Scan - Making string manipulation efficient

One of the many things that makes iRules so powerful is the ability to inspect data and break it up into custom variables. You can store whatever information you need in your desired format and put it into memory on the BIG-IP for use later. Whether you decide to use it for the same request, the same connection or whenever you see the next connection with similar data come through, that's completely up to you. To make variables so valuable, however, you need to be able to not only pull data off the wire, but to break it up and manipulate it into usable formats and pieces.

TCL, which iRules is based off of, offers many ways of performing this type of data manipulation. Depending on your needs you can split, format, sort, concat, increment, join, and list data. There's even the powerful string command and its many sub-commands that allow you to manipulate strings to great effect. An extremely useful and powerful command that is often overlooked, however, is the scan command. Not only is this command full of utility, but it's efficient as well, making it a great choice in many situations once you understand how to use it.

The scan command's definition over on sourceforge says - "scan - Parse string using conversion specifiers in the style of sscanf" - so what does that mean? It means that you can use the scan command in lieu of other commands, depending on the situation, to do things like string conversions and splitting data into substrings. Think of it almost like a limited regular expression for a single string, but much, much faster.

The way the scan command works is by taking a string and breaking it down based on user supplied "conversion characters". A conversion character is a character specified for the scan command that dictates what type of data is being read in, and the type of output the command is going to have. These characters always begin with %, and can take some supplied arguments such as maximum string length, which you can read more about on sourceforge. Here are the available conversion characters:

d
     The input substring must be a decimal integer. It is read in and the integer value is stored in the variable, truncated as required by the size modifier value.

o
     The input substring must be an octal integer. It is read in and the integer value is stored in the variable, truncated as required by the size modifier value.

x
     The input substring must be a hexadecimal integer. It is read in and the integer value is stored in the variable, truncated as required by the size modifier value.

u
     The input substring must be a decimal integer. The integer value is truncated as required by the size modifier value, and the corresponding unsigned value for that truncated range is computed and stored in the variable as a decimal string. The conversion makes no sense without reference to a truncation range, so the size modifier ll is not permitted in combination with conversion character u.

i
     The input substring must be an integer. The base (i.e. decimal, binary, octal, or hexadecimal) is determined in the same fashion as described in expr. The integer value is stored in the variable, truncated as required by the size modifier value.

c
     A single character is read in and its Unicode value is stored in the variable as an integer value. Initial white space is not skipped in this case, so the input substring may be a white-space character.

s
     The input substring consists of all the characters up to the next white-space character; the characters are copied to the variable.

e or f or g
     The input substring must be a floating-point number consisting of an optional sign, a string of decimal digits possibly containing a decimal point, and an optional exponent consisting of an e or E followed by an optional sign and a string of decimal digits. It is read in and stored in the variable as a floating-point value.

[chars]
     The input substring consists of one or more characters in chars. The matching string is stored in the variable. If the first character between the brackets is a ] then it is treated as part of chars rather than the closing bracket for the set. If chars contains a sequence of the form a-b then any character between a and b (inclusive) will match. If the first or last character between the brackets is a -, then it is treated as part of chars rather than indicating a range.

[^chars]
     The input substring consists of one or more characters not in chars. The matching string is stored in the variable. If the character immediately following the ^ is a ] then it is treated as part of the set rather than the closing bracket for the set. If chars contains a sequence of the form a-b then any character between a and b (inclusive) will be excluded from the set. If the first or last character between the brackets is a -, then it is treated as part of chars rather than indicating a range value.

n
     No input is consumed from the input string. Instead, the total number of characters scanned from the input string so far is stored in the variable.

That's a lot of options, I know, but we won't try and cover them all here. You can get the official sourceforge documentation for the scan command here. I'll let you read up on the entirety of the command options there. We'll just go over a few here to get you started and show you how the scan command can be useful in your iRules.

