Getting Started with iRules LX, Part 1: Introduction & Conceptual Overview

What is iRules LX?

When BIG-IP TMOS 12.1.0 released a couple weeks ago, Danny wrote an intro piece to a new feature called iRules LX. iRules LX is the next generation of iRules that allows you to programmatically extend the functionality of BIG-IP with Node.js. In this article series, you will learn the basics of what iRules LX is, some use cases, how the components fit together, troubleshooting techniques, and some introductory code solutions.

What is Node.js ?

Node.js provides an environment for running JavaScript independent of a web browser. With Node.js you can write both clients and servers, but it is mostly used for writing servers. There are 2 main componets to Node.js -

Google v8 JavaScript Engine

Node.js starts with the same open source JavaScript engine used in the Google Chrome web browser. v8 has a JIT complier that optimizes JavaScript code to allow it to run incrediable fast. It also has an optimizing complier that, during run time, further optimizes the code to increase the performance up to 200 times faster.

LibUV Asynchronous I/O Library

A JavaScript engine by itself would not allow a full set of I/O operations that are needed for a server (you dont want your web browser accessing the file system of computer after all). LubUV gives Node.js the capability to have those I/O operations. Because v8 is a single threaded process, I/O (which have the most latency) would normally be blocking; that is, the CPU would be doing nothing while waiting from a response of the I/O operation. LibUV allows for the v8 to continue running JavaScript while waiting for the response, maximizing the use of CPU cycles and providing great concurrency.

Why Node.js?

Some might ask "Why was Node.js chosen?" While there is more than one reason, the biggest factor is that you can use JavaScript. JavaScript has been a staple of web development for almost 2 decades and is a common skillset amongst developers. Also, Node.js has a vibrant community that not only contributes to the Node.js core itself, but also a plethora of people writing libraries (called modules). As of May 2016 there are over 280,000 Node.js modules available in the Node Package Manager (NPM) registry, so chances are you can find a library for almost any use case. An NPM client is included with distributions of Node.js.

For more information about the specifics of Node.js on BIG-IP, please refer to AskF5 Solution Article SOL16221101.

iRules LX Conceptual Operation

Unlike iRules which has a TCL interpreter running inside the Traffic Management Micro-kernel (TMM), Node.js is running as a stand alone processes outside of TMM. Having Node.js outside of TMM is an advantage in this case because it allow F5 to keep the Node.js version more current than if it was integrated into TMM.

In order to get data from TMM to the Node.js processes, a Remote Procedural Call (RPC) is used so that TMM can send data to Node.js and tell it what "procedure" to run. The conceptual model of iRules LX looks something like this -

To make this call to Node.js , we still need iRules TCL. We will cover the coding much more extensively in a few days, so dont get too wrapped up in understanding all the new commands just yet. 

Here is an example of TCL code for doing RPC to Node.js - 

when HTTP_REQUEST {
   set rpc_hdl [ILX::init my_plugin my_extension]
   set result [ILX::call $rpc_hdl my_method $arg1]
    
   if { $result eq "yes" } {
      # Do something
   } else {
      # Do something else
   } 
}

In line 2 you can see with the

ILX::init
command that we establish an RPC handle to a specific Node.js plugin and extension (more about those tomorrow). Then in line 3 we use the
ILX::call
command with our handle to call the method
my_method
(our "remote procedure") in our Node.js process and we send it the data in variable
arg1
. The result of the call will be stored in the variable
result
. With that we can then evaluate the result as we do in lines 5-9. 

Here we have an example of Node.js code using the iRules LX API we provide -

ilx.addMethod('my_method', function(req, res){
  var data = req.params()[0];
 
  <…ADDITIONAL_PROCESSING…>
 
  res.reply('<some_reply>');
});

You can see the first argument

my_method
in
addMethod
is the name of our method that we called from TCL. The second argument is our callback function that will get executed when we call
my_method
. On line 2 we get the data we sent over from TCL and in lines 3-5 we perform whatever actions we wish with JavaScript. Then with line 6 we send our result back to TCL.

