What is memprof and why do I care?

memprof is a Ruby gem which supplies memory profiler functionality similar to bleak_house without patching the Ruby VM. You just install the gem, call a function or two, and off you go.

Where do I get it?

memprof is available on gemcutter, so you can just:

gem install memprof

Feel free to browse the source code at: http://github.com/ice799/memprof.

How do I use it?

Using memprof is simple. Before we look at some examples, let me explain more precisely what memprof is measuring.

memprof is measuring the number of objects created and not destroyed during a segment of Ruby code. The ideal use case for memprof is to show you where objects that do not get destroyed are being created:

• Objects are created and not destroyed when you create new classes. This is a good thing.
• Sometimes garbage objects sit around until garbage_collect has had a chance to run. These objects will go away.
• Yet in other cases you might be holding a reference to a large chain of objects without knowing it. Until you remove this reference, the entire chain of objects will remain in memory taking up space.

memprof will show objects created in all cases listed above.

OK, now Let’s take a look at two examples and their output.

A simple program with an obvious memory “leak”:

require 'memprof'

@blah = Hash.new([])

Memprof.start
100.times {
  @blah[1] << "aaaaa"
}

1000.times {
   @blah[2] << "bbbbb"
}
Memprof.stats
Memprof.stop

This program creates 1100 objects which are not destroyed during the start and stop sections of the file because references are held for each object created.

Let's look at the output from memprof:

   1000 test.rb:11:String
    100 test.rb:7:String

In this example memprof shows the 1100 created, broken up by file, line number, and type.

Let's take a look at another example:

require 'memprof'
Memprof.start
require "stringio"
StringIO.new
Memprof.stats

This simple program is measuring the number of objects created when requiring stringio.

Let's take a look at the output:

    108 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:__node__
     14 test2.rb:3:String
      2 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:Class
      1 test2.rb:4:StringIO
      1 test2.rb:4:String
      1 test2.rb:3:Array
      1 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:Enumerable

This output shows an internal Ruby interpreter type __node__ was created (these represent code), as well as a few Strings and other objects. Some of these objects are just garbage objects which haven't had a chance to be recycled yet.

What if nudge the garbage_collector along a little bit just for our example? Let's add the following two lines of code to our previous example:

GC.start
Memprof.stats

We're now nudging the garbage collector and outputting memprof stats information again. This should show fewer objects, as the garbage collector will recycle some of the garbage objects:

    108 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:__node__
      2 test2.rb:3:String
      2 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:Class
      1 /custom/ree/lib/ruby/1.8/x86_64-linux/stringio.so:0:Enumerable

As you can see above, a few Strings and other objects went away after the garbage collector ran.

Which Rubies and systems are supported?

• Only unstripped binaries are supported. To determine if your Ruby binary is stripped, simply run: file `which ruby`. If it is, consult your package manager's documentation. Most Linux distributions offer a package with an unstripped Ruby binary.
• Only x86_64 is supported at this time. Hopefully, I'll have time to add support for i386/i686 in the immediate future.
• Linux Ruby Enterprise Edition (1.8.6 and 1.8.7) is supported.
• Linux MRI Ruby 1.8.6 and 1.8.7 built with --disable-shared are supported. Support for --enable-shared binaries is coming soon.
• Snow Leopard support is experimental at this time.
• Ruby 1.9 support coming soon.

How does it work?

If you've been reading my blog over the last week or so, you'd have noticed two previous blog posts (here and here) that describe some tricks I came up with for modifying a running binary image in memory.

memprof is a combination of all those tricks and other hacks to allow memory profiling in Ruby without the need for custom patches to the Ruby VM. You simply require the gem and off you go.

memprof works by inserting trampolines on object allocation and deallocation routines. It gathers metadata about the objects and outputs this information when the stats method is called.

What else is planned?

Myself, Jake Douglas, and Aman Gupta have lots of interesting ideas for new features. We don't want to ruin the surprise, but stay tuned. More cool stuff coming really soon :)

Thanks for reading and don't forget to subscribe (via RSS or e-mail) and follow me on twitter.

