error code, the health check will be considered failed.

The other options "inter 5000 rise 3 fall 3" mean that the health check is issued by HAProxy every 5,000 ms, and that the health check needs to succeed 3 times ("rise 3") in order for the node to be considered up, and it needs to fail 3 times ("fall 3") in order for the node to be considered down.

I hasten to add that the master-master load balancing has its disadvantages. It did save my butt one Sunday morning when db101 went down hard (after all, it was an EC2 instance), and traffic was directed by HAProxy to db201 in a totally transparent fashion to the application.

But....I have also seen the situation where db201, as a slave to db101, lagged in its replication, and so when db101 was considered down and traffic was sent to db201, the state of the data was stale from an application point of view. I consider this disadvantage to weigh more than the automatic failover advantage, so I actually ended up taking db201 out of HAProxy. If db101 ever goes down hard again, I'll just manually point HAProxy to db201, after making sure the state of the data on db201 is what I expect.

So all this being said, I recommend the automated failover scenario only when load balance against a read-only farm of MySQL servers, which are all probably slaves of some master. In this case, although reads can also get out of sync, at least you won't attempt to do creates/updates/deletes against stale data.

The sad truth is that there is no good way of doing automated load balancing AND failover with MySQL without resorting to things such as DRBD which are not cloud-friendly. I am aware of Yves Trudeau's blog posts on "High availability for MySQL on Amazon EC2" but the setup he describes strikes me as experimental and I wouldn't trust it in a large-scale production setup.

In any case, I hope somebody will find the tornado handler I wrote useful for their own MySQL health checks, or actually any TCP-based health check they need to do within HAProxy.

Thursday, October 14, 2010

Introducing project "Overmind"

Overmind is the brainchild of Miquel Torres. In its current version, released today, Overmind is what is sometimes called a 'controller fabric' for managing cloud instances, based on libcloud. However, Miquel's Roadmap for the project is very ambitious, and includes things like automated configuration management and monitoring for the instances launched and managed via Overmind.

A little bit of history: Miquel contacted me via email in late July because he read my blog post on "Automated deployment systems: push vs. pull" and he was interested in collaborating on a queue-based deployment/config management system. The first step in such a system is to actually deploy the instances you need configured. Hence the need for something like Overmind.

I'm sure you're asking yourself -- why do these guys wanted to roll their own system? Why not use something like OpenStack? Note in late July OpenStack had only just been announced, and to this day (mid-October 2010) they have yet to release their controller fabric code. In the mean time, we have a pretty functional version of a deployment tool in Overmind, supporting Amazon EC2 and Rackspace, with a Django Web interface, and also with a REST API interface.

I am aware there are many other choices out there in terms of managing and deploying cloud instances -- Cloudkick, RightScale, Scalarium ...and the list goes on. The problem is that none of these is Open Source. They do have great ideas though that we can steal ;-)

I am also aware of Ruby-based tools such as Marionette Collective and its close integration with Puppet (which is now even closer since it has been acquired by Puppet Labs). The problem is that it's Ruby and not Python ;-)

In short, what Overmind brings to the table today is a Python-based, Django-based, libcloud-based tool for deploying (and destroying, but be careful out there) cloud instances. For the next release, Miquel and I are planning to add some configuration management capabilities. We're looking at kokki as a very interesting Python-based alternative to chef, although we're planning on supporting chef-solo too.

If you're interested in contributing to the project, please do! Miquel is an amazingly talented, focused and relentless developer, but he can definitely use more help (my contributions have been minimal in terms of actual code; I mostly tested Miquel's code and did some design and documentation work, especially in the REST API area).

Here are some pointers to Overmind-related resources:

Monday, September 27, 2010

Getting detailed I/O stats with Munin

Ever since Vladimir Vuksan pointed me to his Ganglia script for getting detailed disk stats, I've been looking for something similar for Munin. The iostat and iostat_ios Munin plugins, which are enabled by default when you install Munin, do show disk stats across all devices detected on the system. I wanted more in-depth stats per device though. In my case, the devices I'm interested in are actually Amazon EBS volumes mounted on my database servers.

I finally figured out how to achieve this, using the diskstat_ Munin plugin which gets installed by default when you install munin-node.

If you run

/usr/share/munin/plugins/diskstat_ suggest

you will see the various symlinks you can create for the devices available on your server.

In my case, I have 2 EBS volumes on each of my database servers, mounted as /dev/sdm and /dev/sdn. I created the following symlinks for /dev/sdm (and similar for /dev/sdn):


ln -snf /usr/share/munin/plugins/diskstat_ /etc/munin/plugins/diskstat_latency_sdm
ln -snf /usr/share/munin/plugins/diskstat_ /etc/munin/plugins/diskstat_throughput_sdm
ln -snf /usr/share/munin/plugins/diskstat_ /etc/munin/plugins/diskstat_iops_sdm

Here's what metrics you get from these plugins:

  • from diskstat_iops: Read I/O Ops/sec, Write I/O Ops/sec, Avg. Request Size, Avg. Read Request Size, Avg. Write Request Size
  • from diskstat_latency: Device Utilization, Avg. Device I/O Time, Avg. I/O Wait Time, Avg. Read I/O Wait Time, Avg. Write I/O Wait Time
  • from diskstat_throughput: Read Bytes, Write Bytes
My next step is to follow the advice of Mark Seger (the author of collectl) and graph the output of collectl in real time, so that the stats are displayed in fine-grained intervals of 5-10 seconds instead of the 5-minute averages that RRD-based tools offer.

Tuesday, September 21, 2010

Quick note on installing and configuring Ganglia

I decided to give Ganglia a try to see if I like its metric visualizations and its plugins better than Munin's. I am still in the very early stages of evaluating it. However, I already banged my head against the wall trying to understand how to configure it properly. Here are some quick notes:

1) You can split your servers into clusters for ease of metric aggregation.

2) Each node in a cluster needs to run gmond. In Ubuntu, you can do 'apt-get install ganglia-monitoring' to install it. The config file is in /etc/ganglia/gmond.conf. More on the config file in a minute.

3) Each node in a cluster can send its metrics to a designated node via UDP.

4) One server in your infrastructure can be configured as both the overall metric collection server, and as the web front-end. This server needs to run gmetad, which in Ubuntu can be installed via 'apt-get install gmetad'. Its config file is /etc/gmetad.conf.

Note that you can have a tree of gmetad nodes, with the root of the tree configured to actually display the metric graphs. I wanted to keep it simple, so I am running both gmetad and the Web interface on the same node.

5) The gmetad server periodically polls one or more nodes in each cluster and retrieves the metrics for that cluster. It displays them via a PHP web interface which can be found in the source distribution.

That's about it in a nutshell in terms of the architecture of Ganglia. The nice thing is that it's scalable. You split nodes in clusters, you designate one or more nodes in a cluster to gather metrics from all the other nodes, and you have one ore more gmetad node(s) collecting the metrics from the designated nodes.

Now for the actual configuration. I have a cluster of DB servers, each running gmond. I also have another server called bak01 that I keep around for backup purposes. I configured each DB server to be part of a cluster called 'db'. I also configured each DB server to send the metrics collected by gmond to bak01 (via UDP on the non-default port of 8650). To do this, I have these entries in /etc/ganglia/gmond.conf on each DB server:


cluster {
  name = "db"
  owner = "unspecified"
  latlong = "unspecified"
  url = "unspecified"
}

udp_send_channel { 
  host = bak01
  port = 8650
} 

On host bak01, I also defined a udp_recv_channel and a tcp_accept_channel:

udp_recv_channel { 
  port = 8650
} 

/* You can specify as many tcp_accept_channels as you like to share 
   an xml description of the state of the cluster */ 
tcp_accept_channel { 
  port = 8649 
} 

The upd_recv_channel is necessary so bak01 can receive the metrics from the gmond nodes. The tcp_accept_channel is necessary so that bak01 can be contacted by the gmetad node.

