More Tuning Golang Apps for High Concurrency Tasks on Linux

I have a project that is fairly straightforward. Again it's work related, so I have to fuzz some details, and again my memory is naturally fuzzy so I doubt it's an issue.

Background

This program I've been working on makes calls to a service (a REST endpoint) that in turn pulls data from a database, then my application parses that information into components and checks the disk to see if the file already exists. If it doesn't exist on disk already, the program makes a call to the API endpoint again asking for specific record information and writes it to the disk. In the end I get a huge set of files sorted in a structure resembling ./files/year/month/day/datatype/subtype/filename.txt.

There are literally millions of records to sort through. A single thread handling this would probably take weeks. Therefore, the program uses several (configurable!) goroutines to pull records simultaneously.

First Problem: Too Many Open Files

I wrote about this fix earlier in the blog, but I'll give a quick recap.

At first everything seemed fine. I have a simple bit of math being output periodically to the console, and it was chugging along at around 20,000 records/minute. The system was functioning fine, no errors were showing up. All was right with the world.

Then a few hours later a few alerts arrived in the email. At this point the utility was running on its own instance with its own storage and was making calls to a load balancer that held a few endpoint servers behind it. The only change to the system that could possibly prevent the system from making connections was the utility I was running, so I killed it, and when the API servers were checked there were still 18,000+ network connections in TIME_WAIT on each system.

Linux systems treat files on the disk as well as sockets as "open files" due to the way Linux handles file handles. Too Many Open Files can mean literally too many files are open or it can mean too many network connections are open, but it usually is a combination of the two.

Research time. The problem here is usually related to "you didn't close the connections." That wasn't the cause here. The calls were straightforward; I had a function that created a transport, created a client, made the connection, and called GET then read the data to return to the caller. It was a textbook example fragment of Go adapted to my purposes, and that included a defer Close() call so when the function exited it should make really sure everything was closed properly.

Check the "did you close the connection" off the list. And I also read the data from the socket before closing it, so that can be checked off the list. I had a hacked together bit of logic to retry connections if there was an error, but it also printed that status to the console when that happened. Nothing appeared as the too many open files errors popped up, so even if that caused a socket leak, it wasn't the likely cause.

The issue was the call to instantiate a transport each time the function was called. Transports hold the pool of client connections; the system should be re-using connections. Because the transport was destroyed each time the function returned, it was creating a new pool of connections, which meant new sets of client connections to the server instead of recycling previous connections and that led to thousands of "open files".

The solution was to create the transport and pass it as a parameter to the call to GET the web endpoint. This allowed the transport to continue to manage the client pool outside the scope of the function call, and that allowed the system to keep a managed pool of connections for re-use.

This wouldn't have shown up if I were making periodic, occasional calls to different websites every few minutes. The problem would still be there, but chances are the connections would eventually close and time out before piling up and becoming a problem.

Too Many Files Leads to Terrible Times

There are a few things that are obvious in affecting the speed information is being processed, and at the risk of sounding immodest, I've been told I'm pretty good at spotting the obvious.

Warning: I'm not a Go expert. I'm citing information here that is just my current understanding, so if I'm wrong, please correct me in the comments.

Because I'm writing files, the drive can definitely affect performance. I have multiple processes that could be trying multiple disk operations in parallel at a given time. To that end, disk seek times, write times, and cache can directly impact the utility's speed.

I'm dealing with millions of files. During the initial testing and design of the utility, I had to deal with a file that would unzip into a directory holding around 100,000 files; then I had to deal with several of those 100K-file containing directories for processing. If you haven't tried that on a Macintosh using the HFS+ filesystem, it's not fun. EXT4 doesn't really handle it well either. Even on an SSD, getting a directory listing is downright painful. Too many files in one directory is difficult for some filesystems to handle.

One solution is to split the directory into more subdirectories, reducing the number of entries the system has to track per directory. This is in fact the solution I used, splitting information into logical subsets.

Timing Out Connections

Another fun fact I learned during this project; by default, the Golang client doesn't have timeouts set. This leads to some fun havoc with stray connections left in weird states that if you're using them to hit random sites in a semi-random fashion, you'd probably never notice. Hammer the same site with hundreds of requests per second, and you can bet this can have some ramifications.

