Monitoring

Monitoring module injects user custom metrics and monitors the process. It supports multiple backends, protocols and data formats.

Table of contents

1. Installation
2. Getting started
3. Advanced features
4. System monitoring and server-side backends installation and configuration

Installation

Click here if you don’t have aliBuild installed

• Compile Monitoring and its dependencies via aliBuild

  aliBuild build Monitoring --defaults o2-dataflow

• Load the environment for Monitoring (in the alice directory)

  alienv load Monitoring/latest

Getting started

Monitoring instance

Get an instance from MonitoringFactory by passing backend’s URI(s) as a parameter (comma separated if more than one).
The factory is accessible from o2::monitoring namespace.

#include <MonitoringFactory.h>
using namespace o2::monitoring;
std::unique_ptr<Monitoring> monitoring = MonitoringFactory::Get("backend[-protocol]://host:port[/verbosity][?query]");

See the table below to find URIs for supported backends:

Metrics

A metric consist of 5 parameters:

• name - metric name
• values - vector of value and value name pairs
• timestamp - time of creation
• verbosity - metric “severity”
• tags - metric metadata represented as map

A metric can be constructed by providing required parameters (value and metric name, value name is set to value):

Metric{10, "name"}

Values

By default metric can be created with zero or one value (in such case value name is set to value). Any additional value may be added using .addValue method, therefore the following two metrics are identical:

Metric{10, "name"}
Metric{"name"}.addValue(10, "value")

Tags

1. Metric tags
   Each metric can be tagged with any number of predefined tags.
   In order to do so use addTag(tags::Key, tags::Value) or addTag(tags::Key, unsigned short) methods. The latter method allows assigning numeric value to a tag.

Metric{10, "name"}.addTag(tags::Key::Subsystem, tags::Value::QC)

See the example: examples/2-TaggedMetrics.cxx.

1. Global tags
   Global tags are added to each metric sent eg. hostname tag is added by default by the library.

You can add your own global tag by calling addGlobalTag(std::string_view key, std::string_view value) or addGlobalTag(tags::Key, tags::Value) on Monitoring object.

1. Run number
   Run number is special case of a global tag, its value can be overwritten at any time, therefore it benefits simplified handling: setRunNumber(uint32_t). Value 0 is unique and means no run number is set.

Sending metric

Pass metric object to send method as l-value reference:

send({10, "name"})
send(Metric{20, "name"}.addTag(tags::Key::CRU, 123))
send(Metric{"throughput"}.addValue(100, "tx").addValue(200, "rx"))

See how it works in the example: examples/1-Basic.cxx.

Advanced features

Metric verbosity

There are 3 verbosity levels (the same as for backends): Debug, Info, Prod. By default it is set to Verbosity::Info. The default value can be overwritten using: Metric::setDefaultVerbosity(verbosity).
To overwrite verbosity on per metric basis use third, optional parameter to metric constructor:

Metric{10, "name", Verbosity::Prod}

Metrics need to match backends verbosity in order to be sent, eg. backend with /info verbosity will accept Info and Prod metrics only.

Buffering metrics

In order to avoid sending each metric separately, metrics can be temporary stored in the buffer and flushed at the most convenient moment.
This feature can be controlled with following two methods:

monitoring->enableBuffering(const std::size_t maxSize)
...
monitoring->flushBuffer();

enableBuffering takes maximum buffer size as its parameter. The buffer gets full all values are flushed automatically.

See how it works in the example: examples/10-Buffering.cxx.

Calculating derived values

This feature can calculate derived values. To do so, use optional DerivedMetricMode mode parameter of send method:

send(Metric&& metric, [DerivedMetricMode mode])

Two modes are available:

• DerivedMetricMode::RATE - rate between two following values,
• DerivedMetricMode::INCREMENT - sum of all passed values.
• DerivedMetricMode::SUPPRESS - suppresses forthcoming metric with same value, this happens until timeout is reached (configurable using DerivedMetrics::mSuppressTimeout)

The derived value is generated only from the first value of the metric and it is added to the same metric with the value name suffixed with _rate, _increment accordingly.

See how it works in the example: examples/4-RateDerivedMetric.cxx.

Process monitoring

This feature provides basic performance status of the process. Note that is runs in separate thread.

enableProcessMonitoring([interval in seconds, {PmMeasurement list}]);

List of valid measurement lists:

• PmMeasurement::Cpu
• PmMeasurement::Mem
• PmMeasurement::Smaps - Beware. Enabling this will trigger kernel to run smaps_account periodically.