That's it in terms of configuring gmond.

On the gmetad node, I made one modification to the default /etc/gmetad.conf file by specifying the cluster I want to collect metrics for, and the node where I want to collect the metrics from:

data_source "eosdb" 60 bak01

I then restarted gmetad via '/etc/init.d/gmetad restart'.

Ideally, these instructions would get you to a state where you would be able to see the graphs for all the nodes in the cluster. 

I automated the process of installing and configuring gmond on all the nodes via fabric. Maybe it all happened too fast for the collecting node (bak01), because it wasn't collecting metrics correctly for some of the nodes. I noticed that if I did 'telnet localhost 8649' on bak01, some of the nodes had no metrics associated with them. My solution was to stop and start gmond on those nodes, and that kicked things off. Strange though...

In any case, my next step is to install all kinds of Ganglia plugins, especially related to MySQL, but also for more in-depth disk I/O metrics.

Wednesday, September 15, 2010

Managing Rackspace CloudFiles with python-cloudfiles

I've started to use Rackspace CloudFiles as an alternate storage for database backups. I have the backups now on various EBS volumes in Amazon EC2, AND in CloudFiles, so that should be good enough for Disaster Recovery purposes, one would hope ;-)

I found the documentation for the python-cloudfiles package a bit lacking, so here's a quick post that walks through the common scenarios you encounter when managing CloudFiles containers and objects. I am not interested in the CDN aspect of CloudFiles for my purposes, so for that you'll need to dig on your own.

A CloudFiles container is similar to an Amazon S3 bucket, with one important difference: a container name cannot contain slashes, so you won't be able to mimic a file system hierarchy in CloudFiles the way you can do it in S3. A CloudFiles container, similar to an S3 bucket, contains objects -- which for CloudFiles have a max. size of 5 GB. So the CloudFiles storage landscape consists of 2 levels: a first level of containers (you can have an unlimited number of them), and a second level of objects embedded in containers. More details in the CloudFiles API Developer Guide (PDF).

Here's how you can use the python-cloudfiles package to perform CRUD operations on containers and objects.

Getting a connection to CloudFiles

First you need to obtain a connection to your CloudFiles account. You need a user name and an API key (the key can be generated via the Web interface at https://manage.rackspacecloud.com).

conn = cloudfiles.get_connection(username=USERNAME, api_key=API_KEY, serviceNet=True)

When specifying serviceNet=True, the docs say that you will use the Rackspace ServiceNet network to access Cloud Files, and not the public network.

Listing containers and objects

Once you get a connection, you can list existing containers, and objects within a container:

containers = conn.get_all_containers()
for c in containers:
    print "\nOBJECTS FOR CONTAINER: %s" % c.name
    objects = c.get_objects()
    for obj in objects:
        print obj.name

Creating containers

container = conn.create_container(container_name)

Creating objects in a container

Assuming you have a list of filenames you want to upload to a given container:

for f in files:
    print 'Uploading %s to container %s' % (f, container_name)
    basename = os.path.basename(f)
    o = container.create_object(basename)
    o.load_from_filename(f)

(note that the overview in the python-cloudfiles index.html doc has a typo -- it specifies 'load_from_file' instead of the correct 'load_from_filename')

Deleting containers and objects

You first need to delete all objects inside a container, then you can delete the container itself:

print 'Deleting container %s' % c.name
print 'Deleting all objects first'
objects = c.get_objects()
for obj in objects:
    c.delete_object(obj.name)
print 'Now deleting the container'
conn.delete_container(c.name)

Retrieving objects from a container

Remember that you don't have a backup process in place until you tested restores. So let's see how you retrieve objects that are stored in a CloudFiles container:

container_name = sys.argv[1]
containers = conn.get_all_containers()
c = None
for c in containers:
    if container_name == c.name:
        break
if not c:
    print "No countainer found with name %s" % container_name
    sys.exit(1)

target_dir = container_name
os.system('mkdir -p %s' % target_dir)
objects = c.get_objects()
for obj in objects:
    obj_name = obj.name
    print "Retrieving object %s" % obj_name
    target_file = "%s/%s" % (target_dir, obj_name)
    obj.save_to_filename(target_file)

Wednesday, September 01, 2010

MySQL InnoDB hot backups and restores with Percona XtraBackup

I blogged a while ago about MySQL fault-tolerance and disaster recovery techniques. At that time I was experimenting with the non-free InnoDB Hot Backup product. In the mean time I discovered Percona's XtraBackup (thanks Robin!). Here's how I tested XtraBackup for doing a hot backup and a restore of a MySQL database running Percona XtraDB (XtraBackup works with vanilla InnoDB too).

First of all, I use the following Percona .deb packages on a 64-bit Ubuntu Lucid EC2 instance:


# dpkg -l | grep percona
ii libpercona-xtradb-client-dev 5.1.43-xtradb-1.0.6-9.1-60.jaunty.11 Percona SQL database development files
ii libpercona-xtradb-client16 5.1.43-xtradb-1.0.6-9.1-60.jaunty.11 Percona SQL database client library
ii percona-xtradb-client-5.1 5.1.43-xtradb-1.0.6-9.1-60.jaunty.11 Percona SQL database client binaries
ii percona-xtradb-common 5.1.43-xtradb-1.0.6-9.1-60.jaunty.11 Percona SQL database common files (e.g. /etc
ii percona-xtradb-server-5.1 5.1.43-xtradb-1.0.6-9.1-60.jaunty.11 Percona SQL database server binaries

I tried using the latest stable XtraBackup .deb package from the Percona downloads site but it didn't work for me. I started a hot backup with /usr/bin/innobackupex-1.5.1 and it ran for a while before dying with "InnoDB: Operating system error number 9 in a file operation." See this bug report for more details.

After unsuccessfully trying to compile XtraBackup from source, I tried XtraBackup-1.3-beta for Lucid from the Percona downloads. This worked fine.

Here's the scenario I tested against a MySQL Percona XtraDB instance running with DATADIR=/var/lib/mysql/m10 and a customized configuration file /etc/mysql10/my.cnf. I created and attached an EBS volume which I mounted as /xtrabackup on the instance running MySQL.

1) Take a hot backup of all databases under that instance:

/usr/bin/innobackupex-1.5.1 --defaults-file=/etc/mysql10/my.cnf --user=root --password=xxxxxx /xtrabackup

This will take a while and will create a timestamped directory under /xtrabackup, where it will store the database files from DATADIR. Note that the InnoDB log files are not created unless you apply step 2 below.

As the documentation says, make sure the output of innobackupex-1.5.1 ends with:

100901 05:33:12 innobackupex-1.5.1: completed OK!

2) Apply the transaction logs to the datafiles just created, so that the InnoDB logfiles are recreated in the target directory:

/usr/bin/innobackupex-1.5.1 --defaults-file=/etc/mysql10/my.cnf --user=root --password=xxxxxx --apply-log /xtrabackup/2010-09-01_05-21-36/

At this point, I tested a disaster recovery scenario by stopping MySQL and moving all files in DATADIR to a different location.

