Indexing is a crucial aspect of optimizing database queries for time series applications, as it enables faster search and retrieval of data by creating shortcuts to specific values. However, indexing can become oversized if too many columns or fields are indexed, leading to memory consumption and slowing down processes. To mitigate this, it's essential to be aware of the schema at all times and use indexing judiciously. Another critical factor is query scope, which can significantly impact performance; using time ranges, sub-queries, and filtering data can help limit queries and avoid data overload. Retention policies are also vital in managing high volumes of time series data by setting up processes to delete expired data, while cardinality refers to the number of unique values in a column or series, which can significantly impact performance; optimizing for fewer series with more points rather than more series with fewer points can improve efficiency. By understanding and addressing these factors, developers can craft efficient queries that meet the demands of time series applications.