Let's start with something simple and go through a few examples. Perhaps I have a string that I want to separate into multiple parts. Maybe it's a three part host name like you'd often see on the web, such as "www.mydomain.com". If I wanted to break this apart into three separate variables, I could use scan like this:

when HTTP_REQUEST {
  scan [HTTP::host] %s.%s.%s prefix host suffix
  log "Prefix = $prefix; Host = $host; Suffix = $suffix"
}

In the above example we're using the conversion character s, which reads in all characters until the next whitespace, and separating the hostname into three separate values. We're then logging those values, which would result in an entry in /var/log/ltm like:

Apr  1 16:18:50 tmm tmm[293245]: Rule rule_log_hosts : client Prefix = www; Host = mydomain; Suffix = com

As you can see, the string was split apart much like using the split command, which would have resulted in a TCL list. The scan command, however, is more efficient, not to mention you get to specify your own variable names, which can be helpful at times, and save you from having to perform further commands to dump list values into single variables later, if that's the format you want them in.

Another thing that you can do with scan, which it excels at, is number manipulation. In this example let's split apart a date string. We'll take the string from the above log entry - "Apr 1 16:18:50". What if we wanted that separated into individual strings containing each date part? It would look something like:

when CLIENT_DATA {
  set myString "Apr  1 16:18:50"
  scan $myString %s%s%d:%d:%d month date hour minute second
  log "Month = $month; Date = $date; Hour = $hour; Minute = $minute; Second = $second"
}

Notice how we use a single command to extract many different values in multiple data types separated by different characters. This would take many split and string operations to achieve, or a more expensive regular expression. The above log statement should output:

Apr  24 11:24:50 tmm tmm[142512]: Rule rule_log_dateparts : Month = Apr; Date = 1; Hour = 16; Minute = 18; Second = 50

As you can see we now have the individual values stored in their respective variables, ready for use in whatever manner we need them. Now that you've seen a couple of ways to split data up with scan, for this last example I'll show you a way that you can verify string formatting with scan, another handy use case. In the below example we're going to use the hostname example from earlier and modify it a little. In this case we want to ensure that the host that's being requested always has a three part domain name, like outlined in the example above. Again, we could do this with string and split commands, but scan makes it straight-forward and efficient, like this:

when HTTP_REQUEST {
  if { [scan [HTTP::host] %s.%s.%s prefix host suffix] != 3 } {
    HTTP::respond 200 content "The host name you supplied was invalied, please try again."
  } else {
    pool httpPool
  }
}

As you can see the scan command has many uses ranging from very simple to far more advanced. Hopefully this de-mystifies it enough to get you started. For some more advanced examples of using the scan command, check out some of these great examples in our codeshare that make use of it in some more interesting and advanced ways:

 

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Published Apr 24, 2008
Version 1.0

3 Comments

  • So would it be safe to say that

     

     

    scan [HTTP::host] %s.%s.%s prefix host suffix

     

     

    is more efficient than

     

     

    set prefix [getfield [HTTP::host] "." 1]

     

    set host [getfield [HTTP::host] "." 2]

     

    set suffix [getfield [HTTP::host] "." 3]

     

     

    ?

     

     

    Or are the gains negligible?

     

     

  • It's almost certainly faster, yes. As far as whether the gains are negligible or not, that's a very debatable topic. It really depends on how many requests you're serving, what utilization you're seeing on your system already, etc. etc. There are too many variables to say how much of an effect it's going to have without testing in your specific environment. It should be at least a small gain, though. ;)

     

     

    Thanks for the follow-up!

     

     

    Colin
  • There is a slight problem with your example

     

     

    scan [HTTP::host] %s.%s.%s prefix host suffix

     

     

    Since %s reads in all characters until the next whitespace, the whole [HTTP::host] string ends up in the variable 'prefix' and nothing is left to store in the succeeding variables. In addition, rather than setting these other variables to empty strings, they don't get defined at all so any succeeding reference to them generates a TCL error. The following would work

     

     

    scan [HTTP::host] %\[^.\].%\[^.\].%s prefix host suffix

     

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One of the premises of #SDN and #cloud scalability is that it's easy to simply replicate services - whether they be application or network focused - and distribute traffic across them to scale infinitely.

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In theory, this is absolutely the case. In theory, one can continue to add capacity to any layer of the data center and simply distribute requests across the layer to scale out as necessary.

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Where reality puts a big old roadblock in the way is when services are stateful. This is the case with many applications - much to the chagrin of cloud and REST purists, by the way - and it is also true with a significant number of network devices. Unfortunately, it is often these devices that proponents of network virtualization target without offering a clear path to addressing the challenges inherent in scaling stateful network devices.