To bring the databases back to normal from the XtraBackup hot backup, I did the following:

1) Brought back up a functioning MySQL instance to be used by the XtraBackup restore operation:

i) Copied the contents of the default /var/lib/mysql/mysql database under /var/lib/mysql/m10/ (or you can recreate the mysql DB from scratch)

ii) Started mysqld_safe manually:

mysqld_safe --defaults-file=/etc/mysql10/my.cnf

This will create the data files and logs under DATADIR (/var/lib/mysql/m10) with the sizes specified in the configuration file. I had to wait until the messages in /var/log/syslog told me that the MySQL instance is ready and listening for connections.

2) Copied back the files from the hot backup directory into DATADIR

Note that the copy-back operation below initially errored out because it tried to copy the mysql directory too, and it found the directory already there under DATADIR. So the 2nd time I ran it, I moved /var/lib/mysql/m10/mysql to mysql.bak. The copy-back command is:

/usr/bin/innobackupex-1.5.1 --defaults-file=/etc/mysql10/my.cnf --user=root --copy-back /xtrabackup/2010-09-01_05-21-36/

You can also copy the files from /xtrabackup/2010-09-01_05-21-36/ into DATADIR using vanilla cp.

NOTE: verify the permissions on the restored files. In my case, some files in DATADIR were owned by root, so MySQL didn't start up properly because of that. Do a 'chown -R mysql:mysql DATADIR' to be sure.

3) If everything went well in step 2, restart the MySQL instance to make sure everything is OK.

At this point, your MySQL instance should have its databases restored to the point where you took the hot backup.

IMPORTANT: if the newly restored instance needs to be set up as a slave to an existing master server, you need to set the correct master_log_file and master_log_pos parameters via a 'CHANGE MASTER TO' command. These parameters are saved by innobackupex-1.5.1 in a file called xtrabackup_binlog_info in the target backup directory.

In my case, the xtrabackup_binlog_info file contained:

mysql-bin.000041 23657066

Here is an example of a CHANGE MASTER TO command I used:

STOP SLAVE;

CHANGE MASTER TO MASTER_HOST='masterhost', MASTER_PORT=3316, MASTER_USER='masteruser', MASTER_PASSWORD='masterpass', MASTER_LOG_FILE='mysql-bin.000041', MASTER_LOG_POS=23657066;

START SLAVE;

Note that XtraBackup can also run in a 'stream' mode useful for compressing the files generated by the backup operation. Details in the documentation.

Tuesday, August 31, 2010

Poor man's MySQL disaster recovery in EC2 using EBS volumes

First of all, I want to emphasize that this is NOT a disaster recovery strategy I recommend. However, in a pinch, it might save your ass. Here's the scenario I have:

  • 2 m1.large instances running Ubuntu 10.04 64-bit and the Percona XtraDB MySQL builds (for the record, the exact version I'm using is "Server version: 5.1.43-60.jaunty.11-log (Percona SQL Server (GPL), XtraDB 9.1, Revision 60")
  • I'll call the 2 servers db101 and db201
  • each server is running 2 MySQL instances -- I'll call them m1 and m2
  • instance m1 on db101 and instance m1 on db201 are set up in master-master replication (and similar for instance m2)
  • the DATADIR for m1 is /var/lib/mysql/m1 on each server; that file system is mounted from an EBS volume (and similar for m2)
  • the configuration files for m1 are in /etc/mysql1 on each server -- that directory was initially a copy of the Ubuntu /etc/mysql configuration directory, which I then customized (and similar for m2)
  • the init.d script for m1 is in /etc/init.d/mysql1 (similar for m2)
What I tested:
  • I took a snapshot of each of the 2 EBS volumes associated with each of the DB servers (4 snapshots in all)
  • I terminated the 2 m1.large instances
  • I launched 2 m1.xlarge instances and installed the same Percona distribution (this was done via a Chef recipe at instance launch time); I'll call the 2 new instances xdb101 and xdb102
  • I pushed the configuration files for m1 and m2, as well as the init.d scripts (this was done via fabric)
  • I created new volumes from the EBS snapshots (note that these volumes can be created in any EC2 availability zone)
  • On xdb101, I attached the 2 volumes created from the EBS snapshots on db101; I specified /dev/sdm and /dev/sdn as the device names (similar on xdb201)
  • On xdb101, I created /var/lib/mysql/m1 and mounted /dev/sdm there; I also created /var/lib/mysql/m2 and mounted /dev/sdn there (similar on xdb201)
  • At this point, the DATADIR directories for both m1 and m2 are populated with 'live files' from the moment when I took the EBS snapshot
  • I made sure syslog-ng accepts UDP traffic from localhost (by default it doesn't); this is because by default in Ubuntu mysql log messages are sent to syslog --> to do this, I ensured that "udp(ip(127.0.0.1) port(514));" appears in the "source s_all" entry in /etc/syslog-ng/syslog-ng.conf
At this point, I started up the first MySQL instance on xdb101 via "/etc/init.d/mysql1 start". This script most likely will show [fail] on the console, because MySQL will not start up normally. If you look in /var/log/syslog, you'll see entries similar to:

Aug 31 18:03:21 xdb101 mysqld: 100831 18:03:21 [Note] Plugin 'FEDERATED' is disabled.
Aug 31 18:03:21 xdb101 mysqld: InnoDB: The InnoDB memory heap is disabled
Aug 31 18:03:21 xdb101 mysqld: InnoDB: Mutexes and rw_locks use GCC atomic builtins
Aug 31 18:03:22 xdb101 mysqld: 100831 18:03:22  InnoDB: highest supported file format is Barracuda.
Aug 31 18:03:23 xdb101 mysqld: InnoDB: The log sequence number in ibdata files does not match
Aug 31 18:03:23 xdb101 mysqld: InnoDB: the log sequence number in the ib_logfiles!
Aug 31 18:03:23 xdb101 mysqld: 100831 18:03:23  InnoDB: Database was not shut down normally!
Aug 31 18:03:23 xdb101 mysqld: InnoDB: Starting crash recovery.

If you wait a bit longer (and if you're lucky), you'll see entries similar to:

Aug 31 18:04:20 xdb101 mysqld: InnoDB: Restoring possible half-written data pages from the doublewrite
Aug 31 18:04:20 xdb101 mysqld: InnoDB: buffer...
Aug 31 18:04:24 xdb101 mysqld: InnoDB: In a MySQL replication slave the last master binlog file
Aug 31 18:04:24 xdb101 mysqld: InnoDB: position 0 15200672, file name mysql-bin.000015
Aug 31 18:04:24 xdb101 mysqld: InnoDB: and relay log file
Aug 31 18:04:24 xdb101 mysqld: InnoDB: position 0 15200817, file name ./mysqld-relay-bin.000042
Aug 31 18:04:24 xdb101 mysqld: InnoDB: Last MySQL binlog file position 0 17490532, file name /var/lib/mysql/m1/mysql-bin.000002
Aug 31 18:04:24 xdb101 mysqld: 100831 18:04:24 InnoDB Plugin 1.0.6-9.1 started; log sequence number 1844705956
Aug 31 18:04:24 xdb101 mysqld: 100831 18:04:24 [Note] Recovering after a crash using /var/lib/mysql/m1/mysql-bin
Aug 31 18:04:24 xdb101 mysqld: 100831 18:04:24 [Note] Starting crash recovery...
Aug 31 18:04:24 xdb101 mysqld: 100831 18:04:24 [Note] Crash recovery finished.

At this point, you can do "/etc/init.d/mysql1 restart" just to make sure that both stopping and starting that instance work as expected. Repeat for instance m2, and also repeat on server xdb201.

