Design tables using dedicated SQL pool in Azure Synapse Analytics
This article provides key introductory concepts for designing tables in dedicated SQL pool.
Determine table category
A star schema organizes data into fact and dimension tables. Some tables are used for integration or staging data before it moves to a fact or dimension table. As you design a table, decide whether the table data belongs in a fact, dimension, or integration table. This decision informs the appropriate table structure and distribution.
Fact tables contain quantitative data that are commonly generated in a transactional system, and then loaded into the dedicated SQL pool. For example, a retail business generates sales transactions every day, and then loads the data into a dedicated SQL pool fact table for analysis.
Dimension tables contain attribute data that might change but usually changes infrequently. For example, a customer's name and address are stored in a dimension table and updated only when the customer's profile changes. To minimize the size of a large fact table, the customer's name and address don't need to be in every row of a fact table. Instead, the fact table and the dimension table can share a customer ID. A query can join the two tables to associate a customer's profile and transactions.
Integration tables provide a place for integrating or staging data. You can create an integration table as a regular table, an external table, or a temporary table. For example, you can load data to a staging table, perform transformations on the data in staging, and then insert the data into a production table.
Schema and table names
Schemas are a good way to group tables, used in a similar fashion, together. If you're migrating multiple databases from an on-prem solution to a dedicated SQL pool, it works best to migrate all of the fact, dimension, and integration tables to one schema in a dedicated SQL pool.
CREATE SCHEMA wwi;
To show the organization of the tables in dedicated SQL pool, you could use fact, dim, and int as prefixes to the table names. The following table shows some of the schema and table names for
|WideWorldImportersDW table||Table type||Dedicated SQL pool|
Tables store data either permanently in Azure Storage, temporarily in Azure Storage, or in a data store external to dedicated SQL pool.
A regular table stores data in Azure Storage as part of dedicated SQL pool. The table and the data persist regardless of whether a session is open. The following example creates a regular table with two columns.
CREATE TABLE MyTable (col1 int, col2 int );
A temporary table only exists for the duration of the session. You can use a temporary table to prevent other users from seeing temporary results and also to reduce the need for cleanup.
Temporary tables utilize local storage to offer fast performance. For more information, see Temporary tables.
An external table points to data located in Azure Storage blob or Azure Data Lake Store. When used with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into dedicated SQL pool.
As such, external tables are useful for loading data. For a loading tutorial, see Use PolyBase to load data from Azure blob storage.
Dedicated SQL pool supports the most commonly used data types. For a list of the supported data types, see data types in CREATE TABLE reference in the CREATE TABLE statement. For guidance on using data types, see Data types.
A fundamental feature of dedicated SQL pool is the way it can store and operate on tables across distributions. Dedicated SQL pool supports three methods for distributing data: round-robin (default), hash and replicated.
A hash distributed table distributes rows based on the value in the distribution column. A hash distributed table is designed to achieve high performance for queries on large tables. There are several factors to consider when choosing a distribution column.
For more information, see Design guidance for distributed tables.
A replicated table has a full copy of the table available on every Compute node. Queries run fast on replicated tables since joins on replicated tables don't require data movement. Replication requires extra storage, though, and isn't practical for large tables.
For more information, see Design guidance for replicated tables.
A round-robin table distributes table rows evenly across all distributions. The rows are distributed randomly. Loading data into a round-robin table is fast. Keep in mind that queries can require more data movement than the other distribution methods.
For more information, see Design guidance for distributed tables.
Common distribution methods for tables
The table category often determines which option to choose for distributing the table.
|Table category||Recommended distribution option|
|Fact||Use hash-distribution with clustered columnstore index. Performance improves when two hash tables are joined on the same distribution column.|
|Dimension||Use replicated for smaller tables. If tables are too large to store on each Compute node, use hash-distributed.|
|Staging||Use round-robin for the staging table. The load with CTAS is fast. Once the data is in the staging table, use INSERT...SELECT to move the data to production tables.|
For recommendations on the best table distribution strategy to use based on your workloads, see the Azure Synapse SQL Distribution Advisor.
A partitioned table stores and performs operations on the table rows according to data ranges. For example, a table could be partitioned by day, month, or year. You can improve query performance through partition elimination, which limits a query scan to data within a partition. You can also maintain the data through partition switching. Since the data in SQL pool is already distributed, too many partitions can slow query performance. For more information, see Partitioning guidance. When partition switching into table partitions that are not empty, consider using the TRUNCATE_TARGET option in your ALTER TABLE statement if the existing data is to be truncated. The below code switches in the transformed daily data into the SalesFact overwriting any existing data.
ALTER TABLE SalesFact_DailyFinalLoad SWITCH PARTITION 256 TO SalesFact PARTITION 256 WITH (TRUNCATE_TARGET = ON);
By default, dedicated SQL pool stores a table as a clustered columnstore index. This form of data storage achieves high data compression and query performance on large tables.
