Historical Monitoring

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

Execution Time per Read/Write Query

The elapsed time of read/write query

To identify changes in the pattern of execution time per read/write query from the historical norm. This may indicate an issue with an application or changes in your workload.

Threads - Connected

The number of open connections (threads_connected) to the database relative to the maximum limit (max_connections)

To identify if the database is approaching the maximum allowed connections, which is indicated by a utilization near 100%. This can potentially lead to performance issues, as queries may need to wait in a queue until threads become available to process them.

Threads - Running

The number of threads that are actively running queries (threads_running) relative to the maximum limit (max_connection_threads)

To identify if the system is approaching its capacity with regard to the number of queries that can be executed in parallel, which is indicated by a utilization near 100%. This can potentially lead to resource pressure, system unresponsiveness, latency spikes, and eventual failures.

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.

Memory Utilization

The percentage of a host’s memory that is being used

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

Memory Usage Breakdown

The memory in use categorized by Data, Query, Reserved & Other Internal Memory allocators compared to the total memory available

To perform capacity planning for memory and identify if the cluster is not performing optimally due to workloads in general, write workloads, or workload spikes in particular, and to discover where memory is allocated (table, query, etc.).

Memory Used - Data

The data memory in use

To perform capacity planning for data memory and identify if given write workloads are putting the cluster at risk of running out of memory.

Memory Used - Query

The query memory in use

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

Memory Used - Other

The memory used by SingleStoreDB’s memory allocators

To identify why memory allocations have increased, or are anomalously large, when there are no other indicators of increased memory use, such as workload or data, and to discover where memory is allocated (table, query, etc.).

Persistent Cache Utilization

Persistent cache utilization for the workspace.

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

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

Distribution of Components Using Cache

Distribution of cache utilization by data, plancache, auditlogs, and tracelogs

To understand how the cache is being utilized.

Analyzing cache usage can reveal if certain artifacts (such as data, plancache, audit logs, or trace logs) are consuming an excessive amount of space. This can either cause performance issues, or require additional resources to maintain optimal operation.

Monitoring cache usage and activity can also help identify performance bottlenecks, which may require cache policies to be adjusted, additional resources to be allocated, and/or your workload to be optimized.

Breakdown of Cache Utilization by Data

Cache consumption breakdown by "Data" category.

Adding utilization across blobs, transaction logs, snapshots, temp blobs, etc. will be equal to the total cache utilized by "Data."

Distribution of Databases Using Cache

Distribution of cache utilization by databases.

Adding utilization across databases will be equal to the total cache utilized by "Data."

Blob Cache Downloaded per Second (by Database)

Rate at which the blob cache is downloading files from remote storage.

To understand how SingleStoreDB Cloud blob cache is performing.

By understanding and monitoring the rate at which the blob cache is downloading files from remote storage, potential performance bottlenecks and/or issues related to blob cache activity can be identified.

For example, if high download rates are observed relative to the size of your database and scale of your hardware, you may consider increasing the local cache size.

Regularly reviewing this metric can help you make well-informed decisions for optimizing the performance of SingleStoreDB Cloud.

Blob Cache Evicted per Second (by Database)

Rate at which the blob cache is evicting files.

To understand how SingleStoreDB Cloud blob cache is performing.

By understanding and monitoring the rate at which the blob cache is evicting files, system resource utilization can be optimized based on your data management needs.

A high eviction rate may indicate that the cache size is insufficient, or that your workload is imposing a high cache turnover. To improve overall cluster performance, reviewing data access patterns and adjusting the cache size is recommended.

Regularly reviewing this metric can help you identify potential performance bottlenecks and make well-informed decisions for optimizing the performance of SingleStoreDB Cloud.