![]() ![]() ![]() This network intensive task will take 80–90% of the query execution and may run for several hours before results can be retrieved.Ī simple hack is creating HASH value for all the composite key columns and distribute data on that. Depending on the table size, composite key joins will have lot of data that needs to be re-distributed/ broadcasted to different nodes. However, if a table is distributed on a key, only one key can participate. Users may need to join different tables where there could be a composite key join. There will be instances where the default warehouse isn’t going to help with ad-hoc analysis or deep analysis. Having right compression on columns will improve performance multi-folds. The lesser the IO, the faster will be the query execution and column compression plays a key role. Lot of people doesn’t put much into sortkeys but, do it wrong, it will have a detrimental effect. It determines whether to scan the whole table or it can prune data by filtering right away. Since redshift doesn’t have any concept of indexes, sorting plays a very crucial role while retrieving data. However, when joining with other tables there will not be any data distribution. When ALL is chosen, redshift will store complete data in every slice. When two tables with same key distribution participates in join, redshift will do a colocated joins and pull data from each slice and sends it to leader node for final data presentation which means, there isn’t a need to distribute data to other nodes/ slices. Columns with high cardinality and which are frequently used for joining are preferred as good candidates for key distribution. When Key is chosen, redshift distributes data based on the key specified. Depending on how the table is used, this may or may not be a right choice. When EVEN is chosen, data is distributed evenly across slices randomly by redshift. But, users can specify Key/ ALL distribution as well. When a table is created, data will be distributed to slices in a node based on the distribution style chosen. Redshift will have a leader node and one or more compute/storage nodes. There are three core areas that will determine the performance (excluding how WLMs are configured) of a query Since redshift is MPP system, parallelism is one of the key feature of the system. Putting in decent amount of time to understand how the table is going to fit in the entire warehouse ecosystem is very critical. Everything on redshift comes down to how a table is designed. But, the performance of queries will change. The underlying architecture allows redshift to scale horizontally so, disk space will not be a problem as the data grows. This article touches overview of table internals but doesn’t go in depth to internal implementations. How can we trick redshift not to distribute but do a composite key join with lightening fast processing. What this means irrespective of what kind of distribution strategy is used (except ALL which we cannot use for facts), there will be data distribution. The main focus of this article is on ETL where there will be a need to join tables on composite keys. Users have the ability to fine tune and customize every thing to their specific use cases. Redshift is a Massive Parallel Processing Columnar database storage engine offering from AWS. ![]()
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