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從Prometheus服務器加載指標

在TensorFlow.org上查看 在Google Colab中運行 在GitHub上查看源代碼 下載筆記本

總覽

本教程將來自Prometheus服務器的CoreDNS指標加載到tf.data.Dataset ,然後使用tf.keras進行訓練和推斷。

CoreDNS是一台專注於服務發現的DNS服務器,已廣泛部署為Kubernetes集群的一部分。出於這個原因,它通常由devops運營密切監視。

本教程是一個示例,可供開發人員通過機器學習在其操作中尋求自動化的示例。

設置和使用

安裝所需的tensorflow-io軟件包並重新啟動運行時

 import os
 
 try:
  %tensorflow_version 2.x
except Exception:
  pass
 
TensorFlow 2.x selected.

pip install tensorflow-io
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 from datetime import datetime

import tensorflow as tf
import tensorflow_io as tfio
 

安裝和設置CoreDNS和Prometheus

出於演示目的,在本地開放端口9053的CoreDNS服務器可以接收DNS查詢,而開放端口9153 (默認)的CoreDNS服務器可以公開進行刮擦的指標。以下是CoreDNS的基本Corefile配置,可以下載

 .:9053 {
  prometheus
  whoami
}
 

有關安裝的更多詳細信息,請參見CoreDNS的文檔

 !curl -s -OL https://github.com/coredns/coredns/releases/download/v1.6.7/coredns_1.6.7_linux_amd64.tgz
!tar -xzf coredns_1.6.7_linux_amd64.tgz

!curl -s -OL https://raw.githubusercontent.com/tensorflow/io/master/docs/tutorials/prometheus/Corefile

!cat Corefile
 
.:9053 {
  prometheus
  whoami
}

 # Run `./coredns` as a background process.
# IPython doesn't recognize `&` in inline bash cells.
get_ipython().system_raw('./coredns &')
 

下一步是設置Prometheus服務器,並使用Prometheus刮擦從上方在端口9153上公開的CoreDNS指標。用於配置的prometheus.yml文件也可以下載

 !curl -s -OL https://github.com/prometheus/prometheus/releases/download/v2.15.2/prometheus-2.15.2.linux-amd64.tar.gz
!tar -xzf prometheus-2.15.2.linux-amd64.tar.gz --strip-components=1

!curl -s -OL https://raw.githubusercontent.com/tensorflow/io/master/docs/tutorials/prometheus/prometheus.yml

!cat prometheus.yml
 
global:
  scrape_interval:     1s
  evaluation_interval: 1s
alerting:
  alertmanagers:

  - static_configs:
    - targets:
rule_files:
scrape_configs:
- job_name: 'prometheus'
  static_configs:
  - targets: ['localhost:9090']
- job_name: "coredns"
  static_configs:
  - targets: ['localhost:9153']

 # Run `./prometheus` as a background process.
# IPython doesn't recognize `&` in inline bash cells.
get_ipython().system_raw('./prometheus &')
 

為了顯示一些活動,可以使用dig命令針對已設置的CoreDNS服務器生成一些DNS查詢:

sudo apt-get install -y -qq dnsutils
dig @127.0.0.1 -p 9053 demo1.example.org

; <<>> DiG 9.11.3-1ubuntu1.11-Ubuntu <<>> @127.0.0.1 -p 9053 demo1.example.org
; (1 server found)
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 53868
;; flags: qr aa rd; QUERY: 1, ANSWER: 0, AUTHORITY: 0, ADDITIONAL: 3
;; WARNING: recursion requested but not available

;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 4096
; COOKIE: 855234f1adcb7a28 (echoed)
;; QUESTION SECTION:
;demo1.example.org.     IN  A

;; ADDITIONAL SECTION:
demo1.example.org.  0   IN  A   127.0.0.1
_udp.demo1.example.org. 0   IN  SRV 0 0 45361 .

