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How to create a table with billions of rows?
Here we generate a table with different timestamps in order to satisify the request to index and search on a timestamp column, creation takes a bit longer because to_timestamp (int) is substantially more slow than now () (which is cached for the transaction) So in 83.321 ms we can aggregate 86,401 records in a table with 1.7 Billion rows.
What’s the best approach for optimizing large tables?
The best first step is clustering on stock. Insertion speed is of no consequence at all until you are looking at multiple records inserted per second – I don’t see anything anywhere near that activity here.
How to calculate the number of Records in a month?
20 000+ locations, 720 records per month (hourly measurements, approximately 720 hours per month), 120 months (for 10 years back) and many years into the future. Simple calculations yield the following results: 20 000 locations x 720 records x 120 months (10 years back) = 1 728 000 000 records.
How many records are imported in a month?
Simple calculations yield the following results: 20 000 locations x 720 records x 120 months (10 years back) = 1 728 000 000 records. These are the past records, new records will be imported monthly, so that’s approximately 20 000 x 720 = 14 400 000 new records per month.
How to join tables with millions of rows?
In my application I have to join tables with millions of rows. I have a query like this:
Is it slow to join 10 million rows?
The table “files” has 10 million rows, and the table “value_text” has 40 million rows. This query is too slow, it takes between 40s (15000 results) – 3 minutes (65000 results) to be executed. I had thought about divide the two queries, but I can’t because sometimes I need to order by the joined column (value)… What can I do?
Can a de normalized table be used in a database?
Be prepared for some horrendous queries against the data and vast amounts of pivoting. Don’t be afraid to create a de-normalized table for result sets that are just too large to compute on the fly. General tip: I store most of the data between two databases, the first is straight-up time series data and is normalized.