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Features

  • Dashboards <<<<<<< HEAD
    • Performance overview to see longest running queries, bad join patterns, red flags on table settings, and capacity metrics, and to drill into lists of related queries
    • Query inspection to get both-high level metrics about a particular query's resource usage, as well as query plan and step by step resources
    • Admin dashboard to review ETL history and errors, tables, and capacity =======
      • Performance overview to see longest running queries, bad join patterns, red flags on table settings, and capacity metrics, and to drill into lists of related queries
      • Query inspection to get both-high level metrics about a particular query's resource usage, as well as query plan and step by step resources
      • Admin dashboard to review ETL history and errors, tables, and capacity

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  • Explores
    • Tables
    • Queries for the past day
    • Query Plans for the past day
    • Execution metrics for the past day

Implementation Instructions

The model is very self contained, with no references to other views/models, and all global object names prefixed with "redshift_". As a result, implementation should be straight-forward:

  • Copy the view and dashboard files into your project
    • If your scratch schema is not called looker_scratch, search for looker_scratch within the view file and replace it with the name of your scratch schema
  • Copy the model file into your project and set the connection <<<<<<< HEAD
    • The connection and its associated user in Redshift have an impact on the results of reports. Some of the admin views are only available to superusers, and all of the views return information specific to the user if not a super user. So choose your connection based on your needs for the block:
      • With a separate superuser connection: To view all activity on Redshift
      • With a standard connection: To view all Looker activity on Redshift
      • With a parameterized connection: For each end user to view activity from the specific connection as parameterized
    • Alternately, you can splice the model file contents into an existing model file that uses your redshift connection. Then search and replace your model name. Search for "redshift_model"
  • Optionally unhide any explores that you want to be visible from your explore menu

Great! Now what?

Identify hotspots from the performance dashboard, and drill into the query inspection to pinpoint causes. Here are typical problems and associated fixes

  • Do queries contain unintentional Nested Loops, especially with large tables? Understand how a hash join works and correct any join predicates that prevent hash joins
  • Do queries result in frequent broadcasts of inner tables? Consider setting the dist style for these tables to all.
  • Do queries result in frequent distribution of the outer (or both) tables? See if a distribution key on the outer table can eliminate some of these
  • Do queries result in large amounts of disk scanning? Redshift does a lot of disk scanning since it doesn't have indexes
    • Are your tables compressed? Using the most appropriate compression method for your datatype?
    • Do your tables have a sortkey? Is it on the most typically filtered on column? If multiple columns are frequently used, consider interleaved sort keys.
    • Is your table sorting up to date? Periodic vacuum jobs can help.
    • Are very large tables set to dist-style all? They will be scanned for each node, rather than each node scanning a share of the data
    • Are your filter columns wider than they could be? (e.g. json encoded data rather than a separate column for a field that is often filtered on)
  • Do queries with optimization opportunities use CTE's that prevent the query optimizer from optimizing the subquery based on the outer query? (Particularly for explores with filters.) =======
    • Alternately, you can splice the model file contents into an existing model file that uses your redshift connection. Then search and replace your model name. Search for "redshift_model"
  • The connection and its associated user in Redshift have an impact on the results of reports. Choose your connection based on your needs for the block:
    • (Recommended) With a standard connection
    • With a separate superuser connection
      • This will allow you to view all activity on Redshift across users
      • Note that when creating a superuser connection, Looker users with (develop or sql_runner permissions AND with access to the model) or with manage_models would be able to run arbitrary queries through the super-user connection.
    • Parameterized connections currently do not support PDTs, and will not work with the block.
  • Optional: Unhide any explores that you want to be visible from your explore menu
  • Optional: Adjust time of day that PDT's rebuild
  • Warning: If you see errors in your project, doublecheck your other models to ensure they aren't indiscriminantly including all the dashboards/views in your project. If they are, modify their include statements to only include dashboards/views that make sense in those models. (You can leverage file prefixes for better model organization.)

Great! Now what?

Check our Looker Discourse article for a guide on how to use the block to optimize your performance.

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Known Issues

  • Sometimes, drilling into a list of queries doesn't return any records. As far as we can tell, this is due to categorically wrong result sets from Redshift for certain where filters. As a workaround, remove some filters, such as redshift_tables.sortkey1_enc
  • Sometimes, the query execution table has 0 distribution bytes, despite the query plan and table distributions both suggesting that there was distribution activity. This zeroing out is present in each of SVL_QUERY_SUMMARY, SVL_QUERY_REPORT, and STL_DIST. Always check query execution metrics to ensure they're in the right ballpark before relying on them.
  • "Rows out" according to the query plan are estimates. The are often highly inflated. If anything, this is an indication that you should update table statistics in Redshift so it can generate better query plans.
  • Need to think about metrics for scans (e.g. bytes, rows emitted, and emitted rows to table rows ratio). When a table has dist style='all', the measures are increased by a factor of the number of slices. This is unintuitive since, for example the ratio is then typically >100%, but this may be a good thing.