监控oracle数据io,Prometheus监控Oracle数据库

背景

本文简单介绍下，Prometheus如何通过exporters监控Oracle数据库，以及应该注意哪些指标。

oracledb_exporter

oracledb_exporter是一个连接到Oracle数据库并生成Prometheus metrics的应用程序，

设置

展示下如何安装和设置oracledb_exporter，以使用Prometheus来监控Oracle数据库。oracledb_exporter部署在k8s集群中

在k8s使用Deployment部署oracledb_exporter，并添加注解，以实现Prometheus自动发现oracledb_exporter断点并收集指标

spec:

template:

metadata:

annotations:

prometheus.io/scrape: "true"

prometheus.io/port: "9161"

prometheus.io/path: "/metrics"

oracledb_exporter需要Oracle的连接信息才能访问和生成指标，此参数作为环境变量传递到exporter。由于连接信息包含用于访问数据库的用户和密码，因此我们将使用Kubernetes Secret来存储它。

要创建到Oracle数据库的连接字符串的密码，可以使用以下命令：

kubectl create secret generic oracledb-exporter-secret \

--from-literal=datasource='YOUR_CONNECTION_STRING'

在deployment中，这样配置环境变量

env:

- name: DATA_SOURCE_NAME

valueFrom:

secretKeyRef:

key: datasource

要确保连接信息是否正确：

system/password@//database_url:1521/database_name.your.domain.com

可以使用 sqlplus docker镜像进行检测

docker run --net='host' --rm --interactive guywithnose/sqlplus sqlplus system/password@//database_url:1521/database_name.my.domain.com

下面添加一些自定义指标，包括慢查询(slow queries)，错误查询(bug queries)

为了使用自定义指标：

在deployment中，我们将添加另一个环境变量，该变量具有到新指标的文件的路由。

从ConfigMap将此新文件挂载为volume

完整配置如下：

apiVersion: apps/v1

kind: Deployment

metadata:

namespace: database-namespace

spec:

selector:

matchLabels:

app: oracledb-exporter

replicas: 1

template:

metadata:

labels:

app: oracledb-exporter

annotations:

prometheus.io/scrape: "true"

prometheus.io/port: "9161"

prometheus.io/path: "/metrics"

spec:

containers:

- name: oracledb-exporter

ports:

- containerPort: 9161

image: iamseth/oracledb_exporter

env:

- name: DATA_SOURCE_NAME

valueFrom:

secretKeyRef:

key: datasource

- name: CUSTOM_METRICS

value: /tmp/custom-metrics.toml

volumeMounts:

- name: custom-metrics

mountPath: /tmp/custom-metrics.toml

subPath: custom-metrics.toml

volumes:

- name: custom-metrics

configMap:

defaultMode: 420

ConfigMap:

apiVersion: v1

kind: ConfigMap

metadata:

namespace: database-namespace

data:

custom-metrics.toml: |

[[metric]]

context = "slow_queries"

metricsdesc = { p95_time_usecs= "Gauge metric with percentile 95 of elapsed time.", p99_time_usecs= "Gauge metric with percentile 99 of elapsed time." }

request = "select percentile_disc(0.95) within group (order by elapsed_time) as p95_time_usecs, percentile_disc(0.99) within group (order by elapsed_time) as p99_time_usecs from v$sql where last_active_time >= sysdate - 5/(24*60)"

[[metric]]

context = "big_queries"

metricsdesc = { p95_rows= "Gauge metric with percentile 95 of returned rows.", p99_rows= "Gauge metric with percentile 99 of returned rows." }

request = "select percentile_disc(0.95) within group (order by rownum) as p95_rows, percentile_disc(0.99) within group (order by rownum) as p99_rows from v$sql where last_active_time >= sysdate - 5/(24*60)"

[[metric]]

context = "size_user_segments_top100"

metricsdesc = {table_bytes="Gauge metric with the size of the tables in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as table_bytes from user_segments where segment_type='TABLE' group by segment_name) order by table_bytes DESC FETCH NEXT 100 ROWS ONLY"

[[metric]]

context = "size_user_segments_top100"

metricsdesc = {table_partition_bytes="Gauge metric with the size of the table partition in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as table_partition_bytes from user_segments where segment_type='TABLE PARTITION' group by segment_name) order by table_partition_bytes DESC FETCH NEXT 100 ROWS ONLY"

[[metric]]

context = "size_user_segments_top100"

metricsdesc = {cluster_bytes="Gauge metric with the size of the cluster in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as cluster_bytes from user_segments where segment_type='CLUSTER' group by segment_name) order by cluster_bytes DESC FETCH NEXT 100 ROWS ONLY"

[[metric]]

context = "size_dba_segments_top100"

metricsdesc = {table_bytes="Gauge metric with the size of the tables in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as table_bytes from dba_segments where segment_type='TABLE' group by segment_name) order by table_bytes DESC FETCH NEXT 100 ROWS ONLY"

[[metric]]

context = "size_dba_segments_top100"

metricsdesc = {table_partition_bytes="Gauge metric with the size of the table partition in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as table_partition_bytes from dba_segments where segment_type='TABLE PARTITION' group by segment_name) order by table_partition_bytes DESC FETCH NEXT 100 ROWS ONLY"

[[metric]]

context = "size_dba_segments_top100"

metricsdesc = {cluster_bytes="Gauge metric with the size of the cluster in user segments."}

labels = ["segment_name"]

request = "select * from (select segment_name,sum(bytes) as cluster_bytes from dba_segments where segment_type='CLUSTER' group by segment_name) order by cluster_bytes DESC FETCH NEXT 100 ROWS ONLY"

创建Secret和ConfigMap之后，就可以应用Deployment并检查它是否正在从Oracle数据库的端口9161中获取指标。

如果一切正常，Prometheus将自动发现exporter带注释的pod，并在几分钟内开始抓取指标。可以在Prometheus Web界面的target部分中对其进行检查，以查找以oracledb_开头的任何指标。

监控什么

性能指标

等待时间: exporter在Oracle数据库的不同活动中提供一系列等待时间的指标。它们都以oracledb_wait_time_前缀开头，它们有助于评估数据库在哪里花费了更多时间。它可以存在于I/O，网络，提交，并发等中。通过这种方式，我们可以确定系统中可能影响Oracle数据库整体性能的瓶颈。