Apache Zookeeper and Elasticsearch 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 Zookeeper Telegraf plugin collects and reports metrics from Zookeeper servers, facilitating monitoring and performance analysis. It utilizes the ‘mntr’ command output to gather essential statistics critical for maintaining Zookeeper’s operational health.
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
Apache Zookeeper
The Zookeeper plugin for Telegraf is designed to collect vital statistics from Zookeeper servers by executing the ‘mntr’ command. This plugin serves as a monitoring tool that captures important metrics related to Zookeeper’s performance, including connection details, latency, and various operational statistics, facilitating the assessment of the health and efficiency of Zookeeper deployments. In contrast to the Prometheus input plugin, which is recommended when the Prometheus metrics provider is enabled, the Zookeeper plugin accesses raw output from the ‘mntr’ command, rendering it tailored for configurations that do not adopt Prometheus for metrics reporting. This unique approach allows administrators to gather Java Properties formatted metrics directly from Zookeeper, ensuring comprehensive visibility into Zookeeper’s operational state and enabling timely responses to performance anomalies. It specifically excels in environments where Zookeeper operates as a centralized service for maintaining configuration information and names for distributed systems, thus providing immeasurable insights essential for troubleshooting and capacity planning.
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
Apache Zookeeper
[[inputs.zookeeper]]
## An array of address to gather stats about. Specify an ip or hostname
## with port. ie localhost:2181, 10.0.0.1:2181, etc.
## If no servers are specified, then localhost is used as the host.
## If no port is specified, 2181 is used
servers = [":2181"]
## Timeout for metric collections from all servers. Minimum timeout is "1s".
# timeout = "5s"
## Float Parsing - the initial implementation forced any value unable to be
## parsed as an int to be a string. Setting this to "float" will attempt to
## parse float values as floats and not strings. This would break existing
## metrics and may cause issues if a value switches between a float and int.
# parse_floats = "string"
## Optional TLS Config
# enable_tls = false
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## If false, skip chain & host verification
# insecure_skip_verify = true
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
Apache Zookeeper
-
Cluster Health Monitoring: Integrate the Zookeeper plugin to monitor the health and performance of a distributed application relying on Zookeeper for configuration management and service discovery. By tracking metrics such as session count, latency, and data size, DevOps teams can identify potential issues before they escalate, ensuring high availability and reliability across applications.
-
Performance Benchmarks: Utilize the plugin to benchmark Zookeeper performance in varying workload scenarios. This not only helps in understanding how Zookeeper behaves under load but also assists in tuning configurations to optimize throughput and reduce latency during peak operations.
-
Alerting for Anomalies: Combine this plugin with alerting tools to create a proactive monitoring system that notifies engineers if specific Zookeeper metrics exceed threshold limits, such as open file descriptor counts or high latency values. This enables teams to respond promptly to issues that could impact service reliability.
-
Historical Data Analysis: Store the metrics collected by the Zookeeper plugin in a time-series database to analyze historical performance trends. This allows teams to evaluate the impact of changes over time, assess the effectiveness of scaling actions, and plan for future capacity needs.
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
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
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