Use Cases

While TCL programming is still needed for performing many of the tasks, you can offload the most complicated portions to Node.js using any number of the available modules. Some of the things that are great candidates for this are -

Sideband connections - Anyone that has used sideband in iRules TCL knows how difficult it is to implement a protocol from scratch, even if it is only part of the protocol. Simply by downloading the appropriate module with NPM, you can easily accomplish many of the following with ease -

  • Database lookups - SQL, LDAP, Memcached, Redis
  • HTTP and API calls - RESTful APIs, CAPTCHA, SSO protocols such as SAML, etc.

Data parsing -

  • JSON - Parser built natively into JavaScript
  • XML
  • Binary protocols

 

In the next installment we will be covering iRules LX configuration and workflow.

Updated Jun 06, 2023
Version 2.0

1 Comment

  • A quick question relating to the scenario where an external user connects to an ADFS Farm which is secured by F5+APM (i.e. not using an ADFS Proxy). In the instructions above, after a user enters AD credentials on the F5 APM page, and the following occurs (as described above), would the user need to re-enter their credentials on the AD FS page, or will the credentials be passed through? * Authenticated users are directed to an AD FS server farm member.
  • I noticed that passwords are stored in clear text in the APM log (within the initial_req_body session variable) when using access profiles... is this not a rather fundamental security flaw with this method?
  • I tried clicking on the link to Part 1 in this series, but it redirects me to the Articles main page.
  • Greg, I have read your articles on this topic front to back a few times and I am still having a hard time applying them to my scenario. As best as I can describe here goes:

     

     

    It seems as you are using the BigIP to front end your ADFS environment where ADFS is acting as the IdP in order for users on your LAN to authenticate to Office 365, the SP. What I am trying to do is retrofit this concept to the opposite, where I need to accept inbound assertions from an external IdP and allow access to an internal resource on my LAN. I have ADFS built and I am ingesting assertions from the external IdP but I would like to use the BigIP as the reverse proxy for not only the connection between the IdP and my ADFS server, but to also provide a secure front end that mirrors that of the default STS logon page on ADFS, ultimately allowing an external user to auth against their IdP, ADFS process the claims, issue a new token for my internal sharepoint site to that client and then redirect them to the VS that will be front ending my sharepoint site. Are these concepts you discuss in this series still applicable or is this a completely different scenario requiring another solution? I would love to see this laid out if you have gone through this scenario as well.
  • Hi Greg,

     

     

    Firstly thanks for these articles, they've been a great resource in my recent Exchange/APM work.

     

     

    I've implemented F5 APM federated Office 365 for students in our university, and we're so happy with how it's working that we're keen to investigate the hybridisation of our on premises exchange org (used exclusively for staff) with a new office 365 tenancy, again using F5 for the federation/ saml aspects of the architecture. So far I've been focussing on getting to grips with the ADFS method of implementation (4 ADFS servers? Overkill much?) and I think I can see where F5 is going to be able to take over those roles. All my reading is leaving me a little confusd though, can i competely remove the need for ADFS servers in a Exchange 2010/office 365 hybrid deployment? Or is there still going to be a requirement for internal ADFS servers? It would be great to hear about that particular topology, and if it's possible? Cheers - Gavin Connell-Otten - Victoria University, NZ
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I was asked internally about some performance graphs with python, and while I've done some work with python and iControl over the years, but I've yet to take on graphing. Most of the python scripts I've written output results to the console, so there was no need for fancy things like graphics. I was thinking of a web-based app, but decided to stick with a console app and use the matplotlib 2D plotting libary to generate the graphs. In addition to the normal modules I use for a python script (suds, bigsuds, getpass, argparse) interacting with the BIG-IP, there are few additions:

\n\n

\n\n

Thankfully, Joe wrote on performance graphs for many moons ago in his icontrol 101 #11 article. So the only real work I had to do was python-ize his code, and do my best to one-up his solution!