I read about this in a blog post warning against using the default settings in http.Client. After reviewing that information, I went back to the source code and added some timeouts, like so:

 tr := &http.Transport{
  Dial: (&net.Dialer{
   Timeout: 30 * time.Second,
  }).Dial,
  TLSHandshakeTimeout: 30 * time.Second,
 }

 client := &http.Client{
  Transport: tr,
  Timeout:   time.Second * 10,
 }

This is a modification I made to the most intensively-used connection set; I didn't move the transport's scope for a far less-used connection in another function, figuring that yes, they would pile up to a degree, but they should properly close and age out as closed connections. This set will hammer the server with thousands of connections in parallel.

This basically added some sane timeouts to functions that previously did not have any timeouts. This helped noticeably reduce my ghost connections disappear.

Remove a Hindrance, Create a New One

The initial run finished a few days later. I realized that there was a bug in my loop logic. There were some bad words uttered and an updated version compiled.

At this point we also moved the utility, and the volume to which data was being saved, to the same system that held the API endpoint server. Basically the server being queried for information was now also hosting the client requesting and processing results from the API queries.

This eliminated what before was creating a kind of natural bottleneck that throttled performance; hundreds of connections per second simultaneously hitting the server but separated by the network transit time. Sure, it was on the scale of tens of milliseconds (if things were working well), but it really added up.

Now the client was requesting it from the localhost. *Bam*. Within a few moments, the number of open connections (using netstat |wc -l, since I only needed a rough estimate) ballooned to 40,000 connections before this appeared on the console:

dial tcp <ip address redacted>: can't assign requested address

Because dial was in the error, it was most likely the client causing the issue. After some poking around, I ended up making two more changes.

First, I tried to make a change to the number of idle connections the client keeps open. The default is two; more than that, and the client was closing the connections in the idle pool instead of making more efficient use of re-using the clients. Again, working with random connections aren't so bad, but hammering the same IP will highlight the need to alter this (and you probably don't want to change this if you're not making a large number of frequent calls to the same host):

 tr := &http.Transport{
  Dial: (&net.Dialer{
   Timeout: 30 * time.Second,
  }).Dial,
  TLSHandshakeTimeout: 30 * time.Second,
  MaxIdleConnsPerHost: intIdleConns,
 }

The changed setting is MaxIdleConnsPerHost in the transport. Here I set it to a variable that in turn is set from the command line so I could tune it at runtime, but instead of the default 2 I set it closer to 400.

The next change was an alteration on the host server. There is some guidance on a SO question explaining some tuning tweaks, but the gist of the change I made is this...

When the TCP connection is made, the connection is made to an ephemeral port. When I have a ton of tcp connections hitting the server, it would starve the number of ephemeral ports available. The next step was to try increasing the number of ports available, and then the server could support more connections per second, hopefully at a level where the connections would close and age out properly before overloading the system.

In this case, I changed net.ipv4.ip_local_port_range from "32768 61000" to "9000 64500".  From the SO question, this means I changed the connectivity from (61000-32768)/60 = 470 sockets/second to (64500-9000)/60 = 925 sockets/second.

There was another change I could make from the page that involved changing the net.ipv4.tcp_fin_timeout setting, along with a couple of others. I avoided that, opting instead to test these changes because the tuning advice was more like "change this on the client" or "change this on the server", not really geared to a situation where the server and client were eating resources on the same host. Making minimal changes to keep it working, for this project, would be fine.

I ran netstat in a loop while the application ran again. This time the open connections quickly climbed to 70,000 connections before leveling out, and it held steady. After 15 hours of elapsed runtime, it had 3 connection errors show up. Otherwise it kept up with the load just fine.

I should also mention that I ran 4 parallel processing tasks, one for each core. When I boosted that number it seemed to be a hindrance to the processing speed; keeping it at 4, the estimated processing speed was over 100K records/minute, easily holding sustained bursts 5 or 6 times the processing speed when the client was on a separate machine.

This Was a Minimal Set of Changes

There were a number of lessons learned so far; above the basic novice checking that connections are properly read from a network client response before calling Close(), be aware that the transport is what controls the pool of connection clients for efficient re-use. 

Next, be aware that by default timeouts are missing from the transport and client. Add them. 

Also if you're hitting a particular server or set of servers with requests, change your MaxIdleConnsPerHost. Otherwise you're wasting connection use.

Last, an easy way to boost connection rates is to increase the number of ephemeral ports available. There are limits to this...and you don't want to starve other resources by taking away those ports from other clients or servers on the host.

There are plenty of other changes that can be made to increase horsepower of your servers. Some additional changes are in the SO question I linked to; another good blog post discusses how MigratoryData scaled servers to 12 million concurrent connections. I'd only caution that not every task requires this kind of engineering and you might want to exercise restraint in changing things when a few tweaks can accomplish decent performance for your use case. 

