Amazon ECS Metrics
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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.
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ECS is short for "Elastic Container Service," and Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that makes running your containerized environment secure, reliable and available. It supports serverless options like AWS Fargate and is integrated with a number of Amazon services like Amazon SageMaker, AWS Batch, Amazon Lex, and AWS App Mesh.
In a larger sense, an Amazon ECS service allows you to run and maintain a specific number of instances of a task at the same time in an Amazon ECS cluster. This also allows you to run your service behind a load balancer if necessary, which itself is an important part of the development process.
Why use a Telegraf plugin for Amazon ECS?
In order to maintain a reliable, available and performant instance of Amazon ECS and your other AWS solutions, you need to collect metrics and events from all components of your AWS solution. This will allow you to easily pinpoint the area that may be causing a failure.
Essentially, Amazon ECS is all about providing you a much-needed context so that you don't just know what is going on, but that you also know why. If you know not just that a problem has occurred but what conditions led to the failure, you know what you need to do to fix it. More than that, you also need to know what you must do to stop it from happening again.
The Amazon ECS Telegraf Input Plugin helps you easily pull metrics that let you know how well your Amazon ECS is performing. It collects metrics about the cluster, the tasks, memory and cpu consumption and more. Pair this with one of the many Telegraf plugins to monitor the application in the containers, and you will gain full visibility into your stack.
How to monitor Amazon ECS using the Telegraf plugin
The Amazon ECS Telegraf plugin is Amazon ECS and Amazon Fargate compatible and uses the Amazon ECS metadata and stats v2 or [v3][task-metadata-endpoint-v3] API endpoints to gather metrics on the running containers in a Task. Please note that the telegraf container must be run in the same Task as the workload it is inspecting. This is similar to the Docker input plugin, with some ECS specific modifications for AWS metadata and stats formats.
Key Amazon ECS metrics to use for monitoring
As always, the Amazon ECS metrics that you choose to monitor will ultimately vary depending on where you are in the development process, what problems or other performance-related issues you’re concerned about, and even what type of application you’re developing in the first place. Having said that, some of the important Amazon ECS metrics that you should proactively monitor include:
- ECS task metrics
- Tags:
cluster
task_arn
family
revision
id
name
- Fields:
revision (string)
desired_status (string)
known_status (string)
limit_cpu (float)
limit_mem (float)
- Tags:
- ECS container metrics
- Tags:
cluster
task_arn
family
revision
id
name
- Fields:
container_id
active_anon
active_file
cache
hierarchical_memory_limit
inactive_anon
Inactive_file
mapped_file
pgfault
Pgmajfault
pgpgin
pgpgout
rss
rss_huge
Total_active_anon
total_active_file
total_cache
total_inactive_anon
total_inactive_file
Total_mapped_file
Total_pgfault
Total_pgmajfault
total_pgpgin
total_pgpgout
Total_rss
Total_rss_huge
total_unevictable
Total_writeback
Unevictable
writeback
Fail_count
limit
max_usage
usage
usage_percent
- Tags:
- ECS container cpu metrics
- Tags:
cluster
task_arn
family
revision
id
name
usage_total
usage_in_usermode
usage_in_kernelmode
Usage_system
throttling_periods
throttling_throttled_periods
throttling_throttled_time
usage_percent
usage_total
- Fields:
container_id
- Tags:
- ECS container net metrics
- Tags:
cluster
task_arn
family
revision
id
name
- Fields:
container_id
rx_packets
rx_dropped
rx_bytes
rx_errors
tx_packets
tx_dropped
tx_bytes
tx_errors
- Tags:
- ECS container blkio metrics
- Tags:
cluster
task_arn
family
revision
id
name
- Fields:
container_id
io_service_bytes_recursive_async
io_service_bytes_recursive_read
io_service_bytes_recursive_sync
io_service_bytes_recursive_total
io_service_bytes_recursive_write
io_serviced_recursive_async
io_serviced_recursive_read
io_serviced_recursive_sync
io_serviced_recursive_total
io_serviced_recursive_write
- Tags:
- ECS container meta metrics
- Tags:
cluster
task_arn
family
revision
id
name
- Fields:
container_id
docker_name
image
Image_id
desired_status
known_status
Limit_cpu
limit_mem
created_at
started_at
type
- Tags:
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