AMQP and Elasticsearch Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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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 AMQP Consumer Input Plugin allows you to ingest data from an AMQP 0-9-1 compliant message broker, such as RabbitMQ, enabling seamless data collection for monitoring and analytics purposes.
The Telegraf Elasticsearch Plugin seamlessly sends metrics to an Elasticsearch server. The plugin handles template creation and dynamic index management, and supports various Elasticsearch-specific features to ensure data is formatted correctly for storage and retrieval.
Integration details
AMQP
This plugin provides a consumer for use with AMQP 0-9-1, a prominent implementation of which is RabbitMQ. AMQP, or Advanced Message Queuing Protocol, was originally developed to enable reliable, interoperable messaging between diverse systems in a network. The plugin reads metrics from a topic exchange using a configured queue and binding key, delivering a flexible and efficient means of collecting data from AMQP-compliant messaging systems. This enables users to leverage existing RabbitMQ implementations to monitor their applications effectively by capturing detailed metrics for analysis and alerting.
Elasticsearch
This plugin writes metrics to Elasticsearch, a distributed, RESTful search and analytics engine capable of storing large amounts of data in near real-time. It is designed to handle Elasticsearch versions 5.x through 7.x and utilizes its dynamic template features to manage data type mapping properly. The plugin supports advanced features such as template management, dynamic index naming, and integration with OpenSearch. It also allows configurations for authentication and health monitoring of the Elasticsearch nodes.
Configuration
AMQP
[[inputs.amqp_consumer]]
## Brokers to consume from. If multiple brokers are specified a random broker
## will be selected anytime a connection is established. This can be
## helpful for load balancing when not using a dedicated load balancer.
brokers = ["amqp://localhost:5672/influxdb"]
## Authentication credentials for the PLAIN auth_method.
# username = ""
# password = ""
## Name of the exchange to declare. If unset, no exchange will be declared.
exchange = "telegraf"
## Exchange type; common types are "direct", "fanout", "topic", "header", "x-consistent-hash".
# exchange_type = "topic"
## If true, exchange will be passively declared.
# exchange_passive = false
## Exchange durability can be either "transient" or "durable".
# exchange_durability = "durable"
## Additional exchange arguments.
# exchange_arguments = { }
# exchange_arguments = {"hash_property" = "timestamp"}
## AMQP queue name.
queue = "telegraf"
## AMQP queue durability can be "transient" or "durable".
queue_durability = "durable"
## If true, queue will be passively declared.
# queue_passive = false
## Additional arguments when consuming from Queue
# queue_consume_arguments = { }
# queue_consume_arguments = {"x-stream-offset" = "first"}
## A binding between the exchange and queue using this binding key is
## created. If unset, no binding is created.
binding_key = "#"
## Maximum number of messages server should give to the worker.
# prefetch_count = 50
## Max undelivered messages
## This plugin uses tracking metrics, which ensure messages are read to
## outputs before acknowledging them to the original broker to ensure data
## is not lost. This option sets the maximum messages to read from the
## broker that have not been written by an output.
##
## This value needs to be picked with awareness of the agent's
## metric_batch_size value as well. Setting max undelivered messages too high
## can result in a constant stream of data batches to the output. While
## setting it too low may never flush the broker's messages.
# max_undelivered_messages = 1000
## Timeout for establishing the connection to a broker
# timeout = "30s"
## Auth method. PLAIN and EXTERNAL are supported
## Using EXTERNAL requires enabling the rabbitmq_auth_mechanism_ssl plugin as
## described here: https://www.rabbitmq.com/plugins.html
# auth_method = "PLAIN"
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Content encoding for message payloads, can be set to
## "gzip", "identity" or "auto"
## - Use "gzip" to decode gzip
## - Use "identity" to apply no encoding
## - Use "auto" determine the encoding using the ContentEncoding header
# content_encoding = "identity"
## Maximum size of decoded message.
## Acceptable units are B, KiB, KB, MiB, MB...
## Without quotes and units, interpreted as size in bytes.
# max_decompression_size = "500MB"
## 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"
Elasticsearch
[[outputs.elasticsearch]]
## The full HTTP endpoint URL for your Elasticsearch instance
## Multiple urls can be specified as part of the same cluster,
## this means that only ONE of the urls will be written to each interval
urls = [ "http://node1.es.example.com:9200" ] # required.