\n\n

Grabbing the Data

\n\n

This section of the main loop consists of importing all the modules we'll need, initializing the BIG-IP, asking the user what graph they'd like to see, and grabbing that data from the BIG-IP via an iControl call.

\n\n

    import bigsuds as pc\n    import getpass\n    import argparse\n    import matplotlib.pyplot as plt\n    import matplotlib.dates as mdates\n    import numpy as np\n    import csv, time, base64\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\"-s\",  \"--system\", required=True)\n    parser.add_argument(\"-u\", \"--username\", required=True)\n\n    args = vars(parser.parse_args())\n\n    print \"%s, enter your \" % args['username'],\n    upass = getpass.getpass()\n\n    b = pc.BIGIP(args['system'], args['username'], upass)\n    stats = b.System.Statistics\n\n    graphs = stats.get_performance_graph_list()\n    graph_obj_name = []\n    count = 1\n    print \"Graph Options\"\n    for x in graphs:\n        graph_obj_name.append(x['graph_name'])\n        print \"%d> %s, %s\" % (count, x['graph_description'], x['graph_name'])\n        count +=1\n\n    num_select = int(raw_input(\"Please select the number for the desired graph data: \"))\n\n    graphstats = stats.get_performance_graph_csv_statistics(\n        [{'object_name': graph_obj_name[num_select - 1],\n          'start_time': 0,\n          'end_time': 0,\n          'interval': 0,\n          'maximum_rows': 0}])\n\n

\n\n

In the for loop, I'm just iterating through the data returned by the get_performance_graph_list method and printing it to the console so the user can select the graph they want. Next, I take the user's selection and graph the performance data for that graph with the get_performance_graph_csv_statistics method.

\n\n

Storing and Manipulating the Data

\n\n

Now that I have the data from the BIG-IP, I store it in a .csv file just in case the user would like to use it afterward. Before writing it to said .csv however, I need to b64decode the data as show below.

\n\n

    statsFile = \"%s_rawStats.csv\" % graph_obj_name[num_select-1]\n    f = open(statsFile, \"w\")\n    f.write(base64.b64decode(graphstats[0]['statistic_data']))\n    f.close()\n\n

\n\n

This results in cvs data that looks like this:

\n\n

timestamp,\"Total Phys Memory\",\"OS Used Memory\",\"TMM Alloc Memory\",\"TMM Used Memory\",\"OS Used Swap\"\n1387226160,             nan,             nan,             nan,             nan,             nan\n1387226400,4.1607127040e+09,4.1204910763e+09,1.7070817280e+09,6.0823478033e+08,2.4167403520e+08\n1387226640,4.1607127040e+09,4.1187021824e+09,1.7070817280e+09,6.0832352860e+08,2.4183606613e+08\n\n

\n\n

After storing the raw data, I re-open that file and read it in line by line, reformatting the timestamp and writing that to a new file:

\n\n

    outputFile = \"%s_modStats.csv\" % graph_obj_name[num_select-1]\n    with open(statsFile) as infile, open(outputFile, \"w\") as outfile:\n        r = csv.reader(infile)\n        w = csv.writer(outfile)\n        w.writerow(next(r))\n        for row in r:\n            fl = float(row[0])\n            row[0] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(fl))\n            w.writerow(row)\n\n

\n\n

This results in updated csv data.

\n\n

timestamp,Total Phys Memory,OS Used Memory,TMM Alloc Memory,TMM Used Memory,OS Used Swap\n2013-12-16 14:36:00,             nan,             nan,             nan,             nan,             nan\n2013-12-16 14:40:00,4.1607127040e+09,4.1204910763e+09,1.7070817280e+09,6.0823478033e+08,2.4167403520e+08\n2013-12-16 14:44:00,4.1607127040e+09,4.1187021824e+09,1.7070817280e+09,6.0832352860e+08,2.4183606613e+08\n\nI made sure that each graph would end up with its own set of csv files. I'm sure there's a smarter more pythonic way to handle this, but hey, it works.\n

\n\n

Calling the Graph Functions

\n\n

This is the simplest section of code, an if / elif chain! There is no switch statement in python like in Tcl, but there are other solutions available. The if / elif works just fine, so it remains.

