Tencent Cloud MongoDB¶

Use the 「Guance Synchronization」 series of script packages in the script market to synchronize data from cloud monitoring cloud assets to the observation cloud

Config¶

Install Func¶

Recommend opening 「Integrations - Extension - DataFlux Func (Automata)」: All preconditions are installed automatically, Please continue with the script installation

If you deploy Func yourself,Refer to Self-Deployment of Func

Installation script¶

Tip：Please prepare Aliyun AK that meets the requirements in advance（For simplicity's sake,，You can directly grant the global read-only permissionReadOnlyAccess）

To synchronize the monitoring data of ECS cloud resources, we install the corresponding collection script：「Guance Integration（Tencent Cloud - MongoDBCollect）」(ID：guance_tencentcloud_mongodb)

Click 【Install】 and enter the corresponding parameters: Aliyun AK, Aliyun account name.。

tap【Deploy startup Script】，The system automatically creates Startup script sets，And automatically configure the corresponding startup script。

After this function is enabled, you can view the automatic triggering configuration in「Management / Crontab Config」。Click【Run】，you can immediately execute once, without waiting for a regular time。After a while, you can view task execution records and corresponding logs。

If you want to collect logs, you must enable the corresponding log collection script. If you want to collect bills, start the cloud bill collection script.

We collected some configurations by default, as described in the Metrics column Configure custom cloud object metrics

Verify¶

In「Management / Crontab Config」check whether the automatic triggering configuration exists for the corresponding task,In addition, you can view task records and logs to check whether exceptions exist
On the observation cloud platform, click 「Infrastructure / Custom」 to check whether asset information exists
On the observation cloud platform, press 「Metrics」 to check whether monitoring data exists

Metric¶

Configure Tencent Cloud OSS monitoring. The default metric set is as follows. You can collect more metrics by configuring them Tencent Cloud Monitor Metrics Details

Request class¶

Metric Id	Metric name	Implication	Unit	Dimensions
Inserts_sum	Number of write requests	Number of writes per unit time	Times	target（Instance ID）
Reads_sum	Number of read requests	Number of reads per unit time	Times	target（Instance ID）
Updates_sum	Number of update requests	Number of updates per unit time	Times	target（Instance ID）
Deletes_sum	Number of delete requests	Number of deletes per unit time	Times	target（Instance ID）
Counts_sum	Number of count requests	Number of counts per unit time	Times	target（Instance ID）
Success_sum	Number of successful requests	Number of successful requests per unit time	Times	target（Instance ID）
Commands_sum	Number of command requests	Number of command requests per unit time	Times	target（Instance ID）
Qps_sum	Number of requests per second	Number of operations per second, including CRUD operations	Times/second	target（Instance ID）
CountPerSecond_sum	Number of count requests per second	Number of count requests per second	Times/second	target（Instance ID）
DeletePerSecond_sum	Number of delete requests per second	Number of delete requests per second	Times/second	target（Instance ID）
InsertPerSecond_sum	Number of insert requests per second	Number of insert requests per second	Times/second	target（Instance ID）
ReadPerSecond_sum	Number of read requests per second	Number of read requests per second	Times/second	target（Instance ID）
UpdatePerSecond_sum	Number of update requests per second	Number of update requests per second	Times/second	target（Instance ID）

Delay request class¶

Metric Id	Metric name	Implication	Unit	Dimensions
Delay10_sum	Number of requests with delay between 10 - 50 ms	Number of successful requests with delay between 10ms - 50ms per unit time	Times	target（Instance ID）
Delay50_sum	Number of requests with delay between 50 - 100 ms	Number of successful requests with delay between 50ms - 100ms per unit time	Times	target（Instance ID）
Delay100_sum	Number of requests with delay over 100 ms	Number of successful requests with delay over 100ms per unit time	Times	target（Instance ID）
AvgAllRequestDelay_sum	Average delay of all requests	Average delay of all requests	ms	target（Instance ID）