So....IF you are lucky and the InnoDB crash recovery process did its job, you should have 2 functional MySQL instances one each of xdb101 and xdb201. I tested this with several pairs of servers and it worked for me every time, but I hasten to say that YMMV, so DO NOT bet on this as your disaster recovery strategy!

At this point I still had to re-establish the master-master replication between m1 on xdb101 and m1 on xdb201 (and similar for m2). 

When I initially set up this replication between the original m1.large servers, I used something like this on both db101 and db201:

CHANGE MASTER TO MASTER_HOST='master1', MASTER_PORT=3306, MASTER_USER='masteruser', MASTER_PASSWORD='xxxxxx';"

The trick for me is that master1 points to db201 in db101's /etc/hosts, and vice-versa.

On the newly created xdb101 and xdb201, there are no entries for master1 in /etc/hosts, so replication is broken. Which is a good thing initially, because you want to have the MySQL instances on each server be brought back up without throwing replication into the mix.

Once I added an entry for master1 in xdb101's /etc/hosts pointing to xdb201, and did the same on xdb201, I did a 'stop slave; start slave; show slave status\G' on the m1 instance on each server. In all cases I tested, one of the slaves was showing everything OK, while the other one was complaining about   not being able to read from the master's log file. This was fairly simply to fix. Let's assume xdb101 is the one complaining. I did the following:
  • on xdb201, I ran 'show master status\G' and noted the file name (for example "mysql-bin.000017") and the file position (for example 106)
  • on xdb101, I ran the following command: "stop slave; change master to master_log_file='mysql-bin.000017', master_log_pos=106; start slave;"
  • not a 'show slave status\G' on xdb101 should show everything back to normal
Some lessons:
  • take periodic snapshots of your EBS volumes (at least 1/day)
  • for a true disaster recovery strategy, use at least mysqldump to dump your DB to disk periodically, or something more advanced such as Percona XtraBackup; I recommend dumping the DB to an EBS volume and taking periodic snapshots of that volume
  • the procedure I detailed above is handy when you want to grow your instance 'vertically' -- for example I went from m1.large to m1.xlarge

Friday, August 20, 2010

Visualizing MySQL metrics with the munin-mysql plugin

Munin is a great tool for resource visualization. Sometimes though installing a 3rd party Munin plugin is not as straightforward as you would like. I have been struggling a bit with one such plugin, munin-mysql, so I thought I'd spell it out for my future reference. My particular scenario is running multiple MySQL instances on various port numbers (3306 and up) on the same machine. I wanted to graph in particular the various InnoDB metrics that munin-mysql supports. I installed the plugin on various Ubuntu flavors such as Jaunty and Lucid.

Here are the steps:

1) Install 2 pre-requisite Perl modules for munin-mysql: IPC-ShareLite and Cache-Cache

2) git clone http://github.com/kjellm/munin-mysql

3) cd munin-mysql; edit Makefile and point PLUGIN_DIR to the directory where your munin plugins reside (if you installed Munin on Ubuntu via apt-get, that directory is /usr/share/munin/plugins)

4) make install --> this will copy the mysql_ Perl script to PLUGIN_DIR, and the mysql_.conf file to /etc/munin/plugin-conf.d

5) Edit /etc/munin/plugin-conf.d/mysql_.conf and customize it with your specific MySQL information.

For example, if you run 2 MySQL instances on ports 3306 and 3307, you could have something like this in mysql_.conf:


[mysql_3306_*]
env.mysqlconnection DBI:mysql:mysql;host=127.0.0.1;port=3306
env.mysqluser myuser1
env.mysqlpassword mypassword1

[mysql_3307_*]
env.mysqlconnection DBI:mysql:mysql;host=127.0.0.1;port=3307
env.mysqluser myuser2
env.mysqlpassword mypassword2

6) Run "/usr/share/munin/plugins/mysql_ suggest" to see what metrics are supported by the plugin. Then proceed to create symlinks in /etc/munin/plugins, adding the port number and the metric name as the suffix.

For example, to track InnoDB I/O metrics for the MySQL instance running on port 3306, you would create this symlink:

ln -s /usr/share/munin/plugins/mysql_ /etc/munin/plugins/mysql_3306_innodb_io

(replace 3306 with 3307 to track this metric for the other MySQL instance running on port 3307)

Of course, it's easy to automate this by a simple shell script.

7) Restart munin-node and wait 10-15 minutes for the munin master to receive the information about the new metrics.

Important! If you need to troubleshoot this plugin (and any Munin plugin), do not make the mistake of simply running the plugin script directly in the shell. If you do this, it will not read the configuration file(s) correctly, and it will most probably fail. Instead, what you need to do is to follow the "Debugging Munin plugins" documentation, and run the plugin through the munin-run utility. For example:


# munin-run mysql_3306_innodb_io
ib_io_read.value 34
ib_io_write.value 57870
ib_io_log.value 8325
ib_io_fsync.value 55476

One more thing: you should probably automate all these above steps. I have most of it automated via a fabric script. The only thing I do by hand is to create the appropriate symlinks for the specific port numbers I have on each server.

That's it! Enjoy staring for hours at your brand new MySQL metrics!


Monday, August 16, 2010

MySQL and AppArmor on Ubuntu

This is just a quick post that I hope will save some people some headache when they try to customize their MySQL setup on Ubuntu. I've spent some quality time with this problem over the weekend. I tried in vain for hours to have MySQL read its configuration files from a non-default location on an Ubuntu 9.04 server, only to figure out that it was all AppArmor's fault.

My ultimate goal was to run multiple instances of MySQL on the same host. In the past I achieved this with MySQL Sandbox, but this time I wanted to use MySQL installed from Debian packages and not from a tarball of the binary distribution, and MySQL Sandbox has some issues with that.

Here's what I did: I copied /etc/mysql to /etc/mysql0, then I edited /etc/mysql0/my.cnf and modified the location of the socket file, the pid file and the datadir to non-default locations. Then I tried to run:

/usr/bin/mysqld_safe --defaults-file=/etc/mysql0/my.cnf

At this point, /var/log/daemon.log showed this error:

mysqld[25133]: Could not open required defaults file: /etc/mysql0/my.cnf
mysqld[25133]: Fatal error in defaults handling. Program aborted

It took me as I said a few hours trying all kinds of crazy things until I noticed lines like these in /var/log/syslog:

kernel: [18593519.090601] type=1503 audit(1281847667.413:22): operation="inode_permission" requested_mask="::r" denied_mask="::r" fsuid=0 name="/etc/mysql0/my.cnf"
 pid=4884 profile="/usr/sbin/mysqld"

This made me realize it's AppArmor preventing mysqld from opening non-default files. I don't need AppArmor on my servers, so I just stopped it with 'service apparmor stop' and chkconfig-ed it off....at which point every customization I had started to work perfectly.

At least 2 lessons:

1) when you see mysterious, hair-pulling errors, check security-related processes on your server: iptables, AppArmor, SELinux etc.

2) check all log files in /var/log -- I was focused on daemon.log and didn't notice the errors in syslog quickly enough

Google didn't help when I searched for "mysqld Could not open required defaults file". I couldn't find any reference to AppArmor, only to file permissions.

Tuesday, August 03, 2010

What automated deployment/config mgmt tools do you use?

I posted this question yesterday as a quick tweet. I got a bunch of answers already that I'll include here, but feel free to add your answers as comments to this post too. Or reply to @griggheo on Twitter.

I started by saying I have 2 favorite tools: Fabric for pushing app state (pure Python) and Chef for pulling/bootstraping OS/package state (pure Ruby). For more discussions on push vs. pull deployment tools, see this post of mine.