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SDN's claims to supporting load balancing, at least at layer 4, are almost certainly based on traditional, dumb layer 4 load balancing. We use the term \"dumb\" to simply mean that it doesn't care about the payload or the application or anything else other than its destination port and service and does not participate in the flow. In most layer 4 load balancing scenarios for which this is the case, the only time the load balancer examines the traffic is when processing a new connection. The load balancer may buffer enough packets to determine some basic networking details - source and destination IP and TCP ports - and then it establishes a connection between the client and the server. From this point on, generally speaking, the load balancer assumes the role of a simple forwarder. Subsequent packets with the same pattern are simply forwarded on to the destination.

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If you think about it, this is so close to the behavior described by an SDN-enabled network as to be virtually the same. In an SDN-enabled network, a new flow (session if you will, in the load balancing vernacular) would be directed to the SDN controller for processing. The SDN controller would determine its destination and inform the appropriate network components of that decision. Subsequent packets with the same pattern would be forwarded on to the destination according to the information in the FIB (Forwarding Information Base). As the load balancing service was scaled out, inevitably packets would be distributed to components lacking an entry in the FIB. Said components would query the controller, which would simply return the appropriate entry to the device.

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In such a way, simple layer 4 load balancing can be achieved via SDN*.

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However, the behavior of the layer 4 load balancing service described is stateless. It does not actively manage the flow. Aside from the initial inspection and routing decision, the load balancing service is actually just a bump in the wire, forwarding packets much in the same manner as any other switch in the network.

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But what happens when the load balancing service is actively participating in the flow, i.e. it is stateful.

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Scaling Stateful Devices

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Stateful devices are those that actively manage a flow. That is, they may inspect, manipulate, or otherwise interact with flows in real-time. These devices are often used for security - both ingress and egress - as well as acceleration and optimization of application exchanges. They are also use for content transformation purposes, such as XML or SOA gateways, API management, and other application-focused scenarios. The most common use of stateful devices is persistent load balancing, aka sticky sessions, aka server affinity. Persistent load balancing requires the load balancing service (or device) maintain a mapping of user to application instance (or server, in traditional, non-virtualized environments). This mapping is unique to the device, and without it a wide variety of applications break when scaled - VDI being the most recent example of an application relying on persistence of sessions  .

\n

In all these cases, however, one thing is true: the device providing the service is an active participant. The device maintains service-specific information regarding a variety of variables including the user, the device, the traffic, the application, the data. The entire context of the session is often maintained by one or more devices along the traffic chain.

\n

What that means is that, like stateful, shared-nothing applications, it matters to which device a specific request is directed. While certainly the same model used at layer 4 and below in which a central controller (or really bank of controllers) maintains this information and doles it on on-demand, the result is that depending on the distribution algorithm used, every stateful device would end up with the same flows installed. In the interim, the network is frantically applying optimization and acceleration policies to traffic that may be offset by the latency introduced by the need to query the controller for session state information, resulting in a net loss of performance experienced by the end-user.

\n

And we're not even considering the impact of secured traffic on such a model, where any device needing to make decisions on such traffic must have access to the certificates and keys used to encrypt the traffic in order to decrypt, examine, and usually re-encrypt the traffic. Stateful network devices - application delivery controllers, intrusion prevention and detection systems, secure gateways, etc... - are often required to manage secured content, which means distributing and managing certificates and keys across what may be an ever-expanding set of network devices.

\n

The reality is that stateful network devices are a necessary and integral component of not just networks but applications today. While modern network architectures like SDN bring much needed improvements to provisioning and management of large scale networks, their scaling models are based on the premise of stateless, relatively simple devices not actively participating in flows. For those devices that rely upon deep participation in the flow, this model introduces a variety of challenges that may not find a solution that fits well with SDN without compromising on performance outside new protocols capable of carrying that state persistently throughout the lifetime of a session. 

\n

 

\n

* This does not address the issue of resources required to maintain said forwarding tables in a given device, which given current capacity of commoditized switches supported for such a role seems unlikely to be realistically achieved.

\n
\n

 

\n
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One of the premises of #SDN and #cloud scalability is that it's easy to simply replicate services - whether they be application or network focused - and distribute traffic across them to scale infinitely.

\n

In theory, this is absolutely the case. In theory, one can continue to add capacity to any layer of the data center and simply distribute requests across the layer to scale out as necessary.

