Specify compression types for columns which reduces disk size and disk I/O subsequenty. File names follow a standard naming convention as 'filelabelMonthNameYYYYBatch01.CSV'. If data is not going to change in Redshift you can keep them in S3 (becomes a data lake) and using method 1 above you can read it from Redshift. ĭefine relevant distribution styles, which will distribute data across multiple slices and will impact disk I/O across the cluster. I have job in Redshift that is responsible for pulling 6 files every month from S3. Load the file into a table with every single column (or you date columns) defined as varchar, transform as a second pass. VACUUM & ANALYSE are time consuming activities as well, if thr is any sort key and the data in your csv is also in the same sorted order, the above operation should be faster.ĭefine relevant sort keys which will have an impact on disk I/O and columnar compression & Load data in the sort key order. for instance, at s3://my-bucket/unit1 I have files like below. If so, please execute VACUUM and ANALYSE commands before/after the load. And I have created manifest file at each prefix of the files. Solution 2.2: First COPY all the underlying tables, and then CREATE VIEW on Redshift. copy category from s3://mybucket/categoryobjectpaths. Is it an empty table or does the table possess any data. Solution 2.1: Create views as a table and then COPY, if you don’t care its either a view or a table on Redshift. To load from the Avro data file in the previous example, run the following COPY command. You may disable the COMPUPDATE option during load if it is unnecessary. You can specify the files to be loaded by using an Amazon S3 object prefix or by. We use s3 COPY command to move data from S3 to Redshift table. If you may want only specific columns from the CSV, you may use the column list to ignore a few columns. Use the COPY command to load a table in parallel from data files on Amazon S3. If it is small, you may consider, uploading the csv files directly without compressing.
If the size of the each user*.csv.gz file is very small, then Redshift might be spending some compute effort in uncompressing.
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