If you enjoy this article, subscribe (via RSS or e-mail) and follow me on twitter.

A few days ago, Aman (@tmm1) was complaining to me about a slow running process:

I want to see what is happening in userland and trace calls to extensions. Why doesn’t ltrace work for Ruby processes? I want to figure out which MySQL queries are causing my app to be slow.

It turns out that ltrace did not have support for libraries loaded with libdl. This is a problem for languages like Ruby, Python, PHP, Perl, and others because in many cases extensions, libraries, and plugins for these languages are loaded by the VM using libdl. This means that ltrace is somewhat useless for tracking down performance issues in dynamic languages.

A couple late nights of hacking and I managed to finagle libdl support in ltrace. Since most people probably don’t care about the technical details of how it was implemented, I’ll start with showing how to use the patch I wrote and what sort of output you can expect. This patch has made tracking down slow queries (among other things) really easy and I hope others will find this useful.

How to use ltrace:

After you’ve applied my patch (below) and rebuilt ltrace, let’s say you’d like to trace MySQL queries and have ltrace tell you when the query was executed and how long it took. There are two steps:

1. Give ltrace info so it can pretty print – echo “int mysql_real_query(addr,string,ulong);” > custom.conf
2. Tell ltrace you want to hear about mysql_real_query: ltrace -F custom.conf -ttTgx mysql_real_query -p <pid>

Here’s what those arguments mean:

• -F use a custom config file when pretty-printing (default: /etc/ltrace.conf, add your stuff there to avoid -F if you wish).
• -tt print the time (including microseconds) when the call was executed
• -T time the call and print how long it took
• -x tells ltrace the name of the function you care about
• -g avoid placing breakpoints on all library calls except the ones you specify with -x. This is optional, but it makes ltrace produce much less output and is a lot easier to read if you only care about your one function.

PHP

Test script

mysql_connect("localhost", "root");
while(true){
    mysql_query("SELECT sleep(1)");
}

ltrace output

22:31:50.507523 zend_hash_find(0x025dc3a0, "mysql_query", 12) = 0 <0.000029>
22:31:50.507781 mysql_real_query(0x027bc540, "SELECT sleep(1)", 15) = 0 <1.000600>
22:31:51.508531 zend_hash_find(0x025dc3a0, "mysql_query", 12) = 0 <0.000025>
22:31:51.508675 mysql_real_query(0x027bc540, "SELECT sleep(1)", 15) = 0 <1.000926>

ltrace command

ltrace -ttTg -x zend_hash_find -x mysql_real_query -p [pid of script above]

Python

Test script

import MySQLdb
db = MySQLdb.connect("localhost", "root", "", "test")
cursor = db.cursor()
sql = """SELECT sleep(1)"""
while True:
	cursor.execute(sql)
	data = cursor.fetchone()
db.close()

ltrace output

22:24:39.104786 PyEval_SaveThread() = 0x21222e0 <0.000029>
22:24:39.105020 PyEval_SaveThread() = 0x21222e0 <0.000024>
22:24:39.105210 PyEval_SaveThread() = 0x21222e0 <0.000024>
22:24:39.105303 mysql_real_query(0x021d01d0, "SELECT sleep(1)", 15) = 0 <1.002083>
22:24:40.107553 PyEval_SaveThread() = 0x21222e0 <0.000026>
22:24:40.107713 PyEval_SaveThread()= 0x21222e0 <0.000024>
22:24:40.107909 PyEval_SaveThread() = 0x21222e0 <0.000025>
22:24:40.108013 mysql_real_query(0x021d01d0, "SELECT sleep(1)", 15) = 0 <1.001821>

ltrace command

ltrace -ttTg -x PyEval_SaveThread -x mysql_real_query -p [pid of script above]

Perl

Test script

#!/usr/bin/perl
use DBI;

