LinuxCzar

Python, Linux, and Operations.

Jack Neely

Jack is a Linux IT professional who enjoys coding, singing tenor, and growing orchids and other ornamental flowers.

Guilty as charged. I enjoy changing my websites and playing with different technologies more than writing actual content. Things have been very busy, and will be busier yet. In brief, here are some things that need their very own write-up.

Website, Powered By Hugo

I love Python’s RestructuredText markup language which is what I used for my Pelican based website. I, however, was less enthused when none of the themes had any support for RestructuredText’s more “advanced” features. Or anything beyond what Markdown can do. Nor did I want to dig into the Sass to do more in depth work on the theme.

The last 9 months or so I’ve been very enthralled by Go. Simplicity and efficiency make it a winning choice when working and larger scales. I also encountered Hugo and was very interested in the power and flexibility it had for maintaining a website. This led me to re-design the website with Hugo 0.13 and Bootstrap 3.3.2. Its also completely hosted on AWS S3. The only negative I have so far is that I’ve lost my IPv6 presence.

Projects

Git repositories once hosted at http://linuxczar.net/git/ now live in my repositories at GitHub. At least, the still relevant ones.

StatsRelay

StatsRelay, my first real Go project has been remarkably stable and efficient. With it I’m able to handle more than 350,000 packets/metrics per second to my Statsd service. In testing, I’ve been up toward 800,000 packets per second. I haven’t even rebuilt it with Go 1.4.

Whisper-Backup

How do you backup large Graphite clusters? I know folks that run a secondary cluster to mirror data to. That would have been incredibly expensive for me. So why not use OpenStack Swift or Amazon S3? Compression, retention, high speed, locking, and other fine features. Storage format allows for manual restores in an emergency. Check out Whisper-Backup.

Carbontools

Carbontools is just an idea and some bad code right now and probably not its final name either. The biggest problem I have with my Graphite cluster is manipulating data in a sane amount of time. The Python implementation of whisper-fill gets really slow when you need to operate of a few million WSP files.

  • Can I make a whisper-fill that’s an order of magnitude faster?
  • In a rebalance or expansion routine I want a near-atomic method of moving a WSP file. Faster, and decrease query-strangeness that happens in those operations.
  • Perform basic metric manipulations: tar archives, deletes, restores, build the WebUI search index, etc…across large consistent hashing clusters.

In Go, of course.

Today I’m doing these with some Fabric tasks. I’ve far exceeded what Fabric can really do, and the Python/SSH/Python setup at my scale is quite slow.

Family Time

My wife and I expect a baby girl very soon. Very soon. Surely that will add exacting blog posts. Surely.

Working with a large and consistent hashing Graphite cluster I came across corrupt files. Corrupt files prevent carbon-cache.py from storing data to that specific metric database file. The backlog was starting to tank the cluster. I whipped out find and removed all zero-length files, as that is a common corruption case.

find /opt/graphite/storage/whisper -depth -name *.wsp -size 0c -type f -delete

However, I had a few more cases that were not zero-length files. A quick bit of Google’ing did not find much. Usually, reading the header of the WSP file is enough to have the Whisper code throw an exception, so using that I wrote Whisper-FSCK.

It will scan your tree of Whisper files and look for corrupted ones. With the optional -f argument it will move those files out of the way.

Pull requests welcome!

Introducing StatsRelay, a proxy daemon for Statsd style metrics written in Go.

What does it do?

StatsRelay is designed to help you scale out your ingestion of Statsd metrics. It is a simple proxy that you send your Statsd metrics to. It will then forward your metrics to a list of backend Statsd daemons. A consistent hashing function is used with each metric name to determine which of the Statsd backends will receive the metric. This ensures that only one Statsd backend daemon is responsible for a specific metric. This prevents Graphite or your upstream time series database from recording partial results.

Why would you use it?

Do you have an application tier of multiple machines that send updates for the same metric into Statsd?