\n\n

    if num_select == 1:\n        graph_memory(outputFile)\n    elif num_select == 2:\n        graph_cpu(outputFile)\n    elif num_select == 3:\n        graph_active_connections(outputFile)\n    elif num_select == 4:\n        graph_new_connections(outputFile)\n    elif num_select == 5:\n        graph_throughput(outputFile)\n    elif num_select == 7:\n        graph_http_requests(outputFile)\n    elif num_select == 8:\n        graph_ramcache_util(outputFile)\n    elif num_select == 12:\n        graph_active_ssl(outputFile)\n    else:\n        print \"graph function not yet defined, please select 1, 2, 3, 4, 5, 7, 8, or 12.\"\n\n

\n\n

The else is there for the myriad of graphs I have not generated, I'll leave that as an exercise for you!

\n\n

Generating the Graphs

\n\n

On my system, collecting the graphs from the get_performance_graph_list method results in 38 different graphs. Many of these graphs have different number of columns of data, so consolidating all these functions into one and working out the data passing and error correction was not an exercise I was willing to take on. So...lots of duplicate code for each graph function, but again, it works. The graphs I have built functions for are memory, cpu, active and new connections, throughput (bits), http requests, ramcache utilization, and active ssl connections. The function is essentially the same for all of them, the changes are related to number of plot lines, labels, etc. The memory graph function is below.

\n\n

def graph_memory(csvdata):\n    timestamp, total_mem, os_used, tmm_alloc, tmm_used, swap_used = np.genfromtxt(csvdata, delimiter=',',\n                                                           skip_header=2, skip_footer=2, unpack=True,\n                                                           converters= {0: mdates.strpdate2num('%Y-%m-%d %H:%M:%S')})\n    fig = plt.figure()\n    plt.plot_date(x=timestamp, y=total_mem, fmt='-')\n    plt.plot(timestamp, total_mem, 'k-', label=\"Total Memory\")\n    plt.plot(timestamp, tmm_used, 'r-', label=\"TMM Used\")\n    plt.plot(timestamp, tmm_alloc, 'c-', label=\"Tmm Allocated\")\n    plt.plot(timestamp, os_used, 'g-', label=\"OS Used\")\n    plt.plot(timestamp, swap_used, 'b-', label=\"OS Used Swap\")\n    plt.legend(title=\"Context\", loc='upper left', shadow=True)\n    plt.title('Global Memory')\n    plt.ylabel('GB')\n    plt.show()\n\n

\n\n

In this function, numpy (np.genfromtxt) is used to pull in the csv data to arrays that matplotlib can use. The rest of the function is just placing and formatting data for the graph.

\n\n

Running the Script

\n\n

C:\\>python stats1.py -s 192.168.1.1 -u citizen_elah\n

\n\n

citizen_elah, enter your Password:\n\n\nGraph Options. Not all options supported at this time, please select only 1, 2, 3, 4, 5, 7, 8, or 12.\n\n\n1> Memory Used, memory\n2> System CPU Usage %, CPU\n3> Active Connections, activecons\n4> Total New Connections, newcons\n5> Throughput(bits), throughput\n6> Throughput(packets), throughputpkts\n7> HTTP Requests, httprequests\n8> RAM Cache Utilization, ramcache\n9> Blade Memory Usage, b0memory\n10> Active Connections, detailactcons1\n11> Active Connections per Blade, detailactcons2\n12> Active SSL Connections, detailactcons3\n13> New Connections, detailnewcons1\n14> New PVA Connections, detailnewcons2\n15> New ClientSSL Profile Connections, detailnewcons3\n16> New TCP Accepts/Connects, detailnewcons4\n17> Client-side Throughput, detailthroughput1\n18> Server-side Throughput, detailthroughput2\n19> HTTP Compression Rate, detailthroughput3\n20> SSL Transactions/Sec, SSLTPSGraph\n21> GTM Requests and Resolutions, GTMGraph\n22> GTM Requests, GTMrequests\n23> GTM Resolutions, GTMresolutions\n24> GTM Resolutions Persisted, GTMpersisted\n25> GTM Resolutions Returned to DNS, GTMret2dns\n26> GTM Requests per DNS type, GTMRequestBreakdownGraph\n27> Active Sessions, act_sessions\n28> New Sessions, new_sessions\n29> Access Requests, access_requests\n30> ACL Actions, acl_stats\n31> Active Network Access Connections, NAActiveConnections\n32> New Network Access Connections, NANewConnections\n33> Network Access Throughput, NAthroughput\n34> Rewrite Transactions, rewrite_trans\n35> Rewrite Transaction Data, rewrite_bytes\n36> GTM requests for IPv4 addresses, GTMarequests\n37> GTM requests for IPv6 addresses, GTMaaaarequests\n38> CPU Usage, blade0cpuset1\n\n\nPlease select the number for the desired graph data: 1\n