Performance is a scale. Some things can be overcome with throwing lots of hardware at it. Sometimes a few tweaks will make your app run 5 or 6 times faster. 

Happy tuning!

Golang: HTTP Client Opens Too Many Sockets ("Too many open files")

This relates to a project that is work related, so I have to fuzz some of the details. But on the other hand, some details are naturally fuzzed because I have to remember some of the details and my memory is naturally fuzzy...

I'm working on a utility that is, on the surface, simple. It makes a call to an API endpoint using the http.Client, compares some quick results, and if certain conditions are met it makes a series of API calls to save the JSON responses.

The processing/checking process is carried out in a set of goroutines because the comparisons are easy to do in parallel. If the routine needs to pull a JSON reply, it calls a function that is laid out pretty much like the standard examples from the "here's how you get a web page in Go!" sites.

func GetReply(strAPI string, strServer string) string {

    // The URL to request
    strURL := strServer + “/service/" + strAPI

        // Also add timeouts for connections
        tr := &http.Transport{
            Dial: (&net.Dialer{
               Timeout: 5 * time.Second,
            }).Dial,
            TLSHandshakeTimeout: 5 * time.Second,
        }
    client := &http.Client{
        Transport: tr,
        Timeout:   time.Second * 10,
    }

    // Turn it into a request
    req, err := http.NewRequest("GET", strURL, nil)
    if err != nil {
        fmt.Println("\nError forming request: " + err.Error())
        return ""
    }
    req.Header.Set("Content-Type", "application/json")
    req.Header.Set("Accept", "application/json")

    // Get the URL
    res, err := client.Do(req)
    if err != nil {
        fmt.Println("\nError reading response body: " + err.Error())
        if res != nil {
            res.Body.Close()
        }
        return ""
    }

    // What was the response status from the server?
    var strResult string
    if res.StatusCode != 200 {
        fmt.Println("\nError reading response body, status code: " + res.Status)
        if res != nil {
            res.Body.Close()
        }
        return ""
    }

    // Read the reply
    body, err := ioutil.ReadAll(res.Body)
    if err != nil {
        fmt.Println("\nError reading response body: " + err.Error())
        if res != nil {
            res.Body.Close()
        }
        return ""  
   }
    res.Body.Close()

    // Cut down on calls to convert this
    strResult = string(body)

    // Done
    return strResult

}

This is actually a modified version of what I've pulled from various tutorials and examples, adding more calls to Close() and doing a check for whether res is nil before performing that call in an error.

I also added a timeout to the client because by default it is set to 0; no timeout. As you can probably guess this version was modified while troubleshooting.

After a few hours of the application running we had alerts come in about failing functions on the production servers. When I opened logs I discovered a number of "too many files open" errors, and a developer on the call said there were over 18,000 socket connections on each of the balanced servers.

The only difference was my use of this test program, so I killed it. The socket count fell.

Welp...guess we found the cause. But why?

There are a couple basics for beginners when using http.Client requests.
1) Close the response body after reading.
2) Clients are reused.
3) If you defer a call to Close() (as this one originally did, and most tutorials show) the function should call Close() when the function returns. The modified sample I posted simply closes it after reading the Body and checking for errors.

At first I thought it was due to clients not closing; they must close in order to be re-used. I traced the execution path a dozen ways and added more explicit Close()'s in error checks...but those errors were never printing anything during the run, so errors shouldn't be causing spill of sockets.

I added timeouts to the client and dialer. While that didn't hurt and probably made things a little cleaner, it still didn't help the too many open files/sockets error.

Another lead came from a close reading of a Stack Overflow answer. The function is creating a new Transport, tr, with each call. That Transport is what holds the Clients pool for reuse. See where I'm going with that?

Another answer on that page talked about creating a global client for his functions to reuse.

The theme was scope of variables matters when dealing with what allows re-use. Because I'm hitting the same server repeatedly and the function kept re-instantiating the mechanism that was used to govern client re-use,  the number of new connections and left-open sockets ballooned.

My next move was to go to the goroutines that were in charge of processing the replies from the API endpoints and have them create Transport instances, then when they call the function they passed the Transport as a parameter.

I uploaded the program to a remote system instance and re-ran it while watching netstat on the server and the client systems. After initially ballooning to about 4,000 connections it soon settled down to well under 100 connections (using netstat |wc -l).

Takeaways:
1) Modify the default client, and maybe the transport dialer, to add sane timeouts.
2) If you're hitting the same server repeatedly, do it all within the same scope as your transport instantiation or create a transport and pass it as a parameter to functions so you optimize the re-use of the client pool
3) Check that you properly close the response body so the client can be re-used. Check in error paths that it can be properly closed without panicking.

