HTTP and MongoDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Time series database
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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 HTTP plugin allows for the collection of metrics from specified HTTP endpoints, handling various data formats and authentication methods.
The MongoDB Plugin allows you to send metrics to a MongoDB instance.
Integration details
HTTP
The HTTP plugin collects metrics from one or more HTTP(S) endpoints, which should have metrics formatted in one of the supported input data formats. It also supports secrets from secret-stores for various authentication options and includes globally supported configuration settings.
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
HTTP
[[inputs.http]]
## One or more URLs from which to read formatted metrics.
urls = [
"http://localhost/metrics",
"http+unix:///run/user/420/podman/podman.sock:/d/v4.0.0/libpod/pods/json"
]
## HTTP method
# method = "GET"
## Optional HTTP headers
# headers = {"X-Special-Header" = "Special-Value"}
## HTTP entity-body to send with POST/PUT requests.
# body = ""
## HTTP Content-Encoding for write request body, can be set to "gzip" to
## compress body or "identity" to apply no encoding.
# content_encoding = "identity"
## Optional Bearer token settings to use for the API calls.
## Use either the token itself or the token file if you need a token.
# token = "eyJhbGc...Qssw5c"
# token_file = "/path/to/file"
## Optional HTTP Basic Auth Credentials
# username = "username"
# password = "pa$$word"
## OAuth2 Client Credentials. The options 'client_id', 'client_secret', and 'token_url' are required to use OAuth2.
# client_id = "clientid"
# client_secret = "secret"
# token_url = "https://indentityprovider/oauth2/v1/token"
# scopes = ["urn:opc:idm:__myscopes__"]
## HTTP Proxy support
# use_system_proxy = false
# http_proxy_url = ""
## Optional TLS Config
## Set to true/false to enforce TLS being enabled/disabled. If not set,
## enable TLS only if any of the other options are specified.
# tls_enable =
## Trusted root certificates for server
# tls_ca = "/path/to/cafile"
## Used for TLS client certificate authentication
# tls_cert = "/path/to/certfile"
## Used for TLS client certificate authentication
# tls_key = "/path/to/keyfile"
## Password for the key file if it is encrypted
# tls_key_pwd = ""
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Minimal TLS version to accept by the client
# tls_min_version = "TLS12"
## List of ciphers to accept, by default all secure ciphers will be accepted
## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
## Use "all", "secure" and "insecure" to add all support ciphers, secure
## suites or insecure suites respectively.
# tls_cipher_suites = ["secure"]
## Renegotiation method, "never", "once" or "freely"
# tls_renegotiation_method = "never"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Optional Cookie authentication
# cookie_auth_url = "https://localhost/authMe"
# cookie_auth_method = "POST"
# cookie_auth_username = "username"
# cookie_auth_password = "pa$$word"
# cookie_auth_headers = { Content-Type = "application/json", X-MY-HEADER = "hello" }
# cookie_auth_body = '{"username": "user", "password": "pa$$word", "authenticate": "me"}'
## cookie_auth_renewal not set or set to "0" will auth once and never renew the cookie
# cookie_auth_renewal = "5m"
## Amount of time allowed to complete the HTTP request
# timeout = "5s"
## List of success status codes
# success_status_codes = [200]
## Data format to consume.
## Each data format has its own unique set of configuration options, read
## more about them here:
## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
# data_format = "influx"
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
HTTP
- Collecting Metrics from Localhost: The plugin can fetch metrics from an HTTP endpoint like
http://localhost/metrics
, allowing for easy local monitoring. - Using Unix Domain Sockets: You can specify metrics collection from services over Unix domain sockets by using the http+unix scheme, for example,
http+unix:///path/to/service.sock:/api/endpoint
.
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
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