\n\n

\n\n

Wrapping Up

\n\n

This was a fun walk through the matplotlib and the iControl interface to generate some graphs. The script in full is here in the codeshare. Happy coding!

\n\n


","body@stringLength":"11377","rawBody":"

I was asked internally about some performance graphs with python, and while I've done some work with python and iControl over the years, but I've yet to take on graphing. Most of the python scripts I've written output results to the console, so there was no need for fancy things like graphics. I was thinking of a web-based app, but decided to stick with a console app and use the matplotlib 2D plotting libary to generate the graphs. In addition to the normal modules I use for a python script (suds, bigsuds, getpass, argparse) interacting with the BIG-IP, there are few additions:

\n\n

\n\n

Thankfully, Joe wrote on performance graphs for many moons ago in his icontrol 101 #11 article. So the only real work I had to do was python-ize his code, and do my best to one-up his solution!

\n\n

Grabbing the Data

\n\n

This section of the main loop consists of importing all the modules we'll need, initializing the BIG-IP, asking the user what graph they'd like to see, and grabbing that data from the BIG-IP via an iControl call.

\n\n

    import bigsuds as pc\n    import getpass\n    import argparse\n    import matplotlib.pyplot as plt\n    import matplotlib.dates as mdates\n    import numpy as np\n    import csv, time, base64\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\"-s\",  \"--system\", required=True)\n    parser.add_argument(\"-u\", \"--username\", required=True)\n\n    args = vars(parser.parse_args())\n\n    print \"%s, enter your \" % args['username'],\n    upass = getpass.getpass()\n\n    b = pc.BIGIP(args['system'], args['username'], upass)\n    stats = b.System.Statistics\n\n    graphs = stats.get_performance_graph_list()\n    graph_obj_name = []\n    count = 1\n    print \"Graph Options\"\n    for x in graphs:\n        graph_obj_name.append(x['graph_name'])\n        print \"%d> %s, %s\" % (count, x['graph_description'], x['graph_name'])\n        count +=1\n\n    num_select = int(raw_input(\"Please select the number for the desired graph data: \"))\n\n    graphstats = stats.get_performance_graph_csv_statistics(\n        [{'object_name': graph_obj_name[num_select - 1],\n          'start_time': 0,\n          'end_time': 0,\n          'interval': 0,\n          'maximum_rows': 0}])\n\n

\n\n

In the for loop, I'm just iterating through the data returned by the get_performance_graph_list method and printing it to the console so the user can select the graph they want. Next, I take the user's selection and graph the performance data for that graph with the get_performance_graph_csv_statistics method.

\n\n

Storing and Manipulating the Data

\n\n

Now that I have the data from the BIG-IP, I store it in a .csv file just in case the user would like to use it afterward. Before writing it to said .csv however, I need to b64decode the data as show below.