When you need to engineer a scalable Statsd ingestion service you need a way to balance between more than one Statsd daemon. StatsRelay provides that functionality. You can also use multiple StatsRelay daemons behind a UDP load balancer like LVS to further scale out your infrastructure.

StatsRelay is designed to be fast and is the primary reason it is written in Go. The StatsRelay daemon has been benchmarked at handling 200,000 UDP packets per second. It batches the metrics it receives into larger UDP packets before sending them off to the Statsd backends. As the string processing is faster than system calls, this further increases the amount of metrics that each Statsd daemon is able to handle.

When shouldn’t you use StatsRelay?

In many cases you might want to run Statsd on each client machine and let it aggregate and report metrics to Graphite from that point. If each client only produces unique metrics names this is the approach you should use. This doesn’t work, however, when you have multiple machines than need to increment the same counter, for example.

What’s wrong with Statsd?

Etsy’s Statsd tool is really quite excellent. Its written in NodeJS which, event driven it may be, is not what I would call fast. The daemon is a single process which only scales so far. Testing showed that the daemon would drop packets as it approached 40,000 packets per second as it would peg the CPU core it ran on at 100%. I needed a solution for an order of magnitude more traffic.

But, Hey! Statsd comes with a proxy tool!

New versions of Etsy’s Statsd distribution do come with a NodeJS proxy implementation that does much the same thing. Similar to the Statsd daemon the code, in single process mode, would top out around 40,000 packets per second and 100% CPU. Testing showed that the underlying Statsd daemons were not getting all of that traffic either.

I checked back on this proxy after it had been developed further to find that it had a forkCount configuration parameter and what looked like a good start at a multi-process mode. I tested it again with my statsd load generator which produced about 175,000 packets per second, which was well inside the packets per second I needed to support in production. Setting the forkCount to 4 I found 4 processes each consuming 200% CPU and 2G of memory each. The code was still dropping packets.

At about 175,000 packets per second this Go implementation uses about 10M of memory and about 60% CPU. No packets lost.

Contributing

Fork the StatsRelay repository and submit a pull request on GitHub.

Things that need work:

  • Add health checking of the underlying Statsd daemons
  • Profile and tune for speed and packet throughput

Docker encourages its users to build containers that log to standard out or standard error. In fact, its now common practice to do so. Your process controller (uWSGI/Gunicorn) should combine the logs from all processes and do something useful with them. Like write them all to a file without your app having to worry about locking. Or, maybe even to Syslog.

Docker supports this practice and collects logs for us. In JSON to add missing timestamps and to work well with LogStash. But, what is a show stopping issue for us is that these files grow boundlessly. You cannot use the logrotate utility with them because the Docker daemon will not re-open the file. Well, unless you stop/start the specific container. Docker logging issues are an ongoing topic and this is clearly an area where Docker will improve in the future.

There are two other widely accepted ways of working around this:

  • Bind mount in /dev/log and off load logs to the host’s Syslog
  • Mount a volume from the host or a different container where logs will be processed.

The second point is out. Same problem of not being able to easily tell the app to re-open files for log rotation without restarting the container.

Using /dev/log and off loading logs to the system’s log daemon sounds like a good idea. The Docker host can provide this service arbitrarily to all containers. Containers need not deal with (much) logging complexity inside them.

This approach has multiple problems.

Off loading logs to the host’s Syslog most likely means that you want to add some additional configuration to rsyslog which requires a restart of the rsyslog daemon. (Say, you want to stick your logs in a specific, app-specific file.) The first thing rsyslog does when it starts is (re-)create the /dev/log socket. At this point, any running Docker container that has already bind mounted /dev/log now has an old socket not the newly created one. In any case, rsyslog is no longer listening to any of the currently running containers for logs. Full stop. This method doesn’t pass the smoke test.

