Vertica MC chart understanding
+2Hi, I am using vertica management console and I would like to know the meaning of the categories in the "User Query Type Distribution" chart. I can see a legend with these categories: DDL, QUERY, SET,TRUNCATE and UTILITY. Can someone tell me which operations typically match each category ? (For example database designer, loading data, deleting data, modifying data, reorganize projections...) While in the "Query Concurrency" chart, I'd like to know why I find a peak every 5 minutes for system queries even if I am not working on the database. Thanks, Pietro.
+1Hi! 1. * DDL - data definition language, aka queries from type CREATE/DROP/ALTER * QUERY - data manipulation language(DML): SELECT/INSERT/PROFILE/UPDATE/DELETE requests on user tables * SET - parameters modification(queries that starts from "SET", for example: "set session autocommit to off") * TRUNCATE - its really very hard 
queries from type TRUNCATE TABLE * UTILITY - inner db activity: TupleMover , DBD, move partitions and so on + EXPLAIN 2. May be inner db activity like AnalyzeRowCount, TupleMover ?
queries from type TRUNCATE TABLE * UTILITY - inner db activity: TupleMover , DBD, move partitions and so on + EXPLAIN 2. May be inner db activity like AnalyzeRowCount, TupleMover ?+2thank you Daniel!! what about "Query Concurrency" chart ? Can you explain those peaks? thanks again, Pietro
Pietro -
The peaks every 5m or so will typically represent the various internal services of vertica. This includes things like tuple mover and/or mergeout processes. They will kickoff to see if there is any work to do - which is what you are most likely seeing.
You can click on the data point which will drilldown into actual queries themselves that make up that time period and view system/user queries which would confirm my statement.
-Jason
The peaks every 5m or so will typically represent the various internal services of vertica. This includes things like tuple mover and/or mergeout processes. They will kickoff to see if there is any work to do - which is what you are most likely seeing.
You can click on the data point which will drilldown into actual queries themselves that make up that time period and view system/user queries which would confirm my statement.
-Jason
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