Here are the replies I got on Twitter so far:

@keyist : Fabric and Chef for me as well. use Fabric to automate uploading cookbooks+json and run chef-solo on server

@vvuksan : mcollective for control, puppet for config mgmt/OS config. Some reasons why outlined here http://j.mp/cAKarI

@RackerHacker : There is another solution besides ssh and for loops? :-P

@chris_mahan : libcloud, to pop debian stable on cloud instance, fabric to set root passwd, install python2.6.5, apt-get nginx php django fapws.

@alfredodeza : I'm biased since I wrote it, but I use Pacha: http://code.google.com/p/pacha

@tcdavis : Fabric for remote calls; distribute/pip for python packaging and deps; Makefile to eliminate any repetition.

@competentgirl : Puppet for its expandability and integration w/ other tools (ie svn)

@yashh : Fabric. bundle assets, push to bunch of servers and restart in a shot.. love it

@bitprophet : I'm biased but I use Fab scripts for everything. Was turned off by daemons/declarative/etc aspects of Chef/Puppet style systems.

@kumar303 : @bitprophet we use Cap only because we do 2-way communication with remote SSH procs. Been meaning to look at patching Fab for this

Update with more Twitter replies:

@lt_kije : cfengine on work cluster (HPC/HTC), radmind on personal systems (OpenBSD)

@zenmatt : check out devstructure, built on puppet, more natural workflow for configuring servers.

@almadcz : paver for building, fabric or debian repo for deployment, buildbot for forgetting about it.

@geo_kollias: I use fabric for everything as @bitprophet, but i am thinking about making use of @hpk42 's execnet soon.

Wednesday, July 21, 2010

Bootstrapping EC2 instances with Chef

This is the third installment of my Chef post series (read the first and the second). This time I'll show how to use the Ubuntu EC2 instance bootstrap mechanism in conjunction with Chef and have the instance configure itself at launch time. I had a similar post last year, in which I was accomplishing a similar thing with puppet.

Why Chef this time, you ask? Although I am a Python guy, I prefer learning a smattering of Ruby rather than a proprietary DSL for configuration management. Also, when I upgraded my EC2 instances to the latest Ubuntu Lucid AMIs, puppet stopped working, so I was almost forced to look into Chef -- and I've liked what I've seen so far. I don't want to bad-mouth puppet though, I recommend you look into both if you need a good configuration management/deployment tool.

Here is a high-level view of the bootstrapping procedure I'm using:

1) You create Chef roles and tie them to cookbooks and recipes that you want executed on machines which will be associated with these roles.
2) You launch an EC2 Ubuntu AMI using any method you want (the EC2 Java-based command-line API, or scripts based on boto, etc.). The main thing here is that you pass a custom shell script to the instance via a user-data file.
3) When the EC2 instance boots up, it runs your custom user-data shell script. The script installs chef-client and its prerequisites, downloads the files necessary for running chef-client, runs chef-client once to register with the chef master and to run the recipes associated with its role, and finally runs chef-client in the background so that it wakes up and executed every N minutes.

Here are the 3 steps in more detail.

1) Create Chef roles, cookbooks and recipes

I already described how to do this in my previous post.

For the purposes of this example, let's assume we have a role called 'base' associated with a cookbook called 'base' and another role called 'myapp' associated with a cookbook called 'myapp'.

The 'base' cookbook contains recipes that can do things like installing packages that are required across all your applications, creating users and groups that you need across all server types, etc.

The 'myapp' cookbook contains recipes that can do things specific to one of your particular applications -- in my case, things like installing and configuring tornado/nginx/haproxy.

As a quick example, here's how to add a user and a group both called "myoctopus". This can be part of the default recipe in the cookbook 'base' (in the file cookbooks/base/recipes/default.rb).

The home directory is /home/myoctopus, and we make sure that directory exists and is owned by the user and group myoctopus.

group "myoctopus" do
action :create
end

user "myoctopus" do
gid "myoctopus"
home "/home/myoctopus"
shell "/bin/bash"
end

%w{/home/myoctopus}.each do |dir|
directory dir do
owner "myoctopus"
group "myoctopus"
mode "0755"
action :create
not_if "test -d #{dir}"
end
end


The role 'base' looks something like this, in a file called roles/base.rb:

name "base"
description "Base role (installs common packages)"
run_list("recipe[base]")


The role 'myapp' looks something like this, in a file called roles/myapp.rb:


name "myapp"
description "Installs required packages and applications for an app server"
run_list "recipe[memcached]", "recipe[myapp::tornado]"


Note that the role myapp specifies 2 recipes to be run: one is the default recipe of the 'memcached' cookbook (which is part of the Opscode cookbooks), and one is a reciped called tornado which is part of the myapp cookbook (the file for that recipe is cookbooks/myapp/recipes/tornado.rb). Basically, to denote a recipe, you either specify its cookbook (if the recipe is the default recipe of that cookbook), or you specify cookbook::recipe_name (if the recipe is non-default).

So far, we haven't associated any clients with these roles. We're going to do that on the client EC2 instance. This way the Chef server doesn't have to do any configuration operations during the bootstrap of the EC2 instance.

2) Launching an Ubuntu EC2 AMI with custom user-data

I wrote a Python wrapper around the EC2 command-line API tools. To launch an EC2 instance, I use the ec2-run-instances command-line tool. My Python script also takes a command line option called chef_role, which specifies the Chef role I want to associate with the instance I am launching. The main ingredient in the launching of the instance is the user-data file (passed to ec2-run-instances via the -f flag).

I use this template for the user-data file. My Python wrapper replaces HOSTNAME with an actual host name that I pass via a cmdline option. The Python wrapper also replaces CHEF_ROLE with the value of the chef_role cmdline option (which defaults to 'base').

The shell script which makes up the user-data file does the following:

a) Overwrites /etc/hosts with a version that has hardcoded values for chef.mycloud and mysite.com. The chef.mycloud.com box is where I run Chef server, and mysite.com is a machine serving as a download repository for utility scripts.

b) Downloads Eric Hammond's runurl script, which it uses to run other utility scripts.

c) Executes via runurl the script mysite.com/customize/hostname and passes it the real hostname of the machine being launched. The hostname script simply sets the hostname on the machine:

#!/bin/bash
hostname $1
echo $1 > /etc/hostname


d) Executes via runurl the script mysite.com/customize/hosts and passes it 2 arguments: add and self. Here's the hosts script:

#!/bin/bash
if [[ "$1" == "add" ]]; then
IPADDR=`ifconfig | grep 'inet addr:'| grep -v '127.0.0.1' | cut -d: -f2 | awk '{ print $1}'`
HOSTNAME=`hostname`
sed -i "s/127.0.0.1 localhost.localdomain localhost/127.0.0.1 localhost.localdomain localhost\n$IPADDR $HOSTNAME.mycloud.com $HOSTNAME\n/g" /etc/hosts
fi

What this does is it adds the internal IP of the machine being launched to /etc/hosts and associates it with both the FQDN and the short hostname. The FQDN bit is important for chef configuration purposes. It needs to come before the short form in /etc/hosts. I could have obviously also used DNS, but at bootstrap time I prefer to deal with hardcoded host names for now.

Update 07/22/10

Patrick Lightbody sent me a note saying that it's easier to get the local IP address of the machine by using one of the handy EC2 internal HTTP queries.

If you run "curl -s http://169.254.169.254/latest/meta-data" on any EC2 instance, you'll see a list of variables that you can inspect that way. For the local IP, I modified my script above to use:

IPADDR=`curl -s http://169.254.169.254/latest/meta-data/local-ipv4`

e) Finally, and most importantly for this discussion, executes via runurl the script mysite.com/install/chef-client and passes it the actual value of the cmdline argument chef_role. The chef-client script does the heavy lifting in terms of installing and configuring chef-client on the instance being launched. As such, I will describe it in the next step.