What ended up working for me was using the network, but it added complexity to the Docker host. I’m managing Docker hosts with Ansible so this wasn’t a huge problem. I’d rather tune my Docker hosts than alter each image and container. I set the network range on the docker0 bridge interface to a specific and private IP range. Now, my Docker hosts always have a known IP address that my Docker containers can make connections to. In /etc/default/docker:

DOCKER_OPTS="--ip 127.0.0.1 --bip 172.30.0.1/16"

I configured rsyslog on the host to listen for UDP traffic and bind only to this private address:

$ModLoad imudp
$UDPServerRun 514
$UDPServerAddress 172.30.0.1

I then built my image to run the process with its output piped to logger using the -n option to specify my syslog server. Guess what. No logs.

The util-linux in Ubuntu Trusty (and other releases) is 2.20 which dates from 2011-ish. The logger utility has known bugs. Specifically that the -n option is ignored silently unless you also specify a local UNIX socket to write to. This version of util-linux also does not have the nsenter command which is very handy when working with Docker containers either. (See here for nsenter.) This is a pretty big frustration.

The final solution was to make my incantations in my Dockerfiles slightly more complex for apps that do not directly support Syslog. But, it works. :

CMD foobar-server --options 2>&1 \
    | logger -n 172.30.0.1 -u /dev/null -t foobar-server -p local0.notice

I promise I’m not logging to /dev/null.

I’ve been thinking about and wanting to write about packages for a long time. DEBs. RPMs. Pip for Python. CPAN for Perl. Galaxy for Ansible. Registry and Docker. Puppet modules from Puppet Forge. Vagrant Boxes. Every technology comes with its own distribution format and tool it seems.

My recent transition from RHEL to Ubuntu has made one thing very clear. This mess of packages is intractable. No package format is aware of the others yet they usually have dependencies that interconnect different package types. Pip has no knowledge of C libraries required by many Python packages to build. Us SAs usually end up crossing the streams to produce a working environment. Or we spend hours building packages of one specific type. (Only to spend even more time on them later.) The end result is often different package management systems stepping on each other and producing an unmaintainable and unreproducible system.

I’ve spent, probably, years of my career doing nothing but packaging. The advantages of packages are still just as relevant today as they were in the past. Its a core skill set for running large infrastructures.

Recently, I’ve just about given up trying to deal with packages. Throw-away VMs. Isolation environments. Images. Advanced configuration management tools. Applications with conflicting requirements. Does maintaining a well managed server even matter any more?

I believe it does. A well managed host system keeps things simple and the SAs sane. However, I believe that there should be a line drawn in the sand to keep the OS – and tools that manage the OS – separate from the applications running on that machine or VM. On the OS side of the line, RPMs or DEBs rule. Configuration management has an iron fist. Your configuration management and automation should also deploy your application containers. But now we find the line in the sand.

Your applications, its crazy requirements, as well as whatever abominable package management scheme needed to get the job done should live in Docker containers. Here, your configuration management is a git repo where you can easily rebuild your images. Here, we can use the tools we need that work the best for the situation at hand without causing harm to the host system or another application.

Perhaps Docker “packages” are, finally, the one packaging system to rule them all.

There’s just one thing that itches. I know Fedora out right bans it. Packaging libraries with your applications means that when OpenSSL has a security vulnerability, you have to patch your OS – and find everywhere else that library has been stuffed. Itch. Docker containers seem reasonable about this, but it still means rebuilding and restarting all containers. Itch.

The last several months have been a deep dive into Ansible. Deterministic. Simplistic. Ideally push based. Uses standard YAML. (I’ve never been much for inventing your own language.) Most of this work has been with Amazon’s EC2 service and Ansible’s included dynamic inventory plugin for EC2 has been indispensable.

However, there’s a lot more this inventory plugin could do. I’ve developed a series of patches, some of which I’ve submitted pull requests for. All of which can be had by cloning the master branch of my GitHub Ansible fork.

