Guidance for designing distributed tables using dedicated SQL pool in Azure Synapse Analytics
This article contains recommendations for designing hash-distributed and round-robin distributed tables in dedicated SQL pools.
This article assumes you are familiar with data distribution and data movement concepts in dedicated SQL pool. For more information, see Azure Synapse Analytics architecture.
What is a distributed table?
A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
Hash-distribution improves query performance on large fact tables, and is the focus of this article. Round-robin distribution is useful for improving loading speed. These design choices have a significant impact on improving query and loading performance.
Another table storage option is to replicate a small table across all the Compute nodes. For more information, see Design guidance for replicated tables. To quickly choose among the three options, see Distributed tables in the tables overview.
As part of table design, understand as much as possible about your data and how the data is queried. For example, consider these questions:
- How large is the table?
- How often is the table refreshed?
- Do I have fact and dimension tables in a dedicated SQL pool?
A hash-distributed table distributes table rows across the Compute nodes by using a deterministic hash function to assign each row to one distribution.
Since identical values always hash to the same distribution, SQL Analytics has built-in knowledge of the row locations. In dedicated SQL pool this knowledge is used to minimize data movement during queries, which improves query performance.
Hash-distributed tables work well for large fact tables in a star schema. They can have very large numbers of rows and still achieve high performance. There are some design considerations that help you to get the performance the distributed system is designed to provide. Choosing a good distribution column or columns is one such consideration that is described in this article.
Consider using a hash-distributed table when:
- The table size on disk is more than 2 GB.
- The table has frequent insert, update, and delete operations.
A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random. Unlike hash-distributed tables, rows with equal values are not guaranteed to be assigned to the same distribution.
As a result, the system sometimes needs to invoke a data movement operation to better organize your data before it can resolve a query. This extra step can slow down your queries. For example, joining a round-robin table usually requires reshuffling the rows, which is a performance hit.
Consider using the round-robin distribution for your table in the following scenarios:
- When getting started as a simple starting point since it is the default
- If there is no obvious joining key
- If there is no good candidate column for hash distributing the table
- If the table does not share a common join key with other tables
- If the join is less significant than other joins in the query
- When the table is a temporary staging table
The tutorial Load New York taxicab data gives an example of loading data into a round-robin staging table.
Choose a distribution column
A hash-distributed table has a distribution column or set of columns that is the hash key. For example, the following code creates a hash-distributed table with
ProductKey as the distribution column.
CREATE TABLE [dbo].[FactInternetSales] ( [ProductKey] int NOT NULL , [OrderDateKey] int NOT NULL , [CustomerKey] int NOT NULL , [PromotionKey] int NOT NULL , [SalesOrderNumber] nvarchar(20) NOT NULL , [OrderQuantity] smallint NOT NULL , [UnitPrice] money NOT NULL , [SalesAmount] money NOT NULL ) WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([ProductKey]) );
Hash distribution can be applied on multiple columns for a more even distribution of the base table. Multi-column distribution will allow you to choose up to eight columns for distribution. This not only reduces the data skew over time but also improves query performance. For example:
CREATE TABLE [dbo].[FactInternetSales] ( [ProductKey] int NOT NULL , [OrderDateKey] int NOT NULL , [CustomerKey] int NOT NULL , [PromotionKey] int NOT NULL , [SalesOrderNumber] nvarchar(20) NOT NULL , [OrderQuantity] smallint NOT NULL , [UnitPrice] money NOT NULL , [SalesAmount] money NOT NULL ) WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([ProductKey], [OrderDateKey], [CustomerKey] , [PromotionKey]) );
Multi-column distribution in Azure Synapse Analytics can be enabled by changing the database's compatibility level to
50 with this command.
ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = 50;
For more information on setting the database compatibility level, see ALTER DATABASE SCOPED CONFIGURATION. For more information on multi-column distributions, see CREATE MATERIALIZED VIEW, CREATE TABLE, or CREATE TABLE AS SELECT.
Data stored in the distribution column(s) can be updated. Updates to data in distribution column(s) could result in data shuffle operation.
Choosing distribution column(s) is an important design decision since the values in the hash column(s) determine how the rows are distributed. The best choice depends on several factors, and usually involves tradeoffs. Once a distribution column or column set is chosen, you cannot change it. If you didn't choose the best column(s) the first time, you can use CREATE TABLE AS SELECT (CTAS) to re-create the table with the desired distribution hash key.
Choose a distribution column with data that distributes evenly
For best performance, all of the distributions should have approximately the same number of rows. When one or more distributions have a disproportionate number of rows, some distributions finish their portion of a parallel query before others. Since the query can't complete until all distributions have finished processing, each query is only as fast as the slowest distribution.
- Data skew means the data is not distributed evenly across the distributions
- Processing skew means that some distributions take longer than others when running parallel queries. This can happen when the data is skewed.
To balance the parallel processing, select a distribution column or set of columns that:
- Has many unique values. The distribution column(s) can have duplicate values. All rows with the same value are assigned to the same distribution. Since there are 60 distributions, some distributions can have > 1 unique values while others may end with zero values.
- Does not have NULLs, or has only a few NULLs. For an extreme example, if all values in the distribution column(s) are NULL, all the rows are assigned to the same distribution. As a result, query processing is skewed to one distribution, and does not benefit from parallel processing.
- Is not a date column. All data for the same date lands in the same distribution, or will cluster records by date. If several users are all filtering on the same date (such as today's date), then only 1 of the 60 distributions do all the processing work.
