The core issue is that a single query is attempting to access more than 1,000 tables, which is the hard limit in BigQuery. The current date-sharding approach (a new table for each day) results in approximately 1,095 tables for a three-year period, thus exceeding this limit.

The officially recommended solution for this scenario is to migrate from date-sharded tables to a single, time-partitioned table. By consolidating all daily logs into one table partitioned by date, a query over any time range will only ever reference this single table. BigQuery's query planner will then use the date filter to efficiently scan only the relevant partitions (days), resolving the 1,000-table limit issue while also improving performance and simplifying data management.

A. Convert all daily log tables into date-partitioned tables: This option is less precise and potentially misleading. The goal is to create a single partitioned table to consolidate the data, not to convert each of the 1,000+ daily tables into its own partitioned table.

C. Enable query caching so you can cache data from previous months: Query caching stores the results of successfully executed queries. It cannot prevent a query from failing in the first place due to exceeding fundamental service limits like the number of tables referenced.

D. Create separate views to cover each month, and query from these views: A view is a logical representation of a query. A query against multiple monthly views would still expand to reference all the underlying daily tables, ultimately hitting the same 1,000-table limit.

1. Google Cloud Documentation - Introduction to partitioned tables: "When you create tables that are sharded by date, you have to create a new table for each date. Another approach is to use time-partitioned tables. ... Partitioning is the recommended approach over table sharding." This source establishes that partitioning is the correct replacement for sharding.

2. Google Cloud Documentation - BigQuery Quotas and limits: Under the "Query jobs" section, the limit for "Maximum number of tables referenced per query" is listed as "1,000". This confirms the root cause of the user's problem.

3. Google Cloud Documentation - Migrating sharded tables to partitioned tables: This document provides a tutorial for the exact solution. The introduction states, "This tutorial shows you how to migrate data from multiple, date-sharded tables into a single time-partitioned table." This directly supports option B as the correct procedure.