\n\n

    statsFile = \"%s_rawStats.csv\" % graph_obj_name[num_select-1]\n    f = open(statsFile, \"w\")\n    f.write(base64.b64decode(graphstats[0]['statistic_data']))\n    f.close()\n\n

\n\n

This results in cvs data that looks like this:

\n\n

timestamp,\"Total Phys Memory\",\"OS Used Memory\",\"TMM Alloc Memory\",\"TMM Used Memory\",\"OS Used Swap\"\n1387226160,             nan,             nan,             nan,             nan,             nan\n1387226400,4.1607127040e+09,4.1204910763e+09,1.7070817280e+09,6.0823478033e+08,2.4167403520e+08\n1387226640,4.1607127040e+09,4.1187021824e+09,1.7070817280e+09,6.0832352860e+08,2.4183606613e+08\n\n

\n\n

After storing the raw data, I re-open that file and read it in line by line, reformatting the timestamp and writing that to a new file:

\n\n

    outputFile = \"%s_modStats.csv\" % graph_obj_name[num_select-1]\n    with open(statsFile) as infile, open(outputFile, \"w\") as outfile:\n        r = csv.reader(infile)\n        w = csv.writer(outfile)\n        w.writerow(next(r))\n        for row in r:\n            fl = float(row[0])\n            row[0] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(fl))\n            w.writerow(row)\n\n

\n\n

This results in updated csv data.

\n\n

timestamp,Total Phys Memory,OS Used Memory,TMM Alloc Memory,TMM Used Memory,OS Used Swap\n2013-12-16 14:36:00,             nan,             nan,             nan,             nan,             nan\n2013-12-16 14:40:00,4.1607127040e+09,4.1204910763e+09,1.7070817280e+09,6.0823478033e+08,2.4167403520e+08\n2013-12-16 14:44:00,4.1607127040e+09,4.1187021824e+09,1.7070817280e+09,6.0832352860e+08,2.4183606613e+08\n\nI made sure that each graph would end up with its own set of csv files. I'm sure there's a smarter more pythonic way to handle this, but hey, it works.\n

\n\n

Calling the Graph Functions

\n\n

This is the simplest section of code, an if / elif chain! There is no switch statement in python like in Tcl, but there are other solutions available. The if / elif works just fine, so it remains.

\n\n

    if num_select == 1:\n        graph_memory(outputFile)\n    elif num_select == 2:\n        graph_cpu(outputFile)\n    elif num_select == 3:\n        graph_active_connections(outputFile)\n    elif num_select == 4:\n        graph_new_connections(outputFile)\n    elif num_select == 5:\n        graph_throughput(outputFile)\n    elif num_select == 7:\n        graph_http_requests(outputFile)\n    elif num_select == 8:\n        graph_ramcache_util(outputFile)\n    elif num_select == 12:\n        graph_active_ssl(outputFile)\n    else:\n        print \"graph function not yet defined, please select 1, 2, 3, 4, 5, 7, 8, or 12.\"\n\n

\n\n

The else is there for the myriad of graphs I have not generated, I'll leave that as an exercise for you!

\n\n

Generating the Graphs

\n\n

On my system, collecting the graphs from the get_performance_graph_list method results in 38 different graphs. Many of these graphs have different number of columns of data, so consolidating all these functions into one and working out the data passing and error correction was not an exercise I was willing to take on. So...lots of duplicate code for each graph function, but again, it works. The graphs I have built functions for are memory, cpu, active and new connections, throughput (bits), http requests, ramcache utilization, and active ssl connections. The function is essentially the same for all of them, the changes are related to number of plot lines, labels, etc. The memory graph function is below.

\n\n

def graph_memory(csvdata):\n    timestamp, total_mem, os_used, tmm_alloc, tmm_used, swap_used = np.genfromtxt(csvdata, delimiter=',',\n                                                           skip_header=2, skip_footer=2, unpack=True,\n                                                           converters= {0: mdates.strpdate2num('%Y-%m-%d %H:%M:%S')})\n    fig = plt.figure()\n    plt.plot_date(x=timestamp, y=total_mem, fmt='-')\n    plt.plot(timestamp, total_mem, 'k-', label=\"Total Memory\")\n    plt.plot(timestamp, tmm_used, 'r-', label=\"TMM Used\")\n    plt.plot(timestamp, tmm_alloc, 'c-', label=\"Tmm Allocated\")\n    plt.plot(timestamp, os_used, 'g-', label=\"OS Used\")\n    plt.plot(timestamp, swap_used, 'b-', label=\"OS Used Swap\")\n    plt.legend(title=\"Context\", loc='upper left', shadow=True)\n    plt.title('Global Memory')\n    plt.ylabel('GB')\n    plt.show()\n\n

\n\n

In this function, numpy (np.genfromtxt) is used to pull in the csv data to arrays that matplotlib can use. The rest of the function is just placing and formatting data for the graph.