$dsn = "DBI:mysql:database=test;host=localhost";
$dbh = DBI->connect($dsn, "root", "");
$drh = DBI->install_driver("mysql");
@databases = DBI->data_sources("mysql");
$sth = $dbh->prepare("SELECT SLEEP(1)");

while (1) {
  $sth->execute;
}

ltrace output

22:42:11.194073 Perl_push_scope(0x01bd3010) =  <0.000028>
22:42:11.194299 mysql_real_query(0x01bfbf40, "SELECT SLEEP(1)", 15) = 0 <1.000876>
22:42:12.195302 Perl_push_scope(0x01bd3010) =  <0.000024>
22:42:12.195408 mysql_real_query(0x01bfbf40, "SELECT SLEEP(1)", 15) = 0 <1.000967>

ltrace command

ltrace -ttTg -x mysql_real_query -x Perl_push_scope -p [pid of script above]

Ruby

Test script

require 'rubygems'
require 'sequel'

DB = Sequel.connect('mysql://root@localhost/test')

while true
  p DB['select sleep(1)'].select.first
  GC.start
end

snip of ltrace output

22:10:00.195814 garbage_collect()  = 0 <0.022194>
22:10:00.218438 mysql_real_query(0x02740000, "select sleep(1)", 15) = 0 <1.001100>
22:10:01.219884 garbage_collect() = 0 <0.021401>
22:10:01.241679 mysql_real_query(0x02740000, "select sleep(1)", 15) = 0 <1.000812>

ltrace command used:

ltrace -ttTg -x garbage_collect -x mysql_real_query -p [pid of script above]

Where to get it

• On github: http://github.com/ice799/ltrace/tree/libdl
• Raw patch (NOTE: This should apply cleanly against ltrace 0.5.3): ltrace.patch

How ltrace works normally

ltrace works by setting software breakpoints on entries in a process’ Procedure Linkage Table (PLT).

What is a software breakpoint

A software breakpoint is just a series of bytes (0xcc on the x86 and x86_64) that raise a debug interrupt (interrupt 3 on the x86 and x86_64). When interrupt 3 is raised, the CPU executes a handler installed by the kernel. The kernel then sends a signal to the process that generated the interrupt. (Want to know more about how signals and interrupts work? Check out an earlier blog post: here)

What is a PLT and how does it work?

A PLT is a table of absolute addresses to functions. It is used because the link editor doesn’t know where functions in shared objects will be located. Instead, a table is created so that the program and the dynamic linker can work together to find and execute functions in shared objects. I’ve simplified the explanation a bit¹, but at a high level:

1. Program calls a function in a shared object, the link editor makes sure that the program jumps to a slot in the PLT.
2. The program sets some data up for the dynamic linker and then hands control over to it.
3. The dynamic linker looks at the info set up by the program and fills in the absolute address of the function that was called in the PLT.
4. Then the dynamic linker calls the function.
5. Subsequent calls to the same function jump to the same slot in the PLT, but every time after the first call the absolute address is already in the PLT (because when the dynamic linker is invoked the first time, it fills in the absolute address in the PLT).

Since all calls to library functions occur via the PLT, ltrace sets breakpoints on each PLT entry in a program.

Why ltrace didn’t work with libdl loaded libraries

Libraries loaded with libdl are loaded at run time and functions (and other symbols) are accessed by querying the dynamic linker (by calling dlsym()). The compiler and link editor don’t know anything about libraries loaded this way (they may not even exist!) and as such no PLT entries are created for them.

Since no PLT entries exist, ltrace can’t trace these functions.

What needed to be done to make ltrace libdl-aware

OK, so we understand the problem. ltrace only sets breakpoints on PLT entries and libdl loaded libraries don’t have PLT entries. How can this be fixed?

Luckily, the dynamic linker and ELF all work together to save your ass.