;; Query time: 0 msec
;; SERVER: 127.0.0.1#9053(127.0.0.1)
;; WHEN: Tue Mar 03 22:35:20 UTC 2020
;; MSG SIZE  rcvd: 132


dig @127.0.0.1 -p 9053 demo2.example.org

; <<>> DiG 9.11.3-1ubuntu1.11-Ubuntu <<>> @127.0.0.1 -p 9053 demo2.example.org
; (1 server found)
;; global options: +cmd
;; Got answer:
;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 53163
;; flags: qr aa rd; QUERY: 1, ANSWER: 0, AUTHORITY: 0, ADDITIONAL: 3
;; WARNING: recursion requested but not available

;; OPT PSEUDOSECTION:
; EDNS: version: 0, flags:; udp: 4096
; COOKIE: f18b2ba23e13446d (echoed)
;; QUESTION SECTION:
;demo2.example.org.     IN  A

;; ADDITIONAL SECTION:
demo2.example.org.  0   IN  A   127.0.0.1
_udp.demo2.example.org. 0   IN  SRV 0 0 42194 .

;; Query time: 0 msec
;; SERVER: 127.0.0.1#9053(127.0.0.1)
;; WHEN: Tue Mar 03 22:35:21 UTC 2020
;; MSG SIZE  rcvd: 132


現在是一個CoreDNS服務器,其度量標準已由Prometheus服務器抓取並準備由TensorFlow使用。

為CoreDNS指標創建數據集並在TensorFlow中使用它

可以使用tfio.experimental.IODataset.from_prometheus為PostgreSQL服務器創建可用於CoreDNS指標的數據集。最少需要兩個參數。 query將傳遞到Prometheus服務器以選擇度量標準, length是要加載到數據集中的時間段。

您可以從"coredns_dns_request_count_total""5" (秒)開始以創建以下數據集。由於在本教程的前面發送了兩個DNS查詢,因此在時間序列末尾, "coredns_dns_request_count_total"的度量標準應為"2.0"

 dataset = tfio.experimental.IODataset.from_prometheus(
      "coredns_dns_request_count_total", 5, endpoint="http://localhost:9090")


print("Dataset Spec:\n{}\n".format(dataset.element_spec))

print("CoreDNS Time Series:")
for (time, value) in dataset:
  # time is milli second, convert to data time:
  time = datetime.fromtimestamp(time // 1000)
  print("{}: {}".format(time, value['coredns']['localhost:9153']['coredns_dns_request_count_total']))
 
Dataset Spec:
(TensorSpec(shape=(), dtype=tf.int64, name=None), {'coredns': {'localhost:9153': {'coredns_dns_request_count_total': TensorSpec(shape=(), dtype=tf.float64, name=None)} } })

CoreDNS Time Series:
2020-03-03 22:35:17: 2.0
2020-03-03 22:35:18: 2.0
2020-03-03 22:35:19: 2.0
2020-03-03 22:35:20: 2.0
2020-03-03 22:35:21: 2.0

進一步研究數據集的規範:

 (
  TensorSpec(shape=(), dtype=tf.int64, name=None),
  {
    'coredns': {
      'localhost:9153': {
        'coredns_dns_request_count_total': TensorSpec(shape=(), dtype=tf.float64, name=None)
      }
    }
  }
)

 

顯而易見,數據集由一個(time, values)元組組成,其中values字段是一個python dict,擴展為:

 "job_name": {
  "instance_name": {
    "metric_name": value,
  },
}
 

在上面的示例中, 'coredns'是作業名稱, 'localhost:9153'是實例名稱, 'coredns_dns_request_count_total'是度量標準名稱。請注意,根據所使用的Prometheus查詢,可能會返回多個作業/實例/度量。這也是在數據集結構中使用python dict的原因。

