Endoscope - inspect live Perl systems
use Endoscope;
my $scope = Endoscope->new();
$scope->add(__FILE__, __LINE__ + 3, '$foo');
$scope->apply();
my $foo = "super cool data";
my $bar = "baz"; # print: Endoscope: test.pl/6/$foo = 'super cool data (len 15)'
Endoscope is an endoscope for live Perl programs.
It provides dynamic run-time introspection of Perl variables at arbitrary
locations in the program. Think of it like inserting say Dumper($foo) at
just the right location in your code to figure out why it is misbehaving --
without restarting perl or worrying whether $foo contains gigabytes of
state.
It accomplishes this with low performance impact. See "PERFORMANCE" for more information on overhead. It is a major goal for this module and its subcomponents to be suitable for always-on production usage.
This is a very powerful capability with significant implications for the
security of the data in a program's memory. As such, any usage of
Endoscope should carefully guard access to the control or reporting
interfaces. See "SECURITY" for a more comprehensive discussion.
my $e = Endoscope->new(%options);
Create a new Endoscope object. %options may be empty, or contain any of the
following keys:
-
monitorSubroutine to invoke with the result of the query. Use this to push to a logging pipeline or other human-facing debugging tool.
Default implementation:
sub { my ($file, $line, $query, $result) = @_; say STDERR "Endoscope: $file/$line/$query = $result"; }
my $e = Endoscope->new();
$e->add("foo.pl", 42, '$foo->[0]');
Add a Devel::Optic query to the scope. Takes filename, line number, and query as arguments. An optional fourth argument, if true, will cause the query to fire every time the codepath is executed, rather than just once. Use that option with care.
$e->remove("foo.pl", 42);
Remove any query assigned to the file/line pair.
$e->apply();
apply synchronizes the set of 'added' or 'removed' queries with the
underlying system, Devel::Probe. Call this after 'adding' or 'removing'
queries, or to reset 'once' queries after they've fired. If Endoscope is
integrated with a web application, this would be called once per request early
in the request handling lifecycle.
clear removes all queries from settings. Call apply to remove them for
real.
Endoscope and supporting libraries Devel::Probe and Devel::Optic
attempt to be suitable for usage in performance sensitive production
environments. However, 'performance sensitive' covers a wide range of
situations. As a rule of thumb, if the code you're querying strives to minimize
subroutine calls for performance reasons, it would be best to stick to the
default 'once' setting for queries, and be mindful of the amount of work
performed in the 'monitor'.
Benchmarking is very difficult, and for the sake of this document I'm going to
quote results from my laptop. The goal of this benchmark report is to give you
a general sense of how Endoscope performs. Your milage may vary.
NOTE: all of the Endoscope tests are conducted with at least one query
active and firing each time the associated code is executed. If no queries are
configured, Endoscope has no measurable overhead. The recommended setup is
for Endoscope to be installed and listening, and have the program expose
a privileged interface for system operators to set queries which execute once,
dump some information, and then remove themselves. This model of integration
should be suitable for all but the tightest performance requirements.
The testbed is a "Hello World" Mojolicious application using Mojolicious in the following configuration:
$ mojo version
CORE
Perl (v5.28.1, linux)
Mojolicious (8.17, Supervillain)
OPTIONAL
Cpanel::JSON::XS 4.04+ (4.09)
EV 4.0+ (4.25)
IO::Socket::Socks 0.64+ (n/a)
IO::Socket::SSL 2.009+ (2.066)
Net::DNS::Native 0.15+ (n/a)
Role::Tiny 2.000001+ (2.000006)
This version is up to date, have fun!
The test machine has 16gb of RAM and an Intel Core i7-8650U (4 cores, 8 threads) CPU.
Baseline program:
use Mojolicious::Lite;
get '/hello' => sub {
my $c = shift;
my $app = app;
$c->render(text => "hello!\n");
};
app->start;
Endoscope variant program:
use Mojolicious::Lite;
use Endoscope;
my $scope = Endoscope->new(monitor => sub {
my ($file, $line, $query, $result) = @_;
app->log->debug("$file/$line/$query = $result");
});
$scope->add(__FILE__, __LINE__ + 6, '$app', 1); # 1 means 'run it every time that line executes'
$scope->apply();
get '/hello' => sub {
my $c = shift;
my $app = app;
$c->render(text => "hello!\n");
};
app->start;
These programs store 'app' into $app in order to give Endoscope a large structure to query.