3) Installing and configuring chef-client on the newly launched instance

Here is the chef-client script I'm using. The comments are fairly self-explanatory. Because I am passing CHEF_ROLE as its first argument, the script knows which role to associate with the client. It does it by downloading the appropriate chef.${CHEF_ROLE}.json. To follow the example, I have 2 files corresponding to the 2 roles I created on the Chef server.

Here is chef.base.json:

{
"bootstrap": {
"chef": {
"url_type": "http",
"init_style": "init",
"path": "/srv/chef",
"serve_path": "/srv/chef",
"server_fqdn": "chef.mycloud.com"
}
},
"run_list": [ "role[base]" ]
}

The only difference in chef.myapp.json is the run_list, which in this case contains both roles (base and myapp):

{
"bootstrap": {
"chef": {
"url_type": "http",
"init_style": "init",
"path": "/srv/chef",
"serve_path": "/srv/chef",
"server_fqdn": "chef.mycloud.com"
}
},
"run_list": [ "role[base]", "role[myapp]" ]
}

The chef-client script also downloads the client.rb file which contains information about the Chef server:



log_level :info
log_location STDOUT
ssl_verify_mode :verify_none
chef_server_url "http://chef.mycloud.com:4000"

validation_client_name "chef-validator"
validation_key "/etc/chef/validation.pem"
client_key "/etc/chef/client.pem"

file_cache_path "/srv/chef/cache"
pid_file "/var/run/chef/chef-client.pid"

Mixlib::Log::Formatter.show_time = false

Note that the client knows the IP address of chef.mycloud.com because we hardcoded it in /etc/hosts.

The chef-client script also downloads validation.pem, which is an RSA key file used by the Chef server to validate the client upon the initial connection from the client.

The last file downloaded is the init script for launching chef-client automatically upon reboots. I took the liberty of butchering this sample init script and I made it much simpler (see the gist here but beware that it contains paths specific to my environment).

At this point, the client is ready to run this chef-client command which will contact the Chef server (via client.rb), validate itself (via validation.pem), download the recipes associated with the roles specified in chef.json, and run these recipes:

chef-client -j /etc/chef/chef.json -L /var/log/chef.log -l debug

I run the command in debug mode and I specify a log file location (the default output is stdout) so I can tell what's going on if something goes wrong.

That's about it. At this point, the newly launched instance is busy configuring itself via the Chef recipes. Time to sit back and enjoy your automated bootstrap process!

The last lines in chef-client remove the validation.pem file, which is only needed during the client registration, and run chef-client again, this time in the background, via the init script. The process running in the background looks something like this in my case:
/usr/bin/ruby1.8 /usr/bin/chef-client -L /var/log/chef.log -d -j /etc/chef/chef.json -c /etc/chef/client.rb -i 600 -s 30

The -i 600 option means chef-client will contact the Chef server every 600 seconds (plus a random interval given by -s 30) and it will inquire about additions or modifications to the roles it belongs to. If there are new recipes associated with any of the roles, the client will download and run them.

If you want to associate the client to new roles, you can just edit the local file /etc/chef/chef.json and add the new roles to the run_list.

Thursday, July 15, 2010

Tracking and visualizing mail logs with MongoDB and gviz_api

To me, nothing beats a nice dashboard for keeping track of how your infrastructure and your application are doing. At Evite, sending mail is a core part of our business. One thing we need to ensure is that our mail servers are busily humming away, sending mail out to our users. To this end, I built a quick outgoing email tracking tool using MongoDB and pymongo, and I also put together a dashboard visualization of that data using the Google Visualization API via the gviz_api Python module.

Tracking outgoing email from the mail logs with pymongo

Mail logs are sent to a centralized syslog. I have a simple Python script that tails the common mail log file every 5 minutes, counts the lines that conform to a specific regular expression (looking for a specific msgid pattern), then inserts that count into a MongoDB database. Here's the snippet of code that does that:

import datetime
from pymongo import Connection

conn = Connection(host="myhost.example.com")
db = conn.logs
maillogs = db.mail
d = {}
now = datetime.now()
d['insert_time'] = now
d['msg_count'] = msg_count
maillogs.save(d)

I use the pymongo module to open a connection to the host running the mongod daemon, then I declare a database called logs and a collection called maillogs within that database. Note that both the database and the collection are created on the fly in case they don't exist.

I then instantiate a Python dictionary with two keys, insert_time and msg_count. Finally, I use the save method on the maillogs collection to insert the dictionary into the MongoDB logs database. Can't get any easier than this.

Visualizing the outgoing email count with graph_viz

I have another simple Python script which queries the MongoDB logs database for all documents that have been inserted in the last hour. Here's how I do it:


MINUTES_AGO=60
conn = Connection()
db = conn.logs
maillogs = db.mail
now = datetime.datetime.now()
minutes_ago = now + datetime.timedelta(minutes=-MINUTES_AGO)
rows = maillogs.find({'insert_time': {"$gte": minutes_ago}})

As an aside, when querying MongoDB databases that contain documents with timestamp fields, the datetime module will become your intimate friend.

Just remember that you need to pass datetime objects when you put together a pymongo query. In the case above, I use the now() method to get the current timestamp, then I use timedelta with minutes=-60 to get the datetime object corresponding to 'now minus 1 hour'.

The gviz_api module has decent documentation, but it still took me a while to figure out how to use it properly (thanks to my colleague Dan Mesh for being the trailblazer and providing me with some good examples).

I want to graph the timestamps and message counts from the last hour. Using the pymongo query above, I get the documents inserted in MongoDB during the last hour. From that set, I need to generate the data that I am going to pass to gviz_api:


chart_data = []
for row in rows:
insert_time = row['insert_time']
insert_time = insert_time.strftime(%H:%M')
msg_count = int(row['msg_count'])
chart_data.append([insert_time, msg_count])

jschart("Outgoing_mail", chart_data)


In my case, chart_data is a list of lists, each list containing a timestamp and a message count.

I pass the chart_data list to the jschart function, which does the Google Visualization magic:

def jschart(name, chart_data):
description = [
("time", "string"),
("msg_count", "number", "Message count"),
]

data = []
for insert_time, msg_count in chart_data:
data.append((insert_time, msg_count))

# Loading it into gviz_api.DataTable
data_table = gviz_api.DataTable(description)
data_table.LoadData(data)

# Creating a JSON string
json = data_table.ToJSon()

name = "OUTGOING_MAIL"
html = TEMPL % {"title" : name, "json" : json}
open("charts/%s.html" % name, "w").write(html)

The important parts in this function are the description and the data variables. According to the docs, they both need to be of the same type, either dictionary or list. In my case, they're both lists. The description denotes the schema for the data I want to chart. I declare two variables I want to chart, insert_time of type string, and msg_count of type number. For msg_count, I also specify a user-friendly label called 'Message count', which will be displayed in the chart legend.

After constructing the data list based on chart_data, I declare a gviz_api DataTable, I load the data into it, I call the ToJSon method on it to get a JSON string, and finally I fill in a template string, passing it a title for the chart and the JSON data.

The template string is an HTML + Javascript snippet that actually talks to the Google Visualization backend and tells it to create an Area Chart. Click on this gist to view it.

That's it. I run the gviz_api script every 5 minutes via crontab and I generate an HTML file that serves as my dashboard.

