Mesos and Redis 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
This input plugin gathers metrics from Mesos.
The Redis plugin enables users to send metrics collected by Telegraf directly to Redis. This integration is ideal for applications that require robust time series data storage and analysis.
Integration details
Mesos
The Mesos plugin for Telegraf is designed to collect and report metrics from Apache Mesos clusters, which is essential for monitoring and observability in container orchestration and resource management. Mesos, known for its scalability and ability to manage diverse workloads, generates various metrics about resource usage, tasks, frameworks, and overall system performance. By utilizing this plugin, users can track the health and efficiency of their Mesos clusters, gather insights into resource distribution, and ensure that applications receive the necessary resources in a timely manner. The configuration allows users to specify the relevant Mesos master’s details, along with the desired metric groups to collect, making it adaptable to different deployments and monitoring needs. Overall, this plugin integrates seamlessly within the Telegraf collection pipeline, supporting detailed observability for cloud-native environments.
Redis
The Redis Telegraf plugin is designed for writing metrics to RedisTimeSeries, a specialized Redis database module for time series data. This plugin facilitates the integration of Telegraf with RedisTimeSeries, allowing for the efficient storage and retrieval of timestamped data. With RedisTimeSeries, users can take advantage of enhanced capabilities for managing time series data, including aggregated views and range queries. The plugin offers various configuration options to enable the flexibility needed to connect securely to your Redis database, including support for Authentication, Timeouts, data type conversions, and TLS configurations. The underlying technology leverages Redis’ efficiency and scalability, making it an excellent choice for high-volume metric environments, where real-time processing is essential.
Configuration
Mesos
[[inputs.mesos]]
## Timeout, in ms.
timeout = 100
## A list of Mesos masters.
masters = ["http://localhost:5050"]
## Master metrics groups to be collected, by default, all enabled.
master_collections = [
"resources",
"master",
"system",
"agents",
"frameworks",
"framework_offers",
"tasks",
"messages",
"evqueue",
"registrar",
"allocator",
]
## A list of Mesos slaves, default is []
# slaves = []
## Slave metrics groups to be collected, by default, all enabled.
# slave_collections = [
# "resources",
# "agent",
# "system",
# "executors",
# "tasks",
# "messages",
# ]
## 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
Redis
[[outputs.redistimeseries]]
## The address of the RedisTimeSeries server.
address = "127.0.0.1:6379"
## Redis ACL credentials
# username = ""
# password = ""
# database = 0
## Timeout for operations such as ping or sending metrics
# timeout = "10s"
## Enable attempt to convert string fields to numeric values
## If "false" or in case the string value cannot be converted the string
## field will be dropped.
# convert_string_fields = true
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
# insecure_skip_verify = false
Input and output integration examples
Mesos
-
Resource Utilization Monitoring: Use the Mesos plugin to continually monitor CPU, memory, and disk usage across your Mesos cluster. For a rapidly scaling application, tracking these metrics helps ensure that resources are dynamically allocated according to workloads, preventing bottlenecks and optimizing performance.
-
Framework Performance Analysis: Integrate this plugin to measure the performance of different frameworks running on Mesos. By comparing active frameworks and their task success rates, you can identify which frameworks provide the best resource efficiency or may require optimization.
-
Alerts for System Health: Set up alerts based on metrics collected by the Mesos plugin to notify engineering teams when resource utilization exceeds key thresholds or when specific tasks fail. This allows for proactive intervention and maintenance before critical failures occur.
-
Capacity Planning: Utilize gathered metrics to analyze historical resource usage patterns to assist in capacity planning. By understanding peak loads and resource utilization trends, teams can make informed decisions on scaling infrastructure and deploying additional resources as needed.
Redis
-
Monitoring IoT Sensor Data: Utilize the Redis Telegraf plugin to collect and store data from IoT sensors in real-time. By connecting the plugin to a RedisTimeSeries database, users can analyze trends in temperature, humidity, or other environmental factors. The ability to query historical sensor data efficiently will aid in predictive maintenance and help in resource management.
-
Financial Market Data Aggregation: Employ this plugin to track and store time-sensitive financial data from various sources. By sending metrics to Redis, financial institutions can aggregate and analyze market trends or price changes over time, providing them with actionable insights derived from reliable time series analytics.
-
Application Performance Monitoring (APM): Implement the Redis plugin for gathering application performance metrics such as response times and CPU usage. Users can visualize their application’s performance over time with RedisTimeSeries, allowing them to identify bottlenecks and optimize resource allocation swiftly.
-
Energy Consumption Tracking: Leverage this plugin to monitor energy usage in buildings over time. By integrating with smart meters and sending data to RedisTimeSeries, municipalities or enterprises can analyze energy consumption patterns, helping to implement energy-saving measures and sustainability practices.
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