The Mojo app is running in 'production' mode.
$ perl test.pl daemon -m production
This avoids measuring the performance of printing logs to STDERR.
The load generator is wrk2, invoked in the following way:
$ wrk 'http://localhost:3000/hello' -R 2500 -d 60
The test cases use a target request rate of 2500 RPS. This exceeds the baseline single-core performance of Mojolicious on my laptop. As such, the latency numbers look really high: we are saturating the test programs.
I did this because lower request rates, like 2000 RPS, resulted in both test programs easily managing the request rates with average latencies in the single-digit millisecond range. This demonstrated no clear relationship between the two programs: sometimes the program that did strictly more work was faster, which is a sign of a broken benchmark.
Due to the saturation, the latency numbers are not very meaningful.
However, the request rate that the program manages to output in the face of
saturation is useful: the difference in RPS delivered by the baseline vs. the
Endoscope variant can be read as the "overhead" introduced by Endoscope.
$ wrk 'http://localhost:3000/hello' -R 2500 -d 60
Running 1m test @ http://localhost:3000/hello
2 threads and 10 connections
Thread calibration: mean lat.: 271.343ms, rate sampling interval: 1068ms
Thread calibration: mean lat.: 298.969ms, rate sampling interval: 1011ms
Thread Stats Avg Stdev Max +/- Stdev
Latency 1.89s 802.43ms 3.61s 60.09%
Req/Sec 1.18k 15.16 1.22k 67.37%
141956 requests in 1.00m, 20.20MB read
Requests/sec: 2365.95
Transfer/sec: 344.70KB
$ wrk 'http://localhost:3000/hello' -R 2500 -d 60
Running 1m test @ http://localhost:3000/hello
2 threads and 10 connections
Thread calibration: mean lat.: 686.950ms, rate sampling interval: 2496ms
Thread calibration: mean lat.: 680.839ms, rate sampling interval: 2420ms
Thread Stats Avg Stdev Max +/- Stdev
Latency 4.59s 1.87s 8.80s 58.91%
Req/Sec 1.09k 10.68 1.11k 70.00%
130455 requests in 1.00m, 18.56MB read
Requests/sec: 2174.22
Transfer/sec: 316.77KB
The baseline program delivered 2365 requests per second in the face of clients
demanding 2500 requests per second. The Endoscope variant delivered 2174
requests per second, or 91.92% of baseline. In other words, Endoscope in the
given configuration reduces capacity by about 8.1%.
8.1% can be seen as a lower bound on overhead with a query firing once per request on saturated, CPU-bound Mojolicious web apps. Queries that fire more than once per request, or which do expensive work while exporting data, may have a higher impact. However, most real-world applications:
- Do not run at their 'red line' of capacity, and
- Do significantly more work than render out "Hello World".
So, you are encouraged to measure for yourself.
In order to avoid misrepresenting the performance of Mojolicious (or my laptop :)), here's an example "unsaturated" test case, which is representative of the performance of both the baseline and the variant. I won't specify which one this is, because the variance from run to run is too high to get a meaningful ordering:
$ wrk 'http://localhost:3000/hello' -R 2000 -d 60
Running 1m test @ http://localhost:3000/hello
2 threads and 10 connections
Thread calibration: mean lat.: 5.213ms, rate sampling interval: 10ms
Thread calibration: mean lat.: 5.041ms, rate sampling interval: 10ms
Thread Stats Avg Stdev Max +/- Stdev
Latency 4.28ms 0.88ms 21.57ms 92.20%
Req/Sec 1.05k 122.54 1.67k 65.38%
119971 requests in 1.00m, 17.07MB read
Requests/sec: 1999.48
Transfer/sec: 291.31KB
Endoscope is a powerful tool for debugging running systems by inspecting
their memory. This means that anyone who is able to configure Endoscope
queries and view their output can read the contents of nearly any variable
present in memory. As such, access to these capabilities should be carefully
guarded.
For example, if Endoscope is integrated into a web framework and exposes
a special HTTP endpoint for configuring queries, that endpoint should only be
accessible from the host where the application is running, not externally.
Additionally, that HTTP endpoint should be gated by strong
authentication/authorization.