{"id":4812,"date":"2023-04-17T07:00:00","date_gmt":"2023-04-17T07:00:00","guid":{"rendered":"http:\/\/www.sumologic.com\/blog\/plan-better-preempt-bottlenecks-with-predict-for-metrics"},"modified":"2025-06-17T10:31:11","modified_gmt":"2025-06-17T18:31:11","slug":"plan-better-preempt-bottlenecks-with-predict-for-metrics","status":"publish","type":"blog","link":"https:\/\/www.sumologic.com\/blog\/plan-better-preempt-bottlenecks-with-predict-for-metrics","title":{"rendered":"Plan better and preempt bottlenecks with predict for metrics"},"content":{"rendered":"\n
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\"Predict<\/figure>\n<\/div>\n\n\n

<\/p>\n\n\n\n

Nothing is certain in this world except for death, taxes, and that you will eventually run out of disk space.<\/p>\n\n\n\n

You may have used our unique predict operator<\/a> to query logs and forecast future values (we\u2019ve even heard of customers predicting their ingest volume for Sumo Logic log data to better forecast their usage and budget!) \u2014 and wanted to do the same with metrics. <\/p>\n\n\n\n

With the recent general availability of the predict for metrics operator<\/a>, you can. This metrics forecast query can help you preempt bottlenecks and assist with capacity planning.<\/p>\n\n\n\n

How to get started with predict for metrics with a common DynamoDB use case<\/h3>\n\n\n\n

Teams like yours use Sumo Logic dashboards to monitor service usage and prepare for capacity conversations with their cloud vendors. You can easily track errors, understand their context, and set alerts for when they\u2019re going above certain thresholds. <\/p>\n\n\n\n

Our predict for metrics operator can enhance this workflow.<\/p>\n\n\n\n

For example, take the common use case of monitoring Amazon DynamoDB<\/a> performance. While AWS infrastructure does much of the heavy lifting to support DynamoDB, teams often track how they\u2019re using the service to ensure optimal performance \u2014 focusing on if their requests are being throttled and if they\u2019ve exceeded their allotted capacity.<\/p>\n\n\n\n

With the predict for metrics operator, you create a quick query in the metrics explorer to see if you have sufficient DynamoDB read capacity to support your expected consumption. And remember, you can even run queries, including this predict for metrics operator, on metrics pulled from logs<\/a>.<\/p>\n\n\n

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\"Predict<\/figure>\n<\/div>\n\n\n

In the above example, Series B provides the input for the actual Read Capacity Consumed time series. Series C [ #B | predict model=ar forecast=24h <\/em>] takes Series B as an input to forecast 24 hours into the future using an auto-regression model. In addition to the auto-regression model, the predict operator supports a linear model.<\/p>\n\n\n\n

The red dotted line represents the forecasted consumption over 24 hours versus the actual consumption in green. The results suggest that the user is not expected to exceed their provisioned capacity during the applied time period.<\/p>\n\n\n\n

Improve your capacity planning and stop bottlenecks before they start<\/h3>\n\n\n\n

Modern observability demands rich analytics for your telemetry. With predict operators available to query both logs and metrics, the Sumo Logic platform<\/a> is uniquely equipped to help your teams anticipate and avoid what otherwise may seem inevitable (except, unfortunately, for death and taxes). If you want to get started with the predict for metrics operator and need more information, please consult our technical documentation<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":231,"featured_media":0,"template":"","meta":{"_acf_changed":false,"show_custom_date":false,"custom_date":"","featured":false,"featured_image":0,"learn_more_label":"","image_alt_text":"","learn_more_type":"","show_popup":false,"learn_more_link_file":0,"event_date":false,"event_start_date":"","event_end_date":"","place_holder_image_url":"","post_reading_time":"2","notification_enabled":false,"notification_text":"","notification_logo":"","notification_expiration_time":0,"is_enable_transparent_header":false,"selected_taxonomy_terms":{"blog-category":[125],"blog-tag":[]},"selected_primary_terms":[],"learn_more_link":[],"featured_page_list":[],"notification_enabled_post_list":[],"_gspb_post_css":"","_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"4668,71369,71176","_relevanssi_noindex_reason":"","inline_featured_image":false,"footnotes":""},"blog-category":[125],"blog-tag":[],"class_list":["post-4812","blog","type-blog","status-publish","hentry","blog-category-devops-it-operations"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog\/4812","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/users\/231"}],"version-history":[{"count":3,"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog\/4812\/revisions"}],"predecessor-version":[{"id":26877,"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog\/4812\/revisions\/26877"}],"wp:attachment":[{"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/media?parent=4812"}],"wp:term":[{"taxonomy":"blog-category","embeddable":true,"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog-category?post=4812"},{"taxonomy":"blog-tag","embeddable":true,"href":"https:\/\/www.sumologic.com\/wp-json\/wp\/v2\/blog-tag?post=4812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}