What about separating not just the Transport, but also the Client, then passing the Client around as a parameter? I didn't test that because I wasn't sure how "goroutine-safe" that would be against race conditions, despite the one answer on that Stack Overflow that demonstrated using a global Client instance for use.  It's possible it works fine. At this point it looks like passing the Transport worked fine, though.

I'll also note that my usual self-loathing and insecurity isn't getting the better of me this time because the top answer on that question that inspired me to try this solution was the usual advice I found repeatedly in other sites and blogs (and SO answers); check that you close your response properly. It's the top answer by a significant margin. It was almost an afterthought to realize that maybe what I was doing was pummeling one particular website with multiple instantiations of Client pools so Client reuse was a minimum.

Happy HTTP Client-ing!

Your Experiences Create Your Methods

Sounds obvious, doesn't it?

But at the same time, I feel like it's one of those things that shapes our worldview to the point where you lose sight of the fact that it's obvious; we end up taking our views for granted and ignoring why you approach problems the way you do.

(Or, perhaps worse, we ignore why other people approach problems the way they do, which in turn you react towards them in a possibly negative fashion.)

I'm thinking of this because the other day I was working with a coworker on a problem assigned to us by a manager. Without getting into too many details, one step involved a program reading a list from a text file.

The file was tens of thousands of lines; the program expected the format:
12345,string of text,state_name

The file we got was formatted:
12345,"string of text",state_abbreviation

We were coming up with a game plan and reviewing steps when the file came in, and were divvying up the work needed to get the ball rolling.

My very first thought was to write a Go program that read the file contents into a slice, range over the slice to replace the comma-quote and quote-comma with just commas, then split by comma and replace the item[2] with the full state name using a map I could copy and paste from a previous program I had worked on. Give the size of the file to work on, it shouldn't have taken too long, from my estimation.

My coworker volunteered to reformat the file. After he completed it, I asked him how he cleaned the file. His background is ostensibly in sales, although he also does programming in PHP and can create mockups and web utilities for other employees to use in pulling reports and demos, so I expected he ran it through a one- or two-line PHP filter or something similar.

He pulled up Excel, imported the file as a comma-delimited file, then showed me a formula that pulled state abbreviations-to-full-names from another spreadsheet he already had set up.

In a way the approach wasn't too different. We split the lines into fields and used what amounted to a map to do a value replace, then export the results to a new text file. But the execution was very different. His sales experiences, and having to deal with formatting reports from our system, meant the first tool he used to solve the problem was a spreadsheet (which was a faster and more efficient solution than I was going to use for a one-off reformatting job like this.)

I've been working heavily on Go-based utilities; manipulating log files, manipulating APIs, making text dance as it was processed through pipelines and sending results through databases and monitoring systems. When I saw this text file I immediately saw strings.Split and map[string]string solutions running through my head.

What other solutions are there? Tons, no doubt. Filtering through a series of AWKs and pipes and redirects...maybe PERL...maybe PHP...I know there's plenty of people who would have used Excel to import it and alter it by hand. While I'd probably argue that the manual method could be considered "wrong", I'm equally sure there are people who would have arguments why every approach considered (or used) would have been "wrong."

In the end it was the (timely) results that mattered.

So next time you see someone with a different approach to doing something, don't be quick to criticize. Think about why that person has that approach. Maybe they do know something that is more efficient. Maybe not. Sometimes it's interesting to learn how someone came to use the methods they use and you'll learn something about what it's like for people who aren't you.

Programming a Stargate

I've really loved using the Go language. Part of my exploration and tinkering has involved side projects where I'd pull information from outside sources, usually websites, and parse the response for the information I'm looking for.

I always try to be a good citizen for web scraping; I pull the minimum information I need, close connections once I get the response, insert delays between multiple page views, etc. I always try to put only as much load on a service as a regular user would when web browsing.

"What does that have to do with Stargates?"

I really like Stargate. SG-1, Atlantis, or Discovery, doesn't matter (except the animated series...I pretend that doesn't exist.)

Some people hate it when geeks watch movies and get nitpicky about details. "CAN'T YOU JUST ENJOY THE MOVIE?!"

Not always, no. When I enjoy something, I'm the type of person who enjoys not just the story, but the universe in which it is set; this means learning about the feasibility of that story universe. Oh, sure, there are some rules you have to accept in order for that story to work (such as faster than light travel magic handwaving or using lightsabers and not having them vaporize anything too close to the wielder since, you know, REALLY HOT PLASMA...)

