Rollup Jobsedit

A rollup job is a periodic task that aggregates data from indices specified by an index pattern, and then rolls it into a new index. Rollup indices are a good way to compactly store months or years of historical data for use in visualizations and reports.

To get started, open the menu, then go to Stack Management > Data > Rollup Jobs. With this UI, you can:

][List of currently active rollup jobs

Before using this feature, you should be familiar with how rollups work. Rolling up historical data is a good source for more detailed information.

Create a rollup jobedit

Kibana makes it easy for you to create a rollup job by walking you through the process. You fill in the name, data flow, and how often you want to roll up the data. Then you define a date histogram aggregation for the rollup job and optionally define terms, histogram, and metrics aggregations.

When defining the index pattern, you must enter a name that is different than the output rollup index. Otherwise, the job will attempt to capture the data in the rollup index. For example, if your index pattern is metricbeat-*, you can name your rollup index rollup-metricbeat, but not metricbeat-rollup.

][Wizard that walks you through creation of a rollup job

Start, stop, and delete rollup jobsedit

Once you’ve saved a rollup job, you’ll see it the Rollup Jobs overview page, where you can drill down for further investigation. The Manage menu enables you to start, stop, and delete the rollup job. You must first stop a rollup job before deleting it.

][Rollup job details

You can’t change a rollup job after you’ve created it. To select additional fields or redefine terms, you must delete the existing job, and then create a new one with the updated specifications. Be sure to use a different name for the new rollup job—reusing the same name can lead to problems with mismatched job configurations. You can read more at rollup job configuration.

Try it: Create and visualize rolled up dataedit

This example creates a rollup job to capture log data from sample web logs. To follow along, add the sample web logs data set.

In this example, you want data that is older than 7 days in the target index pattern kibana_sample_data_logs to roll up once a day into the index rollup_logstash. You’ll bucket the rolled up data on an hourly basis, using 60m for the time bucket configuration. This allows for more granular queries, such as 2h and 12h.

Create the rollup jobedit

As you walk through the Create rollup job UI, enter the data:

Field Value

Name

logs_job

Index pattern

kibana_sample_data_logs

Rollup index name

rollup_logstash

Frequency

Every day at midnight

Page size

1000

Delay (latency buffer)

7d

Date field

@timestamp

Time bucket size

60m

Time zone

UTC

Terms

geo.src, machine.os.keyword

Histogram

bytes, memory

Histogram interval

1000

Metrics

bytes (average)

The terms, histogram, and metrics fields reflect the key information to retain in the rolled up data: where visitors are from (geo.src), what operating system they are using (machine.os.keyword), and how much data is being sent (bytes).

You can now use the rolled up data for analysis at a fraction of the storage cost of the original index. The original data can live side by side with the new rollup index, or you can remove or archive it using Index Lifecycle Management.

Visualize the rolled up dataedit

Your next step is to visualize your rolled up data in a vertical bar chart. Most visualizations support rolled up data, with the exception of Timelion and Vega visualizations.

  1. Go to Stack Management > Kibana > Index Patterns.
  2. Click Create index pattern, and select Rollup index pattern from the dropdown.

    ][Create rollup index pattern
  3. Enter rollup_logstash,kibana_sample_logs as your Index Pattern and @timestamp as the Time Filter field name.

    The notation for a combination index pattern with both raw and rolled up data is rollup_logstash,kibana_sample_data_logs. In this index pattern, rollup_logstash matches the rolled up index pattern and kibana_sample_data_logs matches the index pattern for raw data.

  4. Go to Visualize and create a vertical bar chart.
  5. Choose rollup_logstash,kibana_sample_data_logs as your source to see both the raw and rolled up data.

    ][Create visualization of rolled up data
  6. Look at the data in your visualization.

    ][Visualization of rolled up data
  7. Optionally, create a dashboard that contains visualizations of the rolled up data, raw data, or both.

    ][Dashboard with rolled up data