Connection number class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`ClusterConn_max`	Cluster connection number	Total number of connections received by the current cluster proxy	Times	target（Instance ID）
`Connper_max`	Connection usage rate	The ratio of the number of connections in the current cluster to the total connection configuration	%	target（Instance ID）

System class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`ClusterDiskusage`	Disk usage rate	The ratio of the actual occupied storage space of the cluster to the total capacity configuration	%	target（Instance ID）

In/Out flow class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`ClusterNetin`	Inflow	Cluster network inflow	Bytes	target（Instance ID）
`ClusterNetout`	Outflow	Cluster network outflow	Bytes	target（Instance ID）

MongoDB Replica set¶

1. System class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`ReplicaDiskusage`	Disk usage rate	Replica set capacity usage rate	%	target（Replica set ID）

2. Master-slave class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`SlaveDelay`	Master-slave delay	Average delay of master and slave in unit time	Seconds	target（Replica set ID）
`Oplogreservedtime`	Oplog retention time	The time difference between the last operation and the first operation in the oplog record	Hours	target（Replica set ID)

3. Cache class¶

Metric Id	Metric name	Implication	Unit	Dimensions
CacheDirty	Percentage of dirty data in Cache	Percentage of dirty data in current memory Cache	%	target（Replica set ID)
CacheUsed	Cache usage percentage	Current Cache usage percentage	%	target（Replica set ID)
HitRatio	Cache hit rate	Current Cache hit rate	%	target（Replica set ID)

Mongo Node¶

1. System class¶

Metric Id	Metric name	Implication	Unit	Dimensions
`CpuUsage`	CPU usage rate	CPU usage rate	%	target（Node ID)
`MemUsage`	Memory usage rate	Memory usage rate	%	target（Node ID)
`NetIn`	Network inflow	Network inflow	MB/s	target（Node ID)
`NetOut`	Network outflow	Network outflow	MB/s	target（Node ID)
`Disk`	Node disk usage	Node disk usage	MB	target（Node ID)
`Conn`	Number of connections	Number of connections	Times	target（Node ID)
`ActiveSession`	Number of active sessions	Number of active sessions	Times	target（Node ID)
`NodeOplogReservedTime`	Oplog retention duration	Node oplog retention duration	-	target（Node ID)
`NodeHitRatio`	Cache hit rate	Cache hit rate	%	target（Node ID)
`NodeCacheUsed`	Cache usage percentage	Percentage of Cache memory in total memory	%	target（Node ID)
`NodeSlavedelay`	Master-slave delay	Delay of slave nodes	Seconds	target（Node ID)
`Diskusage`	Node disk usage rate	Node disk usage rate	%	target（Node ID)
`Ioread`	Number of disk reads	Disk read IOPS	Times/second	target（Node ID)
`Iowrite`	Number of disk writes	Disk write IOPS	Times/second	target（Node ID)
`NodeCacheDirty`	Percentage of dirty data in Cache	Percentage of dirty data in Cache	%	target（Node ID)

2. Read/Write class¶

Metric Id	Metric name	Implication	Unit	Dimensions
Qr	Number of read requests in the waiting queue	Number of read requests in the waiting queue	Count	target（Node ID)
Qw	Number of write requests in the waiting queue	Number of write requests in the waiting queue	Count	target（Node ID)
Ar	ActiveRead of WT engine	Number of active read requests	Count	target（Node ID)
Aw	ActiveWrite of WT engine	Number of active write requests	Count	target（Node ID)

