Managing workload resources in an Eon Mode database

You primarily control workloads in an Eon Mode database using subclusters.

You primarily control workloads in an Eon Mode database using subclusters. For example, you can create subclusters for specific use cases, such as ETL or query workloads, or you can create subclusters for different groups of users to isolate workloads. Within each subcluster, you can create individual resource pools to optimize resource allocation according to workload. See Managing subclusters for more information about how Vertica uses subclusters.

Global and subcluster-specific resource pools

You can define global resource pool allocations that affect all nodes in the database. You can also create resource pool allocations at the subcluster level. If you create both, the subcluster-level settings override the global settings.

You can use this feature to remove global resource pools that the subcluster does not need. Additionally, you can create a resource pool with settings that are adequate for most subclusters, and then tailor the settings for specific subclusters as needed.

Optimizing ETL and query subclusters

Overriding resource pool settings at the subcluster level allows you to isolate built-in and user-defined resource pools and optimize them by workload. You often assign specific roles to different subclusters:

  • Subclusters dedicated to ETL workloads and DDL statements that alter the database.

  • Subclusters dedicated to running in-depth, long-running analytics queries. These queries need more resources allocated for the best performance.

  • Subclusters that run many short-running "dashboard" queries that you want to finish quickly and run in parallel.

After you define the type of queries executed by each subcluster, you can create a subcluster-specific resource pool that is optimized to improve efficiency for that workload.

The following scenario optimizes 3 subclusters by workload:

  • etl: A subcluster that performs ETL that you want to optimize for Tuple Mover operations.

  • dashboard: A subcluster that you want to designate for short-running queries executed by a large number of users to refresh a web page.

  • analytics: A subcluster that you want to designate for long-running queries.

See Best practices for managing workload resources for additional scenarios about resource pool tuning.

Configure an ETL subcluster to improve TM performance

Vertica chooses the subcluster that has the most ROS containers involved in a mergeout operation in its depot to execute a mergeout (see The Tuple Mover in Eon Mode Databases). Often, a subcluster performing ETL will be the best candidate to perform a mergeout because the data it loaded is involved in the mergeout. You can choose to improve the performance of mergeout operations on a subcluster by altering the TM pool's MAXCONCURRENCY setting to increase the number of threads available for mergeout operations. You cannot change this setting at the subcluster level, so you must set it globally:


See Tuning tuple mover pool settings for additional information about Tuple Mover resources.

Configure the dashboard query subcluster

By default, secondary subclusters have memory allocated to Tuple Mover resource pools. This pool setting allows Vertica to assign mergeout operations to the subcluster, which can add a small overhead. If you primarily use a secondary subcluster for queries, the best practice is to reclaim the memory used by the TM pool and prevent mergeout operations being assigned to the subcluster.

To optimize your dashboard query secondary subcluster, set their TM pool's MEMORYSIZE and MAXMEMORYSIZE settings to 0:


To confirm the overrides, query the SUBCLUSTER_RESOURCE_POOL_OVERRIDES table:

=> SELECT pool_oid, name, subcluster_name, memorysize, maxmemorysize

     pool_oid      | name | subcluster_name | memorysize | maxmemorysize
 45035996273705046 | tm   | dashboard       | 0%         | 0%
(1 row)

To optimize the dashboard subcluster for short-running queries on a web page, create a dash_pool subcluster-level resource pool that uses 70% of the subcluster's memory. Additionally, increase PLANNEDCONCURRENCY to use all of the machine's logical cores, and limit EXECUTIONPARALLELISM to no more than half of the machine's available cores:

     MEMORYSIZE '70%'

Configure the analytic query subcluster

To optimize the analytics subcluster for long-running queries, create an analytics_pool subcluster-level resource pool that uses 60% of the subcluster's memory. In this scenario, you cannot allocate more memory to this pool because the nodes in this subcluster still have memory assigned to their TM pools. Additionally, set EXECUTIONPARALLELISM to AUTO to use all cores available on the node to process a query, and limit PLANNEDCONCURRENCY to no more than 8 concurrent queries:

=> CREATE RESOURCE POOL analytics_pool FOR SUBCLUSTER analytics
      MEMORYSIZE '60%'