  • Do you have more than one AWS account? Need to manage machines in all of them? The multipleaawsaaccounts branch teaches ec2.py to query multiple accounts given the proper IAM credentials.
  • Making groups from various tags and attributes in AWS is handy. But I wanted a way to just make arbitrary groups happen. The arbitraryggroups branch supports reading groups from the ansible_groups AWS tag on your EC2 instances.
  • Need additional information about the EBS stores mapped to your instnaces? The blockddevicemmapping branch exposes this as inventory variables from ec2.py.

One thing I’ve always have difficulty keeping straight is what the free command in Linux tells you about buffers and cache memory. Is that buffers and cache used and free (making you do the math of how much real free memory you have) or does the free command do the math for you? In tuning a Nagios setup, I researched it to make sure I had it right.

Misunderstandings about how Linux uses RAM are common and those misconceptions can lead to quite a few false positives in monitoring machines for memory pressure conditions. Linux doesn’t eat RAM, although it will try to use as much RAM as possible to cache block devices which makes your machine much faster.

Remember, free does the math for you. Its goal is to inform you about free memory, not make you do math. :

$ free -m
             total       used       free     shared    buffers     cached
Mem:        257948     256570       1377          0       3336     238963
-/+ buffers/cache:      14270     243677
Swap:       262127          1     262126

The -/+ buffers/cache line’s two values are:

  1. Buffer/cache memory used subtracted from total used memory.
  2. Buffer/cache memory used added to total free memory.

So, in the above example, the value 243677 is the value you want to monitor with Nagios / Graphite and what us humans would like to see as the free memory metric.

More precisely the two values above are calculated like so:

  1. Total Used Memory - Buffers - Cached
  2. Total RAM - Value obtained in #1

See /proc/meminfo for the gory details.

I’ve been thinking about my keyboards of late. This is mine.

keyboard

I’ve had a collection of IBM Model M keyboards since before college and I’ve picked up a few more since. My keyboards are old enough to drink. They click at each other in the ABC store. They feel like home.

Fortunately, what I have known for years (that mechanical keyboards are superior for us that code or work in operations all day) has gone main stream. There are actually quite a few options for new keyboards. A friend of mine recently got a Das Keyboard (the completely blank and black one). I have been looking at the more expensive CODE Keyboard. The backlit keys and minimalistic design strike me.

Are they as good as ye olde Model Ms? Less key travel to actuation perhaps? Not quite as loud? (I’m on the headset a lot with the new job.) Or are buckling springs the best for a RSI free life?

As I’ve been thinking about this through the last week…my mouse button fails.

Nothing ever quite stays the same. The best SAs understand this almost unconsciously. I no longer work for NC State University and have started with a small company called 42 Lines. Its been a challenge, and I’ve been drinking from the fire-hose. Instead of teaching and advising I’ve been learning.

Clearly, this calls for a website make-over. So, welcome to the new LinuxCzar powered by Pelican. I’ve been wanting to move to something other than Wordpress for lots of reasons. Its time to get back to Python, lose the ever annoying comment spam, and make one less Wordpress install to maintain. Best of all, the website can live in Git as all things should.

For a Python fan, I am using the Octopress theme ported to Pelican. This them has stood out to me as fantastic design for a long time. Google fonts and the works. However, it does require Ruby tools to modify the CSS and doesn’t seem to have a support for a lot of reStructuredText directives. So it may not last. Know of a better theme Pelican theme?

However, there is nothing better than being able to write in reStructuredText.

I don’t like partitions.  MS-DOS partition tables or GUID Partition Tables (GPT) alike.  We use them because you always partition a disk, right?  We use them without understanding the ramifications.  The MS-DOS partition table was designed in 1981.  We still blindly use it on almost every machine today.  Do we still surf the Internet with a Commodore 64?

MS-DOS partition tables cannot handle drives of more than 2TiB.  Intel designed GPT in the 1990s to deal with this and add more than 4 (count ‘em, 4) primary partitions.  Its better, and is now part of the EFI standard.  For booting your hardware, a partition table can be quite handy.  Or even required.

