Skip to main content

Historical Monitoring

SingleStoreDB Cloud provides native monitoring which allows users to quickly and easily understand their application workloads and debug performance-related issues. Monitoring leverages the system information and statistics generated within SingleStoreDB Cloud, and retains and visualizes them using a dedicated and highly-available cloud monitoring infrastructure.

The following tables list the provided dashboards, their associated functionality, and how/when to use them to identify trends, troubleshoot and/or optimize workloads, and take action to remediate issues.

View the Dashboards

Cluster View

Chart Name

What it shows

When to use it

CPU Utilization

The percentage of the host’s CPU that is being used:

  • max single-core load: The maximum CPU load across all of the available CPU cores

  • avg core load: The average CPU load across all of the available CPU cores

  • min single-core load: The minimum CPU load across all of the available CPU cores

To understand CPU usage and host resource usage in general, or for a given workload.

Memory Utilization

The percent of the host’s memory that is being used

To understand host memory usage for a given workload over time.

Local Disk Utilization

The local disk utilization for the workspace.

Total storage can be managed by dropping obsolete tables and/or purging older data in large tables.

To identify the amount of warm data so preventive actions can be taken to better handle the load, thereby ensuring stability and optimum performance.

Read/Write Queries per Second

The number of reads/writes per second of the queries running on the system

To understand typical (“normal”) cluster activity to benchmark workloads and their query rate and identify anomalies in the read/write workload.

If the number of rows read or written is very high or uneven, it could indicate that some queries or operations are taking longer to process than others. This can be due to poor indexing, inefficient queries, or database design issues.

Failed Read/Write Queries per Second

The number of reads/writes failed per second of the queries running on the system

Rows Read or Written

The number of rows read/written

Historical Workload Monitoring

Chart Name

What it shows

When to use it

CPU time by Database

The CPU time spent by each query activity, grouped by database

To identify which databases incur the most CPU usage. Note: A blank database indicates system activity that is not related to a user database.

Execution Count

The number of queries executed in a given time

To perform capacity planning for workloads and identify if workloads in general, or workload spikes in particular, are putting the workspace at risk of running out of memory.

Metrics by Query Plan

The queries executed and their relative resource consumption

To identify which queries are expensive, including how long queries are taking to complete, their CPU times, failure rates etc.

Pipeline Dashboards

Note

Currently, pipeline dashboards are a preview feature.

Pipeline Summary

Chart Name

What it shows

When to use it

State Distribution

A high-level overview of all pipelines, including the number of pipelines in running, stopped, and error states, and the percentage of each.

To identify potential issues by comparing the number of running pipelines to those that have either stopped or produced an error.

Historical Pipeline State

The state of all pipelines over a period of time.

To identify potential issues by examining how a pipeline behaves over time.

Summary

The current state of all pipelines.

To identify which pipelines are currently running, stopped, or in an errored state along with their associated database.

Pipeline Performance

Chart Name

What it shows

When to use it

Execution Count

The total number of executions that have run in a pipeline.

To estimate how frequently a query is executed in a pipeline and whether a query has failed. Useful for optimizing queries.

Avg CPU Time per Execution

The average CPU time for each execution in a pipeline.

To identify which pipelines are consuming excessive CPU cycles.

Avg Elapsed Time per Execution

The average elapsed time for each execution in a pipeline.

To identify which pipelines are experiencing degraded performance over time.

Avg I/O per Execution

The average disk I/O (number of bytes that SingleStoreDB read and wrote to the filesystem or the in-memory transaction log) per execution in a pipeline.

To identify if a pipeline is experiencing I/O-related performance issues (typically when this value is consistently high).

Avg Memory User per Execution

The average memory usage per execution in a pipeline.

To identify which pipelines are exhibiting excessive memory use.

Avg Network Bytes per Execution

The average network bytes per execution in a pipeline.

To identify which pipelines are experiencing degraded performance due to network constraints.

Pipeline Errors

Which pipelines have produced an error, including the pipeline name, error ID, error code, error message, and the time the error occurred.

To identify and troubleshoot pipelines that have produced an error.