I can easily also write a Nagios plugin based on the pymongo query, which would alert me for example if the number of outgoing email messages is too low or too high. It's very easy to write a Nagios plugin by just having a script that exits with 0 for success, 1 for warnings and 2 for critical errors. Here's a quick example, where wlimit is the warning threshold and climit is the critical threshold:


def check_maillogs(wlimit, climit):
# MongoDB
conn = Connection()
db = conn.logs
maillogs = db.mail
now = datetime.datetime.now()
minutes_ago = now + datetime.timedelta(minutes=-MINUTES_AGO)
count = maillogs.find({'insert_time': {"$gte": minutes_ago}}).count()
rc = 0
if count > wlimit:
rc = 1
if count > climit:
rc = 2
print "%d messages sent in the last %d minutes" % (count, MINUTES_AGO)
return rc


Update #1
See Mike Dirolf's comment on how to properly insert and query timestamp-related fields. Basically, use datetime.datetime.utcnow() instead of now() everywhere, and convert to local time zone when displaying.

Update #2
Due to popular demand, here's a screenshot of the chart I generate. Note that the small number of messages is a very, very small percentage of our outgoing mail traffic. I chose to chart it because it's related to some new functionality, and I want to see if we're getting too few or too many messages in that area of the application.

Friday, July 09, 2010

Working with Chef cookbooks and roles

Welcome to the second installment of my Chef saga (you can read the first one here). This time I will walk you through creating your own cookbook, modifying an existing cookbook, creating a role and adding a client machine to that role. As usual, I got much help from the good people on the #chef IRC channel, especially the omnipresent @kallistec. All these tasks are also documented in one form or another on the Chef wiki, but I found it hard to put them all together, hence this blog post.

Downloading the Opscode Chef cookbooks

Step 0 for this task is to actually clone the Opscode repository. I created a directory called /srv/chef/repos on my Chef server box and ran this command inside it:

# git clone git://github.com/opscode/chef-repo.git

This will create /srv/chef/repos/chef-repo with a bunch of sub-directories, one of them being cookbooks.
I deleted the cookbooks directory and cloned the Opscode cookbooks in its place:

# cd /srv/chef/repos/chef-repo
# git clone git://github.com/opscode/cookbooks



Uploading the cookbooks to the Chef server

Just downloading the cookbooks somewhere on the file system is not enough. The Chef server needs to be made aware of their existence. You do that with the following knife command (which I ran on the Chef server box):

# knife cookbook upload -a -o /srv/chef/repos/chef-repo/cookbooks

BTW, if you specified a non-default configuration file location for knife when you configured it (I specified /etc/chef/knife.rb for example) then you need to make a symlink from that file to ~/.chef/knife.rb, otherwise knife will complain about not finding a config file. At least it complained to me in its strange Swedish accent.

To go back to the knife command above: it says to upload all (-a) cookbooks it finds under the directory specified with -o.

Modifying an existing cookbook

If you look closely under the Opscode cookbooks, there's one called python. A cookbook contains one or more recipes, which reside under COOKBOOK_NAME/recipes. Most cookbooks have only one recipe which is a file called default.rb. In the case of the python cookbook, this recipe ensures that certain python packages such as python-dev, python-imaging, etc. get installed on the node running Chef client. To add more packages, simply edit default.rb (there's a certain weirdness in modifying a Ruby file to make a node install more Python packages....) and add your packages of choice.

Again, modifying a cookbook recipe on the file system is not enough; you need to let the Chef server know about the modification, and you do it by uploading the modified cookbook to the Chef server via knife:

# knife cookbook upload python -o /srv/chef/repos/chef-repo/cookbooks

Note the modified version of the 'knife cookbook upload' command. In this case, we don't specify '-a' for all cookbooks, but instead we specify a cookbook name (python). However, the directory remains the same. Do not make the mistake of specifying /srv/chef/repos/chef-repo/cookbooks/python as the target of your -o parameter, because it will not work. Trust me, I tried it until I was enlightened by @kallistec on the #chef IRC channel.

Creating your own cookbook

It's time to bite the bullet and create your own cookbook. Chef makes it easy to create all the files needed inside a cookbook. Run this command when in the top-level chef repository directory (/srv/chef/repos/chef-repo in my case):

# rake new_cookbook COOKBOOK=octopus

(like many other people, I felt the urge to cook Paul the Octopus when he accurately predicted Germany's loss to Spain)

This will create a directory cscp neo-app01:/opt/evite/etc/ad_urls.json .
alled octopus under chef-repo/cookbooks and it will populate it with many other directories and files. At a minimum, you need to modify only octopus/recipes/default.rb. Let's assume you want to install some packages. You can take some inspiration from other recipes (build-essential for example), but it boils down to something like this:

include_recipe "build-essential"
include_recipe "ntp"
include_recipe "python"
include_recipe "screen"
include_recipe "git"

%w{chkconfig libssl-dev syslog-ng munin-node}.each do |pkg|
  package pkg do
    action :install
  end
end

Note that I'm also including other recipes in my default.rb file. They will be executed on the Chef client node, BUT they also need to be specified in the metadata file cookbooks/octopus/metadata.rb as dependencies, so that the Chef client node knows that it needs to download them before running them. Trust me on this one, I speak again from bitter experience. This is how my metadata.rb file looks like:

maintainer        "My Organization"
maintainer_email  "admin@example.com"
license           "Apache 2.0"
description       "Installs required packages for My Organization applications"
version           "0.1"
recipe            "octopus", "Installs required packages for My Organization applications"
depends           "build-essential"
depends           "ntp"
depends           "python"
depends           "screen"
depends           "git"

%w{ fedora redhat centos ubuntu debian }.each do |os|
  supports os
end

Now it's time to upload our brand new cookbook to the Chef server. As before, we'll use the knife utility:

# knife cookbook upload octopus -o /srv/chef/repos/chef-repo/cookbooks

If you have any syntax errors in the recipe file or the metadata file, knife will let you know about them. To verify that the cookbook was uploaded successfully to the server, run this command, which should list your cookbook along the others that you uploaded previously:

# knife cookbook list

Creating a role and associating a chef client machine with it

Chef supports the notion of roles, which is very important for automated configuration management because you can assign a client node to one or more roles (such as 'web' or 'db' for example) and have it execute recipes associated with those roles.

To add a role, simply create a file under chef-repo/roles. I called mine base.rb, with the contents:

name "base"
description "Base role (installs common packages)"
run_list("recipe[octopus]")

It's pretty self-explanatory. Clients associated with the 'base' role will run the 'octopus' recipe.

As with cookbooks, we need to upload the newly created role to the Chef server. The following knife command will do it (assuming you're in the chef-repo directory):

# knife role from file roles/base.rb

To verify that the role has been uploaded, you can run:

# knife role show base

and it should show a JSON output similar to:

{
    "name": "base",
    "default_attributes": {
    },
    "json_class": "Chef::Role",
    "run_list": [
      "recipe[octopus]"
    ],
    "description": "Base role (installs common packages)",
    "chef_type": "role",
    "override_attributes": {
    }
}

Now it's time to associate a Chef client with the new role. You can see which clients are already registered with your Chef server by running:

# knife client list

Now pick a client and see which roles it is associated with:

# knife node show client01.example.com -r
{
  "run_list": [
  ]
}

The run_list is empty for this client. Let's associate it with the role we created, called 'base':

# knife node run_list add client01.example.com "role[base]"

You should now see the 'base' role in the run_list:


# knife node show client01.example.com -r
{
  "run_list": [
    "role[base]"
  ]
}

The next time client01.example.com will run chef-client, it will figure out it is part of the 'base' role, and it will follow the role' run_list by downloading and running the 'octopus' recipe from the Chef server.