Executable and Linking Format (ELF) is a file format for executables, shared libraries, and more². The file format can get a bit complicated, but all you really need to know is: ELF consists of different sections which hold different types of entries. There is a section called .dynamic which has an entry named DT_DEBUG. This entry stores the address of a debugging structure in the address space of the process. In Linux, this struct has type struct r_debug.

How to use struct r_debug to win the game

The debug structure is updated by the dynamic linker at runtime to reflect the current state of shared object loading. The structure contains 3 things that will help us in our quest:

1. state – the current state of the mapping change taking place (begin add, begin delete, consistent)
2. brk – the address of a function internal to the dynamic linker that will be called when the linker maps, unmaps, or has completed mapping a shared object.
3. link map – Pointer to the start of a list of currently loaded objects. This list is called the link map and is represented as a struct link_map in Linux.

Tie it all together and bring it home

To add support for libdl loaded libraries to ltrace, the steps are:

1. Find the address of the debug structure in the .dynamic section of the program.
2. Set a software breakpoint on brk.
3. When the dynamic linker updates the link map, it will trigger the software breakpoint.
4. When the breakpoint is triggered, check state in the debug structure.
5. If a new library has been added, walk the link map and figure out what was added.
6. Search the added library’s symbol table for the symbols we care about.
7. Set a software breakpoints on whatever is found.
8. Steps 3-8 repeat.

That isn’t too hard all thanks to the dynamic linker providing a way for us to hook into its internal events.

Conclusion

• Read the System V ABI for your CPU. It is filled with insanely useful information that can help you be a better programmer.
• Use the source. A few times while hacking on this patch I looked through the source for GDB and glibc to help me figure out what was going on.
• Understanding how things work at a low-level can help you build tools to solve your high-level problems.

Thanks for reading and don’t forget to subscribe (via RSS or e-mail) and follow me on twitter.

References

1. System V Application Binary Interface AMD64 Architecture Processor Supplement, p 78 [↩]
2. Executable and Linking Format (ELF) Specification [↩]

The other day at Kickball Labs we were discussing whether linking Ruby against tcmalloc (or ptmalloc3, nedmalloc, or any other malloc) would have any noticeable effect on application latency. After taking a side in the argument, I started wondering how we could test this scenario.

We had a couple different ideas about testing:

• Look at other people’s benchmarks
  BUT do the memory workloads tested in the benchmarks actually match our own workload at all?
• Run different allocators on different Ruby backends
  BUT different backends will get different users who will use the system differently and cause different allocation patterns
• Try to recreate our applications memory footprint and test that against different mallocs
  BUT how?

I decided to explore the last option and came up with an interesting solution. Let’s dive into how to do this.

Get the code:

http://github.com/ice799/malloc_wrap/tree/master

Step 1: We need to get a memory footprint of our process

So we have some random binary  (in this case it happens to be a Ruby interpreter, but it could be anything) and we’d like to track when it calls malloc/realloc/calloc and free (from now on I’ll refer to all of these as malloc-family for brevity). There are two ways to do this, the right way and the wrong/hacky/unsafe way.

• The “right” way to do this, with libc malloc hooks:

  Edit your application code to use the malloc debugging hooks provided by libc. When a malloc-family function is called, your hook executes and outputs to a file which function was called and what arguments were passed to it.

• The “wrong/hacky/unsafe” way to do this, with LD_PRELOAD:

  Create a shim library and point LD_PRELOAD at it. The shim exports the malloc-family symbols, and when your application calls one of those functions, the shim code gets executed. The shim logs which function was called and with what arguments. The shim then calls the libc version of the function (so that memory is actually allocated/freed) and returns control to the application.

I chose to do it the second way, because I like living on the edge. The second way is unsafe because you can’t call any functions which use a malloc-family function before your hooks are setup. If you do, you can end up in an infinite loop and crash the application.

You can check out my implementation for the shim library here: malloc_wrap.c

Why does your shim output such weirdly formatted data?

Answer is sort of complicated, but let’s keep it simple: I originally had a different idea about how I was going to use the output. When that first try failed, I tried something else and translated the data to the format I needed it in, instead of re-writing the shim. What can I say, I’m a lazy programmer.