One of the key bits to Stargate involves using the Stargate; the dial home device for Earth's portal was not found with the gate. The device can, however, be manually "dialed", which is what SG command does...they have a computer control massive motors that sets each of the chevrons into a lock position, as well as reading diagnostic signals from the gate.

The show handwaves a lot of this process away, but I think it's implied that someone had to program the computer to attempt dialing control and reading (and sending) signals to control the gate. It's a black box; they needed to figure out "If I do X, do I get Y?" and more importantly, "Do I get Y consistently?" (Then maybe figure out what Y means. I mean, you're screwing around with an alien device that connects to other worlds, after all...) I like to think about what it took for that person to approach that black box and coax information out of it in a way that was useful.

Getting information from these websites, designed for human interaction using a web client, is like trying to programmatically poke a stargate. In the process I've discovered that our many websites are frustrating and inconsistent (I sometimes wonder, when I just want to get a list of text to parse, how many common websites are compliant for devices used by people with poor eyesight or braille systems.)

For example, I tried looking at a way to query the status of my orders from a frequently used store site. I thought it would be simple...log in and pull the orders page. Nope. If you order too many items, you might have to query another page with more order details. Sometimes order statuses change in unexpected ways. The sort order of your items isn't always consistent, either. And those were the simpler problems I encountered...figuring out consistency in delivery estimate

I tried a similar quick command line checker for a computer parts company. Turned out they had far more order statuses than I thought they did, and alerting me to changes in that order status was an interesting exercise in false alarms when they'd abruptly change from shipped to unknown and back again.

Another mini-utility I worked on was checking validity of town locations. Pray you never have to work with FIPS...

The website I chose seemed to be fairly consistent in the format of the information. Turns out I was naive in how various towns are designated, and this website was not internally consistent in showing information in a particular order. I get all sorts of interesting but very weird results for different areas around the country.

I'm sure that if I had a dial-home device (in this case, a clear API to the websites or access to an internal database) these lookups would be more straightforward. As it stands, the closest API I can use is the same as anyone with a mouse and keyboard...parsing the web page.

While frustrating at times, I am thankful that these mini-projects have taught me a few things.

• Websites, some of which I've routinely used, are not as standardized as I thought within their own site. I just hadn't noticed when I'm searching for particular information the items I click on to get what I'm searching for.
• I end up rethinking a lot of parsing logic when digging and sorting through human language.
• Web sites implement some seemingly convoluted logic for interacting with clients and I now have a new appreciation for web browsers.
• I also have a new appreciation for the usefulness of a good API. If I start a business and there's anything that can be exposed through API, I'm making it available through an API.

Learning By Creating Support Applications

Not long ago I started a job with a company whose primary product is a very custom application that is comprised of many smaller interoperating applications. Without getting into too much detail, the applications communicate through various APIs, many of which are not well documented.

(What follows are thoughts that are not focused solely on the new employer, but rather a set of experiences I've gathered over the years from several jobs and interactions with others in the technology field. In other words, this isn't about the current employer. It's a conglomeration of experiences, and it's my own opinion. Just figured I'd have to clarify that...)

As a company focuses on growth, there comes a time when maintenance and monitoring is moved to staff that are dedicated to those tasks so the developers no longer have to do triple duty; for the new hire tasked with pioneering that position, gathering statistics to get a feel for the behavior of their systems over time, and taking care of regular maintenance and basic troubleshooting is very daunting when there is little (or no) documentation available outlining how to get the necessary metrics for gauging the health of the system.

And it isn't just a lack of documentation that acts as an obstacle. When a software-based company is first conceived and grows, it's natural for the programmers to work on getting the product into a usable, testable state. This means overcoming problems as they arise and focusing on results, not laying framework for delegating future operations.

That fosters institutional knowledge. The more of your system that is developed in-house, the more information future maintainers must glean about your system without help of outside references. Sites like Serverfault can help when you're trying to figure out why a new deployment of Nginx won't work, but it won't be useful when a log contains output from a Java application Bob, three desks away, wrote while debugging a particular reply encountered from another subsystem's API response.

Small companies with a small number of developers may feel it is inconvenient to be interrupted by the new person's constant questions about why application A is dependant on application B, or how application C discovers a service status on server 3. As a new hire, I feel a little hesitant to approach others with these types of questions, preferring to try looking for answers through other means before taking someone else's time.

