Table: aws_elasticache_redis_metric_engine_cpu_utilization_hourly - Query AWS ElastiCache Redis using SQL
The AWS ElastiCache Redis is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. It provides a high-performance, scalable, and cost-effective caching solution, while removing the complexity associated with managing a distributed cache environment. This service is primarily used to improve the performance of web applications by retrieving information from fast, managed, in-memory caches, instead of relying entirely on slower disk-based databases.
Table Usage Guide
The aws_elasticache_redis_metric_engine_cpu_utilization_hourly
table in Steampipe gives you information about the hourly CPU utilization metrics for AWS ElastiCache Redis. This table enables you, as a DevOps engineer, database administrator, or other technical professional, to query time-series data related to CPU usage. As a result, you can monitor performance, identify potential bottlenecks, and optimize resource allocation. The schema outlines various attributes of the CPU utilization metrics for you, including the timestamp, average, maximum, and minimum CPU utilization, among others.
The aws_elasticache_redis_metric_engine_cpu_utilization_hourly
table provides you with metric statistics at 1 hour intervals for the most recent 60 days.
Examples
Basic info
Explore the performance of your AWS ElastiCache Redis instances by analyzing CPU utilization over time. This can help optimize resource allocation and identify instances where performance tuning may be required.
select cache_cluster_id, timestamp, minimum, maximum, average, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlyorder by cache_cluster_id, timestamp;
select cache_cluster_id, timestamp, minimum, maximum, average, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlyorder by cache_cluster_id, timestamp;
CPU Over 80% average
Discover instances where your AWS ElastiCache Redis clusters are experiencing high CPU usage, specifically over 80% on average. This can help identify potential performance issues and allow for proactive troubleshooting.
select cache_cluster_id, timestamp, round(minimum :: numeric, 2) as min_cpu, round(maximum :: numeric, 2) as max_cpu, round(average :: numeric, 2) as avg_cpu, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlywhere average > 80order by cache_cluster_id, timestamp;
select cache_cluster_id, timestamp, round(minimum, 2) as min_cpu, round(maximum, 2) as max_cpu, round(average, 2) as avg_cpu, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlywhere average > 80order by cache_cluster_id, timestamp;
CPU hourly average < 2%
Analyze the performance of your AWS ElastiCache Redis clusters by identifying instances where the average CPU usage is less than 2% on an hourly basis. This can help pinpoint potential inefficiencies or underutilized resources, optimizing your cloud infrastructure management and cost efficiency.
select cache_cluster_id, timestamp, round(minimum :: numeric, 2) as min_cpu, round(maximum :: numeric, 2) as max_cpu, round(average :: numeric, 2) as avg_cpu, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlywhere average < 2order by cache_cluster_id, timestamp;
select cache_cluster_id, timestamp, round(minimum, 2) as min_cpu, round(maximum, 2) as max_cpu, round(average, 2) as avg_cpu, sample_countfrom aws_elasticache_redis_metric_engine_cpu_utilization_hourlywhere average < 2order by cache_cluster_id, timestamp;
Schema for aws_elasticache_redis_metric_engine_cpu_utilization_hourly
Name | Type | Operators | Description |
---|---|---|---|
_ctx | jsonb | Steampipe context in JSON form. | |
account_id | text | =, !=, ~~, ~~*, !~~, !~~* | The AWS Account ID in which the resource is located. |
average | double precision | The average of the metric values that correspond to the data point. | |
cache_cluster_id | text | The cache cluster id. | |
maximum | double precision | The maximum metric value for the data point. | |
metric_name | text | The name of the metric. | |
minimum | double precision | The minimum metric value for the data point. | |
namespace | text | The metric namespace. | |
partition | text | The AWS partition in which the resource is located (aws, aws-cn, or aws-us-gov). | |
region | text | The AWS Region in which the resource is located. | |
sample_count | double precision | The number of metric values that contributed to the aggregate value of this data point. | |
sp_connection_name | text | =, !=, ~~, ~~*, !~~, !~~* | Steampipe connection name. |
sp_ctx | jsonb | Steampipe context in JSON form. | |
sum | double precision | The sum of the metric values for the data point. | |
timestamp | timestamp with time zone | The time stamp used for the data point. | |
unit | text | The standard unit for the data point. |
Export
This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.
You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh
script:
/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- aws
You can pass the configuration to the command with the --config
argument:
steampipe_export_aws --config '<your_config>' aws_elasticache_redis_metric_engine_cpu_utilization_hourly