OK, so once you’ve built the shim (gcc -O2 -Wall -ldl -fPIC -o malloc_wrap.so -shared malloc_wrap.c), you can launch your binary like this:

You should now see output in /tmp/malloc-footprint.pid

Step 2: Translate the data into a more usable format

Yeah, I should have went back and re-written the shim, but nothing happens exactly as planned. So, I wrote a quick ruby script to convert my output into a more usable format. The script sorts through the output and renames memory addresses to unique integer ids starting at 1 (0 is hardcoded to NULL).

The format is pretty simple. The first line of the file has the number of calls to malloc-family functions, followed by a blank line, and then the memory footprint. Each line of the memory footprint has 1 character which represents the function called followed by a few arguments. For the free() function, there is only one argument, the ID of the memory block to free. malloc/calloc/realloc have different arguments, but the first argument following the one character is always the ID of the return value. The next arguments are the arguments usually passed to malloc/calloc/realloc in the same order.

Have a look at my ruby script here: build_trace_file.rb

It might take a while to convert your data to this format, I suggest running this in a screen session, especially if your memory footprint data is large. Just as a warning, we collected 15 *gigabytes* of data over a 10 hour period. This script took *10 hours* to convert the data. We ended up with a 7.8 gigabyte file.

Step 3: Replay the allocation data with different allocators and measure time, memory usage.

OK, so we now have a file which represents the memory footprint of our application. It’s time to build the replayer, link against your malloc implementation of choice, fire it up and start measuring time spent in allocator functions and memory usage.

Have a look at the replayer here: alloc_tester.c
Build the replayer: gcc -ggdb -Wall -ldl -fPIC -o tester alloc_tester.c

Use ltrace

ltrace is similar to strace, but for library calls. You can use ltrace -c to sum the amount of time spent in different library calls and output a cool table at the end, it will look something like this:

% time     seconds  usecs/call     calls      function
------ ----------- ----------- --------- --------------------
86.70   37.305797          62    600003 fscanf
10.64    4.578968          33    138532 malloc
2.36    1.014294          18     55263 free
0.25    0.109550          18      5948 realloc
0.03    0.011407          45       253 printf
0.02    0.010665          42       252 puts
0.00    0.000167          20         8 calloc
0.00    0.000048          48         1 fopen
------ ----------- ----------- --------- --------------------
100.00   43.030896                800260 total

Conclusion

Using a different malloc implementation can provide a speed/memory increases depending on your allocation patterns. Hopefully the code provided will help you test different allocators to determine whether or not swapping out the default libc allocator is the right choice for you. Our results are still pending; we had a lot of allocator data (15g!) and it takes several hours to replay the data with just one malloc implementation. Once we’ve gathered some data about the different implementations and their effects, I’ll post the results and some analysis. As always, stay tuned and thanks for reading!

Huh? RAID status? Consistency status?

The status of your RAID array tells you if your RAID array has degraded and which disk(s) are the culprit. Most RAID statuses will include more information like temperature, installed memory amount, and more.

You also need to run consistency checks to ensure that data on bad blocks will either be moved or rewritten to good blocks. Why is this important? Consider the following scenario: You have a RAID 10 array. One disk dies, say disk A of stripe set 1. You now replace that disk and start a rebuild of the array. You never ran a consistency check and it turns out that there were bad blocks on disk B of stripe set 1 that were never reallocated to good blocks. When data is written to the replacement disk, disk B may not be able to read data from its bad blocks. Corrupt data then gets written to the replacement disk and you likely won’t notice a problem until the box crashes or you are missing data due to corruption

Whoa that is pretty serious. How can I keep track of all that?

The two common failure notifications for a logical failure I’ve seen are alarms and RAID status changes.

In my opinion, alarms are generally useless unless you are sitting near your server. What good is an alarm if you don’t hear it? While I wouldn’t rely on an alarm as the first line of defense against a RAID failure, it can definitely grab the attention of a nearby tech in the data center when a problem arises.