\n\n

Running the Script

\n\n

C:\\>python stats1.py -s 192.168.1.1 -u citizen_elah\n

\n\n

citizen_elah, enter your Password:\n\n\nGraph Options. Not all options supported at this time, please select only 1, 2, 3, 4, 5, 7, 8, or 12.\n\n\n1> Memory Used, memory\n2> System CPU Usage %, CPU\n3> Active Connections, activecons\n4> Total New Connections, newcons\n5> Throughput(bits), throughput\n6> Throughput(packets), throughputpkts\n7> HTTP Requests, httprequests\n8> RAM Cache Utilization, ramcache\n9> Blade Memory Usage, b0memory\n10> Active Connections, detailactcons1\n11> Active Connections per Blade, detailactcons2\n12> Active SSL Connections, detailactcons3\n13> New Connections, detailnewcons1\n14> New PVA Connections, detailnewcons2\n15> New ClientSSL Profile Connections, detailnewcons3\n16> New TCP Accepts/Connects, detailnewcons4\n17> Client-side Throughput, detailthroughput1\n18> Server-side Throughput, detailthroughput2\n19> HTTP Compression Rate, detailthroughput3\n20> SSL Transactions/Sec, SSLTPSGraph\n21> GTM Requests and Resolutions, GTMGraph\n22> GTM Requests, GTMrequests\n23> GTM Resolutions, GTMresolutions\n24> GTM Resolutions Persisted, GTMpersisted\n25> GTM Resolutions Returned to DNS, GTMret2dns\n26> GTM Requests per DNS type, GTMRequestBreakdownGraph\n27> Active Sessions, act_sessions\n28> New Sessions, new_sessions\n29> Access Requests, access_requests\n30> ACL Actions, acl_stats\n31> Active Network Access Connections, NAActiveConnections\n32> New Network Access Connections, NANewConnections\n33> Network Access Throughput, NAthroughput\n34> Rewrite Transactions, rewrite_trans\n35> Rewrite Transaction Data, rewrite_bytes\n36> GTM requests for IPv4 addresses, GTMarequests\n37> GTM requests for IPv6 addresses, GTMaaaarequests\n38> CPU Usage, blade0cpuset1\n\n\nPlease select the number for the desired graph data: 1\n

\n\n

\n\n

Wrapping Up

\n\n

This was a fun walk through the matplotlib and the iControl interface to generate some graphs. The script in full is here in the codeshare. Happy coding!

\n\n


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me!"},"localOverride":false},"ModerationData:moderation_data:279376":{"__typename":"ModerationData","id":"moderation_data:279376","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"TkbReplyMessage:message:279376":{"__typename":"TkbReplyMessage","author":{"__ref":"User:user:51154"},"id":"message:279376","revisionNum":1,"uid":279376,"depth":1,"hasGivenKudo":false,"subscribed":false,"board":{"__ref":"Tkb:board:TechnicalArticles"},"parent":{"__ref":"TkbTopicMessage:message:279374"},"conversation":{"__ref":"Conversation:conversation:279374"},"subject":"Re: Graphing Performance Data with bigsuds","moderationData":{"__ref":"ModerationData:moderation_data:279376"},"body":"

Updated...and that's a good idea, look for an article toward the end of next week!

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Hi  any plan to rewrite this using icontrol rest?

 

and can you amend the codeshare link to

https://devcentral.f5.com/s/articles/python-bigsuds-performance-graphs

 

Thanks

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