以另一個查詢"go_memstats_gc_sys_bytes"為例。由於CoreDNS和Prometheus都是用Golang編寫的,因此"go_memstats_gc_sys_bytes"指標可用於"coredns"作業和"prometheus"作業:

 dataset = tfio.experimental.IODataset.from_prometheus(
    "go_memstats_gc_sys_bytes", 5, endpoint="http://localhost:9090")

print("Time Series CoreDNS/Prometheus Comparision:")
for (time, value) in dataset:
  # time is milli second, convert to data time:
  time = datetime.fromtimestamp(time // 1000)
  print("{}: {}/{}".format(
      time,
      value['coredns']['localhost:9153']['go_memstats_gc_sys_bytes'],
      value['prometheus']['localhost:9090']['go_memstats_gc_sys_bytes']))
 
Time Series CoreDNS/Prometheus Comparision:
2020-03-03 22:35:17: 2385920.0/2775040.0
2020-03-03 22:35:18: 2385920.0/2775040.0
2020-03-03 22:35:19: 2385920.0/2775040.0
2020-03-03 22:35:20: 2385920.0/2775040.0
2020-03-03 22:35:21: 2385920.0/2775040.0

現在可以將創建的Dataset直接傳遞給tf.keras ,以進行訓練或推理。

使用數據集進行模型訓練

創建度量標準數據集後,可以將數據集直接傳遞到tf.keras進行模型訓練或推斷。

出於演示目的,本教程將僅使用一個非常簡單的LSTM模型,該模型具有1個功能和2個步驟作為輸入:

 n_steps, n_features = 2, 1
simple_lstm_model = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(8, input_shape=(n_steps, n_features)),
    tf.keras.layers.Dense(1)
])

simple_lstm_model.compile(optimizer='adam', loss='mae')

 

要使用的數據集是帶有10個樣本的CoreDNS的'go_memstats_sys_bytes'值。但是,由於形成了window=n_stepsshift=1的滑動窗口,因此需要其他樣本(對於任意兩個連續元素,將第一個作為x ,將第二個作為y進行訓練)。總計為10 + n_steps - 1 + 1 = 12秒。

數據值也按比例縮放為[0, 1]

 n_samples = 10

dataset = tfio.experimental.IODataset.from_prometheus(
    "go_memstats_sys_bytes", n_samples + n_steps - 1 + 1, endpoint="http://localhost:9090")

# take go_memstats_gc_sys_bytes from coredns job 
dataset = dataset.map(lambda _, v: v['coredns']['localhost:9153']['go_memstats_sys_bytes'])

# find the max value and scale the value to [0, 1]
v_max = dataset.reduce(tf.constant(0.0, tf.float64), tf.math.maximum)
dataset = dataset.map(lambda v: (v / v_max))

# expand the dimension by 1 to fit n_features=1
dataset = dataset.map(lambda v: tf.expand_dims(v, -1))

# take a sliding window
dataset = dataset.window(n_steps, shift=1, drop_remainder=True)
dataset = dataset.flat_map(lambda d: d.batch(n_steps))


# the first value is x and the next value is y, only take 10 samples
x = dataset.take(n_samples)
y = dataset.skip(1).take(n_samples)

dataset = tf.data.Dataset.zip((x, y))

# pass the final dataset to model.fit for training
simple_lstm_model.fit(dataset.batch(1).repeat(10),  epochs=5, steps_per_epoch=10)
 
Train for 10 steps
Epoch 1/5
10/10 [==============================] - 2s 150ms/step - loss: 0.8484
Epoch 2/5
10/10 [==============================] - 0s 10ms/step - loss: 0.7808
Epoch 3/5
10/10 [==============================] - 0s 10ms/step - loss: 0.7102
Epoch 4/5
10/10 [==============================] - 0s 11ms/step - loss: 0.6359
Epoch 5/5
10/10 [==============================] - 0s 11ms/step - loss: 0.5572

<tensorflow.python.keras.callbacks.History at 0x7f1758f3da90>

上面訓練有素的模型實際上並不是很有用,因為在本教程中設置的CoreDNS服務器沒有任何工作量。但是,這是一條工作流水線,可用於從真正的生產服務器加載指標。然後可以對模型進行改進,以解決devops自動化的現實問題。