## Elasticsearch client timeout, defaults to "5s" if not set.
timeout = "5s"
## Set to true to ask Elasticsearch a list of all cluster nodes,
## thus it is not necessary to list all nodes in the urls config option
enable_sniffer = false
## Set to true to enable gzip compression
enable_gzip = false
## Set the interval to check if the Elasticsearch nodes are available
## Setting to "0s" will disable the health check (not recommended in production)
health_check_interval = "10s"
## Set the timeout for periodic health checks.
# health_check_timeout = "1s"
## HTTP basic authentication details.
## HTTP basic authentication details
# username = "telegraf"
# password = "mypassword"
## HTTP bearer token authentication details
# auth_bearer_token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9"
## Index Config
## The target index for metrics (Elasticsearch will create if it not exists).
## You can use the date specifiers below to create indexes per time frame.
## The metric timestamp will be used to decide the destination index name
# %Y - year (2016)
# %y - last two digits of year (00..99)
# %m - month (01..12)
# %d - day of month (e.g., 01)
# %H - hour (00..23)
# %V - week of the year (ISO week) (01..53)
## Additionally, you can specify a tag name using the notation {{tag_name}}
## which will be used as part of the index name. If the tag does not exist,
## the default tag value will be used.
# index_name = "telegraf-{{host}}-%Y.%m.%d"
# default_tag_value = "none"
index_name = "telegraf-%Y.%m.%d" # required.
## Optional Index Config
## Set to true if Telegraf should use the "create" OpType while indexing
# use_optype_create = false
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## Template Config
## Set to true if you want telegraf to manage its index template.
## If enabled it will create a recommended index template for telegraf indexes
manage_template = true
## The template name used for telegraf indexes
template_name = "telegraf"
## Set to true if you want telegraf to overwrite an existing template
overwrite_template = false
## If set to true a unique ID hash will be sent as sha256(concat(timestamp,measurement,series-hash)) string
## it will enable data resend and update metric points avoiding duplicated metrics with different id's
force_document_id = false
## Specifies the handling of NaN and Inf values.
## This option can have the following values:
## none -- do not modify field-values (default); will produce an error if NaNs or infs are encountered
## drop -- drop fields containing NaNs or infs
## replace -- replace with the value in "float_replacement_value" (default: 0.0)
## NaNs and inf will be replaced with the given number, -inf with the negative of that number
# float_handling = "none"
# float_replacement_value = 0.0
## Pipeline Config
## To use a ingest pipeline, set this to the name of the pipeline you want to use.
# use_pipeline = "my_pipeline"
## Additionally, you can specify a tag name using the notation {{tag_name}}
## which will be used as part of the pipeline name. If the tag does not exist,
## the default pipeline will be used as the pipeline. If no default pipeline is set,
## no pipeline is used for the metric.
# use_pipeline = "{{es_pipeline}}"
# default_pipeline = "my_pipeline"
#
# Custom HTTP headers
# To pass custom HTTP headers please define it in a given below section
# [outputs.elasticsearch.headers]
# "X-Custom-Header" = "custom-value"
## Template Index Settings
## Overrides the template settings.index section with any provided options.
## Defaults provided here in the config
# template_index_settings = {
# refresh_interval = "10s",
# mapping.total_fields.limit = 5000,
# auto_expand_replicas = "0-1",
# codec = "best_compression"
# }
Input and output integration examples
AMQP
-
Integrating Application Metrics with AMQP: Use the AMQP Consumer plugin to gather application metrics that are published to a RabbitMQ exchange. By configuring the plugin to listen to specific queues, teams can gain insights into application performance, track request rates, error counts, and latency metrics, all in real-time. This setup not only aids in anomaly detection but also provides valuable data for capacity planning and system optimization.
-
Event-Driven Monitoring: Configure the AMQP Consumer to trigger specific monitoring events whenever certain conditions are met within an application. For instance, if a message indicating a high error rate is received, the plugin can feed this data into monitoring tools, generating alerts or scaling events. This integration can improve responsiveness to issues and automate parts of the operations workflow.
-
Cross-Platform Data Aggregation: Leverage the AMQP Consumer plugin to consolidate metrics from various applications distributed across different platforms. By utilizing RabbitMQ as a centralized message broker, organizations can unify their monitoring data, allowing for comprehensive analysis and dashboarding through Telegraf, thus maintaining visibility across heterogeneous environments.
-
Real-Time Log Processing: Extend the use of the AMQP Consumer to capture log data sent to a RabbitMQ exchange, processing logs in real time for monitoring and alerting purposes. This application ensures that operational issues are detected and addressed swiftly by analyzing log patterns, trends, and anomalies as they occur.
Elasticsearch
-
Time-based Indexing: Use this plugin to store metrics in Elasticsearch to index each metric based on the time collected. For example, CPU metrics can be stored in a daily index named <code
telegraf-2023.01.01
, allowing easy time-based queries and retention policies. -
Dynamic Templates Management: Utilize the template management feature to automatically create a custom template tailored to your metrics. This allows you to define how different fields are indexed and analyzed without manually configuring Elasticsearch, ensuring an optimal data structure for querying.
-
OpenSearch Compatibility: If you are using AWS OpenSearch, you can configure this plugin to work seamlessly by activating compatibility mode, ensuring your existing Elasticsearch clients remain functional and compatible with newer cluster setups.
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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