背景
本文简略介绍下，Prometheus如何通过exporters监控Oracle数据库，以及应该留神哪些指标。
oracledb_exporter
oracledb_exporter是一个连贯到Oracle数据库并生成Prometheus metrics的应用程序，
设置
展现下如何装置和设置oracledb_exporter，以应用Prometheus来监控Oracle数据库。oracledb_exporter部署在k8s集群中
在k8s应用Deployment部署oracledb_exporter，并增加注解，以实现Prometheus主动发现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须要Oracle的连贯信息能力拜访和生成指标，此参数作为环境变量传递到exporter。因为连贯信息蕴含用于拜访数据库的用户和明码，因而咱们将应用Kubernetes Secret来存储它。
要创立到Oracle数据库的连贯字符串的明码，能够应用以下命令：
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: name: oracledb-exporter-secret key: datasource要确保连贯信息是否正确：
system/password@//database_url:1521/database_name.your.domain.com能够应用 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上面增加一些自定义指标，包含慢查问（slow queries），谬误查问（bug queries）
为了应用自定义指标：
- 在deployment中，咱们将增加另一个环境变量，该变量具备到新指标的文件的路由。
- 从ConfigMap将此新文件挂载为volume
残缺配置如下：
apiVersion: apps/v1kind: Deploymentmetadata: name: oracledb-exporter namespace: database-namespacespec: 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: name: oracledb-exporter-secret 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 name: custom-metrics ConfigMap:
apiVersion: v1kind: ConfigMapmetadata: name: custom-metrics namespace: database-namespacedata: 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"创立Secret和ConfigMap之后，就能够利用Deployment并查看它是否正在从Oracle数据库的端口9161中获取指标。
如果一切正常，Prometheus将主动发现exporter带正文的pod，并在几分钟内开始抓取指标。能够在Prometheus Web界面的target局部中对其进行查看，以查找以oracledb_结尾的任何指标。
监控什么
性能指标
等待时间: exporter在Oracle数据库的不同流动中提供一系列等待时间的指标。它们都以oracledb_wait_time_前缀结尾，它们有助于评估数据库在哪里破费了更多工夫。它能够存在于I/O，网络，提交，并发等中。通过这种形式，咱们能够确定零碎中可能影响Oracle数据库整体性能的瓶颈。
慢查问：某些查问返回后果所花的工夫可能比其余查问长。如果此工夫高于应用程序中配置的接管响应的超时工夫，它将认为这是来自数据库的超时谬误，而后重试查问。这种行为可能会使零碎超负荷工作，并影响整体性能。
在下面显示的配置中，有两个自定义指标可提供最近5分钟内执行查问的响应工夫的百分比95和99的信息。这些指标是：
- oracledb_slow_queries_p95_time_usecs
- oracledb_slow_queries_p99_time_usecs
流动会话：监督Oracle数据库中流动会话很重要。如果超过配置的限度，则数据库将回绝新连贯，从而导致应用程序谬误。提供此信息的指标是oracledb_sessions_value，标签status能够提供更多信息。
流动：监督数据库执行的操作也很重要。为此，咱们能够依附以下指标：
- oracledb_activity_execute_count
- oracledb_activity_parse_count_total
- oracledb_activity_user_commits
- oracledb_activity_user_rollbacks