![]() ![]() Now you want to run some sophisticated analysis on your data on Redshift – like predicting your churn rate, or calculate your customer engagement / product usage KPIs. You found a way to load your data into Redshift and keep that data in sync with your primary data source. Unless absolutely necessary, it’s worth the effort of converting the JSON field to a set of first class columns with the right data type. This allows tricks like storing ‘deltas’ between sequential values of a column, which wouldn’t be possible with a row-based storage. Since Redshift is a column-store, compression is available on a per-column basis. The table will be bigger in size because compression will be less effective.These column statistics are critical in the performance of columns participating in sorting and grouping operations, joins and query predicated. With a first class column, Redshift knows how many NULL values and other distribution properties of a column. Defeats the query planner because Redshift cannot make use of statistical metadata on the keys of the field.Each query will have to load the entire JSON document, causing unneeded IO load. These keys and values of the JSON field are stored sequentially not making use of Redshift’s columnar store architecture.The problem is that the performance of querying JSON fields is slow as these fields do not take advantage of Redshift’s unique architecture: What’s not to like? You don’t have to maintain the schema, it’s flexible to add new keys making the schema future proof. SELECT json_extract_path_text ( property, 'country' ) AS country FROM table_name The property column is a JSON field and you can use Redshift’s JSON functions to use any key in the JSON field: Your data table will just have two columns: id It’s tempting to store your data as a JSON field and use Redshift’s JSON functions to query it. Most performance problems on Redshift boil down to incorrectly setting sort and dist keys. dist key: your data is sharded across the Redshift nodes according the dist key.sort key: your data is stored sorted on disk according to the sort key.Each query is a full table scan (though run in parallel across the shards.) It’s your responsibility to optimize your data for greatest throughput for which you have two tools: It also supports better compression ratios while stored on disk because similar data is stored sequentially together on disk.Īs a result of these unique architectural choices, Redshift has no indices. This storage mechanism drastically reduces the amount of data required to be read from disk and stored in memory for processing. Redshift uses a columnar storage, i.e., the values for a particular column are stored sequentially. Indices are not what you’d expect on Redshift "table" FROM 's3:///.csv.gz' CREDENTIALS 'aws_access_key_id= aws_secret_access_key=' REGION 'us-west-1' DELIMITER ',' GZIP 2. ![]()
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