Select row_number() over (order by 1) - return 1.n in place of the original recordįrom pg_catalog.pg_operator a - 659 recordsĬross join pg_catalog.pg_operator b - to get 659^2=434k recordsĬross join pg_catalog.pg_operator c - to get 659^3=286M records Select the row_number() over (order by 1) to turn those created records into an ascending set of numbersĮxample using the Redshift system table pg_catalog.pg_operator (which as of Oct 2020 has 659 records): - Prep, so that you can copy/paste the code sampleĬreate schema if not exists facts - Make sure the schema existsĭrop table if exists facts.numbers - Avoid an error if that table already exists Ĭreate table facts.numbers - Create the table definition.Cross join that table to itself enough times to get the desired number of rows.Take a system table (guaranteed to exist) of a known or stable size.Here is a different suggestion for building the facts.numbers that does not require manual intervention: ^ Be sure to set the numbers at the end for the date range you need Once you have a numbers table, then you can generate a dates table (again, stealing code from elliot land ) : CREATE TABLE facts.dates ( ^ Make sure to remove the leading comma from the copy-pasted list of numbers Then copy-paste the numbers from your results in the query below, after VALUES: INSERT INTO facts.numbers I used a million to get started SELECT ',(' || generate_series(0,1000000,1) || ')' Run the following to start a numbers table: create table facts.numbers In asking the question, I figured it out. because, I assume, INSERT and generate_series are not allowed in the same command in Redshift Which throws this error (500310) Invalid operation: Specified types or functions (one per INFO message) not supported on Redshift tables. Generate days for 81 years starting from 2000. INTERVAL '1' DAY) = extract(DAY FROM datum) The code I was attempting to use: (based on this also-not-working recommendation: ) CREATE TABLE facts.dates (Ĭast(extract(YEAR FROM datum) AS SMALLINT) AS year_number,Ĭast(extract(WEEK FROM datum) AS SMALLINT) AS year_week_number,Ĭast(extract(DOY FROM datum) AS SMALLINT) AS year_day_number,Ĭast(to_char(datum, 'Q') AS SMALLINT) AS qtr_number,Ĭast(extract(MONTH FROM datum) AS SMALLINT) AS month_number,Ĭast(extract(DAY FROM datum) AS SMALLINT) AS month_day_number,Ĭast(to_char(datum, 'D') AS SMALLINT) AS week_day_number,ĬASE WHEN to_char(datum, 'D') IN ('1', '7')Įxtract(DAY FROM (datum + (1 - extract(DAY FROM datum)) :: INTEGER + Is there an easy way to generate a dates table in Redshift? However, most of the tutorials for dates tables use SQL commands that are not available or not fully supported by Redshift (I'm looking at you, generate_series). The best way to tackle this problem is most likely a dates table. Also, I am not guaranteed an instance of a single item per day, especially once a user starts slicing the data. The date range could span months or more, so there could be, say, 5 Mondays but only 4 Sundays, which can make the math a little tricky. I am working on a report where I have to average new items created per day of the week. Preferable without needing large tables already in redshift, needing to upload a csv file. For this, get the definition of the source table i.e web_team using the SHOW TABLE command.Tl dr: I want to generate a dates table in Redshift in order to make a report easier to generate. Suppose you want to build the new table using the web_team table definition. For this, you just need to use the SHOW TABLE command to get the definition of any similar column. ![]() To keep all the database tables with the same pattern, you will need to look at some previous data as it is not usually possible to remember all the details in mind. Now, take an example where you are going to expand your organization and add a new database table for the new software development department for which you have to create a new table named dev_team. ![]() Creating a new table using the old definition Further, the distribution style of the table is set to auto which is a key-based distribution style.īesides, showing the definition of the Redshift table, the SHOW TABLE command also returns the original command to create the new table with the same definition. We can see in the results output that the table admin_team has just two columns named id having integer data type with azr64 encoding and the other column named name with varchar data-type and lzo encoding.
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