SNMP and MongoDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
5B+
Telegraf downloads
#1
Time series database
Source: DB Engines
1B+
Downloads of InfluxDB
2,800+
Contributors
Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Input and output integration overview
The SNMP plugin allows you to collect a variety of metrics from SNMP (Simple Network Management Protocol) agents. It provides flexibility in how data is retrieved, whether collecting single metrics or entire tables.
The MongoDB Plugin allows you to send metrics to a MongoDB instance.
Integration details
SNMP
This plugin uses polling to gather metrics from SNMP agents, supporting retrieval of individual OIDs and complete SNMP tables. It can be configured to handle multiple SNMP versions, authentication, and other features.
MongoDB
This plugin sends metrics to MongoDB, automatically creating time series collections where they don’t already exist. Time series collections require MongoDB 5.0+.
Configuration
SNMP
[[inputs.snmp]]
agents = ["udp://127.0.0.1:161"]
[[inputs.snmp.field]]
oid = "RFC1213-MIB::sysUpTime.0"
name = "sysUptime"
conversion = "float(2)"
[[inputs.snmp.field]]
oid = "RFC1213-MIB::sysName.0"
name = "sysName"
is_tag = true
[[inputs.snmp.table]]
oid = "IF-MIB::ifTable"
name = "interface"
inherit_tags = ["sysName"]
[[inputs.snmp.table.field]]
oid = "IF-MIB::ifDescr"
name = "ifDescr"
is_tag = true
MongoDB
[[outputs.mongodb]]
# connection string examples for mongodb
dsn = "mongodb://localhost:27017"
# dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"
# overrides serverSelectionTimeoutMS in dsn if set
# timeout = "30s"
# default authentication, optional
# authentication = "NONE"
# for SCRAM-SHA-256 authentication
# authentication = "SCRAM"
# username = "root"
# password = "***"
# for x509 certificate authentication
# authentication = "X509"
# tls_ca = "ca.pem"
# tls_key = "client.pem"
# # tls_key_pwd = "changeme" # required for encrypted tls_key
# insecure_skip_verify = false
# database to store measurements and time series collections
# database = "telegraf"
# granularity can be seconds, minutes, or hours.
# configuring this value will be based on your input collection frequency.
# see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
# granularity = "seconds"
# optionally set a TTL to automatically expire documents from the measurement collections.
# ttl = "360h"
Input and output integration examples
SNMP
- Basic SNMP Configuration: Collect metrics from a local SNMP agent using typical SNMP community string settings. This setup is ideal for local monitoring of device performance.
- Advanced SNMPv3 Setup: Securely collect metrics using SNMPv3 with authentication and encryption to enhance security. This configuration is recommended for production environments.
- Collect Interface Metrics: Configure the plugin to collect interface metrics from the device’s SNMP table. Utilize fields to capture specific data points for traffic analysis.
- Join Two SNMP Tables: By using translation fields, join data from two SNMP tables for a comprehensive view of correlated performance metrics.
MongoDB
-
Log Management: Integrate this plugin to send application logs directly to MongoDB for structured storage and flexible querying. You can analyze logs as time series data, aggregating logs by hour, day, or month.
-
Metric Capture: Use the plugin to capture system metrics (CPU, memory usage) in real-time and store them in MongoDB. The time-series collections will allow for efficient queries over time ranges.
-
Monitoring Solutions: Combine this output plugin with inputs from various sources, such as disk usage metrics, network statistics, or application performance data. It allows for consolidated monitoring dashboards with historical trends saved in MongoDB.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Related Integrations
Related Integrations
HTTP and InfluxDB Integration
The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.
View IntegrationKafka and InfluxDB Integration
This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.
View IntegrationKinesis and InfluxDB Integration
The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.
View Integration