In the next installment, I will talk about how to automatically bootstrap a Chef client in an EC2 environment. The goal there is to launch a new EC2 instance, have it install Chef client, have it assign a role to itself, then have it talk to the Chef server, download and run the recipes associated with the role.

Tuesday, July 06, 2010

Chef installation and minimal configuration

I started to play with Chef the other day. The instructions on the wiki are a bit confusing, but help on twitter (thanks @jtimberman) and on the #chef IRC channel (thanks @kallistec) has been great. I am at the very minimal stage of having a chef client talking to a chef server. I hasten to write down what I've done so far, both for my own sake and for others who might want to do the same. My OS is Ubuntu 10.04 32-bit on both machines.

First of all: as the chef wiki says, make sure you have FQDNs correctly set up on both client and server, and that they can ping each other at a minimum using the FQDN. I added the FQDN to the local IP address line in /etc/hosts, so that 'hostname -f' returned the FQDN correctly. In what follows, my Chef server machine is called chef.example.com and my Chef client machine is called client.example.com.

Installing the Chef server


Here I went the Ruby Gems route, because the very latest Chef (0.9.4) had not been captured in the Ubuntu packages yet when I tried to install it.

a) install pre-requisites

# apt-get install ruby ruby1.8-dev libopenssl-ruby1.8 rdoc ri irb build-essential wget ssl-cert

b) install Ruby Gems

# wget http://production.cf.rubygems.org/rubygems/rubygems-1.3.7.tgz
# tar xvfz rubygems-1.3.7.tgz
# cd rubygems-1.3.7
# ruby setup.rb
# ln -sfv /usr/bin/gem1.8 /usr/bin/gem

c) install the Chef gem

# gem install chef

d) install the Chef server by bootstrapping with the chef-solo utility

d1) create /etc/chef/solo.rb with contents:


file_cache_path "/tmp/chef-solo"
cookbook_path "/tmp/chef-solo/cookbooks"
recipe_url "http://s3.amazonaws.com/chef-solo/bootstrap-latest.tar.gz"

d2) create /etc/chef/chef.json with contents:

{
"bootstrap": {
"chef": {
"url_type": "http",
"init_style": "runit",
"path": "/srv/chef",
"serve_path": "/srv/chef",
"server_fqdn": "chef.example.com",
"webui_enabled": true
}
},
"run_list": [ "recipe[bootstrap::server]" ]
}

d3) run chef-solo to bootstrap the Chef server install:

# chef-solo -c /etc/chef/solo.rb -j /etc/chef/chef.json

e) create an initial admin client with the Knife utility, to interact with the API

#knife configure -i
Where should I put the config file? [~/.chef/knife.rb]
Please enter the chef server URL: [http://localhost:4000] http://chef.example.com
Please enter a clientname for the new client: [root]
Please enter the existing admin clientname: [chef-webui]
Please enter the location of the existing admin client's private key: [/etc/chef/webui.pem]
Please enter the validation clientname: [chef-validator]
Please enter the location of the validation key: [/etc/chef/validation.pem]
Please enter the path to a chef repository (or leave blank):

f) create an intial Chef repository

I created a directory called /srv/chef/repos , cd-ed to it and ran this command:

# git clone git://github.com/opscode/chef-repo.git

At this point, you should have a functional Chef server, although it won't help you much unless you configure some clients.

Installing a Chef client

Here's the bare minimum I did to get a Chef client to just talk to the Chef server configured above, without actually performing any cookbook recipe yet (I leave that for another post).

The first steps are very similar to the ones I followed when I installed the Chef server.


a) install pre-requisites

# apt-get install ruby ruby1.8-dev libopenssl-ruby1.8 rdoc ri irb build-essential wget ssl-cert

b) install Ruby Gems

# wget http://production.cf.rubygems.org/rubygems/rubygems-1.3.7.tgz
# tar xvfz rubygems-1.3.7.tgz
# cd rubygems-1.3.7
# ruby setup.rb
# ln -sfv /usr/bin/gem1.8 /usr/bin/gem

c) install the Chef gem

# gem install chef

d) install the Chef client by bootstrapping with the chef-solo utility

d1) create /etc/chef/solo.rb with contents:

file_cache_path "/tmp/chef-solo"
cookbook_path "/tmp/chef-solo/cookbooks"
recipe_url "http://s3.amazonaws.com/chef-solo/bootstrap-latest.tar.gz"
Caveat for this stepCaveat for this step
d2) create /etc/chef/chef.json with contents:

{
"bootstrap": {
"chef": {
"url_type": "http",
"init_style": "runit",
"path": "/srv/chef",
"serve_path": "/srv/chef",
"server_fqdn": "chef.example.com",
"webui_enabled": true
}
},
"run_list": [ "recipe[bootstrap::client]" ]
}

Note that the only difference so far between the Chef server and the Chef client bootstrap files is the one directive at the end of chef.json, which is bootstrap::server for the server and bootstrap::client for the client.

Caveat for this step: if you mess up and bootstrap the client using the wrong chef.json file containing the bootstrap::server directive, you will end up with a server and not a client. I speak from experience -- I did exactly this, then when I tried to run chef-client on this box, I got:

WARN: HTTP Request Returned 401 Unauthorized: Failed to authenticate!

/usr/lib/ruby/1.8/net/http.rb:2097:in `error!': 401 "Unauthorized" (Net::HTTPServerException)

d3) run chef-solo to bootstrap the Chef client install:

# chef-solo -c /etc/chef/solo.rb -j /etc/chef/chef.json

At this point, you should have a file called client.rb in /etc/chef on your client machine, with contents similar to:

#
# Chef Client Config File
#
# Dynamically generated by Chef - local modifications will be replaced
#

log_level :info
log_location STDOUT
ssl_verify_mode :verify_none
chef_server_url "http://chef.example.com:4000"

validation_client_name "chef-validator"
validation_key "/etc/chef/validation.pem"
client_key "/etc/chef/client.pem"

file_cache_path "/srv/chef/cache"
pid_file "/var/run/chef/chef-client.pid"

Mixlib::Log::Formatter.show_time = false
Caveat for this stepCaveat for this stepCaveat for this step
e) validate the client against the server

e1) copy /etc/chef/validation.pem from the server to /etc/chef on the client
e2) run chef-client on the client; for debug purposes you can use:

# chef-client -l debug

If everything goes well, you should see a message of the type:

# chef-client
INFO: Starting Chef Run
INFO: Client key /etc/chef/client.pem is not present - registering
WARN: Node client.example.com has an empty run list.
INFO: Chef Run complete in 1.209376 sec WARN: HTTP Request Returned 401 Unauthorized: Failed to authenticate!
31 < ggheo > 30 /usr/lib/ruby/1.8/nCaveat for this stepet/http.rb:2097:in `error!': 401 "Unauthorized" (Net::HTTPServerException)onds
INFO: Running report handlers
INFO: Report handlers complete

You should also have a file called client.pem containing a private key that the client will be using when talking to the server. At this point, you should remove validation.pem from /etc/chef on the client, as it is not needed any more.

You can also run this command on the server to see if the client got registered with it:

# knife client list -c /etc/chef/knife.rb

The output should be something like:

[
"chef-validator",
"chef-webui",
"chef.example.com",
"root",
"client.example.com"
]

That's it for now. As I warned you, nothing exciting happened here except for having a Chef client that talks to a server but doesn't actually DO anything. Stay tuned for more installments in my continuing chef saga though...


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