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# coding=utf-8
#
# Copyright © Cloud Linux GmbH & Cloud Linux Software, Inc 2010-2020 All Rights Reserved
#
# Licensed under CLOUD LINUX LICENSE AGREEMENT
# http://cloudlinux.com/docs/LICENCE.TXT
#
import os
import platform
import re
from requests import PreparedRequest, RequestException
from prometheus_client.parser import text_string_to_metric_families
from typing import Dict, AnyStr, List
from itertools import groupby
from .collector_base import CollectorBase
from cl_plus.utils.unix_socket import UnixHTTPAdapter
from clcommon.utils import get_virt_type
from cl_plus.consts import METRIC_NAME, METRIC_LABELS, METRIC_VALUE
class NodeExporterCollector(CollectorBase):
def __init__(self, _logger):
super(NodeExporterCollector, self).__init__(_logger)
self._required_metrics = ['node_filesystem_avail_bytes',
'node_filesystem_size_bytes',
'node_cpu_seconds_total',
'node_filesystem_free_bytes',
'node_memory_MemTotal_bytes',
'node_memory_MemAvailable_bytes',
'node_boot_time_seconds',
'node_context_switches_total',
'node_disk_io_time_seconds_total',
'node_disk_read_bytes_total',
'node_disk_read_time_seconds_total',
'node_disk_reads_completed_total',
'node_disk_write_time_seconds_total',
'node_disk_writes_completed_total',
'node_disk_written_bytes_total',
'node_network_receive_bytes_total',
'node_network_transmit_bytes_total',
'node_filefd_allocated',
'node_load1',
'node_load15',
'node_load5',
'node_hwmon_temp_celsius']
self._is_ne_error = None
self._socket_file = '/var/run/cl_node_exporter.socket'
self._metrics_url = 'http://localhost/metrics'
self.virt_type = get_virt_type()
def init(self):
"""
Initialize Node Exporter collector
:return: None
"""
self._aggregated_data = {}
self._logger.info("Node Exporter collector init")
def _get_data_from_socket(self):
request = PreparedRequest()
request.prepare(
method='GET',
url=self._metrics_url,
)
unix_http_adapter = UnixHTTPAdapter(self._socket_file)
response = unix_http_adapter.send(request)
try:
response.raise_for_status()
finally:
unix_http_adapter.close()
return response.text
@staticmethod
def _is_metric_value_exclude(labels):
"""
Determines is metric value with supposed labels need exclude
:param labels: labels for mount dictionary to check. Example:
{'device': '/dev/sda1', 'fstype': 'xfs', 'mountpoint': '/usr/share/cagefs-skeleton'}
:return: True/False - Exclude/not exclude metric value from collector output
"""
if 'mountpoint' not in labels:
return False
# Check for special case if CageFs skeleton path (or it part) is symlink to some another dir
path_to_check = os.path.realpath('/usr/share/cagefs-skeleton')
if labels['mountpoint'].startswith(path_to_check) or '/virtfs/' in labels['mountpoint']:
return True
return False
@staticmethod
def _calculate_available_mem(metrics):
"""
Calculates available mem metric
according to article:
https://access.redhat.com/solutions/406773
"""
return metrics['node_memory_MemFree_bytes'] + \
metrics['node_memory_SReclaimable_bytes'] + \
metrics['node_memory_Buffers_bytes'] + \
metrics['node_memory_Cached_bytes']
def _prepare_node_disk_entries(self, raw_ne_output: AnyStr) -> AnyStr:
"""
Different virtualization types display all devices
in the /proc/diskstats with different titles
Node exporter provides default filtering, but it doesn't work
with non-kvm virtual machines (at least, XEN)
Example on kvm /proc/diskstats output:
vda ...
vda1 ...
sr0 ...
loop0 ...
And default NE behaviour will leave only vda and sr0 devices
Example on XEN:
xvda1 ...