The clustered columnstore index is usually the best choice, but in some cases a clustered index or a heap is the appropriate storage structure.
A heap table can be especially useful for loading transient data, such as a staging table which is transformed into a final table.
For a list of columnstore features, see What's new for columnstore indexes. To improve columnstore index performance, see Maximizing rowgroup quality for columnstore indexes.
The query optimizer uses column-level statistics when it creates the plan for executing a query.
To improve query performance, it's important to have statistics on individual columns, especially columns used in query joins. Creating statistics happens automatically.
Updating statistics doesn't happen automatically. Update statistics after a significant number of rows are added or changed. For example, update statistics after a load. For more information, see Statistics guidance.
Primary key and unique key
PRIMARY KEY is only supported when NONCLUSTERED and NOT ENFORCED are both used. UNIQUE constraint is only supported with NOT ENFORCED is used. Check Dedicated SQL pool table constraints.
Commands for creating tables
You can create a table as a new empty table. You can also create and populate a table with the results of a select statement. The following are the T-SQL commands for creating a table.
|CREATE TABLE||Creates an empty table by defining all the table columns and options.|
|CREATE EXTERNAL TABLE||Creates an external table. The definition of the table is stored in dedicated SQL pool. The table data is stored in Azure Blob storage or Azure Data Lake Store.|
|CREATE TABLE AS SELECT||Populates a new table with the results of a select statement. The table columns and data types are based on the select statement results. To import data, this statement can select from an external table.|
|CREATE EXTERNAL TABLE AS SELECT||Creates a new external table by exporting the results of a select statement to an external location. The location is either Azure Blob storage or Azure Data Lake Store.|
Aligning source data with dedicated SQL pool
Dedicated SQL pool tables are populated by loading data from another data source. To perform a successful load, the number and data types of the columns in the source data must align with the table definition in the dedicated SQL pool. Getting the data to align might be the hardest part of designing your tables.
If data is coming from multiple data stores, you load the data into the dedicated SQL pool and store it in an integration table. Once data is in the integration table, you can use the power of dedicated SQL pool to perform transformation operations. Once the data is prepared, you can insert it into production tables.
Unsupported table features
Dedicated SQL pool supports many, but not all, of the table features offered by other databases. The following list shows some of the table features that aren't supported in dedicated SQL pool:
- Foreign key, Check Table Constraints
- Computed Columns
- Indexed Views
- Sparse Columns
- Surrogate Keys. Implement with Identity.
- Unique Indexes
- User-Defined Types
Table size queries
One simple way to identify space and rows consumed by a table in each of the 60 distributions, is to use DBCC PDW_SHOWSPACEUSED.
However, using DBCC commands can be quite limiting. Dynamic management views (DMVs) show more detail than DBCC commands. Start by creating this view:
CREATE VIEW dbo.vTableSizes AS WITH base AS ( SELECT GETDATE() AS [execution_time] , DB_NAME() AS [database_name] , s.name AS [schema_name] , t.name AS [table_name] , QUOTENAME(s.name)+'.'+QUOTENAME(t.name) AS [two_part_name] , nt.[name] AS [node_table_name] , ROW_NUMBER() OVER(PARTITION BY nt.[name] ORDER BY (SELECT NULL)) AS [node_table_name_seq] , tp.[distribution_policy_desc] AS [distribution_policy_name] , c.[name] AS [distribution_column] , nt.[distribution_id] AS [distribution_id] , i.[type] AS [index_type] , i.[type_desc] AS [index_type_desc] , nt.[pdw_node_id] AS [pdw_node_id] , pn.[type] AS [pdw_node_type] , pn.[name] AS [pdw_node_name] , di.name AS [dist_name] , di.position AS [dist_position] , nps.[partition_number] AS [partition_nmbr] , nps.[reserved_page_count] AS [reserved_space_page_count] , nps.[reserved_page_count] - nps.[used_page_count] AS [unused_space_page_count] , nps.[in_row_data_page_count] + nps.[row_overflow_used_page_count] + nps.[lob_used_page_count] AS [data_space_page_count] , nps.[reserved_page_count] - (nps.[reserved_page_count] - nps.[used_page_count]) - ([in_row_data_page_count] + [row_overflow_used_page_count]+[lob_used_page_count]) AS [index_space_page_count] , nps.[row_count] AS [row_count] from sys.schemas s INNER JOIN sys.tables t ON s.