Following metrics are generated every time interval:
PmMeasurement::Cpu:

• cpuUsedPercentage - percentage of a core usage (kernel + user mode) over time interval
• involuntaryContextSwitches - involuntary context switches over time interval
• cpuUsedAbsolute - amount of time spent on process execution (in user and kernel mode) over time interval (expressed in microseconds)

PmMeasurement::Mem: (Linux only)

• memoryUsagePercentage - ratio of the process’s virtual memory to memory available on the machine
• virtualMemorySize - virtual memory reserved by process (expressed in kB)
• residentSetSize - resident set size reserved by process (expressed in kB)

PmMeasurement::Smaps: (Linux only)

• proportionalSetSize - count of pages it has in memory, where each page is divided by the number of processes sharing it
• memoryPrivateClean - unmodified private pages
• memoryPrivateDirty - modified private pages

Additional metrics are generated at the end of process execution:
CPU measurements:

• cpuTimeConsumedByProcess - total amount of time spent on process execution (in user and kernel mode) (expressed in microseconds)
• averageCpuUsedPercentage - average percentage of a core usage over time interval

Memory measurements: (Linux only)

• averageResidentSetSize - average resident set size used by process (expressed in kB)
• averageVirtualMemorySize - average virtual memory used by process (expressed in kB)

StdOut backend output format

[METRIC] <name>,<type> <values> <timestamp> <tags>

The prefix ([METRIC]) can be changed using query component.

Regex verbosity policy

Overwrite metric verbosity using regex expression:

Metric::setVerbosityPolicy(Verbosity verbosity, const std::regex& regex)

System monitoring, server-side backends installation and configuration

This guide explains manual installation. For ansible deployment see AliceO2Group/system-configuration gitlab repo.

Receiving metrics from Monitoring system (development instructions)

Requirements

• Ubuntu, RHEL9, RHEL8, CS8, macOS, or CC7 with devtoolset-9
• Boost >= 1.83, CMake

Compile Monitoring library with Kafka backend

Manually

• Compile librdkafka

  git clone -b v2.3.0 https://github.com/edenhill/librdkafka && cd librdkafka
  cmake -H. -B./_cmake_build -DENABLE_LZ4_EXT=OFF -DCMAKE_INSTALL_LIBDIR=lib -DRDKAFKA_BUILD_TESTS=OFF -DRDKAFKA_BUILD_EXAMPLES=OFF -DCMAKE_INSTALL_PREFIX=~/librdkafka_install
  cmake --build ./_cmake_build --target install -j

• Compile Monitoring library, make sure to define RdKafka_DIR and point to CMake config directory:

  git clone https://github.com/AliceO2Group/Monitoring && cd Monitoring
  cmake -H. -B./_cmake_build -DRdKafka_DIR=~/librdkafka_install/lib/cmake/RdKafka/ -DCMAKE_INSTALL_PREFIX=~/Monitoring_install
  cmake --build ./_cmake_build --target install -j

aliBuild

• Modify monitoring.sh: add - librdkafka to “requires”
• Compile Monitoring: aliBuild build Monitoring --defaults o2-dataflow --always-prefer-system
• Add Monitoring as dependency of your project

Look for Monitoring library in your CMake

As librdkafka is optional dependency of Monitoring it is not handled by CMakeConfig, therefore you need:

find_package(RdKafka CONFIG REQUIRED)
find_package(Monitoring CONFIG REQUIRED)

And then, link against AliceO2::Monitoring target.

Connect to Monitoring server

#include "Monitoring/MonitoringFactory.h"
...
std::vector<std::string> topics = {"<topic-to-subscribe>"};
auto client = MonitoringFactory::GetPullClient("<kafka-server:9092>", topics, "<client-id>");
for (;;) {
  auto metrics = client->pull();
  if (!metrics.empty()) {
    /// metric.first => topic name; metric.second => metric itself
  } else {
    // wait a bit if no data available
    std::this_thread::sleep_for(std::chrono::milliseconds(100));
  }

Run-time parameters:

• <topic-to-subscribe> - List of topics to subscribe
• <kafka-server:9092> - Kafka broker (staging or production)
• <client_id> - unique, self-explainable string describing the client, eg. dcs-link-status or its-link-status.

Metrics are returned in batch of maximum 100 for each pull() call.

Data format

Native data format is Influx Line Protocol but metrics can be converted into any format listed in here: https://docs.influxdata.com/telegraf/latest/data_formats/output/