\n

Where reality puts a big old roadblock in the way is when services are stateful. This is the case with many applications - much to the chagrin of cloud and REST purists, by the way - and it is also true with a significant number of network devices. Unfortunately, it is often these devices that proponents of network virtualization target without offering a clear path to addressing the challenges inherent in scaling stateful network devices.

\n

SDN's claims to supporting load balancing, at least at layer 4, are almost certainly based on traditional, dumb layer 4 load balancing. We use the term \"dumb\" to simply mean that it doesn't care about the payload or the application or anything else other than its destination port and service and does not participate in the flow. In most layer 4 load balancing scenarios for which this is the case, the only time the load balancer examines the traffic is when processing a new connection. The load balancer may buffer enough packets to determine some basic networking details - source and destination IP and TCP ports - and then it establishes a connection between the client and the server. From this point on, generally speaking, the load balancer assumes the role of a simple forwarder. Subsequent packets with the same pattern are simply forwarded on to the destination.

\n

If you think about it, this is so close to the behavior described by an SDN-enabled network as to be virtually the same. In an SDN-enabled network, a new flow (session if you will, in the load balancing vernacular) would be directed to the SDN controller for processing. The SDN controller would determine its destination and inform the appropriate network components of that decision. Subsequent packets with the same pattern would be forwarded on to the destination according to the information in the FIB (Forwarding Information Base). As the load balancing service was scaled out, inevitably packets would be distributed to components lacking an entry in the FIB. Said components would query the controller, which would simply return the appropriate entry to the device.

\n

In such a way, simple layer 4 load balancing can be achieved via SDN*.

\n

However, the behavior of the layer 4 load balancing service described is stateless. It does not actively manage the flow. Aside from the initial inspection and routing decision, the load balancing service is actually just a bump in the wire, forwarding packets much in the same manner as any other switch in the network.

\n

But what happens when the load balancing service is actively participating in the flow, i.e. it is stateful.

\n

Scaling Stateful Devices

\n

Stateful devices are those that actively manage a flow. That is, they may inspect, manipulate, or otherwise interact with flows in real-time. These devices are often used for security - both ingress and egress - as well as acceleration and optimization of application exchanges. They are also use for content transformation purposes, such as XML or SOA gateways, API management, and other application-focused scenarios. The most common use of stateful devices is persistent load balancing, aka sticky sessions, aka server affinity. Persistent load balancing requires the load balancing service (or device) maintain a mapping of user to application instance (or server, in traditional, non-virtualized environments). This mapping is unique to the device, and without it a wide variety of applications break when scaled - VDI being the most recent example of an application relying on persistence of sessions  .

\n

In all these cases, however, one thing is true: the device providing the service is an active participant. The device maintains service-specific information regarding a variety of variables including the user, the device, the traffic, the application, the data. The entire context of the session is often maintained by one or more devices along the traffic chain.

\n

What that means is that, like stateful, shared-nothing applications, it matters to which device a specific request is directed. While certainly the same model used at layer 4 and below in which a central controller (or really bank of controllers) maintains this information and doles it on on-demand, the result is that depending on the distribution algorithm used, every stateful device would end up with the same flows installed. In the interim, the network is frantically applying optimization and acceleration policies to traffic that may be offset by the latency introduced by the need to query the controller for session state information, resulting in a net loss of performance experienced by the end-user.

\n

And we're not even considering the impact of secured traffic on such a model, where any device needing to make decisions on such traffic must have access to the certificates and keys used to encrypt the traffic in order to decrypt, examine, and usually re-encrypt the traffic. Stateful network devices - application delivery controllers, intrusion prevention and detection systems, secure gateways, etc... - are often required to manage secured content, which means distributing and managing certificates and keys across what may be an ever-expanding set of network devices.

\n

The reality is that stateful network devices are a necessary and integral component of not just networks but applications today. While modern network architectures like SDN bring much needed improvements to provisioning and management of large scale networks, their scaling models are based on the premise of stateless, relatively simple devices not actively participating in flows. For those devices that rely upon deep participation in the flow, this model introduces a variety of challenges that may not find a solution that fits well with SDN without compromising on performance outside new protocols capable of carrying that state persistently throughout the lifetime of a session. 

\n

 

\n

* This does not address the issue of resources required to maintain said forwarding tables in a given device, which given current capacity of commoditized switches supported for such a role seems unlikely to be realistically achieved.

\n
\n

 

\n
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