Choose a distribution column that minimizes data movement
To get the correct query result queries might move data from one Compute node to another. Data movement commonly happens when queries have joins and aggregations on distributed tables. Choosing a distribution column or column set that helps minimize data movement is one of the most important strategies for optimizing performance of your dedicated SQL pool.
To minimize data movement, select a distribution column or set of columns that:
- Is used in
HAVINGclauses. When two large fact tables have frequent joins, query performance improves when you distribute both tables on one of the join columns. When a table is not used in joins, consider distributing the table on a column or column set that is frequently in the
- Is not used in
WHEREclauses. When a query's
WHEREclause and the table's distribution columns are on the same column, the query could encounter high data skew, leading to processing load falling on only few distributions. This impacts query performance, ideally many distributions share the processing load.
- Is not a date column.
WHEREclauses often filter by date. When this happens, all the processing could run on only a few distributions affecting query performance. Ideally, many distributions share the processing load.
Once you design a hash-distributed table, the next step is to load data into the table. For loading guidance, see Loading overview.
How to tell if your distribution is a good choice
After data is loaded into a hash-distributed table, check to see how evenly the rows are distributed across the 60 distributions. The rows per distribution can vary up to 10% without a noticeable impact on performance. Consider the following topics to evaluate your distribution column(s).
Determine if the table has data skew
A quick way to check for data skew is to use DBCC PDW_SHOWSPACEUSED. The following SQL code returns the number of table rows that are stored in each of the 60 distributions. For balanced performance, the rows in your distributed table should be spread evenly across all the distributions.
-- Find data skew for a distributed table DBCC PDW_SHOWSPACEUSED('dbo.FactInternetSales');
To identify which tables have more than 10% data skew:
- Create the view
dbo.vTableSizesthat is shown in the Tables overview article.
- Run the following query:
select * from dbo.vTableSizes where two_part_name in ( select two_part_name from dbo.vTableSizes where row_count > 0 group by two_part_name having (max(row_count * 1.000) - min(row_count * 1.000))/max(row_count * 1.000) >= .10 ) order by two_part_name, row_count;
Check query plans for data movement
A good distribution column set enables joins and aggregations to have minimal data movement. This affects the way joins should be written. To get minimal data movement for a join on two hash-distributed tables, one of the join columns needs to be in distribution column or column(s). When two hash-distributed tables join on a distribution column of the same data type, the join does not require data movement. Joins can use additional columns without incurring data movement.
To avoid data movement during a join:
- The tables involved in the join must be hash distributed on one of the columns participating in the join.
- The data types of the join columns must match between both tables.
- The columns must be joined with an equals operator.
- The join type may not be a
To see if queries are experiencing data movement, you can look at the query plan.
Resolve a distribution column problem
It is not necessary to resolve all cases of data skew. Distributing data is a matter of finding the right balance between minimizing data skew and data movement. It is not always possible to minimize both data skew and data movement. Sometimes the benefit of having the minimal data movement might outweigh the impact of having data skew.
To decide if you should resolve data skew in a table, you should understand as much as possible about the data volumes and queries in your workload. You can use the steps in the Query monitoring article to monitor the impact of skew on query performance. Specifically, look for how long it takes large queries to complete on individual distributions.
Since you cannot change the distribution column(s) on an existing table, the typical way to resolve data skew is to re-create the table with a different distribution column(s).
Re-create the table with a new distribution column set
This example uses CREATE TABLE AS SELECT to re-create a table with a different hash distribution column or column(s).
CREATE TABLE AS SELECT (CTAS) the new table with the new key. Then re-create the statistics and finally, swap the tables by re-naming them.
CREATE TABLE [dbo].[FactInternetSales_CustomerKey] WITH ( CLUSTERED COLUMNSTORE INDEX , DISTRIBUTION = HASH([CustomerKey]) , PARTITION ( [OrderDateKey] RANGE RIGHT FOR VALUES ( 20000101, 20010101, 20020101, 20030101 , 20040101, 20050101, 20060101, 20070101 , 20080101, 20090101, 20100101, 20110101 , 20120101, 20130101, 20140101, 20150101 , 20160101, 20170101, 20180101, 20190101 , 20200101, 20210101, 20220101, 20230101 , 20240101, 20250101, 20260101, 20270101 , 20280101, 20290101 ) ) ) AS SELECT * FROM [dbo].[FactInternetSales] OPTION (LABEL = 'CTAS : FactInternetSales_CustomerKey') ; --Create statistics on new table CREATE STATISTICS [ProductKey] ON [FactInternetSales_CustomerKey] ([ProductKey]); CREATE STATISTICS [OrderDateKey] ON [FactInternetSales_CustomerKey] ([OrderDateKey]); CREATE STATISTICS [CustomerKey] ON [FactInternetSales_CustomerKey] ([CustomerKey]); CREATE STATISTICS [PromotionKey] ON [FactInternetSales_CustomerKey] ([PromotionKey]); CREATE STATISTICS [SalesOrderNumber] ON [FactInternetSales_CustomerKey] ([SalesOrderNumber]); CREATE STATISTICS [OrderQuantity] ON [FactInternetSales_CustomerKey] ([OrderQuantity]); CREATE STATISTICS [UnitPrice] ON [FactInternetSales_CustomerKey] ([UnitPrice]); CREATE STATISTICS [SalesAmount] ON [FactInternetSales_CustomerKey] ([SalesAmount]); --Rename the tables RENAME OBJECT [dbo].[FactInternetSales] TO [FactInternetSales_ProductKey]; RENAME OBJECT [dbo].[FactInternetSales_CustomerKey] TO [FactInternetSales];
To create a distributed table, use one of these statements:
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