Getting started with monitoring | Tarantool
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Администрирование Monitoring Getting started with monitoring

Getting started with monitoring

If you use Tarantool version below 2.11.1, it is necessary to install the latest version of metrics first. For details, see Installing the metrics module.

Примечание

The module is also used in applications based on the Cartridge framework. For details, see the Getting started with Cartridge application section.

  1. First, set the instance name and start to collect the standard set of metrics:

    metrics.cfg{labels = {alias = 'my-instance'}}
    

    When using a metrics module version below 0.17.0, use the following snippet instead of metrics.cfg(...):

    metrics.set_global_labels({alias = 'my-instance'})
    metrics.enable_default_metrics()
    
  2. Then, add a handler to expose metric values.

    For JSON format:

    local json_exporter = require('metrics.plugins.json')
    local function http_metrics_handler(request)
        return request:render({ text = json_exporter.export() })
    end
    

    For Prometheus format:

    local prometheus_exporter = require('metrics.plugins.prometheus').collect_http
    

    To learn how to extend metrics with custom data, check the API reference.

  3. Start the HTTP server and expose metrics:

    local http_server = require('http.server')
    local server = http_server.new('0.0.0.0', 8081)
    server:route({path = '/metrics'}, http_metrics_handler)
    server:start()
    

The metric values are now available via the http://localhost:8081/metrics URL:

[
  {
    "label_pairs": {
      "alias": "my-instance"
    },
    "timestamp": 1679663602823779,
    "metric_name": "tnt_vinyl_disk_index_size",
    "value": 0
  },
  {
    "label_pairs": {
      "alias": "my-instance"
    },
    "timestamp": 1679663602823779,
    "metric_name": "tnt_info_memory_data",
    "value": 39272
  },
  {
    "label_pairs": {
      "alias": "my-instance"
    },
    "timestamp": 1679663602823779,
    "metric_name": "tnt_election_vote",
    "value": 0
  }
]

The data can be visualized in Grafana dashboard.

The full source example is listed below:

-- Import modules
local metrics = require('metrics')
local http_server = require('http.server')
local json_exporter = require('metrics.plugins.json')

-- Define helper functions
local function http_metrics_handler(request)
    return request:render({ text = json_exporter.export() })
end

-- Start the database
box.cfg{
    listen = 3301,
}

-- Configure the metrics module
metrics.cfg{labels = {alias = 'my-tnt-app'}}

-- Run the web server
local server = http_server.new('0.0.0.0', 8081)
server:route({path = '/metrics'}, http_metrics_handler)
server:start()

To enable the collection of HTTP metrics, wrap a handler with a metrics.http_middleware.v1 function:

local metrics = require('metrics')
local httpd = require('http.server').new(ip, port)

-- Create a summary collector for latency
metrics.http_middleware.configure_default_collector('summary')

-- Set a route handler for latency summary collection
httpd:route({ path = '/path-1', method = 'POST' }, metrics.http_middleware.v1(handler_1))
httpd:route({ path = '/path-2', method = 'GET' }, metrics.http_middleware.v1(handler_2))

-- Start HTTP routing
httpd:start()

Примечание

By default, the http_middleware uses the histogram collector for backward compatibility reasons. To collect HTTP metrics, use the summary type instead.

You can collect all HTTP metrics with a single collector. If you use the default Grafana dashboard, don’t change the default collector name. Otherwise, your metrics won’t appear on the charts.

You can create your own metric in two ways, depending on when you need to take measurements:

  • at any arbitrary moment of time
  • when the data collected by metrics is requested

To create custom metrics at any arbitrary moment of time, do the following:

  1. Create the collector:

    local response_counter = metrics.counter('response_counter', 'Response counter')
    
  2. Take a measurement at the appropriate place, for example, in an API request handler:

    local function check_handler(request)
        local label_pairs = {
            path = request.path,
            method = request.method,
        }
        response_counter:inc(1, label_pairs)
        -- ...
    end
    

To create custom metrics when the data collected by metrics is requested, do the following:

  1. Create the collector:

    local other_custom_metric = metrics.gauge('other_custom_metric', 'Other custom metric')
    
  2. Take a measurement at the time of requesting the data collected by metrics:

    metrics.register_callback(function()
        -- ...
        local label_pairs = {
            category = category,
        }
        other_custom_metric:set(current_value, label_pairs)
    end)
    

The full example is listed below:

-- Import modules
local metrics = require('metrics')
local http_server = require('http.server')
local json_exporter = require('metrics.plugins.json')

local response_counter = metrics.counter('response_counter', 'Response counter')

-- Define helper functions
local function http_metrics_handler(request)
    return request:render({ text = json_exporter.export() })
end

local function check_handler(request)
    local label_pairs = {
        path = request.path,
        method = request.method,
    }
    response_counter:inc(1, label_pairs)
    return request:render({ text = 'ok' })
end

-- Start the database
box.cfg{
    listen = 3301,
}

-- Configure the metrics module
metrics.set_global_labels{alias = 'my-tnt-app'}

-- Run the web server
local server = http_server.new('0.0.0.0', 8081)
server:route({path = '/metrics'}, http_metrics_handler)
server:route({path = '/check'}, check_handler)
server:start()

The result looks in the following way:

[
  {
    "label_pairs": {
      "path": "/check",
      "method": "GET",
      "alias": "my-tnt-app"
    },
    "timestamp": 1688385933874080,
    "metric_name": "response_counter",
    "value": 1
  }
]

The module allows to add your own metrics, but there are some subtleties when working with specific tools.

When adding your custom metric, it’s important to ensure that the number of label value combinations is kept to a minimum. Otherwise, combinatorial explosion may happen in the timeseries database with metrics values stored. Examples of data labels:

For example, if your company uses InfluxDB for metric collection, you could potentially disrupt the entire monitoring setup, both for your application and for all other systems within the company. As a result, monitoring data is likely to be lost.

Example:

local some_metric = metrics.counter('some', 'Some metric')

-- THIS IS POSSIBLE
local function on_value_update(instance_alias)
   some_metric:inc(1, { alias = instance_alias })
end

-- THIS IS NOT ALLOWED
local function on_value_update(customer_id)
   some_metric:inc(1, { customer_id = customer_id })
end

In the example, there are two versions of the function on_value_update. The top version labels the data with the cluster instance’s alias. Since there’s a relatively small number of nodes, using them as labels is feasible. In the second case, an identifier of a record is used. If there are many records, it’s recommended to avoid such situations.

The same principle applies to URLs. Using the entire URL with parameters is not recommended. Use a URL template or the name of the command instead.

In essence, when designing custom metrics and selecting labels or tags, it’s crucial to opt for a minimal set of values that can uniquely identify the data without introducing unnecessary complexity or potential conflicts with existing metrics and systems.

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