You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
A. Convert all daily log tables into date-partitioned tables
B. Convert the sharded tables into a single partitioned table
C. Enable query caching so you can cache data from previous months
D. Create separate views to cover each month, and query from these views
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Yes should be B as B solves this issue “some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing”
I think is B (single partitioned), because of this:
“Converting date-sharded tables into ingestion-time partitioned tables
If you have previously created date-sharded tables, you can convert the entire set of related tables into a single ingestion-time partitioned table by using the partition command in the bq command-line tool. The date-sharded tables must use the following naming convention: table_YYYYMMDD. For example, mytable_20160101, … , mytable_20160331.”
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_date-sharded_tables_into_ingestion-time_partitioned_tables
caching wont solve that. Not C
Creating separate views either
We should convert the daily tables into date-partitioned tables (and not single partitioned as B says)
So is A check cloud.google.com/bigquery/docs/creating-partitioned-tables