3. Delay&Request class¶

Metric Id	Metric name	Implication	Unit	Dimensions
NodeAvgAllRequestDelay	Average delay of all requests	Average delay of all requests on node	ms	target（Node ID)
NodeDelay100	Number of requests on node with delay over 100 ms	Number of requests on node with delay over 100 ms	Times	target（Node ID)
NodeDelay10	Number of requests on node with delay between 10-50 ms	Number of requests on node with delay between 10-50 ms	Times	target（Node ID)
NodeDelay50	Number of requests on node with delay between 50-100 ms	Number of requests on node with delay between 50-100 ms	Times	target（Node ID)
NodeSuccessPerSecond	Number of successful requests on node per second	Number of successful requests on node per second	Times/second	target（Node ID)
NodeCountPerSecond	Number of count requests on node per second	Number of count requests on node per second	Times/second	target（Node ID)
NodeDeletePerSecond	Number of delete requests on node per second	Number of delete requests on node per second	Times/second	target（Node ID)
NodeInsertPerSecond	Number of insert requests on node per second	Number of insert requests on node per second	Times/second	target（Node ID)
NodeReadPerSecond	Number of read requests on node per second	Number of read requests on node per second	Times/second	target（Node ID)
NodeUpdatePerSecond	Number of update requests on node per second	Number of update requests on node per second	Times/second	target（Node ID)
SuccessPerSecond	Total requests	Number of successful requests on node per second	Times/second	target（Node ID)

4. TTL Index class¶

Metric Id	Metric name	Implication	Unit	Dimensions
TtlDeleted	Number of data deleted by TTL	Number of data deleted by TTL	Count	target（Node ID)
TtlPass	Number of TTL rotations	Number of TTL rotations	Count	target（Node ID)

Object¶

The collected Tencent Cloud MongoDB object data structure can be seen from the "Infrastructure - Custom" object data

{
  "measurement": "tencentcloud_mongodb",
  "tags": {
    "ClusterType" : "0",
    "InstanceId"  : "cmxxxx",
    "InstanceName": "test_01",
    "InstanceType": "1",
    "MongoVersion": "MONxxxx",
    "NetType"     : "1",
    "PayMode"     : "0",
    "ProjectId"   : "0",
    "RegionId"    : "ap-nanjing",
    "Status"      : "2",
    "VpcId"       : "vpc-nf6xxxxx",
    "Zone"        : "ap-nanjing-1",
    "name"        : "cmxxxx"
  },
  "fields": {
    "CloneInstances"   : "[]",
    "CreateTime"       : "2022-08-24 13:54:00",
    "DeadLine"         : "2072-08-24 13:54:00",
    "ReadonlyInstances": "[]",
    "RelatedInstance"  : "{Instance JSON data}",
    "ReplicaSets"      : "{Instance JSON data}",
    "StandbyInstances" : "[]",
    "message"          : "{Instance JSON data}",
  }
}

Loging¶

Slow query statistics¶

Preconditions¶

Tip 1: The code running of this script depends on the collection of MongoDB instance objects. If the custom collection of MongoDB object is not configured, the slow log script cannot collect slow log data

Installation script¶

On the basis of the previous, you need to install another script for MongoDB slow query statistics log collection

In "Manage/Script Marketplace", click and install the corresponding script package:

「Guance Integration（Tencent Cloud - MongoDB Slow Query Log Collect）」(ID：guance_tencentcloud_mongodb_slowlog)

After data is synchronized, you can view the data in Logs of the observation cloud.

The following is an example of the reported data:

{
  "measurement": "tencentcloud_mongodb_slow_log",
  "tags": {

  },
  "fields": {
      "Slowlog": "Slow log details",
      "message": "{Instance JSON data}"
  }
}

Note: The fields in tags and Fields may change with subsequent updates

Tip 1: The tags value is supplemented by a custom object

Tip 2: 'fields.message' is the JSON serialized string

Tip 3: 'fields.Slowlog' records each record for all slow query details

Tencent Cloud MongoDB¶

Config¶

Install Func¶

Installation script¶

Verify¶

Metric¶

Request class¶

Delay request class¶

Connection number class¶

System class¶

In/Out flow class¶

MongoDB Replica set¶

1. System class¶

2. Master-slave class¶

3. Cache class¶

Mongo Node¶

1. System class¶

2. Read/Write class¶

3. Delay&Request class¶

4. TTL Index class¶

Object¶

Loging¶

Slow query statistics¶

Preconditions¶

Installation script¶

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