Sign inTry Free

Historical Monitoring

On this page

SingleStore Helios 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 SingleStore Helios, 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

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.

Historical Workload Monitoring

Chart Name

What it shows

When to use it

Elapsed Time per Execution by Database

The elapsed time per query, grouped by database

To identify which databases incur the most long-running queries and observe changes in the pattern of execution time per query from the historical norm. This may indicate an issue with an application or changes in your workload.

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.

CPU Time per Execution by Database

The CPU time spent per 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.

Memory Usage per Execution by Database

The memory bytes spent per query activity, grouped by database

To identify which databases incur the most memory usage.

Note: A blank database indicates system activity that is not related to a user database.

Disk Bytes per Execution by Database

The disk bytes spent per query activity, grouped by database

To identify which databases incur the most disk bytes.

Note: A blank database indicates system activity that is not related to a user database.

Network Bytes per Execution by Database

The network bytes spent per query activity, grouped by database

To identify which databases incur the most network bytes.

Note: A blank database indicates system activity that is not related to a user database.

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 Monitoring

Chart Name

What it shows

When to use it

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 SingleStore’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.).

Cache Monitoring

Note: Requires SingleStore Helios version 8.0.23 or later.

Chart Name

What it shows

When to use it

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 SingleStore Helios 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 SingleStore Helios.

Blob Cache Evicted per Second (by Database)

Rate at which the blob cache is evicting files.

To understand how SingleStore Helios 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 SingleStore Helios.

Pipeline Dashboards

Note: Requires SingleStore Helios version 8.0.25 or later.

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 (specifically, the queries that are run in the engine that ingest the data into tables)

To observe the workload from pipelines.

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 SingleStore read and written to the filesystem or the in-memory transaction log) per execution in a pipeline

Note that this is the average value of disk_b from the total of run_count, success_count, and failure_count (from the MV_ACTIVITIES_CUMULATIVE table). This is focused more on the load on the server than the data being ingested.

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

Avg Memory Use 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.

Query History

Chart Name

What it shows

When to use it

Query History

Query runtimes, those queries that have succeeded, and those queries that have failed.

To view query runtimes over time, identify and resolve slow-running and failed queries, and view and optimize workloads in real time.

Refer to Query History for additional information and examples.

Resource Pool Monitoring

Note

This is a Preview feature.

Please contact SingleStore Support to enable this feature.

Note: Requires SingleStore version 8.0.26 or 8.1.24 and later.

Chart Name

What it shows

When to use it

Finished Queries

The number of queries finished on a given resource pool

To perform capacity planning for workloads by resource pool and identify if workloads in general, or workload spikes in particular, are queueing the queries by current resource pool configurations.

Killed Queries

The number of queries killed on a given resource pool

To understand how many queries are killed on a given resource pool.

Queueing Queries

The number of queries queued for a given resource pool

To understand how many queries are queued over time and perform capacity planning to increase resource limits for a given pool as needed.

Queue Time per Queued Query

The average queue time per query for a given resource pool

To understand how long queries are being queued; helps to perform capacity planning to increase resource limits for a given pool as needed.

Last modified: April 2, 2024

Was this article helpful?

On this page

Was this article helpful?

Verification instructions

Note: You must install cosign to verify the authenticity of the SingleStore file.

Use the following steps to verify the authenticity of singlestoredb-server, singlestoredb-toolbox, singlestoredb-studio, and singlestore-client SingleStore files that have been downloaded.

You may perform the following steps on any computer that can run cosign, such as the main deployment host of the cluster.

  1. (Optional) Run the following command to view the associated signature files.

    curl undefined

  2. Download the signature file from the SingleStore release server.

    • Option 1: Click the Download Signature button next to the SingleStore file.

    • Option 2: Copy and paste the following URL into the address bar of your browser and save the signature file.

    • Option 3: Run the following command to download the signature file.

      curl -O undefined

  3. After the signature file has been downloaded, run the following command to verify the authenticity of the SingleStore file.

    echo -n undefined |
        cosign verify-blob --certificate-oidc-issuer https://oidc.eks.us-east-1.amazonaws.com/id/CCDCDBA1379A5596AB5B2E46DCA385BC \
          --certificate-identity https://kubernetes.io/namespaces/freya-production/serviceaccounts/job-worker \
          --bundle undefined \
          --new-bundle-format -
    Verified OK