(In my opinion, if the answer is to check the source code from the repo and read that to get the answers, you may as well have hired a new programmer; recognizing a need for someone dedicated to operating and maintaining your system outside the coterie of coders is a sign that there may be a need to dedicate time to documenting and tooling the application for non-programmer use.)

How can a new hire get a grasp on this situation?

In this case, I've been writing a series of Nagios plugins specifically configured to pull metrics from the various subsystems in the company application. There are cases where I thought a simple task was actually more nuanced that first appeared; each time, I ended up discovering something more about the operation of the system, and I made sure it was documented for later reference.

Each time there's a failure case, I would make a note and start work on a new monitor so we'd know about it in the future. These monitors didn't just collect a snapshot of the current state of a service, it would gather some metric that was then sent to a database and from there plotted on a graphing application for performance monitoring.

The current product relies on database performance; some queries behave different from others, where some are straightforward and others require processing of filters. Some of my checks measure response times.

Others are querying API endpoints for replies of what the services believe are their current health states.

Some queries are pulling the status of database indexing.

In cases where the application is exposing information through Java beans, my plugins are pulling numbers from JMX and checking for values within established expectations.

In other cases, plugins are checking for the existence of files that are supposed to be regularly updated and when certain records are updated in the database.

Each of these plugins, once finished and deployed, are being documented for operation in a way that when new people are hired he or she should be able to easily find a list of how these work and gather indirect information on some aspects of the in-house application operation without programmer-level institutional knowledge.

In the case of my new position, I've gained a higher respect for the value of meta-applications in gaining insight on how a complicated system works. Having information written out or explained to you is enlightening, and I never feel that documenting how something works is a waste of time. But until you find yourself executing on that knowledge, I'm not sure you really understand the subject. Creating support applications that meaningfully interact with the system pushes knowledge into the realm of wisdom the way reading about the science of flight comes alive after building your first remote control plane.

When confronted with the task of comprehending the colossal, try learning about the limited first with applications that monitor and interact with small aspects of the system. Not only will others benefit with the support applications, but you'll benefit with the mental exercise and in the end have a better model of how everything works!

Golang: Remember This When Using Select For Monitoring Channels

I thought I would share something that's easy to overlook when using a loop to listen for messages from channels in Go.

The following is a simple code snippet:

for {

    for a := range structOfChannels {

        select {

        case msg := <- structOfChannels[a].Chan1:

            // Something

        case msg := <- structofChannels[a].Chan2:

            // Something

        default:

        }

    }

}

All this is doing is rotating over a series of channels for a message and processing them. The default case makes sure the loop doesn't get stuck after the first iteration of "select", and the for without conditions means continue until eternity.

I noticed, when running Activity Monitor (this was using Go 1.8 on OS X) that the processor would stay near 100%. The system seemed responsive, but the processor staying that high was, to me, annoying.

The solution was simple; make the loop wait a fraction of a second each iteration.

for {

    tmTimer := time.NewTimer(time.Millisecond * 50)

    <-tmTimer.C

    for a := range structOfChannels {

        select {

        case msg := <- structOfChannels[a].Chan1:

            // Something

        case msg := <- structofChannels[a].Chan2:

            // Something

        default:

        }

    }

}

This just makes the loop wait 50 milliseconds before ranging again, a pause smaller than most humans would perceive but enough for the computer that it dropped the processor use to near nothing. 

There are a few other approaches that work, but have a similar effect. For example, if you're worried about the overhead of creating and freeing the NewTimer(), you could create a NewTicker() outside the for{} scope and keep reusing that. You can also probably lower the milliseconds to smaller values and see where the processor starts kicking up, but I'll leave that to the reader to experiment and tune.

The point is, because the system seemed responsive, it was easy to overlook the effect of a simple for{} loop used to monitor messages from goroutines and there's a possibility this could have an effect when deploying to servers. Check your performance when testing your work!

When To Use "+" And When To Use "%20": Adventures In URL Encoding

I've been working on some Go-based utilities to interact with a website application written in Java. Part of this involves, in many cases, encoding database queries that are submitted to API endpoints on the Java application and interpreting the returned results.

In the process I learned something new about encoding easier to read/more human-like strings to encoded strings for the server. Namely, the standards seem broken.

I jest, but really the trouble was a matter of "it works if you know specifically how to make it work for this case."

My workflow would involve a Curl command line from a coworker with a library of working queries he had scripted out for use in other situations. I'd take that and translate it into the utility or Nagios plugin I was writing.

I took the string used in the Curl sample and feed it into Go's url.QueryEscape(), then send it to the database endpoint with

req, err := http.NewRequest("GET", strURL+"?q="+strQuery, nil)

...which promptly spit back a syntax error. Huh?