[schema_id] = t.[schema_id] INNER JOIN sys.indexes i ON t.[object_id] = i.[object_id] AND i.[index_id] <= 1 INNER JOIN sys.pdw_table_distribution_properties tp ON t.[object_id] = tp.[object_id] INNER JOIN sys.pdw_table_mappings tm ON t.[object_id] = tm.[object_id] INNER JOIN sys.pdw_nodes_tables nt ON tm.[physical_name] = nt.[name] INNER JOIN sys.dm_pdw_nodes pn ON nt.[pdw_node_id] = pn.[pdw_node_id] INNER JOIN sys.pdw_distributions di ON nt.[distribution_id] = di.[distribution_id] INNER JOIN sys.dm_pdw_nodes_db_partition_stats nps ON nt.[object_id] = nps.[object_id] AND nt.[pdw_node_id] = nps.[pdw_node_id] AND nt.[distribution_id] = nps.[distribution_id] AND i.[index_id] = nps.[index_id] LEFT OUTER JOIN (select * from sys.pdw_column_distribution_properties where distribution_ordinal = 1) cdp ON t.[object_id] = cdp.[object_id] LEFT OUTER JOIN sys.columns c ON cdp.[object_id] = c.[object_id] AND cdp.[column_id] = c.[column_id] WHERE pn.[type] = 'COMPUTE' ) , size AS ( SELECT [execution_time] , [database_name] , [schema_name] , [table_name] , [two_part_name] , [node_table_name] , [node_table_name_seq] , [distribution_policy_name] , [distribution_column] , [distribution_id] , [index_type] , [index_type_desc] , [pdw_node_id] , [pdw_node_type] , [pdw_node_name] , [dist_name] , [dist_position] , [partition_nmbr] , [reserved_space_page_count] , [unused_space_page_count] , [data_space_page_count] , [index_space_page_count] , [row_count] , ([reserved_space_page_count] * 8.0) AS [reserved_space_KB] , ([reserved_space_page_count] * 8.0)/1000 AS [reserved_space_MB] , ([reserved_space_page_count] * 8.0)/1000000 AS [reserved_space_GB] , ([reserved_space_page_count] * 8.0)/1000000000 AS [reserved_space_TB] , ([unused_space_page_count] * 8.0) AS [unused_space_KB] , ([unused_space_page_count] * 8.0)/1000 AS [unused_space_MB] , ([unused_space_page_count] * 8.0)/1000000 AS [unused_space_GB] , ([unused_space_page_count] * 8.0)/1000000000 AS [unused_space_TB] , ([data_space_page_count] * 8.0) AS [data_space_KB] , ([data_space_page_count] * 8.0)/1000 AS [data_space_MB] , ([data_space_page_count] * 8.0)/1000000 AS [data_space_GB] , ([data_space_page_count] * 8.0)/1000000000 AS [data_space_TB] , ([index_space_page_count] * 8.0) AS [index_space_KB] , ([index_space_page_count] * 8.0)/1000 AS [index_space_MB] , ([index_space_page_count] * 8.0)/1000000 AS [index_space_GB] , ([index_space_page_count] * 8.0)/1000000000 AS [index_space_TB] FROM base ) SELECT * FROM size ;
Table space summary
This query returns the rows and space by table. It allows you to see which tables are your largest tables and whether they are round-robin, replicated, or hash-distributed. For hash-distributed tables, the query shows the distribution column.
SELECT database_name , schema_name , table_name , distribution_policy_name , distribution_column , index_type_desc , COUNT(distinct partition_nmbr) as nbr_partitions , SUM(row_count) as table_row_count , SUM(reserved_space_GB) as table_reserved_space_GB , SUM(data_space_GB) as table_data_space_GB , SUM(index_space_GB) as table_index_space_GB , SUM(unused_space_GB) as table_unused_space_GB FROM dbo.vTableSizes GROUP BY database_name , schema_name , table_name , distribution_policy_name , distribution_column , index_type_desc ORDER BY table_reserved_space_GB desc ;
Table space by distribution type
SELECT distribution_policy_name , SUM(row_count) as table_type_row_count , SUM(reserved_space_GB) as table_type_reserved_space_GB , SUM(data_space_GB) as table_type_data_space_GB , SUM(index_space_GB) as table_type_index_space_GB , SUM(unused_space_GB) as table_type_unused_space_GB FROM dbo.vTableSizes GROUP BY distribution_policy_name ;
Table space by index type
SELECT index_type_desc , SUM(row_count) as table_type_row_count , SUM(reserved_space_GB) as table_type_reserved_space_GB , SUM(data_space_GB) as table_type_data_space_GB , SUM(index_space_GB) as table_type_index_space_GB , SUM(unused_space_GB) as table_type_unused_space_GB FROM dbo.vTableSizes GROUP BY index_type_desc ;
Distribution space summary
SELECT distribution_id , SUM(row_count) as total_node_distribution_row_count , SUM(reserved_space_MB) as total_node_distribution_reserved_space_MB , SUM(data_space_MB) as total_node_distribution_data_space_MB , SUM(index_space_MB) as total_node_distribution_index_space_MB , SUM(unused_space_MB) as total_node_distribution_unused_space_MB FROM dbo.vTableSizes GROUP BY distribution_id ORDER BY distribution_id ;
After creating the tables for your dedicated SQL pool, the next step is to load data into the table. For a loading tutorial, see Loading data to dedicated SQL pool and review Data loading strategies for dedicated SQL pool in Azure Synapse Analytics.