And default NE behaviour will ignore this entry
So this method checks virt type and leaves only suitable
devices (depending on vm type)
:param raw_ne_output: node exporter output including all devices
for node_disk metrics
:return: output with skipped lines
"""
# Set default regexp value (just like NE)
# All lines with node_disk metrics and devices to filter
# Example of raw output with kvm virtualization:
# # HELP node_disk_io_now The number of I/Os currently in progress.
# # TYPE node_disk_io_now gauge
# node_disk_io_now{device="loop0"} 0
# node_disk_io_now{device="sr0"} 0
# node_disk_io_now{device="vda"} 0
# node_disk_io_now{device="vda1"} 0
# After the regexp substring:
# # HELP node_disk_io_now The number of I/Os currently in progress.
# # TYPE node_disk_io_now gauge
# node_disk_io_now{device="sr0"} 0
# node_disk_io_now{device="vda"} 0
reg = "node_disk.*(ram|loop|fd|(h|s|v|xv)d[a-z]|nvme\\d+n\\d+p)\\d.*\n"
if self.virt_type == "xen":
# Regex, that will include `xvd*{num}` and `vd*{num}` devices
reg = "node_disk.*(ram|loop|fd|(h|s)d[a-z]|nvme\\d+n\\d+p)\\d.*\n"
return re.sub(reg, "", raw_ne_output)
@staticmethod
def filter_samples_for_xen(family) -> List:
"""
Checks for presence of devices without numbers in a title and
ignores all devices with numbers (if any)
If there are only numbered devices - doesn't filter anything
Example of received node_disk metric family:
Name: node_disk_* Labels: {'device': 'xvda'} Value: 811395.84
Name: node_disk_* Labels: {'device': 'xvda1'} Value: 1114.75
Name: node_disk_* Labels: {'device': 'xvda2'} Value: 0.0
Name: node_disk_* Labels: {'device': 'xvdb1'} Value: 813760.16
Name: node_disk_* Labels: {'device': 'xvdb2'} Value: 872.06
Name: node_disk_* Labels: {'device': 'xvdb3'} Value: 0.0
Expected result of filtering:
`xvda` group (since we have sample with device without num):
Name: node_disk_* Labels: {'device': 'xvda'} Value: 811395.84
`xvdb` group (since all samples devices are with num):
Name: node_disk_* Labels: {'device': 'xvdb1'} Value: 813760.16
Name: node_disk_* Labels: {'device': 'xvdb2'} Value: 872.06
Name: node_disk_* Labels: {'device': 'xvdb3'} Value: 0.0
:param family: parsed Metric object with all samples
:return: list of needed samples
"""
filtered_family_samples = []
# Grouping samples of metric by device base
# (device value without numbers)
for device_base, group in groupby(
family.samples, lambda x: re.sub("\\d+", "", x[1]["device"])):
group = list(group)
for sample in group:
# If devices have xvd* title without numbers (`xvda`)
if sample[METRIC_LABELS]["device"] == device_base:
# Leave only this sample from device group
filtered_family_samples.append(sample)
break
else:
# There is no device without a number in title
# Add info about all devices in the group
filtered_family_samples.extend(group)
return filtered_family_samples
def _get_data_from_ne(self):
"""
Retrieve new data from Node Exporter
:return: Dict with NE metrics
"""
# Get data from NE
try:
node_exporter_output = self._get_data_from_socket()
except RequestException as err:
if not self._is_ne_error:
self._logger.warn("[Node exporter collector] socket get_data error: %s", err)
self._is_ne_error = True
return None
self._is_ne_error = False
try:
node_exporter_metrics = {}
# metrics to calculate availableMEM for cl6
memory_metrics = ['node_memory_MemFree_bytes', 'node_memory_SReclaimable_bytes',
'node_memory_Cached_bytes', 'node_memory_Buffers_bytes']
memory_metrics_values = {}
# Filter diskstats entries
node_exporter_output = self._prepare_node_disk_entries(
node_exporter_output)
for family in text_string_to_metric_families(node_exporter_output):
# Exclude all unneeded samples for node_disk and XEN VMs
if family.name.startswith("node_disk") and self.virt_type == "xen":
family.samples = self.filter_samples_for_xen(family)
for sample in family.samples:
# sample example
# Name: node_cpu_seconds_total Labels: {'cpu': '0', 'mode': 'idle'} Value: 811395.84
# Name: node_cpu_seconds_total Labels: {'cpu': '0', 'mode': 'iowait'} Value: 1114.75
# Name: node_cpu_seconds_total Labels: {'cpu': '0', 'mode': 'irq'} Value: 0.0
# Name: node_cpu_seconds_total Labels: {'cpu': '1', 'mode': 'idle'} Value: 813760.16
# Name: node_cpu_seconds_total Labels: {'cpu': '1', 'mode': 'iowait'} Value: 872.06
# Name: node_cpu_seconds_total Labels: {'cpu': '1', 'mode': 'irq'} Value: 0.0
name = sample[METRIC_NAME] # metric name
labels = sample[METRIC_LABELS] # labels dict
value = sample[METRIC_VALUE] # metric value
if name in memory_metrics:
memory_metrics_values[name] = value
if name not in self._required_metrics:
continue
# Skip unneeded mounts
if self._is_metric_value_exclude(labels):
continue
if name not in node_exporter_metrics:
node_exporter_metrics[name] = []
metric_data = {"value": value}
if len(labels) != 0:
metric_data.update({"labels": labels})
node_exporter_metrics[name].append(metric_data)
self._is_ne_error = False
if 'el6' in platform.release() and \
node_exporter_metrics.get('node_memory_MemAvailable_bytes') is None:
available_mem = self._calculate_available_mem(memory_metrics_values)
node_exporter_metrics['node_memory_MemAvailable_bytes'] = [{'value': available_mem}]
return node_exporter_metrics
except Exception:
if not self._is_ne_error:
import traceback
var = traceback.format_exc()
self._logger.warn("[Node exporter collector] generic get_data error: %s" % var)
self._is_ne_error = True
return None
def _collect_new_data(self, new_ne_data_dict: Dict):
"""
Add new NE data
:param new_ne_data_dict: New data from NE
income node exporter dict:
{
'metric_name': [
{'value': 1}
],
'metric_name2': [
{'value': 1, 'labels': {...}},
{'value': 1, 'labels': {...}}
]
}
aggregated data:
{
'metric_name': [
{'value': [1, 2, 3]}
],
'metric_name2': [
{'value': [1, 2, 3], 'labels': {...}},
{'value': [1, 3, 5], 'labels': {...}}
]
}
"""
for metric_name, metric_values_list in new_ne_data_dict.items():
for metric_values_dict in metric_values_list:
if metric_name not in self._aggregated_data:
self._aggregated_data[metric_name] = []
if 'labels' not in metric_values_dict:
if self._aggregated_data[metric_name]:
self._aggregated_data[metric_name][0]['value'].append(metric_values_dict['value'])
else:
self._aggregated_data[metric_name].append({
'value': [metric_values_dict['value']]
})
else:
for aggregated_item_dict in self._aggregated_data[metric_name]:
if metric_values_dict['labels'] == aggregated_item_dict['labels']:
aggregated_item_dict['value'].append(metric_values_dict['value'])
break
else:
self._aggregated_data[metric_name].append({
'value': [metric_values_dict['value']],
'labels': metric_values_dict['labels']
})
def aggregate_new_data(self):
"""
Retrieve and aggregate new data
:return None
"""
json_dict = self._get_data_from_ne()
# Exit if no data
if not json_dict:
return
# New data present - aggregate
self._collect_new_data(json_dict)
def get_averages(self):
"""
Get collector's averages data
:return: dict
{
"node_exporter":
{
"go_gc_duration_seconds": [ { "labels": {"quantile": "0"} , "value": "0" },
{ "labels": {"quantile": "0.25"} , "value": "0" },
{ "labels": {"quantile": "0.5"} , "value": "0" },
{ "labels": {"quantile": "0.75"} , "value": "0" },
{ "labels": {"quantile": "1"} , "value": "0" }
],
"go_gc_duration_seconds_sum": [ { "value": "0" } ]
}
}
or None if can't get data
"""
if not self._aggregated_data:
return None
# calculate average for each metric
for _, metric_values_list in self._aggregated_data.items():
for metric_data in metric_values_list:
# {'metric_name': [{'value': [1,2,3,...], 'labels': {}}, ...], ...} ->
# {'metric_name': [{'value': average([1,2,3,...]), 'labels': {}}, ...], ...}
metric_data['value'] = sum(metric_data['value']) // len(metric_data['value'])
ret_dict = self._aggregated_data.copy()
# Prepare to next collecting cycle - reset all needed variables
self._aggregated_data = {}
return {"node_exporter": ret_dict}