A little digging later and I found that there are standards defining an encoded space can be either "+" or "%20". And it wasn't necessarily clear when each was acceptable, and different languages varied in the strictness of their interpretation of standards.

The first red flag here is that language encoding libraries implement these changes differently, but I still felt kind of stupid at first not knowing what I was doing wrong. My self-flagellation eased a bit when I saw this was even a bug report for Go. It didn't go anywhere in terms of changing things; the language still encoded spaces as pluses and not percent-20's, but it at least acknowledges that I'm not the only one scratching my head why it wasn't working as expected.

A more elaborate answer was found on Stack Overflow. It wasn't the top answer, although each gave some elaboration on the issue, but the best one boiled the situation down to the existence of different standards for what part of the URI was being used, and backwards compatibility meant that %20 is generally safest to use but technically the %20 should be used before the ? in a URI and + be used after the ? for denoting a space.

In my case, Go liked the + for escaping strings and eschewed the percent-20. My fix? Right after running the url.QueryEscape():

strQuery = strings.Replace(strQuery, "+", "%20", -1)

Not the most elegant, but when I submitted that strQuery, the Java application was happy!

My takeaways:
1) Something I thought was simple...feeding a string to an escape function for encoding properly to a URI format...isn't necessarily straightforward. If you have trouble, find out if your application is expecting pluses or %20's for spaces.
2) Computers are binary...it works or it doesn't. But implementations of standards are still influenced by people, and languages (and libraries) are implemented by people, so even given the constraint of binary...people still make things more complicated in practice.
3) Given the confusion of + versus %20 when searching around online, I'm not the only one having this kind of issue.
4) Just use %20. Unless I run into a specific case where the other side isn't translating %20 correctly.

Creating a Test Ensemble with ZooKeeper and VirtualBox

What is ZooKeeper? It's an Apache project creating a server that allows distributed information and message storage for distributed processes. If you have a number of servers that need to coordinate certain information, ZooKeeper might be useful, especially if your project uses Java.

The interface is very reminiscent of a simple filesystem, using "znodes" as files that can have sub-znodes containing more information. In addition to passing and storing small bits of information (less than a megabyte per node, as I recall), znodes can also be set to ephemeral, so when a server connects to the ZooKeeper ensemble (cluster), a znode is registered and other servers can find that server. When the server goes offline, the znode disappears, so your application can be informed (if it sets a watch on that znode) if that server is no longer available to the cluster or it can search for the available servers (znodes) before connecting to that system.

That's the simplest overview.

I created 3 ZooKeeper nodes using VirtualBox.

I first installed 1 Ubuntu-Server VM. I named it Cluster1 for the VM name and hostname, used Ubuntu Server 64 bit with version 16.10. The VM had an 8GB drive (sparse drive so it didn't eat all the space on my workstation right away), 1GB RAM and bridged ethernet.

While running through the install I added the SSH server package when prompted.

Once the VM was running I ran
sudo apt-get update
sudo apt-get upgrade

I also ended up running
sudo apt-get dist-upgrade

At that point it no longer had packages to update nor packages held back.

I shut down the VM and told VirtualBox to clone it. The first was named Cluster2 and the second clone became Cluster3. During the clone wizard step-through I told VB to reinitialize the MAC addresses on the network cards and do a full clone so these are independent VMs.

I fired up Cluster2 and changed the hosts file and hostname file in /etc to reflect the fact that the machine is cluster2, not cluster1, then repeated the step for Cluster 3. A restart of the two machines should now show the proper names for the machines.

Now I have 3 small servers running. In each of them, I ran
sudo apt-get install zookeeper

I edited the /etc/zookeeper/conf/myid file so cluster1 had the value 1, cluster2 had the value 2, and cluster3 had 3. In the /etc/zookeeper/conf/zoo.cfg file, I added the IP's for each of the three machines reflecting the 1,2, and 3 values, like so (just for the specify zookeeper servers section):
server.1=192.168.254.1:2888:3888
server.2=192.168.254.2:2888:3888
server.3=192.168.254.3:2888:3888

I used the IP's for each server because I didn't edit any hosts file or local DNS to allow finding these ZooKeeper servers by name, although it could certainly be done. On the other hand, using the IP means no DNS lookup, so I might have shaved a few milliseconds off communications.

The default install didn't have any service scripts, so "service zookeeper restart" leaves Ubuntu scratching it's head at you. Install some add-on scripts using:
sudo apt-get install zookeeperd

At this point I can run
sudo service zookeeper restart
sudo service zookeeper status

A basic ensemble (or cluster) should now be running!