However, I’ve long advised folks not to wrap partition tables around their dedicated storage arrays. Growing storage arrays comes up multiple times a week for me.  An outage is required if there are partition tables in use.  A longer outage is needed if we have to convert the array to GPT to cross the 2 TiB barrier.  I have always advised folks use raw LVM physical volumes on their storage arrays.  LVM handles all the manipulation for us in a smooth and consistent way in this case.  No reboots either as long as you instruct Linux to rescan the SCSI bus after a resize.

echo 1 > /sys/class/scsi_device/DEVICE/rescan

Then grow the LVM physical volume:

pvresize /dev/DEVICE

Finally, you may resize the logical volumes and your file systems:

lvresize -l+100%FREE -r /dev/mapper/Volume00-foobar

No outage to handle growth in your dedicated storage array.

With so much infrastructure based on VMs (virtual machines) like KVM or VMWare we can take these concepts further.  Growing a root file system on a VM isn’t hard and shouldn’t require a Linux expert to handle.  Many cloud providers have tools that do this for their customers.  Take into consideration:

  • VMWare and other VM tools are fully capable of doing all of our storage virtualization that us Linux folks commonly do with LVM.
  • Certain enterprise Linux distributions turn off the kernel’s feature to re-read partition tables for safety.  In any case, the only way to safely grok a modified partition table is to reboot.  We can manipulate file systems without an outage, so why do we need an outage here?
  • Depending on the VM’s partitioning and layout, it can require a large amount of skill to move and resize partitions to extend file systems.  If the VM uses LVM that helps in this case.
  • What if, by using a standard method of deployment, your worst case scenario is your VM guys extend the file space, and the customer is told to run one or two commands as root?  What if by using standard methods of deployment you could automate this process?

I’ve been attempting to build a better way for us to deploy VMs.  Each of these VM has 3 different virtual disks:

  1. 512MiB for /boot.  This is partitioned with an MS-DOS table so the machine can boot and the MBR is protected for the bootstrap procedure.  Rarely does /boot need to become larger.
  2. Swap.  2GiB.  Vary according to your needs.  Not partitioned!  Raw swap.  Depending on memory load, resizing swap can be done online.
  3. 30GiB, more, or less depending on your needs.  Not partitioned.  This is a raw LVM physical volume used to build your logical volumes for how you would like your file system separated out.

The Red Hat installer doesn’t support this.  So creating and using an image as a template for your VM farm can be very handy.  But whether you use images or, like me, use Kickstart, you need to get the Installer to actually install this layout.  Below are the relevant Kickstart snippets that will install into the above configuration – at least with RHEL 6.  But once the above can be reached, its a simple matter to grow the third virtual HD and use pvresize and lvresize to extend the native filesystem without an outage to your systems. (My devices here are /dev/vda, /dev/vdb, and /dev/vbc.)

clearpart --drives vda --all

part /boot --size 1 --grow --ondisk vda

volgroup Volume00 vdc --useexisting

logvol /          --size 8704 --fstype ext4 --vgname=Volume00 --name=root
logvol /tmp       --size 2048 --fstype ext4 --vgname=Volume00 --name=tmp
logvol /var       --size 7168 --fstype ext4 --vgname=Volume00 --name=var

%pre
# Clean up any possible left overs...
vgremove -v -f Volume00

# Whipe any partition table
dd if=/dev/zero of=/dev/vdb bs=512 count=1
dd if=/dev/zero of=/dev/vdc bs=512 count=1
partprobe

# Create an LVM Volume Group
pvcreate -y /dev/vdc
vgcreate Volume00 /dev/vdc

# Create swap device
mkswap /dev/vdb -L vmswap
swapon -L vmswap
%end

%post
# Setup swap space in fstab
echo "LABEL=vmswap  swap    swap    defaults  0 0" >> /etc/fstab
%end