![]() ![]() 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. Please ensure the 'search_path' contains the schema name, referenced in your python code, else no records will be returned.Tl dr: I want to generate a dates table in Redshift in order to make a report easier to generate. Querying the system table, will return rows referencing schema name, if the table creation was successful. Make sure the logged in user has necessary permissions to view the table. Set search_path to '$user',public, replace_with_schema_nameĬan you confirm if commit() is called after the table creation.Įnsure you are looking at the right aws region, cluster and database name the table was created under. Assuming the existing default values are '$user',public In order to append a new entry to existing search_path. Set search_path to replace_with_schema_name Note: this overwrites existing schema names in the list, SQL script to include a schema to search_path. If the schema mentioned in the python script isn't listed, then you need to reference it. SQL script to view contents of search_path. To confirm the table creation is successful, verify the schema name provided from python is referenced in 'search_path'. If none of the names listed in 'search_path' has a corresponding schema, then create table operation will fail with error "no schema has been selected to create in". If redshift didn't find a schema, it looks for the next schema name in the list which is 'public'. If it finds a schema name with the same name as session user, the table is created here. Here $user corresponds to the SESSION_USER. By default 'search_path' value is '$user', public. When no schema name is provided while creating a table, Redshift relies on 'search_path' to retrieve the default one available. I would be extremely grateful for any hints that could help resolve this problem. Like I said the code runs without errors but when I check the cluster, the table is not there. Sql = """COPY %s FROM '%s' access_key_id '%s' secret_access_key '%s' delimiter '%s' CSV ignoreheader 1 timeformat 'auto' """ % (table_name, s3_path_to_file_name, access_key_id, secret_key,delim) The relevant lines of code are as follows: sql = 'CREATE TABLE IF NOT EXISTS ' + schema_name.table_name + ' (column datatype, column datatype, column datatype) ' I currently use a schema_name.table_name format when writing the create statement because I don't know how else to specify the schema the table should be created in. Both lines run without errors but when I log into AWS and check my cluster, the table is not there. I want to know this because I have a script where I create a table and copy data to it from S3. I would like to know how redshift knows what schema a table should be created in when a database contains multiple schemas and the table is being created programmatically from a python script. ![]()
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