How do you test this...or at least do something with it? There's a Java CLI tool included with ZooKeeper, but it turns out there's a bug where a particular environment variable isn't set. It's not a big deal...just set it before trying to run the tool.
export JAVA=java

Now you can run the tool. This will launch it, and connect to a local server instance.
/usr/share/zookeeper/bin/zkCli.sh -server 127.0.0.1:2181

From here, you can use the "help" command to get a list of available commands. To just kick the tires a little, I ran these commands:
ls /
create /zk_test My_Data
ls /
get /zk_test
set /zk_test test
get /zk_test
delete /zk_test
ls /
quit

And as I ran through the list of commands (creating the zk_test znode, seeing the data stored as the string "My_Data", setting the data to "test", and finally deleting the znode) I would list and set information from different VMs to see that the data was synchronizing properly.

Time Machine With File Vault Corruption: Reformatting the External Drive

I've already written one post that went into detail about reformatting an external drive that acted as my Time Machine backup for my Mac. External USB drives can be bumped and the cables loosened, raising the chances that data corruption will occur; encrypted drives are really cranky when that happens.

In my case, reformatting and starting over is acceptable for recovering and getting the system backed up again as long as there isn't any indication that the hardware itself is failing.

This time around any time I tried accessing the data was met with failure; from the diskutil command line utility, it seemed that there was an encrypted volume being found and mounted by the system (even though it wouldn't appear in Finder nor in Disk Utility...Disk Utility kept hanging with a spinning beach ball and wouldn't show any drives mounted at all when it launched...). Diskutil seemed to show the existence of an unlocked encrypted volume on my Time Machine drive, but it had no data, and attempts to reformat the drive using the command line returned an error to the effect of "resource busy."

After several attempts to read the volume I decided to try just obliterating the data on the drive by wiping as many sectors as I could at the beginning of the drive. Unfortunately, attempting to do that returned a resource busy response. A daemon on OS X was trying to access the drive, and that prevents direct access to the device.

But there is a way around it.

Use this command to identify your external drive (the Time Machine drive, in my case)

diskutil list

Su to root.

sudo su

Disconnect the drive from the USB port. Then use the up arrow and enter key to repeat this command when plugging in the drive again. The goal is to execute this command just after the system sees it, but before the auto-mount daemon tries to be helpful and prevent your access...replace "disk1" with with the correct disk number found with the diskutil command above. Triple check that you have the correct drive. If you overwrite the wrong drive you will be very unhappy and it's not my fault.

cat /dev/random > /dev/disk1

This will overwrite data on the drive with random gibberish. Once enough sectors...like the partition table...the drive will be seen as ready to be formatted by OS X. This process is not going to give much feedback...and since the drive is large, it could take forever to complete. I advise letting it run for several minutes then use control-C to abort the command.

At this point I used Disk Utility to format the drive, then opened Time Machine to remove the previous backup drive and re-add the "new" one.

And yes, I re-encrypted it. I'd rather not let the backups be readable by others, despite the risk of corruption wiping the backup, and creating a new set of backups took only about a day to complete.

Apple Remote Desktop (ARD) Can't Find Machines

One of ARD's more entertaining tricks is "forgetting" machines on the network. I'm still not sure what triggers this, but it certainly is among the more annoying behaviors to crop up.

There are a couple of sites that mention this kind-of sort-of trick to kick ARD in the head, but I thought I'd make a note here for my own quick reference in one place. These notes should work on El Capitan (10.11) and Sierra (10.12).

Summary: Remove cached settings from ARD, remove network DNS/ARP caches on machine, kick ARD in the head...

1. In ARD, go to the All Computers list, highlight the machine names and delete them.
2. Quit ARD.
3. Flush DNS cache: sudo dscacheutil -flushcache;sudo killall -HUP mDNSResponder
4. Flush ARP: sudo arp -ad
5. Kick ARD in the head by restarting the ARD agent (on clients): /System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -restart -agent

Start Remote Desktop again and re-scan the network. Because the clients were removed, attempting to view/connect may require you to re-enter credentials.

Also keep in mind I noted testing this on El Capitan and Sierra. Another annoyance with OS X releases is that the syntax/procedure for flushing DNS changes alarmingly often, so it may take some Googling if your release is different.

The last note I have is that if this doesn't work, check that a network hiccup didn't force the client's wireless to shrug its shoulders and give up, meaning that the actual problem all along was that the client was not able to be remotely managed over the network all along.

Whoops.