To identify queries consuming a specific high volume of I/O (over 100 million), the TotalIOCount metric is the most direct and essential measure. This metric, found in Teradata's Database Query Log (DBQLogTbl), aggregates the total logical I/O operations for the entire query across all AMPs.

To diagnose the cause, such as a large redistribution, the PJI (Product Join Index Skew) metric is highly relevant. A high PJI value indicates that during a product join, the redistribution of rows to the AMPs was severely unbalanced. This skew is a common cause of performance issues, including the excessive I/O associated with large redistributions and potential full table scans of the spooled, duplicated table.

A. MAXAMPIO reflects the I/O of the single busiest AMP, not the query's total I/O, so it cannot be used to verify the "100 million IOs" threshold.

C. I/O SKEW % measures the degree of I/O imbalance but not the total I/O volume. UII (Unique Index Indicator) is not a direct measure of I/O consumption.

D. REQPHYSIO is a system-level I/O metric from ResUsage tables, not a query-specific metric suitable for identifying individual problematic queries from DBQLogTbl.

1. Teradata Vantage™ Data Dictionary, Release 17.20, B035-1092-172K, Chapter 2: Data Dictionary Views and Tables.

Page 1008, DBC.QryLogV View Definition: The TotalIOCount column is defined as "Total I/O count." This directly supports its use for measuring the overall I/O of a query.

Page 999, DBC.QryLogV View Definition: The PJI column is defined as "Product Join Index Skew." The description clarifies it measures the row count on the most-populated AMP as a percentage of the total rows in a product join, directly indicating redistribution skew.

2. Teradata Vantage™ Database Administration, Release 17.20, B035-1093-172K, Chapter 11: Monitoring System Activity.

Section: "Using DBQL to Identify Problem Queries": This section details the use of DBQLogTbl for performance analysis. It emphasizes tracking resource consumption metrics like TotalIOCount to find expensive queries. It also discusses analyzing skew metrics to understand the root cause of high resource usage. The combination of a high TotalIOCount and a high skew metric like PJI is a classic pattern for identifying queries with redistribution problems.