RAID status changes are probably the most useful way to determine when a RAID array degrades.

For physical disk failures, you’ll only know when a consistency check is run or when you lose data or the box dies. Some RAID adapters can be set up to automatically run consistency checks, others need to be invoke each time.

Speaking of consistency, don’t forget about that battery backup unit (BBU)!

A battery backup unit is necessary for a RAID array which has its write cache enabled. This is because if write requests are in the cache and power is lost to the system, the BBU will provide power so that the outstanding writes can be synced to the array. If you have the write cache enabled, but don’t have a BBU when power is lost to the system, the data on the system could be corrupt because the writes in the cache may not be written to disk.

How do I check my RAID/BBU status?

Checking your RAID/BBU status is very vendor specific. Each vendor has their own method, but the most common method by far is to expose a management interface (in the form of a character device) which listens for different queries from userspace via an ioctl interface.

Most hardware RAID vendors include a small binary or script which will send ioctls to the management interface and give you detailed information about the status of your device. I’ve listed the names of the management apps for Adaptec and 3ware RAID devices below and included a sample output from an aacraid device at the bottom of this post.

Adaptec aacraid – /usr/StorMan/arcconf

3WARE raid – /usr/bin/tw_cli

You can write a script that runs as a cron job, parses the output of the management binary, and sends an email/page when a status change occurs.

How can I run consistency checks?

This is also incredibly vendor specific. The consistency check can usually be run/scheduled via the CLI. You should check the documentation for the CLI tool. With an aacraid controller, a consistency check can be run by using the datascrub command:

/usr/StorMan/arcconf datascrub 1 period 10

This will perform a consistency check in the background that has 10 days to complete.

How can I protect myself from a single disk failure?

There are many different RAID configurations, but the most common ones which can protect you from a single disk failure are:

• RAID 1
• RAID 5
• RAID 6
• RAID 10

What about a multiple disk failure?

Again, there are many different RAID configurations, but there are two major ways to survive multiple disk failure. Unfortunately, one way involves being really lucky.

• RAID 10 – You have to be pretty lucky here. As long as there is one working disk on each stripe set, you should be OK.
• Double Parity RAID 6 – This configuration can survive a failure of any two disks.

Conclusion

Read your RAID device documentation carefully and follow any relevant suggestions. If you don’t have RAID status monitoring set up, do it now. The minimal time investment to set this up can save you down the road when a hardware failure occurs.

You should also set up and run a consistency check as soon as possible and schedule them to run at regular intervals. Check your RAID docs for more info about how to run a consistency check.

Sample output from an aacraid device that doesn’t have consistency checks running:

sudo /usr/StorMan/arcconf getconfig 1 AD

----------------------------------------------------------------------
Controller information
----------------------------------------------------------------------
   Controller Status                        : Optimal
   Channel description                      : SAS/SATA
   Controller Model                         : Adaptec 3405
   Controller Serial Number                 : 7C391118F8E
   Physical Slot                            : 2
   Temperature                              : 43 C/ 109 F (Normal)
   Installed memory                         : 128 MB
   Copyback                                 : Disabled
   Background consistency check             : Disabled
   Automatic Failover                       : Enabled
   Defunct disk drive count                 : 0
   Logical devices/Failed/Degraded          : 1/0/0
   --------------------------------------------------------
   Controller Version Information
   --------------------------------------------------------
   BIOS                                     : 5.2-0 (15753)
   Firmware                                 : 5.2-0 (15753)
   Driver                                   : 1.1-5 (2456)
   Boot Flash                               : 5.2-0 (15753)
   --------------------------------------------------------
   Controller Battery Information
   --------------------------------------------------------
   Status                                   : Optimal
   Over temperature                         : No
   Capacity remaining                       : 100 percent
   Time remaining (at current draw)         : 3 days, 1 hours, 31 minutes