![]() The simple answer is: because it can read the needed data really, really fast and in parallel. during one of our recent benchmark frameworks ![]() Redshift does fare well with other systems like Hive, Impala, Spark, BQ etc. It eventually duplicates data but at the required format to be executed for queries (similar to materialized view) The below blog gives your some information on the above approach. Redshift does not support materialized views but it easily allows you to create (temporary/permant) tables by running select queries on existing tables. If you have an interleaved soft key on (orderid, shipdate) and if your query If you have a composite sortkey on (orderid, shipdate) and if your query only on ship date, Redshift will not be operating efficiently. If you select orderid as your sort key but if your queries are based on shipdate, Redshift will be operating efficiently. If your table structure is lineitem(orderid,linenumber,supplier,quantity,price,discount,tax,returnflat,shipdate). Redshift supports Sort keys, Compound Sort keys and Interleaved Sort keys. To implement an efficient solution, it requires a great deal of knowledge on the above sections and as well as the on the queries that you would run on Amazon Redshift. Implementing the above factors, reduces IO operations on Redshift and eventually providing better performance. provides the power to Redshift to be faster. The combination of columnar storage, compression codings, data distribution, compression, query compilations, optimization etc. The 'life without a btree' section in the below blog explains with examples how an index based out of btree affects OLAP workloads. Instead it uses a secondary structure called zone maps with sort keys. Indexes would not be a right fit for OLAP systems. On the contrary, OLAP systems retrieve a large set of values and performs aggregation on the large set of values. Indexes are basically used in OLTP systems to retrieve a specific or a small group of values. This intern allows RedShift to identify which blocks to read from the other columns This allows Redshift to skip many of those blocks in certain conditions Those stats will say the minimum and maximum values stored by that block RedShift looks at the block statistics (for column z) first If the data is sorted by a then b then x Each column is stored separately from each other columnĪs well as being the storage pattern this effectively becomes a set of pseudo indexes. That becomes kind of possible because of how RedShift implements its column store. Instead of ordering by a THEN b THEN c it effectively orders by each of them at the same time. This is a direct attempt to have multiple independent sort orders. It even has recently introduced INTERLEAVED SORT KEYS. It is simply a list of fields by which the data is ordered (like a composite clustered index). It does have SORT ORDER which is exceptionally similar to a clustered index. Perhaps AWS believe I must be doing something wrong in the first place? I would dispute this and the product I work on maintains its own materialised views and can show significant performance gains from doing so. Possibly because they consider the engine so performant that the gains from having them are minimal. I have no real idea why they make this claim. Although RedShift has neither of these, I'm not sure that's the same as saying it wouldn't benefit from them. It's a bit disingenuous to be honest (in my opinion).
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