• Developer guide
  • API reference

›Monitoring

Getting started

  • Introduction to Imply Polaris
  • Quickstart
  • Navigate the console
  • Key concepts

Ingestion sources

  • Ingestion sources overview
  • Supported data formats
  • Create a connection
  • Ingest from files
  • Ingest from S3
  • Ingest from Kinesis
  • Ingest from Confluent Cloud
  • Kafka Connector for Imply Polaris
  • Push event data

Tables and data

  • Overview
  • Introduction to tables
  • Table schema
  • Create an ingestion job
  • Timestamp expressions
  • Data partitioning
  • Introduction to rollup
  • Approximation algorithms
  • Replace data

Analytics

  • Overview
  • Manage data cubes
  • Visualize data
  • Data cube dimensions
  • Data cube measures
  • Dashboards
  • Create a dashboard
  • Visualizations reference
  • Set up alerts
  • Set up reports
  • Embed visualizations
  • Query data

Monitoring

  • Overview

Management

  • Overview
  • Pause and resume a project

Billing

  • Overview
  • Polaris plans
  • Estimate project costs

Usage

  • Overview

Security

    Polaris access

    • Overview
    • Invite users to your organization
    • Permissions reference
    • Manage user groups
    • Enable SSO
    • SSO settings reference
    • Map IdP groups

    Secure networking

    • Connect to AWS

Developer guide

  • Overview
  • Authentication

    • Overview
    • Authenticate with API keys
    • Authenticate with OAuth
  • Manage users and groups
  • Migrate deprecated resources
  • Create a table
  • Define a schema
  • Upload files
  • Create an ingestion job
  • Ingestion sources

    • Ingest from files
    • Ingest from a table
    • Get ARN for AWS access
    • Ingest from Amazon S3
    • Ingest from Amazon Kinesis
    • Ingest from Confluent Cloud
    • Push event data
    • Kafka Connector for Imply Polaris
    • Kafka Connector reference
  • Filter data to ingest
  • Ingest nested data
  • Ingest and query sketches
  • Query data
  • Update a project
  • Link to BI tools
  • Connect over JDBC
  • Query parameters reference
  • API documentation

    • OpenAPI reference
    • Query API

Product info

  • Release notes
  • Known limitations
  • Druid extensions

Monitoring overview

Imply Polaris includes built-in monitoring to support your production workloads and ensure your database is optimized for your application. The Monitoring section of the Polaris user interface provides dashboards for inspecting query performance and event stream ingestion. Use the provided information to perform comparative analyses for the entire cluster to see how performance has evolved over time. You can also probe into detailed metrics in the prepared data cube interface. Access these dashboards and visualizations in the Monitoring section of the left navigation tree.

This topic provides an overview of the monitoring capabilities included in Polaris.

User queries

The User Queries view provides a single-page dashboard for monitoring query performance.

In this dashboard, you can analyze the following:

  • User activity: Track the number of distinct query users and the top query users. You can filter by user to investigate performance issues for specific users.
  • Query performance: Track the 98th percentile of query execution times, average query latency, total number of queries executed, and total number of failed queries.
  • Query processing: Evaluate the average and 98th percentile of query wait times to determine whether to scale up your project in response to high concurrent load issues.
  • Segment scanning: Assess the number of segments scanned and the segment scan times. High segment scan times indicate that your segment files are too large, which can be resolved by data partitioning. If many scans are occurring, your data may be too fragmented, and you may benefit from configuring data rollup.

Streaming

The Streaming view provides a dashboard to monitor streaming ingestion.

This dashboard displays the following:

  • Volume of incoming events and latency to ingest those events
  • Issues from streaming ingestion, including unparseable events and expired records rejected by Polaris
  • Number of rows output from processed events

Detailed metrics

The Detailed Metrics view provides a data cube where you can investigate specific metrics with the option to filter by table, query type, and query ID.

← Query dataOverview →
  • User queries
  • Streaming
  • Detailed metrics
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